From 835d64d797411141a1b21948566651d6dace882e Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sat, 16 Jan 2021 19:29:47 +0000
Subject: [PATCH 001/178] Initial draft for TensorAlgebra

---
 .gitignore                                    |  1 +
 .../kmath/structures/TensorAlgebra.kt         | 49 +++++++++++++++++++
 .../kmath/structures/TensorStructure.kt       | 11 +++++
 3 files changed, 61 insertions(+)
 create mode 100644 kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt
 create mode 100644 kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorStructure.kt

diff --git a/.gitignore b/.gitignore
index bade7f08c..ea8e65fb4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
 build/
 out/
 .idea/
+.vscode/
 
 # Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)
 !gradle-wrapper.jar
diff --git a/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt
new file mode 100644
index 000000000..f960742ee
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt
@@ -0,0 +1,49 @@
+package kscience.kmath.structures
+
+
+import kscience.kmath.operations.*
+
+public interface TensorAlgebra<T, TorchTensorType : TensorStructure<T>> : Ring<TorchTensorType> {
+
+    public operator fun T.plus(other: TorchTensorType): TorchTensorType
+    public operator fun TorchTensorType.plus(value: T): TorchTensorType
+    public operator fun TorchTensorType.plusAssign(value: T): Unit
+    public operator fun TorchTensorType.plusAssign(b: TorchTensorType): Unit
+
+    public operator fun T.minus(other: TorchTensorType): TorchTensorType
+    public operator fun TorchTensorType.minus(value: T): TorchTensorType
+    public operator fun TorchTensorType.minusAssign(value: T): Unit
+    public operator fun TorchTensorType.minusAssign(b: TorchTensorType): Unit
+
+    public operator fun T.times(other: TorchTensorType): TorchTensorType
+    public operator fun TorchTensorType.times(value: T): TorchTensorType
+    public operator fun TorchTensorType.timesAssign(value: T): Unit
+    public operator fun TorchTensorType.timesAssign(b: TorchTensorType): Unit
+
+    public infix fun TorchTensorType.dot(b: TorchTensorType): TorchTensorType
+
+    public fun diagonalEmbedding(
+        diagonalEntries: TorchTensorType,
+        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+    ): TorchTensorType
+
+    public fun TorchTensorType.transpose(i: Int, j: Int): TorchTensorType
+    public fun TorchTensorType.view(shape: IntArray): TorchTensorType
+
+    public fun TorchTensorType.abs(): TorchTensorType
+    public fun TorchTensorType.sum(): TorchTensorType
+
+}
+
+public interface TensorFieldAlgebra<T, TorchTensorType : TensorStructure<T>> :
+    TensorAlgebra<T, TorchTensorType>, Field<TorchTensorType> {
+
+    public operator fun TorchTensorType.divAssign(b: TorchTensorType)
+
+    public fun TorchTensorType.exp(): TorchTensorType
+    public fun TorchTensorType.log(): TorchTensorType
+
+    public fun TorchTensorType.svd(): Triple<TorchTensorType, TorchTensorType, TorchTensorType>
+    public fun TorchTensorType.symEig(eigenvectors: Boolean = true): Pair<TorchTensorType, TorchTensorType>
+
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorStructure.kt b/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorStructure.kt
new file mode 100644
index 000000000..6e3ac0b7b
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorStructure.kt
@@ -0,0 +1,11 @@
+package kscience.kmath.structures
+
+public abstract class TensorStructure<T>: MutableNDStructure<T> {
+
+    // A tensor can have empty shape, in which case it represents just a value
+    public abstract fun value(): T
+
+    // Tensors are mutable and might hold shared resources
+    override fun equals(other: Any?): Boolean = false
+    override fun hashCode(): Int = 0
+}
\ No newline at end of file

From ecf813803b04f55b602ad50d23d53d13e0b300cb Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 18 Jan 2021 07:57:13 +0000
Subject: [PATCH 002/178] Correcting terminology and failed connection to
 Algebra<T>

---
 .gitignore                                    |  3 +
 .../kmath/structures/TensorAlgebra.kt         | 99 +++++++++++--------
 .../kmath/structures/TensorStructure.kt       | 11 ---
 3 files changed, 62 insertions(+), 51 deletions(-)
 delete mode 100644 kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorStructure.kt

diff --git a/.gitignore b/.gitignore
index ea8e65fb4..f32005cd2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,3 +9,6 @@ out/
 
 # Cache of project
 .gradletasknamecache
+
+# Generated by javac -h
+*.class
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt
index f960742ee..ca7619429 100644
--- a/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt
@@ -1,49 +1,68 @@
 package kscience.kmath.structures
 
-
-import kscience.kmath.operations.*
-
-public interface TensorAlgebra<T, TorchTensorType : TensorStructure<T>> : Ring<TorchTensorType> {
-
-    public operator fun T.plus(other: TorchTensorType): TorchTensorType
-    public operator fun TorchTensorType.plus(value: T): TorchTensorType
-    public operator fun TorchTensorType.plusAssign(value: T): Unit
-    public operator fun TorchTensorType.plusAssign(b: TorchTensorType): Unit
-
-    public operator fun T.minus(other: TorchTensorType): TorchTensorType
-    public operator fun TorchTensorType.minus(value: T): TorchTensorType
-    public operator fun TorchTensorType.minusAssign(value: T): Unit
-    public operator fun TorchTensorType.minusAssign(b: TorchTensorType): Unit
-
-    public operator fun T.times(other: TorchTensorType): TorchTensorType
-    public operator fun TorchTensorType.times(value: T): TorchTensorType
-    public operator fun TorchTensorType.timesAssign(value: T): Unit
-    public operator fun TorchTensorType.timesAssign(b: TorchTensorType): Unit
-
-    public infix fun TorchTensorType.dot(b: TorchTensorType): TorchTensorType
-
-    public fun diagonalEmbedding(
-        diagonalEntries: TorchTensorType,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
-    ): TorchTensorType
-
-    public fun TorchTensorType.transpose(i: Int, j: Int): TorchTensorType
-    public fun TorchTensorType.view(shape: IntArray): TorchTensorType
-
-    public fun TorchTensorType.abs(): TorchTensorType
-    public fun TorchTensorType.sum(): TorchTensorType
-
+public interface TensorStructure<T> : MutableNDStructure<T> {
+    // A tensor can have empty shape, in which case it represents just a value
+    public abstract fun value(): T
 }
 
-public interface TensorFieldAlgebra<T, TorchTensorType : TensorStructure<T>> :
-    TensorAlgebra<T, TorchTensorType>, Field<TorchTensorType> {
+// https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 
-    public operator fun TorchTensorType.divAssign(b: TorchTensorType)
+public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
-    public fun TorchTensorType.exp(): TorchTensorType
-    public fun TorchTensorType.log(): TorchTensorType
+    public operator fun T.plus(other: TensorType): TensorType
+    public operator fun TensorType.plus(value: T): TensorType
+    public operator fun TensorType.plus(other: TensorType): TensorType
+    public operator fun TensorType.plusAssign(value: T): Unit
+    public operator fun TensorType.plusAssign(other: TensorType): Unit
 
-    public fun TorchTensorType.svd(): Triple<TorchTensorType, TorchTensorType, TorchTensorType>
-    public fun TorchTensorType.symEig(eigenvectors: Boolean = true): Pair<TorchTensorType, TorchTensorType>
+    public operator fun T.minus(other: TensorType): TensorType
+    public operator fun TensorType.minus(value: T): TensorType
+    public operator fun TensorType.minus(other: TensorType): TensorType
+    public operator fun TensorType.minusAssign(value: T): Unit
+    public operator fun TensorType.minusAssign(other: TensorType): Unit
+
+    public operator fun T.times(other: TensorType): TensorType
+    public operator fun TensorType.times(value: T): TensorType
+    public operator fun TensorType.times(other: TensorType): TensorType
+    public operator fun TensorType.timesAssign(value: T): Unit
+    public operator fun TensorType.timesAssign(other: TensorType): Unit
+    public operator fun TensorType.unaryMinus(): TensorType
+
+
+    public infix fun TensorType.dot(other: TensorType): TensorType
+    public infix fun TensorType.dotAssign(other: TensorType): Unit
+    public infix fun TensorType.dotRightAssign(other: TensorType): Unit
+
+    public fun diagonalEmbedding(
+        diagonalEntries: TensorType,
+        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+    ): TensorType
+
+    public fun TensorType.transpose(i: Int, j: Int): TensorType
+    public fun TensorType.transposeAssign(i: Int, j: Int): Unit
+
+    public fun TensorType.view(shape: IntArray): TensorType
+
+    public fun TensorType.abs(): TensorType
+    public fun TensorType.absAssign(): Unit
+    public fun TensorType.sum(): TensorType
+    public fun TensorType.sumAssign(): Unit
+}
+
+// https://proofwiki.org/wiki/Definition:Division_Algebra
+
+public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
+    TensorAlgebra<T, TensorType> {
+
+    public operator fun TensorType.div(other: TensorType): TensorType
+    public operator fun TensorType.divAssign(other: TensorType)
+
+    public fun TensorType.exp(): TensorType
+    public fun TensorType.expAssign(): Unit
+    public fun TensorType.log(): TensorType
+    public fun TensorType.logAssign(): Unit
+
+    public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
+    public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
 
 }
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorStructure.kt b/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorStructure.kt
deleted file mode 100644
index 6e3ac0b7b..000000000
--- a/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorStructure.kt
+++ /dev/null
@@ -1,11 +0,0 @@
-package kscience.kmath.structures
-
-public abstract class TensorStructure<T>: MutableNDStructure<T> {
-
-    // A tensor can have empty shape, in which case it represents just a value
-    public abstract fun value(): T
-
-    // Tensors are mutable and might hold shared resources
-    override fun equals(other: Any?): Boolean = false
-    override fun hashCode(): Int = 0
-}
\ No newline at end of file

From 360e0e17e94d997088915fda214a663a0928bfb9 Mon Sep 17 00:00:00 2001
From: Alexander Nozik <altavir@gmail.com>
Date: Tue, 19 Jan 2021 22:24:42 +0300
Subject: [PATCH 003/178] Minor optimization for RealNDAlgebra

---
 .../ast/ExpressionsInterpretersBenchmark.kt   |  4 +-
 .../kscience/kmath/benchmarks/DotBenchmark.kt | 20 ++++-----
 .../kmath/benchmarks/LargeNDBenchmark.kt      | 25 -----------
 .../benchmarks/LinearAlgebraBenchmark.kt      |  8 +---
 .../FunctionalExpressionAlgebra.kt            |  5 ++-
 .../kscience/kmath/linear/LupDecomposition.kt |  1 +
 .../kscience/kmath/operations/Complex.kt      |  2 +
 .../kscience/kmath/structures/RealNDField.kt  | 41 +++++++++----------
 .../kscience/kmath/linear/MatrixTest.kt       |  1 +
 .../kmath/structures/NumberNDFieldTest.kt     |  1 +
 .../kscience/dimensions/DMatrixContextTest.kt |  1 +
 11 files changed, 42 insertions(+), 67 deletions(-)
 delete mode 100644 examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LargeNDBenchmark.kt

diff --git a/examples/src/benchmarks/kotlin/kscience/kmath/ast/ExpressionsInterpretersBenchmark.kt b/examples/src/benchmarks/kotlin/kscience/kmath/ast/ExpressionsInterpretersBenchmark.kt
index 6acaca84d..c5edcdedf 100644
--- a/examples/src/benchmarks/kotlin/kscience/kmath/ast/ExpressionsInterpretersBenchmark.kt
+++ b/examples/src/benchmarks/kotlin/kscience/kmath/ast/ExpressionsInterpretersBenchmark.kt
@@ -28,7 +28,7 @@ internal class ExpressionsInterpretersBenchmark {
     @Benchmark
     fun mstExpression() {
         val expr = algebra.mstInField {
-            symbol("x") * number(2.0) + number(2.0) / symbol("x") - number(16.0)
+            symbol("x") * 2.0 + 2.0 / symbol("x") - 16.0
         }
 
         invokeAndSum(expr)
@@ -37,7 +37,7 @@ internal class ExpressionsInterpretersBenchmark {
     @Benchmark
     fun asmExpression() {
         val expr = algebra.mstInField {
-            symbol("x") * number(2.0) + number(2.0) / symbol("x") - number(16.0)
+            symbol("x") * 2.0 + 2.0 / symbol("x") - 16.0
         }.compile()
 
         invokeAndSum(expr)
diff --git a/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/DotBenchmark.kt b/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/DotBenchmark.kt
index 8823e86db..5c59afaee 100644
--- a/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/DotBenchmark.kt
+++ b/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/DotBenchmark.kt
@@ -2,9 +2,8 @@ package kscience.kmath.benchmarks
 
 import kotlinx.benchmark.Benchmark
 import kscience.kmath.commons.linear.CMMatrixContext
-import kscience.kmath.commons.linear.toCM
 import kscience.kmath.ejml.EjmlMatrixContext
-import kscience.kmath.ejml.toEjml
+
 import kscience.kmath.linear.BufferMatrixContext
 import kscience.kmath.linear.RealMatrixContext
 import kscience.kmath.linear.real
@@ -26,11 +25,11 @@ class DotBenchmark {
         val matrix1 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
         val matrix2 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
 
-        val cmMatrix1 = matrix1.toCM()
-        val cmMatrix2 = matrix2.toCM()
+        val cmMatrix1 = CMMatrixContext { matrix1.toCM() }
+        val cmMatrix2 = CMMatrixContext { matrix2.toCM() }
 
-        val ejmlMatrix1 = matrix1.toEjml()
-        val ejmlMatrix2 = matrix2.toEjml()
+        val ejmlMatrix1 = EjmlMatrixContext { matrix1.toEjml() }
+        val ejmlMatrix2 = EjmlMatrixContext { matrix2.toEjml() }
     }
 
     @Benchmark
@@ -49,22 +48,23 @@ class DotBenchmark {
 
     @Benchmark
     fun ejmlMultiplicationwithConversion() {
-        val ejmlMatrix1 = matrix1.toEjml()
-        val ejmlMatrix2 = matrix2.toEjml()
         EjmlMatrixContext {
+            val ejmlMatrix1 = matrix1.toEjml()
+            val ejmlMatrix2 = matrix2.toEjml()
+
             ejmlMatrix1 dot ejmlMatrix2
         }
     }
 
     @Benchmark
     fun bufferedMultiplication() {
-        BufferMatrixContext(RealField, Buffer.Companion::real).invoke{
+        BufferMatrixContext(RealField, Buffer.Companion::real).invoke {
             matrix1 dot matrix2
         }
     }
 
     @Benchmark
-    fun realMultiplication(){
+    fun realMultiplication() {
         RealMatrixContext {
             matrix1 dot matrix2
         }
diff --git a/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LargeNDBenchmark.kt b/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LargeNDBenchmark.kt
deleted file mode 100644
index 395fde619..000000000
--- a/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LargeNDBenchmark.kt
+++ /dev/null
@@ -1,25 +0,0 @@
-package kscience.kmath.benchmarks
-
-import kscience.kmath.structures.NDField
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
-import org.openjdk.jmh.infra.Blackhole
-import kotlin.random.Random
-
-@State(Scope.Benchmark)
-class LargeNDBenchmark {
-    val arraySize = 10000
-    val RANDOM = Random(222)
-    val src1 = DoubleArray(arraySize) { RANDOM.nextDouble() }
-    val src2 = DoubleArray(arraySize) { RANDOM.nextDouble() }
-    val field = NDField.real(arraySize)
-    val kmathArray1 = field.produce { (a) -> src1[a] }
-    val kmathArray2 = field.produce { (a) -> src2[a] }
-
-    @Benchmark
-    fun test10000(bh: Blackhole) {
-        bh.consume(field.add(kmathArray1, kmathArray2))
-    }
-
-}
\ No newline at end of file
diff --git a/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt b/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt
index ec8714617..5ff43ef80 100644
--- a/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt
+++ b/examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt
@@ -5,10 +5,8 @@ import kotlinx.benchmark.Benchmark
 import kscience.kmath.commons.linear.CMMatrixContext
 import kscience.kmath.commons.linear.CMMatrixContext.dot
 import kscience.kmath.commons.linear.inverse
-import kscience.kmath.commons.linear.toCM
 import kscience.kmath.ejml.EjmlMatrixContext
 import kscience.kmath.ejml.inverse
-import kscience.kmath.ejml.toEjml
 import kscience.kmath.operations.invoke
 import kscience.kmath.structures.Matrix
 import org.openjdk.jmh.annotations.Scope
@@ -35,16 +33,14 @@ class LinearAlgebraBenchmark {
     @Benchmark
     fun cmLUPInversion() {
         CMMatrixContext {
-            val cm = matrix.toCM()             //avoid overhead on conversion
-            inverse(cm)
+            inverse(matrix)
         }
     }
 
     @Benchmark
     fun ejmlInverse() {
         EjmlMatrixContext {
-            val km = matrix.toEjml()      //avoid overhead on conversion
-            inverse(km)
+            inverse(matrix)
         }
     }
 }
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt b/kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt
index 880a4e34c..1a3668855 100644
--- a/kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt
@@ -95,8 +95,9 @@ public open class FunctionalExpressionRing<T, A : Ring<T>>(
         super<FunctionalExpressionSpace>.binaryOperationFunction(operation)
 }
 
-public open class FunctionalExpressionField<T, A : Field<T>>(algebra: A) :
-    FunctionalExpressionRing<T, A>(algebra), Field<Expression<T>> {
+public open class FunctionalExpressionField<T, A : Field<T>>(
+    algebra: A,
+) : FunctionalExpressionRing<T, A>(algebra), Field<Expression<T>> {
     /**
      * Builds an Expression of division an expression by another one.
      */
diff --git a/kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LupDecomposition.kt b/kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LupDecomposition.kt
index f4f998da2..5cf7c8f70 100644
--- a/kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LupDecomposition.kt
+++ b/kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LupDecomposition.kt
@@ -224,6 +224,7 @@ public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext
 ): Matrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
 
 
+@OptIn(UnstableKMathAPI::class)
 public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
     // Use existing decomposition if it is provided by matrix
     val bufferFactory: MutableBufferFactory<Double> = MutableBuffer.Companion::real
diff --git a/kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Complex.kt b/kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Complex.kt
index 5695e6696..c6409c015 100644
--- a/kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Complex.kt
+++ b/kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Complex.kt
@@ -3,6 +3,7 @@ package kscience.kmath.operations
 import kscience.kmath.memory.MemoryReader
 import kscience.kmath.memory.MemorySpec
 import kscience.kmath.memory.MemoryWriter
+import kscience.kmath.misc.UnstableKMathAPI
 import kscience.kmath.structures.Buffer
 import kscience.kmath.structures.MemoryBuffer
 import kscience.kmath.structures.MutableBuffer
@@ -41,6 +42,7 @@ private val PI_DIV_2 = Complex(PI / 2, 0)
 /**
  * A field of [Complex].
  */
+@OptIn(UnstableKMathAPI::class)
 public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, RingWithNumbers<Complex> {
     override val zero: Complex = 0.0.toComplex()
     override val one: Complex = 1.0.toComplex()
diff --git a/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/RealNDField.kt b/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/RealNDField.kt
index 3eb1dc4ca..60e6de440 100644
--- a/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/RealNDField.kt
+++ b/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/RealNDField.kt
@@ -11,7 +11,7 @@ public typealias RealNDElement = BufferedNDFieldElement<Double, RealField>
 public class RealNDField(override val shape: IntArray) :
     BufferedNDField<Double, RealField>,
     ExtendedNDField<Double, RealField, NDBuffer<Double>>,
-    RingWithNumbers<NDBuffer<Double>>{
+    RingWithNumbers<NDBuffer<Double>> {
 
     override val strides: Strides = DefaultStrides(shape)
 
@@ -24,35 +24,31 @@ public class RealNDField(override val shape: IntArray) :
         return produce { d }
     }
 
-    private inline fun buildBuffer(size: Int, crossinline initializer: (Int) -> Double): Buffer<Double> =
-        RealBuffer(DoubleArray(size) { initializer(it) })
-
-    /**
-     * Inline transform an NDStructure to
-     */
-    override fun map(
+    @Suppress("OVERRIDE_BY_INLINE")
+    override inline fun map(
         arg: NDBuffer<Double>,
-        transform: RealField.(Double) -> Double
+        transform: RealField.(Double) -> Double,
     ): RealNDElement {
         check(arg)
-        val array = buildBuffer(arg.strides.linearSize) { offset -> RealField.transform(arg.buffer[offset]) }
+        val array = RealBuffer(arg.strides.linearSize) { offset -> RealField.transform(arg.buffer[offset]) }
         return BufferedNDFieldElement(this, array)
     }
 
-    override fun produce(initializer: RealField.(IntArray) -> Double): RealNDElement {
-        val array = buildBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
+    @Suppress("OVERRIDE_BY_INLINE")
+    override inline fun produce(initializer: RealField.(IntArray) -> Double): RealNDElement {
+        val array = RealBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
         return BufferedNDFieldElement(this, array)
     }
 
-    override fun mapIndexed(
+    @Suppress("OVERRIDE_BY_INLINE")
+    override inline fun mapIndexed(
         arg: NDBuffer<Double>,
-        transform: RealField.(index: IntArray, Double) -> Double
+        transform: RealField.(index: IntArray, Double) -> Double,
     ): RealNDElement {
         check(arg)
-
         return BufferedNDFieldElement(
             this,
-            buildBuffer(arg.strides.linearSize) { offset ->
+            RealBuffer(arg.strides.linearSize) { offset ->
                 elementContext.transform(
                     arg.strides.index(offset),
                     arg.buffer[offset]
@@ -60,16 +56,17 @@ public class RealNDField(override val shape: IntArray) :
             })
     }
 
-    override fun combine(
+    @Suppress("OVERRIDE_BY_INLINE")
+    override inline fun combine(
         a: NDBuffer<Double>,
         b: NDBuffer<Double>,
-        transform: RealField.(Double, Double) -> Double
+        transform: RealField.(Double, Double) -> Double,
     ): RealNDElement {
         check(a, b)
-        return BufferedNDFieldElement(
-            this,
-            buildBuffer(strides.linearSize) { offset -> elementContext.transform(a.buffer[offset], b.buffer[offset]) }
-        )
+        val buffer = RealBuffer(strides.linearSize) { offset ->
+            elementContext.transform(a.buffer[offset], b.buffer[offset])
+        }
+        return BufferedNDFieldElement(this, buffer)
     }
 
     override fun NDBuffer<Double>.toElement(): FieldElement<NDBuffer<Double>, *, out BufferedNDField<Double, RealField>> =
diff --git a/kmath-core/src/commonTest/kotlin/kscience/kmath/linear/MatrixTest.kt b/kmath-core/src/commonTest/kotlin/kscience/kmath/linear/MatrixTest.kt
index 0a582e339..d7755dcb5 100644
--- a/kmath-core/src/commonTest/kotlin/kscience/kmath/linear/MatrixTest.kt
+++ b/kmath-core/src/commonTest/kotlin/kscience/kmath/linear/MatrixTest.kt
@@ -7,6 +7,7 @@ import kscience.kmath.structures.as2D
 import kotlin.test.Test
 import kotlin.test.assertEquals
 
+@Suppress("UNUSED_VARIABLE")
 class MatrixTest {
     @Test
     fun testTranspose() {
diff --git a/kmath-core/src/commonTest/kotlin/kscience/kmath/structures/NumberNDFieldTest.kt b/kmath-core/src/commonTest/kotlin/kscience/kmath/structures/NumberNDFieldTest.kt
index f5e008ef3..22a0d3629 100644
--- a/kmath-core/src/commonTest/kotlin/kscience/kmath/structures/NumberNDFieldTest.kt
+++ b/kmath-core/src/commonTest/kotlin/kscience/kmath/structures/NumberNDFieldTest.kt
@@ -8,6 +8,7 @@ import kotlin.math.pow
 import kotlin.test.Test
 import kotlin.test.assertEquals
 
+@Suppress("UNUSED_VARIABLE")
 class NumberNDFieldTest {
     val array1: RealNDElement = real2D(3, 3) { i, j -> (i + j).toDouble() }
     val array2: RealNDElement = real2D(3, 3) { i, j -> (i - j).toDouble() }
diff --git a/kmath-dimensions/src/commonTest/kotlin/kscience/dimensions/DMatrixContextTest.kt b/kmath-dimensions/src/commonTest/kotlin/kscience/dimensions/DMatrixContextTest.kt
index 5b330fcce..b9193d4dd 100644
--- a/kmath-dimensions/src/commonTest/kotlin/kscience/dimensions/DMatrixContextTest.kt
+++ b/kmath-dimensions/src/commonTest/kotlin/kscience/dimensions/DMatrixContextTest.kt
@@ -6,6 +6,7 @@ import kscience.kmath.dimensions.DMatrixContext
 import kscience.kmath.dimensions.one
 import kotlin.test.Test
 
+@Suppress("UNUSED_VARIABLE")
 internal class DMatrixContextTest {
     @Test
     fun testDimensionSafeMatrix() {

From a11711c336e7698a7cf418e5e727d3ed338cb45c Mon Sep 17 00:00:00 2001
From: Alexander Nozik <altavir@gmail.com>
Date: Tue, 19 Jan 2021 22:25:04 +0300
Subject: [PATCH 004/178] Tensor algebra refactoring

---
 .../{structures => tensors}/TensorAlgebra.kt  | 31 ++++++++++---------
 1 file changed, 17 insertions(+), 14 deletions(-)
 rename kmath-core/src/commonMain/kotlin/kscience/kmath/{structures => tensors}/TensorAlgebra.kt (76%)

diff --git a/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/kscience/kmath/tensors/TensorAlgebra.kt
similarity index 76%
rename from kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt
rename to kmath-core/src/commonMain/kotlin/kscience/kmath/tensors/TensorAlgebra.kt
index ca7619429..c40544579 100644
--- a/kmath-core/src/commonMain/kotlin/kscience/kmath/structures/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/kscience/kmath/tensors/TensorAlgebra.kt
@@ -1,32 +1,36 @@
-package kscience.kmath.structures
+package kscience.kmath.tensors
 
-public interface TensorStructure<T> : MutableNDStructure<T> {
-    // A tensor can have empty shape, in which case it represents just a value
-    public abstract fun value(): T
-}
+import kscience.kmath.operations.Ring
+import kscience.kmath.structures.MutableNDStructure
+
+public typealias Tensor<T> = MutableNDStructure<T>
+
+public val <T : Any> Tensor<T>.value: T
+    get() {
+        require(shape.contentEquals(intArrayOf(1))) { "Value available only for a tensor with no dimensions" }
+        return get(intArrayOf(0))
+    }
 
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
-
-public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
+/**
+ * To be moved to a separate project
+ */
+public interface TensorAlgebra<T, TensorType : Tensor<T>> : Ring<TensorType> {
 
     public operator fun T.plus(other: TensorType): TensorType
     public operator fun TensorType.plus(value: T): TensorType
-    public operator fun TensorType.plus(other: TensorType): TensorType
     public operator fun TensorType.plusAssign(value: T): Unit
     public operator fun TensorType.plusAssign(other: TensorType): Unit
 
     public operator fun T.minus(other: TensorType): TensorType
     public operator fun TensorType.minus(value: T): TensorType
-    public operator fun TensorType.minus(other: TensorType): TensorType
     public operator fun TensorType.minusAssign(value: T): Unit
     public operator fun TensorType.minusAssign(other: TensorType): Unit
 
     public operator fun T.times(other: TensorType): TensorType
     public operator fun TensorType.times(value: T): TensorType
-    public operator fun TensorType.times(other: TensorType): TensorType
     public operator fun TensorType.timesAssign(value: T): Unit
     public operator fun TensorType.timesAssign(other: TensorType): Unit
-    public operator fun TensorType.unaryMinus(): TensorType
 
 
     public infix fun TensorType.dot(other: TensorType): TensorType
@@ -35,7 +39,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     public fun diagonalEmbedding(
         diagonalEntries: TensorType,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+        offset: Int = 0, dim1: Int = -2, dim2: Int = -1,
     ): TensorType
 
     public fun TensorType.transpose(i: Int, j: Int): TensorType
@@ -51,8 +55,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
 // https://proofwiki.org/wiki/Definition:Division_Algebra
 
-public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
-    TensorAlgebra<T, TensorType> {
+public interface TensorPartialDivisionAlgebra<T, TensorType : Tensor<T>> : TensorAlgebra<T, TensorType> {
 
     public operator fun TensorType.div(other: TensorType): TensorType
     public operator fun TensorType.divAssign(other: TensorType)

From 9011579d2c777b4844e4fbb10da9c077ce027c24 Mon Sep 17 00:00:00 2001
From: Alexander Nozik <altavir@gmail.com>
Date: Wed, 20 Jan 2021 11:43:01 +0300
Subject: [PATCH 005/178] [WIP] Tensor algebra design

---
 build.gradle.kts                              |  2 +-
 .../kscience/kmath/tensors/TensorAlgebra.kt   | 74 ++++++++++---------
 2 files changed, 40 insertions(+), 36 deletions(-)

diff --git a/build.gradle.kts b/build.gradle.kts
index d171bd608..0572217af 100644
--- a/build.gradle.kts
+++ b/build.gradle.kts
@@ -4,7 +4,7 @@ plugins {
     id("ru.mipt.npm.project")
 }
 
-internal val kmathVersion: String by extra("0.2.0-dev-5")
+internal val kmathVersion: String by extra("0.2.0-dev-6")
 internal val bintrayRepo: String by extra("kscience")
 internal val githubProject: String by extra("kmath")
 
diff --git a/kmath-core/src/commonMain/kotlin/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/kscience/kmath/tensors/TensorAlgebra.kt
index c40544579..c14f0ee0d 100644
--- a/kmath-core/src/commonMain/kotlin/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/kscience/kmath/tensors/TensorAlgebra.kt
@@ -1,10 +1,13 @@
 package kscience.kmath.tensors
 
+import kscience.kmath.misc.UnstableKMathAPI
+import kscience.kmath.operations.Field
 import kscience.kmath.operations.Ring
 import kscience.kmath.structures.MutableNDStructure
 
 public typealias Tensor<T> = MutableNDStructure<T>
 
+@UnstableKMathAPI
 public val <T : Any> Tensor<T>.value: T
     get() {
         require(shape.contentEquals(intArrayOf(1))) { "Value available only for a tensor with no dimensions" }
@@ -13,59 +16,60 @@ public val <T : Any> Tensor<T>.value: T
 
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 /**
- * To be moved to a separate project
+ * TODO To be moved to a separate project
  */
-public interface TensorAlgebra<T, TensorType : Tensor<T>> : Ring<TensorType> {
+@UnstableKMathAPI
+public interface TensorAlgebra<T, TT : Tensor<T>> : Ring<TT> {
 
-    public operator fun T.plus(other: TensorType): TensorType
-    public operator fun TensorType.plus(value: T): TensorType
-    public operator fun TensorType.plusAssign(value: T): Unit
-    public operator fun TensorType.plusAssign(other: TensorType): Unit
+    public operator fun T.plus(other: TT): TT
+    public operator fun TT.plus(value: T): TT
+    public operator fun TT.plusAssign(value: T): Unit
+    public operator fun TT.plusAssign(other: TT): Unit
 
-    public operator fun T.minus(other: TensorType): TensorType
-    public operator fun TensorType.minus(value: T): TensorType
-    public operator fun TensorType.minusAssign(value: T): Unit
-    public operator fun TensorType.minusAssign(other: TensorType): Unit
+    public operator fun T.minus(other: TT): TT
+    public operator fun TT.minus(value: T): TT
+    public operator fun TT.minusAssign(value: T): Unit
+    public operator fun TT.minusAssign(other: TT): Unit
 
-    public operator fun T.times(other: TensorType): TensorType
-    public operator fun TensorType.times(value: T): TensorType
-    public operator fun TensorType.timesAssign(value: T): Unit
-    public operator fun TensorType.timesAssign(other: TensorType): Unit
+    public operator fun T.times(other: TT): TT
+    public operator fun TT.times(value: T): TT
+    public operator fun TT.timesAssign(value: T): Unit
+    public operator fun TT.timesAssign(other: TT): Unit
 
 
-    public infix fun TensorType.dot(other: TensorType): TensorType
-    public infix fun TensorType.dotAssign(other: TensorType): Unit
-    public infix fun TensorType.dotRightAssign(other: TensorType): Unit
+    public infix fun TT.dot(other: TT): TT
+    public infix fun TT.dotAssign(other: TT): Unit
+    public infix fun TT.dotRightAssign(other: TT): Unit
 
     public fun diagonalEmbedding(
-        diagonalEntries: TensorType,
+        diagonalEntries: TT,
         offset: Int = 0, dim1: Int = -2, dim2: Int = -1,
-    ): TensorType
+    ): TT
 
-    public fun TensorType.transpose(i: Int, j: Int): TensorType
-    public fun TensorType.transposeAssign(i: Int, j: Int): Unit
+    public fun TT.transpose(i: Int, j: Int): TT
+    public fun TT.transposeAssign(i: Int, j: Int): Unit
 
-    public fun TensorType.view(shape: IntArray): TensorType
+    public fun TT.view(shape: IntArray): TT
 
-    public fun TensorType.abs(): TensorType
-    public fun TensorType.absAssign(): Unit
-    public fun TensorType.sum(): TensorType
-    public fun TensorType.sumAssign(): Unit
+    public fun abs(tensor: TT): TT
+    public fun TT.absAssign(): Unit
+    public fun TT.sum(): TT
+    public fun TT.sumAssign(): Unit
 }
 
 // https://proofwiki.org/wiki/Definition:Division_Algebra
 
-public interface TensorPartialDivisionAlgebra<T, TensorType : Tensor<T>> : TensorAlgebra<T, TensorType> {
+public interface TensorPartialDivisionAlgebra<T, TT : Tensor<T>> :
+    TensorAlgebra<T, TT>, Field<TT> {
 
-    public operator fun TensorType.div(other: TensorType): TensorType
-    public operator fun TensorType.divAssign(other: TensorType)
+    public operator fun TT.divAssign(other: TT)
 
-    public fun TensorType.exp(): TensorType
-    public fun TensorType.expAssign(): Unit
-    public fun TensorType.log(): TensorType
-    public fun TensorType.logAssign(): Unit
+    public fun exp(tensor: TT): TT
+    public fun TT.expAssign(): Unit
+    public fun log(tensor: TT): TT
+    public fun TT.logAssign(): Unit
 
-    public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
-    public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
+    public fun svd(tensor: TT): Triple<TT, TT, TT>
+    public fun symEig(tensor: TT, eigenvectors: Boolean = true): Pair<TT, TT>
 
 }
\ No newline at end of file

From f15ac203236355117e72f81159af7a496f5c6706 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 1 Mar 2021 22:52:58 +0000
Subject: [PATCH 006/178] Cannot afford to inherit from RingWithNumbers

---
 .../kmath/tensors/RealTensorAlgebra.kt        | 35 +++++++++----------
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  9 ++---
 2 files changed, 21 insertions(+), 23 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index 0a28ace4f..6023d2b72 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -19,25 +19,6 @@ public class RealTensor(
 
 public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 
-    override fun add(a: RealTensor, b: RealTensor): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun multiply(a: RealTensor, k: Number): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override val zero: RealTensor
-        get() = TODO("Not yet implemented")
-
-    override fun multiply(a: RealTensor, b: RealTensor): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override val one: RealTensor
-        get() = TODO("Not yet implemented")
-
-
     override fun Double.plus(other: RealTensor): RealTensor {
         val n = other.buffer.size
         val arr = other.buffer.array
@@ -51,6 +32,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun RealTensor.plus(other: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.plusAssign(value: Double) {
         TODO("Not yet implemented")
     }
@@ -67,6 +52,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun RealTensor.minus(other: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.minusAssign(value: Double) {
         TODO("Not yet implemented")
     }
@@ -83,6 +72,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun RealTensor.times(other: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.timesAssign(value: Double) {
         TODO("Not yet implemented")
     }
@@ -91,6 +84,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun RealTensor.unaryMinus(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.dot(other: RealTensor): RealTensor {
         TODO("Not yet implemented")
     }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 83c45feec..137230d29 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -1,25 +1,26 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.operations.Ring
-import space.kscience.kmath.operations.RingWithNumbers
-
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
-public interface TensorAlgebra<T, TensorType : TensorStructure<T>>: RingWithNumbers<TensorType> {
+public interface TensorAlgebra<T, TensorType : TensorStructure<T>>{
 
     public operator fun T.plus(other: TensorType): TensorType
     public operator fun TensorType.plus(value: T): TensorType
+    public operator fun TensorType.plus(other: TensorType): TensorType
     public operator fun TensorType.plusAssign(value: T): Unit
     public operator fun TensorType.plusAssign(other: TensorType): Unit
 
     public operator fun T.minus(other: TensorType): TensorType
     public operator fun TensorType.minus(value: T): TensorType
+    public operator fun TensorType.minus(other: TensorType): TensorType
     public operator fun TensorType.minusAssign(value: T): Unit
     public operator fun TensorType.minusAssign(other: TensorType): Unit
 
     public operator fun T.times(other: TensorType): TensorType
     public operator fun TensorType.times(value: T): TensorType
+    public operator fun TensorType.times(other: TensorType): TensorType
     public operator fun TensorType.timesAssign(value: T): Unit
     public operator fun TensorType.timesAssign(other: TensorType): Unit
+    public operator fun TensorType.unaryMinus(): TensorType
 
 
     //https://pytorch.org/docs/stable/generated/torch.matmul.html

From 4aa1df0628c5d531b518691eb8ab22b907946eb0 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 2 Mar 2021 09:32:36 +0000
Subject: [PATCH 007/178] Safe item method and view_as added

---
 .../space/kscience/kmath/tensors/RealTensorAlgebra.kt  | 10 +++++++++-
 .../space/kscience/kmath/tensors/TensorAlgebra.kt      |  1 +
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index 6023d2b72..24612a6aa 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -14,9 +14,13 @@ public class RealTensor(
         TensorStrides(shape),
         RealBuffer(buffer)
     ) {
-    override fun item(): Double = buffer[0]
+    override fun item(): Double {
+        check(buffer.size > 0) { "The tensor is empty" }
+        return buffer[0]
+    }
 }
 
+
 public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 
     override fun Double.plus(other: RealTensor): RealTensor {
@@ -116,6 +120,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun RealTensor.view_as(other: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.abs(): RealTensor {
         TODO("Not yet implemented")
     }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 137230d29..cddda7670 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -40,6 +40,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>>{
 
     //https://pytorch.org/docs/stable/tensor_view.html
     public fun TensorType.view(shape: IntArray): TensorType
+    public fun TensorType.view_as(other: TensorType): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.abs.html
     public fun TensorType.abs(): TensorType

From ed007589cfc97d4b6c8d01a51719f2511d0ee3b3 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 2 Mar 2021 14:54:07 +0000
Subject: [PATCH 008/178] LU decomposition set up

---
 .../kscience/kmath/tensors/RealTensorAlgebra.kt     | 13 +++++++++++++
 .../space/kscience/kmath/tensors/TensorAlgebra.kt   |  3 +++
 2 files changed, 16 insertions(+)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index 24612a6aa..f234d7d6a 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -1,5 +1,6 @@
 package space.kscience.kmath.tensors
 
+import space.kscience.kmath.linear.LupDecompositionFeature
 import space.kscience.kmath.nd.MutableNDBuffer
 import space.kscience.kmath.structures.RealBuffer
 import space.kscience.kmath.structures.array
@@ -164,7 +165,19 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun RealTensor.lu(): Pair<RealTensor, RealTensor> {
+        /**
+         * Main first task for @AndreiKingsley
+         * Compare with the implementation of [LupDecomposition]
+         * and provide a common API
+         */
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {
+        /**
+         * Main first task for @AlyaNovikova
+         */
         TODO("Not yet implemented")
     }
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index cddda7670..03f6f05c2 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -66,6 +66,9 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
     public fun TensorType.log(): TensorType
     public fun TensorType.logAssign(): Unit
 
+    //https://pytorch.org/docs/stable/generated/torch.lu.html
+    public fun TensorType.lu(): Pair<TensorType, TensorType>
+
     //https://pytorch.org/docs/stable/generated/torch.svd.html
     public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
 

From 90f4ff06fd76fb60f4966d8d60fa31c6b4ebf96f Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Thu, 4 Mar 2021 13:22:28 +0000
Subject: [PATCH 009/178] Basic tasks for Alya and Andrei

---
 .../kmath/tensors/RealTensorAlgebra.kt        | 58 +++++++++++--------
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  7 ++-
 .../kscience/kmath/tensors/TensorStructure.kt |  4 ++
 3 files changed, 44 insertions(+), 25 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index f234d7d6a..cad01c4d0 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -1,6 +1,5 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.linear.LupDecompositionFeature
 import space.kscience.kmath.nd.MutableNDBuffer
 import space.kscience.kmath.structures.RealBuffer
 import space.kscience.kmath.structures.array
@@ -15,6 +14,9 @@ public class RealTensor(
         TensorStrides(shape),
         RealBuffer(buffer)
     ) {
+    /*
+     * TODO: Andrei remove item()
+     */
     override fun item(): Double {
         check(buffer.size > 0) { "The tensor is empty" }
         return buffer[0]
@@ -24,6 +26,10 @@ public class RealTensor(
 
 public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 
+    override fun RealTensor.value(): Double {
+        TODO("Andrei")
+    }
+
     override fun Double.plus(other: RealTensor): RealTensor {
         val n = other.buffer.size
         val arr = other.buffer.array
@@ -34,95 +40,95 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
     }
 
     override fun RealTensor.plus(value: Double): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.plus(other: RealTensor): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.plusAssign(value: Double) {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.plusAssign(other: RealTensor) {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun Double.minus(other: RealTensor): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun RealTensor.minus(value: Double): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun RealTensor.minus(other: RealTensor): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun RealTensor.minusAssign(value: Double) {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun RealTensor.minusAssign(other: RealTensor) {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun Double.times(other: RealTensor): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.times(value: Double): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.times(other: RealTensor): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.timesAssign(value: Double) {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.timesAssign(other: RealTensor) {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.unaryMinus(): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.dot(other: RealTensor): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun RealTensor.dotAssign(other: RealTensor) {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun RealTensor.dotRightAssign(other: RealTensor) {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun diagonalEmbedding(diagonalEntries: RealTensor, offset: Int, dim1: Int, dim2: Int): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun RealTensor.transpose(i: Int, j: Int): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun RealTensor.transposeAssign(i: Int, j: Int) {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
     override fun RealTensor.view(shape: IntArray): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.view_as(other: RealTensor): RealTensor {
-        TODO("Not yet implemented")
+        TODO("Andrei")
     }
 
     override fun RealTensor.abs(): RealTensor {
@@ -174,6 +180,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun lu_unpack(A_LU: RealTensor, pivots: RealTensor): Triple<RealTensor, RealTensor, RealTensor> {
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {
         /**
          * Main first task for @AlyaNovikova
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 03f6f05c2..b2586e1e9 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -1,7 +1,9 @@
 package space.kscience.kmath.tensors
 
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
-public interface TensorAlgebra<T, TensorType : TensorStructure<T>>{
+public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
+
+    public fun TensorType.value(): T
 
     public operator fun T.plus(other: TensorType): TensorType
     public operator fun TensorType.plus(value: T): TensorType
@@ -69,6 +71,9 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
     //https://pytorch.org/docs/stable/generated/torch.lu.html
     public fun TensorType.lu(): Pair<TensorType, TensorType>
 
+    //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
+    public fun lu_unpack(A_LU: TensorType, pivots: TensorType): Triple<TensorType, TensorType, TensorType>
+
     //https://pytorch.org/docs/stable/generated/torch.svd.html
     public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
index 6d2d855b6..3775ea2e5 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
@@ -3,6 +3,10 @@ package space.kscience.kmath.tensors
 import space.kscience.kmath.nd.MutableNDStructure
 
 public interface TensorStructure<T> : MutableNDStructure<T> {
+
+    /*
+    * TODO: Andrei remove item() and value()
+     */
     public fun item(): T
 
     // A tensor can have empty shape, in which case it represents just a value

From fe55856a26bd92c7389c57a17df9f908ea2cc343 Mon Sep 17 00:00:00 2001
From: Your Name <kisandy@yandex.ru>
Date: Tue, 9 Mar 2021 22:18:46 +0300
Subject: [PATCH 010/178] basic implements & refactor tensor structure & and
 new tensor algebra methods

---
 .../kmath/tensors/RealTensorAlgebra.kt        | 137 ++++++++++++------
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  14 +-
 .../kscience/kmath/tensors/TensorStrides.kt   |   4 +-
 .../kscience/kmath/tensors/TensorStructure.kt |  23 +--
 4 files changed, 111 insertions(+), 67 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index cad01c4d0..13c57fc18 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -3,6 +3,8 @@ package space.kscience.kmath.tensors
 import space.kscience.kmath.nd.MutableNDBuffer
 import space.kscience.kmath.structures.RealBuffer
 import space.kscience.kmath.structures.array
+import kotlin.js.JsName
+import kotlin.math.abs
 
 
 public class RealTensor(
@@ -13,46 +15,80 @@ public class RealTensor(
     MutableNDBuffer<Double>(
         TensorStrides(shape),
         RealBuffer(buffer)
-    ) {
-    /*
-     * TODO: Andrei remove item()
-     */
-    override fun item(): Double {
-        check(buffer.size > 0) { "The tensor is empty" }
-        return buffer[0]
-    }
-}
-
+    )
 
 public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 
+    //rename to item?
     override fun RealTensor.value(): Double {
-        TODO("Andrei")
+        check(this.dimension == 0) {
+            // todo change message
+            "This tensor has shape ${shape.toList()}"
+        }
+        return this.buffer.array[0]
+    }
+
+    override fun eye(n: Int): RealTensor {
+        val shape = intArrayOf(n, n)
+        val buffer = DoubleArray(n * n) { 0.0 }
+        val res = RealTensor(shape, buffer)
+        for (i in 0 until n) {
+            res[intArrayOf(i, i)] = 1.0
+        }
+        return res
+    }
+
+    override fun zeros(shape: IntArray): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun zeroesLike(other: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun ones(shape: IntArray): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun onesLike(shape: IntArray): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.copy(): RealTensor {
+        TODO("Not yet implemented")
     }
 
     override fun Double.plus(other: RealTensor): RealTensor {
-        val n = other.buffer.size
-        val arr = other.buffer.array
-        val res = DoubleArray(n)
-        for (i in 1..n)
-            res[i - 1] = arr[i - 1] + this
-        return RealTensor(other.shape, res)
+        //todo should be change with broadcasting
+        val resBuffer = DoubleArray(other.buffer.size) { i ->
+            other.buffer.array[i] + this
+        }
+        return RealTensor(other.shape, resBuffer)
     }
 
-    override fun RealTensor.plus(value: Double): RealTensor {
-        TODO("Andrei")
-    }
+    //todo should be change with broadcasting
+    override fun RealTensor.plus(value: Double): RealTensor = value + this
 
     override fun RealTensor.plus(other: RealTensor): RealTensor {
-        TODO("Andrei")
+        //todo should be change with broadcasting
+        val resBuffer = DoubleArray(this.buffer.size) { i ->
+            this.buffer.array[i] + other.buffer.array[i]
+        }
+        return RealTensor(this.shape, resBuffer)
     }
 
     override fun RealTensor.plusAssign(value: Double) {
-        TODO("Andrei")
+        //todo should be change with broadcasting
+        for (i in this.buffer.array.indices) {
+            this.buffer.array[i] += value
+        }
     }
 
     override fun RealTensor.plusAssign(other: RealTensor) {
-        TODO("Andrei")
+        //todo should be change with broadcasting
+        for (i in this.buffer.array.indices) {
+            this.buffer.array[i] += other.buffer.array[i]
+        }
     }
 
     override fun Double.minus(other: RealTensor): RealTensor {
@@ -76,27 +112,43 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
     }
 
     override fun Double.times(other: RealTensor): RealTensor {
-        TODO("Andrei")
+        //todo should be change with broadcasting
+        val resBuffer = DoubleArray(other.buffer.size) { i ->
+            other.buffer.array[i] * this
+        }
+        return RealTensor(other.shape, resBuffer)
     }
 
-    override fun RealTensor.times(value: Double): RealTensor {
-        TODO("Andrei")
-    }
+    //todo should be change with broadcasting
+    override fun RealTensor.times(value: Double): RealTensor = value * this
 
     override fun RealTensor.times(other: RealTensor): RealTensor {
-        TODO("Andrei")
+        //todo should be change with broadcasting
+        val resBuffer = DoubleArray(this.buffer.size) { i ->
+            this.buffer.array[i] * other.buffer.array[i]
+        }
+        return RealTensor(this.shape, resBuffer)
     }
 
     override fun RealTensor.timesAssign(value: Double) {
-        TODO("Andrei")
+        //todo should be change with broadcasting
+        for (i in this.buffer.array.indices) {
+            this.buffer.array[i] *= value
+        }
     }
 
     override fun RealTensor.timesAssign(other: RealTensor) {
-        TODO("Andrei")
+        //todo should be change with broadcasting
+        for (i in this.buffer.array.indices) {
+            this.buffer.array[i] *= other.buffer.array[i]
+        }
     }
 
     override fun RealTensor.unaryMinus(): RealTensor {
-        TODO("Andrei")
+        val resBuffer = DoubleArray(this.buffer.size) { i ->
+            this.buffer.array[i].unaryMinus()
+        }
+        return RealTensor(this.shape, resBuffer)
     }
 
     override fun RealTensor.dot(other: RealTensor): RealTensor {
@@ -124,11 +176,11 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
     }
 
     override fun RealTensor.view(shape: IntArray): RealTensor {
-        TODO("Andrei")
+        return RealTensor(shape, this.buffer.array)
     }
 
-    override fun RealTensor.view_as(other: RealTensor): RealTensor {
-        TODO("Andrei")
+    override fun RealTensor.viewAs(other: RealTensor): RealTensor {
+        return this.view(other.shape)
     }
 
     override fun RealTensor.abs(): RealTensor {
@@ -147,10 +199,18 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun RealTensor.div(value: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.div(other: RealTensor): RealTensor {
         TODO("Not yet implemented")
     }
 
+    override fun RealTensor.divAssign(value: Double) {
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.divAssign(other: RealTensor) {
         TODO("Not yet implemented")
     }
@@ -172,15 +232,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
     }
 
     override fun RealTensor.lu(): Pair<RealTensor, RealTensor> {
-        /**
-         * Main first task for @AndreiKingsley
-         * Compare with the implementation of [LupDecomposition]
-         * and provide a common API
-         */
-        TODO("Not yet implemented")
+        TODO()
     }
 
-    override fun lu_unpack(A_LU: RealTensor, pivots: RealTensor): Triple<RealTensor, RealTensor, RealTensor> {
+    override fun luUnpack(A_LU: RealTensor, pivots: RealTensor): Triple<RealTensor, RealTensor, RealTensor> {
         TODO("Not yet implemented")
     }
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index b2586e1e9..6f42623e0 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -5,6 +5,14 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     public fun TensorType.value(): T
 
+    public fun eye(n: Int): TensorType
+    public fun zeros(shape: IntArray): TensorType
+    public fun zeroesLike(other: TensorType): TensorType
+    public fun ones(shape: IntArray): TensorType
+    public fun onesLike(shape: IntArray): TensorType
+
+    public fun TensorType.copy(): TensorType
+
     public operator fun T.plus(other: TensorType): TensorType
     public operator fun TensorType.plus(value: T): TensorType
     public operator fun TensorType.plus(other: TensorType): TensorType
@@ -42,7 +50,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     //https://pytorch.org/docs/stable/tensor_view.html
     public fun TensorType.view(shape: IntArray): TensorType
-    public fun TensorType.view_as(other: TensorType): TensorType
+    public fun TensorType.viewAs(other: TensorType): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.abs.html
     public fun TensorType.abs(): TensorType
@@ -57,7 +65,9 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
     TensorAlgebra<T, TensorType> {
 
+    public operator fun TensorType.div(value: T): TensorType
     public operator fun TensorType.div(other: TensorType): TensorType
+    public operator fun TensorType.divAssign(value: T)
     public operator fun TensorType.divAssign(other: TensorType)
 
     //https://pytorch.org/docs/stable/generated/torch.exp.html
@@ -72,7 +82,7 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
     public fun TensorType.lu(): Pair<TensorType, TensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
-    public fun lu_unpack(A_LU: TensorType, pivots: TensorType): Triple<TensorType, TensorType, TensorType>
+    public fun luUnpack(A_LU: TensorType, pivots: TensorType): Triple<TensorType, TensorType, TensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.svd.html
     public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
index 3ea8f4bf0..dbaefe907 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
@@ -5,7 +5,7 @@ import space.kscience.kmath.nd.offsetFromIndex
 import kotlin.math.max
 
 
-inline public fun stridesFromShape(shape: IntArray): IntArray {
+public inline fun stridesFromShape(shape: IntArray): IntArray {
     val nDim = shape.size
     val res = IntArray(nDim)
     if (nDim == 0)
@@ -22,7 +22,7 @@ inline public fun stridesFromShape(shape: IntArray): IntArray {
 
 }
 
-inline public fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {
+public inline fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {
     val res = IntArray(nDim)
     var current = offset
     var strideIndex = 0
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
index 3775ea2e5..5463877ce 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
@@ -2,26 +2,5 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.nd.MutableNDStructure
 
-public interface TensorStructure<T> : MutableNDStructure<T> {
+public typealias TensorStructure<T> = MutableNDStructure<T>
 
-    /*
-    * TODO: Andrei remove item() and value()
-     */
-    public fun item(): T
-
-    // A tensor can have empty shape, in which case it represents just a value
-    public fun value(): T {
-        checkIsValue()
-        return item()
-    }
-}
-
-public inline fun <T> TensorStructure<T>.isValue(): Boolean {
-    return (dimension == 0)
-}
-
-public inline fun <T> TensorStructure<T>.isNotValue(): Boolean = !this.isValue()
-
-public inline fun <T> TensorStructure<T>.checkIsValue(): Unit = check(this.isValue()) {
-    "This tensor has shape ${shape.toList()}"
-}
\ No newline at end of file

From 3a37b88b5c4808107e2522d5f33eb07440169fb9 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 9 Mar 2021 21:13:48 +0000
Subject: [PATCH 011/178] Fixed getting value test for tensors

---
 .../space/kscience/kmath/tensors/RealTensorAlgebra.kt     | 8 ++------
 .../kotlin/space/kscience/kmath/tensors/TensorStrides.kt  | 2 +-
 .../kotlin/space/kscience/kmath/tensors/TestRealTensor.kt | 4 ++--
 3 files changed, 5 insertions(+), 9 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index 13c57fc18..366acfb62 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -3,8 +3,6 @@ package space.kscience.kmath.tensors
 import space.kscience.kmath.nd.MutableNDBuffer
 import space.kscience.kmath.structures.RealBuffer
 import space.kscience.kmath.structures.array
-import kotlin.js.JsName
-import kotlin.math.abs
 
 
 public class RealTensor(
@@ -19,11 +17,9 @@ public class RealTensor(
 
 public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 
-    //rename to item?
     override fun RealTensor.value(): Double {
-        check(this.dimension == 0) {
-            // todo change message
-            "This tensor has shape ${shape.toList()}"
+        check(this.shape contentEquals  intArrayOf(1)) {
+            "Inconsistent value for tensor of shape ${shape.toList()}"
         }
         return this.buffer.array[0]
     }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
index dbaefe907..0c10203f0 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
@@ -48,5 +48,5 @@ public class TensorStrides(override val shape: IntArray): Strides
         indexFromOffset(offset, strides, shape.size)
 
     override val linearSize: Int
-        get() = shape.fold(1) { acc, i -> acc * i }
+        get() = shape.reduce(Int::times)
 }
\ No newline at end of file
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
index 7938eb864..d96b00baf 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
@@ -9,9 +9,9 @@ import kotlin.test.assertTrue
 class TestRealTensor {
 
     @Test
-    fun valueTest(){
+    fun valueTest() = RealTensorAlgebra {
         val value = 12.5
-        val tensor = RealTensor(IntArray(0), doubleArrayOf(value))
+        val tensor = RealTensor(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
     }
 

From 723e0e458e7be15e3388e4bef6654db487d2cf03 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Thu, 11 Mar 2021 23:04:42 +0300
Subject: [PATCH 012/178] add functions transpose and transposeAssign

---
 .../space/kscience/kmath/nd/NDStructure.kt    |  9 ++++
 .../kmath/tensors/RealTensorAlgebra.kt        | 26 +++++++++-
 .../kscience/kmath/tensors/TensorStrides.kt   | 20 ++++++++
 .../kmath/tensors/TestRealTensorAlgebra.kt    | 51 +++++++++++++++++++
 4 files changed, 104 insertions(+), 2 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/NDStructure.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/NDStructure.kt
index 54e410ade..e458d0606 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/NDStructure.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/NDStructure.kt
@@ -174,6 +174,11 @@ public interface Strides {
      */
     public fun index(offset: Int): IntArray
 
+    /**
+     * Get next multidimensional index from the current multidimensional index
+     */
+    public fun nextIndex(index: IntArray): IntArray
+
     /**
      * The size of linear buffer to accommodate all elements of ND-structure corresponding to strides
      */
@@ -232,6 +237,10 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
         return res
     }
 
+    override fun nextIndex(index: IntArray): IntArray {
+        TODO("Not yet implemented")
+    }
+
     override fun equals(other: Any?): Boolean {
         if (this === other) return true
         if (other !is DefaultStrides) return false
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index 366acfb62..a5c00e8ec 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -164,11 +164,33 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
     }
 
     override fun RealTensor.transpose(i: Int, j: Int): RealTensor {
-        TODO("Alya")
+        val n = this.buffer.size
+        val resBuffer = DoubleArray(n)
+
+        val resShape = this.shape.copyOf()
+        resShape[i] = resShape[j].also { resShape[j] = resShape[i] }
+
+        val resTensor = RealTensor(resShape, resBuffer)
+
+        for (offset in 0 until n) {
+            val oldMultiIndex = this.strides.index(offset)
+            val newMultiIndex = oldMultiIndex.copyOf()
+            newMultiIndex[i] = newMultiIndex[j].also { newMultiIndex[j] = newMultiIndex[i] }
+
+            val linearIndex = resTensor.strides.offset(newMultiIndex)
+            resTensor.buffer.array[linearIndex] = this.buffer.array[offset]
+        }
+        return resTensor
     }
 
     override fun RealTensor.transposeAssign(i: Int, j: Int) {
-        TODO("Alya")
+        val transposedTensor = this.transpose(i, j)
+        for (i in transposedTensor.shape.indices) {
+            this.shape[i] = transposedTensor.shape[i]
+        }
+        for (i in transposedTensor.buffer.array.indices) {
+            this.buffer.array[i] = transposedTensor.buffer.array[i]
+        }
     }
 
     override fun RealTensor.view(shape: IntArray): RealTensor {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
index 0c10203f0..d6a6f5f16 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
@@ -35,6 +35,23 @@ public inline fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): In
     return res
 }
 
+public inline fun nextIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
+    val res = index.copyOf()
+    var current = nDim - 1
+    var carry = 0
+
+    do {
+        res[current]++
+        if (res[current] >= shape[current]) {
+            carry = 1
+            res[current] = 0
+        }
+        current--
+    } while(carry != 0 && current >= 0)
+
+    return res
+}
+
 
 
 public class TensorStrides(override val shape: IntArray): Strides
@@ -47,6 +64,9 @@ public class TensorStrides(override val shape: IntArray): Strides
     override fun index(offset: Int): IntArray =
         indexFromOffset(offset, strides, shape.size)
 
+    override fun nextIndex(index: IntArray): IntArray =
+        nextIndex(index, shape, shape.size)
+
     override val linearSize: Int
         get() = shape.reduce(Int::times)
 }
\ No newline at end of file
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
index 86caa0338..8e95922b8 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
@@ -13,4 +13,55 @@ class TestRealTensorAlgebra {
         assertTrue(res.buffer.array contentEquals doubleArrayOf(11.0,12.0))
     }
 
+    @Test
+    fun transpose1x1() = RealTensorAlgebra {
+        val tensor = RealTensor(intArrayOf(1), doubleArrayOf(0.0))
+        val res = tensor.transpose(0, 0)
+
+        assertTrue(res.buffer.array contentEquals doubleArrayOf(0.0))
+        assertTrue(res.shape contentEquals intArrayOf(1))
+    }
+
+    @Test
+    fun transpose3x2() = RealTensorAlgebra {
+        val tensor = RealTensor(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val res = tensor.transpose(1, 0)
+
+        assertTrue(res.buffer.array contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
+        assertTrue(res.shape contentEquals intArrayOf(2, 3))
+    }
+
+    @Test
+    fun transpose1x2x3() = RealTensorAlgebra {
+        val tensor = RealTensor(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val res01 = tensor.transpose(0, 1)
+        val res02 = tensor.transpose(0, 2)
+        val res12 = tensor.transpose(1, 2)
+
+        assertTrue(res01.shape contentEquals intArrayOf(2, 1, 3))
+        assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
+        assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2))
+
+        assertTrue(res01.buffer.array contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res02.buffer.array contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res12.buffer.array contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+    }
+
+    @Test
+    fun transposeAssign1x2() = RealTensorAlgebra {
+        val tensor = RealTensor(intArrayOf(1,2), doubleArrayOf(1.0, 2.0))
+        tensor.transposeAssign(0, 1)
+
+        assertTrue(tensor.buffer.array contentEquals doubleArrayOf(1.0, 2.0))
+        assertTrue(tensor.shape contentEquals intArrayOf(2, 1))
+    }
+
+    @Test
+    fun transposeAssign2x3() = RealTensorAlgebra {
+        val tensor = RealTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        tensor.transposeAssign(1, 0)
+
+        assertTrue(tensor.buffer.array contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(tensor.shape contentEquals intArrayOf(3, 2))
+    }
 }
\ No newline at end of file

From bb4894b87ea5d4ed64b290ae0ddcd35c9ebe6d1a Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Thu, 11 Mar 2021 21:46:35 +0000
Subject: [PATCH 013/178] removing assignement for non operator members

---
 .../kmath/tensors/RealTensorAlgebra.kt        | 31 +------------------
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  7 -----
 .../kmath/tensors/TestRealTensorAlgebra.kt    | 18 -----------
 3 files changed, 1 insertion(+), 55 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index a5c00e8ec..16f90341b 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -151,14 +151,6 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Alya")
     }
 
-    override fun RealTensor.dotAssign(other: RealTensor) {
-        TODO("Alya")
-    }
-
-    override fun RealTensor.dotRightAssign(other: RealTensor) {
-        TODO("Alya")
-    }
-
     override fun diagonalEmbedding(diagonalEntries: RealTensor, offset: Int, dim1: Int, dim2: Int): RealTensor {
         TODO("Alya")
     }
@@ -183,15 +175,6 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         return resTensor
     }
 
-    override fun RealTensor.transposeAssign(i: Int, j: Int) {
-        val transposedTensor = this.transpose(i, j)
-        for (i in transposedTensor.shape.indices) {
-            this.shape[i] = transposedTensor.shape[i]
-        }
-        for (i in transposedTensor.buffer.array.indices) {
-            this.buffer.array[i] = transposedTensor.buffer.array[i]
-        }
-    }
 
     override fun RealTensor.view(shape: IntArray): RealTensor {
         return RealTensor(shape, this.buffer.array)
@@ -205,17 +188,12 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.absAssign() {
-        TODO("Not yet implemented")
-    }
+
 
     override fun RealTensor.sum(): RealTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.sumAssign() {
-        TODO("Not yet implemented")
-    }
 
     override fun RealTensor.div(value: Double): RealTensor {
         TODO("Not yet implemented")
@@ -237,17 +215,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.expAssign() {
-        TODO("Not yet implemented")
-    }
-
     override fun RealTensor.log(): RealTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.logAssign() {
-        TODO("Not yet implemented")
-    }
 
     override fun RealTensor.lu(): Pair<RealTensor, RealTensor> {
         TODO()
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 6f42623e0..f1d401f6e 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -35,8 +35,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     //https://pytorch.org/docs/stable/generated/torch.matmul.html
     public infix fun TensorType.dot(other: TensorType): TensorType
-    public infix fun TensorType.dotAssign(other: TensorType): Unit
-    public infix fun TensorType.dotRightAssign(other: TensorType): Unit
 
     //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
     public fun diagonalEmbedding(
@@ -46,7 +44,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     //https://pytorch.org/docs/stable/generated/torch.transpose.html
     public fun TensorType.transpose(i: Int, j: Int): TensorType
-    public fun TensorType.transposeAssign(i: Int, j: Int): Unit
 
     //https://pytorch.org/docs/stable/tensor_view.html
     public fun TensorType.view(shape: IntArray): TensorType
@@ -54,11 +51,9 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     //https://pytorch.org/docs/stable/generated/torch.abs.html
     public fun TensorType.abs(): TensorType
-    public fun TensorType.absAssign(): Unit
 
     //https://pytorch.org/docs/stable/generated/torch.sum.html
     public fun TensorType.sum(): TensorType
-    public fun TensorType.sumAssign(): Unit
 }
 
 // https://proofwiki.org/wiki/Definition:Division_Algebra
@@ -72,11 +67,9 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.exp.html
     public fun TensorType.exp(): TensorType
-    public fun TensorType.expAssign(): Unit
 
     //https://pytorch.org/docs/stable/generated/torch.log.html
     public fun TensorType.log(): TensorType
-    public fun TensorType.logAssign(): Unit
 
     //https://pytorch.org/docs/stable/generated/torch.lu.html
     public fun TensorType.lu(): Pair<TensorType, TensorType>
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
index 8e95922b8..19f0c0ef0 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
@@ -46,22 +46,4 @@ class TestRealTensorAlgebra {
         assertTrue(res02.buffer.array contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
         assertTrue(res12.buffer.array contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
     }
-
-    @Test
-    fun transposeAssign1x2() = RealTensorAlgebra {
-        val tensor = RealTensor(intArrayOf(1,2), doubleArrayOf(1.0, 2.0))
-        tensor.transposeAssign(0, 1)
-
-        assertTrue(tensor.buffer.array contentEquals doubleArrayOf(1.0, 2.0))
-        assertTrue(tensor.shape contentEquals intArrayOf(2, 1))
-    }
-
-    @Test
-    fun transposeAssign2x3() = RealTensorAlgebra {
-        val tensor = RealTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        tensor.transposeAssign(1, 0)
-
-        assertTrue(tensor.buffer.array contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
-        assertTrue(tensor.shape contentEquals intArrayOf(3, 2))
-    }
 }
\ No newline at end of file

From f9500f44ec6fc811a17e76104a9bea7ed6f790cc Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 12 Mar 2021 13:50:06 +0300
Subject: [PATCH 014/178] new tensor def

---
 .../kscience/kmath/tensors/BufferTensor.kt    | 28 +++++++++++++++++++
 .../kmath/tensors/RealTensorAlgebra.kt        | 14 +++++-----
 2 files changed, 35 insertions(+), 7 deletions(-)
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt
new file mode 100644
index 000000000..4d8aea32c
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt
@@ -0,0 +1,28 @@
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.linear.BufferMatrix
+import space.kscience.kmath.linear.RealMatrixContext.toBufferMatrix
+import space.kscience.kmath.nd.MutableNDBuffer
+import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.structures.MutableBuffer
+import space.kscience.kmath.structures.toList
+
+public open class BufferTensor<T>(
+    override val shape: IntArray,
+    buffer: MutableBuffer<T>
+) :
+    TensorStructure<T>,
+    MutableNDBuffer<T>(
+        TensorStrides(shape),
+        buffer
+    )
+
+
+public fun <T : Any> BufferTensor<T>.toBufferMatrix(): BufferMatrix<T> {
+    return BufferMatrix(shape[0], shape[1], this.buffer)
+}
+
+public fun  <T : Any> BufferMatrix<T>.BufferTensor(): BufferTensor<T> {
+    return BufferTensor(intArrayOf(rowNum, colNum), buffer)
+}
+
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index a5c00e8ec..e38056097 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -1,19 +1,19 @@
 package space.kscience.kmath.tensors
 
+import space.kscience.kmath.linear.BufferMatrix
+import space.kscience.kmath.linear.RealMatrixContext.toBufferMatrix
 import space.kscience.kmath.nd.MutableNDBuffer
+import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.RealBuffer
 import space.kscience.kmath.structures.array
+import space.kscience.kmath.structures.toList
 
 
 public class RealTensor(
-    override val shape: IntArray,
+    shape: IntArray,
     buffer: DoubleArray
-) :
-    TensorStructure<Double>,
-    MutableNDBuffer<Double>(
-        TensorStrides(shape),
-        RealBuffer(buffer)
-    )
+) : BufferTensor<Double>(shape, RealBuffer(buffer))
 
 public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 

From 95b814e1637c281b6cc771b0c89e3475a27323c9 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 12 Mar 2021 14:29:51 +0300
Subject: [PATCH 015/178] add fromMatrix

---
 .../kotlin/space/kscience/kmath/tensors/BufferTensor.kt  | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt
index 4d8aea32c..817c51f11 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt
@@ -2,8 +2,12 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.linear.BufferMatrix
 import space.kscience.kmath.linear.RealMatrixContext.toBufferMatrix
+import space.kscience.kmath.nd.Matrix
 import space.kscience.kmath.nd.MutableNDBuffer
+import space.kscience.kmath.nd.Structure2D
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.BufferAccessor2D
 import space.kscience.kmath.structures.MutableBuffer
 import space.kscience.kmath.structures.toList
 
@@ -22,7 +26,8 @@ public fun <T : Any> BufferTensor<T>.toBufferMatrix(): BufferMatrix<T> {
     return BufferMatrix(shape[0], shape[1], this.buffer)
 }
 
-public fun  <T : Any> BufferMatrix<T>.BufferTensor(): BufferTensor<T> {
-    return BufferTensor(intArrayOf(rowNum, colNum), buffer)
+// T???
+public fun BufferMatrix<Double>.BufferTensor(): BufferTensor<Double> {
+    return BufferTensor(intArrayOf(rowNum, colNum),  BufferAccessor2D(rowNum, colNum, Buffer.Companion::real).create(this))
 }
 

From 454d574ccd8876982753e8e09782ff1f4fb0c564 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Fri, 12 Mar 2021 15:20:46 +0300
Subject: [PATCH 016/178] add broadcast and functions plus and minus

---
 .../kmath/tensors/RealTensorAlgebra.kt        | 96 ++++++++++++++++---
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  3 +
 .../kmath/tensors/TestRealTensorAlgebra.kt    | 47 +++++++++
 3 files changed, 135 insertions(+), 11 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index a5c00e8ec..a4ad54bfc 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -2,7 +2,9 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.nd.MutableNDBuffer
 import space.kscience.kmath.structures.RealBuffer
+import space.kscience.kmath.structures.ValueFlag
 import space.kscience.kmath.structures.array
+import kotlin.math.max
 
 
 public class RealTensor(
@@ -54,27 +56,85 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun broadcastShapes(vararg shapes: IntArray): IntArray {
+        var totalDim = 0
+        for (shape in shapes) {
+            totalDim = max(totalDim, shape.size)
+        }
+
+        val totalShape = IntArray(totalDim) {0}
+        for (shape in shapes) {
+            for (i in shape.indices) {
+                val curDim = shape[i]
+                val offset = totalDim - shape.size
+                totalShape[i + offset] = max(totalShape[i + offset], curDim)
+            }
+        }
+
+        for (shape in shapes) {
+            for (i in shape.indices) {
+                val curDim = shape[i]
+                val offset = totalDim - shape.size
+                if (curDim != 1 && totalShape[i + offset] != curDim) {
+                    throw IllegalArgumentException("Shapes are not compatible and cannot be broadcast")
+                }
+            }
+        }
+
+        return totalShape
+    }
+
+    override fun broadcastTensors(vararg tensors: RealTensor): List<RealTensor> {
+        val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
+        val n = totalShape.reduce{ acc, i ->  acc * i }
+
+        val res = ArrayList<RealTensor>(0)
+        for (tensor in tensors) {
+            val resTensor = RealTensor(totalShape, DoubleArray(n))
+
+            for (linearIndex in 0 until n) {
+                val totalMultiIndex = resTensor.strides.index(linearIndex)
+                val curMultiIndex = tensor.shape.copyOf()
+
+                val offset = totalMultiIndex.size - curMultiIndex.size
+
+                for (i in curMultiIndex.indices) {
+                    if (curMultiIndex[i] != 1) {
+                        curMultiIndex[i] = totalMultiIndex[i + offset]
+                    } else {
+                        curMultiIndex[i] = 0
+                    }
+                }
+
+                val curLinearIndex = tensor.strides.offset(curMultiIndex)
+                resTensor.buffer.array[linearIndex] = tensor.buffer.array[curLinearIndex]
+            }
+            res.add(resTensor)
+        }
+
+        return res
+    }
+
     override fun Double.plus(other: RealTensor): RealTensor {
-        //todo should be change with broadcasting
         val resBuffer = DoubleArray(other.buffer.size) { i ->
             other.buffer.array[i] + this
         }
         return RealTensor(other.shape, resBuffer)
     }
 
-    //todo should be change with broadcasting
     override fun RealTensor.plus(value: Double): RealTensor = value + this
 
     override fun RealTensor.plus(other: RealTensor): RealTensor {
-        //todo should be change with broadcasting
-        val resBuffer = DoubleArray(this.buffer.size) { i ->
-            this.buffer.array[i] + other.buffer.array[i]
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.buffer.size) { i ->
+            newThis.buffer.array[i] + newOther.buffer.array[i]
         }
-        return RealTensor(this.shape, resBuffer)
+        return RealTensor(newThis.shape, resBuffer)
     }
 
     override fun RealTensor.plusAssign(value: Double) {
-        //todo should be change with broadcasting
         for (i in this.buffer.array.indices) {
             this.buffer.array[i] += value
         }
@@ -88,19 +148,33 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
     }
 
     override fun Double.minus(other: RealTensor): RealTensor {
-        TODO("Alya")
+        val resBuffer = DoubleArray(other.buffer.size) { i ->
+            this - other.buffer.array[i]
+        }
+        return RealTensor(other.shape, resBuffer)
     }
 
     override fun RealTensor.minus(value: Double): RealTensor {
-        TODO("Alya")
+        val resBuffer = DoubleArray(this.buffer.size) { i ->
+            this.buffer.array[i] - value
+        }
+        return RealTensor(this.shape, resBuffer)
     }
 
     override fun RealTensor.minus(other: RealTensor): RealTensor {
-        TODO("Alya")
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.buffer.size) { i ->
+            newThis.buffer.array[i] - newOther.buffer.array[i]
+        }
+        return RealTensor(newThis.shape, resBuffer)
     }
 
     override fun RealTensor.minusAssign(value: Double) {
-        TODO("Alya")
+        for (i in this.buffer.array.indices) {
+            this.buffer.array[i] -= value
+        }
     }
 
     override fun RealTensor.minusAssign(other: RealTensor) {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 6f42623e0..bd47a1b76 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -13,6 +13,9 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     public fun TensorType.copy(): TensorType
 
+    public fun broadcastShapes(vararg shapes: IntArray): IntArray
+    public fun broadcastTensors(vararg tensors: RealTensor): List<TensorType>
+
     public operator fun T.plus(other: TensorType): TensorType
     public operator fun TensorType.plus(value: T): TensorType
     public operator fun TensorType.plus(other: TensorType): TensorType
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
index 8e95922b8..18639bf3c 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
@@ -2,6 +2,8 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.structures.array
 import kotlin.test.Test
+import kotlin.test.assertFails
+import kotlin.test.assertFailsWith
 import kotlin.test.assertTrue
 
 class TestRealTensorAlgebra {
@@ -64,4 +66,49 @@ class TestRealTensorAlgebra {
         assertTrue(tensor.buffer.array contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
         assertTrue(tensor.shape contentEquals intArrayOf(3, 2))
     }
+
+    @Test
+    fun broadcastShapes() = RealTensorAlgebra {
+        assertTrue(this.broadcastShapes(
+            intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
+        ) contentEquals intArrayOf(1, 2, 3))
+
+        assertTrue(this.broadcastShapes(
+            intArrayOf(6, 7), intArrayOf(5, 6, 1), intArrayOf(7,), intArrayOf(5, 1, 7)
+        ) contentEquals intArrayOf(5, 6, 7))
+    }
+
+    @Test
+    fun broadcastTensors() = RealTensorAlgebra {
+        val tensor1 = RealTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = RealTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor3 = RealTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
+
+        val res = this.broadcastTensors(tensor1, tensor2, tensor3)
+
+        assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3))
+        assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
+        assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
+
+        assertTrue(res[0].buffer.array contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res[1].buffer.array contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+        assertTrue(res[2].buffer.array contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
+    }
+
+    @Test
+    fun minusTensor() = RealTensorAlgebra {
+        val tensor1 = RealTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = RealTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor3 = RealTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
+
+        assertTrue((tensor2 - tensor1).shape contentEquals intArrayOf(2, 3))
+        assertTrue((tensor2 - tensor1).buffer.array contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
+
+        assertTrue((tensor3 - tensor1).shape contentEquals intArrayOf(1, 2, 3))
+        assertTrue((tensor3 - tensor1).buffer.array
+                contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0))
+
+        assertTrue((tensor3 - tensor2).shape contentEquals intArrayOf(1, 1, 3))
+        assertTrue((tensor3 - tensor2).buffer.array contentEquals doubleArrayOf(490.0, 480.0, 470.0))
+    }
 }
\ No newline at end of file

From 70e1861e536febfe9792fcbd3dde3dbd66cb4682 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Fri, 12 Mar 2021 15:25:47 +0300
Subject: [PATCH 017/178] remove extra import

---
 .../kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt     | 1 -
 1 file changed, 1 deletion(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index a4ad54bfc..afe78ae08 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -2,7 +2,6 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.nd.MutableNDBuffer
 import space.kscience.kmath.structures.RealBuffer
-import space.kscience.kmath.structures.ValueFlag
 import space.kscience.kmath.structures.array
 import kotlin.math.max
 

From 626d5c98fa843fe6833cf300f8b71871d54474b1 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Fri, 12 Mar 2021 15:43:44 +0300
Subject: [PATCH 018/178] change IllegalArgumentException to RuntimeException

---
 .../kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index afe78ae08..9e090e376 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -75,7 +75,7 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
                 val curDim = shape[i]
                 val offset = totalDim - shape.size
                 if (curDim != 1 && totalShape[i + offset] != curDim) {
-                    throw IllegalArgumentException("Shapes are not compatible and cannot be broadcast")
+                    throw RuntimeException("Shapes are not compatible and cannot be broadcast")
                 }
             }
         }

From 0911efd4aa1944c89cb9df4913c251daef98d31c Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Sat, 13 Mar 2021 19:30:58 +0300
Subject: [PATCH 019/178] add buffered tensor + lu

---
 .../kscience/kmath/tensors/BufferTensor.kt    | 33 ---------
 .../kscience/kmath/tensors/BufferedTensor.kt  | 33 +++++++++
 .../kmath/tensors/RealTensorAlgebra.kt        | 69 +++++++++++++++----
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  5 +-
 4 files changed, 92 insertions(+), 48 deletions(-)
 delete mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt
deleted file mode 100644
index 817c51f11..000000000
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferTensor.kt
+++ /dev/null
@@ -1,33 +0,0 @@
-package space.kscience.kmath.tensors
-
-import space.kscience.kmath.linear.BufferMatrix
-import space.kscience.kmath.linear.RealMatrixContext.toBufferMatrix
-import space.kscience.kmath.nd.Matrix
-import space.kscience.kmath.nd.MutableNDBuffer
-import space.kscience.kmath.nd.Structure2D
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.BufferAccessor2D
-import space.kscience.kmath.structures.MutableBuffer
-import space.kscience.kmath.structures.toList
-
-public open class BufferTensor<T>(
-    override val shape: IntArray,
-    buffer: MutableBuffer<T>
-) :
-    TensorStructure<T>,
-    MutableNDBuffer<T>(
-        TensorStrides(shape),
-        buffer
-    )
-
-
-public fun <T : Any> BufferTensor<T>.toBufferMatrix(): BufferMatrix<T> {
-    return BufferMatrix(shape[0], shape[1], this.buffer)
-}
-
-// T???
-public fun BufferMatrix<Double>.BufferTensor(): BufferTensor<Double> {
-    return BufferTensor(intArrayOf(rowNum, colNum),  BufferAccessor2D(rowNum, colNum, Buffer.Companion::real).create(this))
-}
-
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
new file mode 100644
index 000000000..9ffe2db61
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
@@ -0,0 +1,33 @@
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.linear.BufferMatrix
+import space.kscience.kmath.nd.MutableNDBuffer
+import space.kscience.kmath.structures.*
+import space.kscience.kmath.structures.BufferAccessor2D
+
+public open class BufferedTensor<T>(
+    override val shape: IntArray,
+    buffer: MutableBuffer<T>
+) :
+    TensorStructure<T>,
+    MutableNDBuffer<T>(
+        TensorStrides(shape),
+        buffer
+    ) {
+
+    public operator fun get(i: Int, j: Int): T{
+            check(this.dimension == 2) {"Not matrix"}
+            return this[intArrayOf(i, j)]
+    }
+
+    public operator fun set(i: Int, j: Int, value: T): Unit{
+        check(this.dimension == 2) {"Not matrix"}
+        this[intArrayOf(i, j)] = value
+    }
+
+}
+
+public class IntTensor(
+    shape: IntArray,
+    buffer: IntArray
+) : BufferedTensor<Int>(shape, IntBuffer(buffer))
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index e38056097..3e21a3a98 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -1,24 +1,19 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.linear.BufferMatrix
-import space.kscience.kmath.linear.RealMatrixContext.toBufferMatrix
-import space.kscience.kmath.nd.MutableNDBuffer
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.RealBuffer
 import space.kscience.kmath.structures.array
-import space.kscience.kmath.structures.toList
+import kotlin.math.abs
 
 
 public class RealTensor(
     shape: IntArray,
     buffer: DoubleArray
-) : BufferTensor<Double>(shape, RealBuffer(buffer))
+) : BufferedTensor<Double>(shape, RealBuffer(buffer))
 
 public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 
     override fun RealTensor.value(): Double {
-        check(this.shape contentEquals  intArrayOf(1)) {
+        check(this.shape contentEquals intArrayOf(1)) {
             "Inconsistent value for tensor of shape ${shape.toList()}"
         }
         return this.buffer.array[0]
@@ -51,7 +46,8 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
     }
 
     override fun RealTensor.copy(): RealTensor {
-        TODO("Not yet implemented")
+        // should be rework as soon as copy() method for NDBuffer will be available
+        return RealTensor(this.shape, this.buffer.array.copyOf())
     }
 
     override fun Double.plus(other: RealTensor): RealTensor {
@@ -249,12 +245,59 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.lu(): Pair<RealTensor, RealTensor> {
-        TODO()
+    override fun RealTensor.lu(): Pair<RealTensor, IntTensor> {
+        // todo checks
+        val lu = this.copy()
+        val m = this.shape[0]
+        val pivot = IntArray(m)
+
+
+        // Initialize permutation array and parity
+        for (row in 0 until m) pivot[row] = row
+        var even = true
+
+        for (i in 0 until m) {
+            var maxA = -1.0
+            var iMax = i
+
+            for (k in i until m) {
+                val absA = abs(lu[k, i])
+                if (absA > maxA) {
+                    maxA = absA
+                    iMax = k
+                }
+            }
+
+            //todo check singularity
+
+            if (iMax != i) {
+
+                val j = pivot[i]
+                pivot[i] = pivot[iMax]
+                pivot[iMax] = j
+                even != even
+
+                for (k in 0 until m) {
+                    val tmp = lu[i, k]
+                    lu[i, k] = lu[iMax, k]
+                    lu[iMax, k] = tmp
+                }
+
+            }
+
+            for (j in i + 1 until m) {
+                lu[j, i] /= lu[i, i]
+                for (k in i + 1 until m) {
+                    lu[j, k] -= lu[j, i] * lu[i, k]
+                }
+            }
+        }
+        return Pair(lu, IntTensor(intArrayOf(m), pivot))
     }
 
-    override fun luUnpack(A_LU: RealTensor, pivots: RealTensor): Triple<RealTensor, RealTensor, RealTensor> {
-        TODO("Not yet implemented")
+    override fun luUnpack(A_LU: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
+        // todo checks
+
     }
 
     override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 6f42623e0..0dbcae044 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -78,11 +78,12 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
     public fun TensorType.log(): TensorType
     public fun TensorType.logAssign(): Unit
 
+    // todo change type of pivots
     //https://pytorch.org/docs/stable/generated/torch.lu.html
-    public fun TensorType.lu(): Pair<TensorType, TensorType>
+    public fun TensorType.lu(): Pair<TensorType, IntTensor>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
-    public fun luUnpack(A_LU: TensorType, pivots: TensorType): Triple<TensorType, TensorType, TensorType>
+    public fun luUnpack(A_LU: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.svd.html
     public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>

From 8f88a101d24faee0504228c57642bb980a7d48e4 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Sat, 13 Mar 2021 20:51:15 +0300
Subject: [PATCH 020/178] complete lu for matrix

---
 .../kmath/tensors/RealTensorAlgebra.kt        | 28 +++++++++++++++++--
 1 file changed, 26 insertions(+), 2 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index 9e040fcd8..f91f44519 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -35,7 +35,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
     }
 
     override fun zeroesLike(other: RealTensor): RealTensor {
-        TODO("Not yet implemented")
+        // TODO refactor very bad!!!
+        val shape = other.shape
+        val buffer = DoubleArray(other.buffer.size) { 0.0 }
+        return RealTensor(shape, buffer)
     }
 
     override fun ones(shape: IntArray): RealTensor {
@@ -340,9 +343,30 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         return Pair(lu, IntTensor(intArrayOf(m), pivot))
     }
 
-    override fun luUnpack(A_LU: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
+    override fun luUnpack(lu: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
         // todo checks
+        val n = lu.shape[0]
+        val p = zeroesLike(lu)
+        pivots.buffer.array.forEachIndexed { i, pivot ->
+            p[i, pivot] = 1.0
+        }
+        val l = zeroesLike(lu)
+        val u = zeroesLike(lu)
 
+        for (i in 0 until n){
+            for (j in 0 until n){
+                if (i == j) {
+                    l[i, j] = 1.0
+                }
+                if (j < i) {
+                    l[i, j] = lu[i, j]
+                }
+                if (j >= i) {
+                    u[i, j] = lu[i, j]
+                }
+            }
+        }
+        return Triple(p, l, u)
     }
 
     override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {

From 384415dc98ae27125f894798057f6052103e2594 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sat, 13 Mar 2021 19:16:13 +0000
Subject: [PATCH 021/178] utils module for tensors

---
 .../kscience/kmath/tensors/BufferedTensor.kt  | 28 ++++--
 .../kmath/tensors/RealTensorAlgebra.kt        | 63 ------------
 .../kscience/kmath/tensors/TensorAlgebra.kt   | 46 +--------
 .../kscience/kmath/tensors/TensorStrides.kt   |  6 +-
 .../space/kscience/kmath/tensors/utils.kt     | 96 +++++++++++++++++++
 .../kmath/tensors/TestRealTensorAlgebra.kt    |  8 +-
 6 files changed, 124 insertions(+), 123 deletions(-)
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
index 9ffe2db61..29605024d 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
@@ -1,9 +1,8 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.linear.BufferMatrix
 import space.kscience.kmath.nd.MutableNDBuffer
 import space.kscience.kmath.structures.*
-import space.kscience.kmath.structures.BufferAccessor2D
+
 
 public open class BufferedTensor<T>(
     override val shape: IntArray,
@@ -15,13 +14,13 @@ public open class BufferedTensor<T>(
         buffer
     ) {
 
-    public operator fun get(i: Int, j: Int): T{
-            check(this.dimension == 2) {"Not matrix"}
-            return this[intArrayOf(i, j)]
+    public operator fun get(i: Int, j: Int): T {
+        check(this.dimension == 2) { "Not matrix" }
+        return this[intArrayOf(i, j)]
     }
 
-    public operator fun set(i: Int, j: Int, value: T): Unit{
-        check(this.dimension == 2) {"Not matrix"}
+    public operator fun set(i: Int, j: Int, value: T): Unit {
+        check(this.dimension == 2) { "Not matrix" }
         this[intArrayOf(i, j)] = value
     }
 
@@ -31,3 +30,18 @@ public class IntTensor(
     shape: IntArray,
     buffer: IntArray
 ) : BufferedTensor<Int>(shape, IntBuffer(buffer))
+
+public class LongTensor(
+    shape: IntArray,
+    buffer: LongArray
+) : BufferedTensor<Long>(shape, LongBuffer(buffer))
+
+public class FloatTensor(
+    shape: IntArray,
+    buffer: FloatArray
+) : BufferedTensor<Float>(shape, FloatBuffer(buffer))
+
+public class RealTensor(
+    shape: IntArray,
+    buffer: DoubleArray
+) : BufferedTensor<Double>(shape, RealBuffer(buffer))
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index f91f44519..b3e54f077 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -6,11 +6,6 @@ import kotlin.math.abs
 import kotlin.math.max
 
 
-public class RealTensor(
-    shape: IntArray,
-    buffer: DoubleArray
-) : BufferedTensor<Double>(shape, RealBuffer(buffer))
-
 public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 
     override fun RealTensor.value(): Double {
@@ -54,64 +49,6 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         return RealTensor(this.shape, this.buffer.array.copyOf())
     }
 
-    override fun broadcastShapes(vararg shapes: IntArray): IntArray {
-        var totalDim = 0
-        for (shape in shapes) {
-            totalDim = max(totalDim, shape.size)
-        }
-
-        val totalShape = IntArray(totalDim) {0}
-        for (shape in shapes) {
-            for (i in shape.indices) {
-                val curDim = shape[i]
-                val offset = totalDim - shape.size
-                totalShape[i + offset] = max(totalShape[i + offset], curDim)
-            }
-        }
-
-        for (shape in shapes) {
-            for (i in shape.indices) {
-                val curDim = shape[i]
-                val offset = totalDim - shape.size
-                if (curDim != 1 && totalShape[i + offset] != curDim) {
-                    throw RuntimeException("Shapes are not compatible and cannot be broadcast")
-                }
-            }
-        }
-
-        return totalShape
-    }
-
-    override fun broadcastTensors(vararg tensors: RealTensor): List<RealTensor> {
-        val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
-        val n = totalShape.reduce{ acc, i ->  acc * i }
-
-        val res = ArrayList<RealTensor>(0)
-        for (tensor in tensors) {
-            val resTensor = RealTensor(totalShape, DoubleArray(n))
-
-            for (linearIndex in 0 until n) {
-                val totalMultiIndex = resTensor.strides.index(linearIndex)
-                val curMultiIndex = tensor.shape.copyOf()
-
-                val offset = totalMultiIndex.size - curMultiIndex.size
-
-                for (i in curMultiIndex.indices) {
-                    if (curMultiIndex[i] != 1) {
-                        curMultiIndex[i] = totalMultiIndex[i + offset]
-                    } else {
-                        curMultiIndex[i] = 0
-                    }
-                }
-
-                val curLinearIndex = tensor.strides.offset(curMultiIndex)
-                resTensor.buffer.array[linearIndex] = tensor.buffer.array[curLinearIndex]
-            }
-            res.add(resTensor)
-        }
-
-        return res
-    }
 
     override fun Double.plus(other: RealTensor): RealTensor {
         val resBuffer = DoubleArray(other.buffer.size) { i ->
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index ec257e45c..280acf8ea 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -13,9 +13,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     public fun TensorType.copy(): TensorType
 
-    public fun broadcastShapes(vararg shapes: IntArray): IntArray
-    public fun broadcastTensors(vararg tensors: RealTensor): List<TensorType>
-
     public operator fun T.plus(other: TensorType): TensorType
     public operator fun TensorType.plus(value: T): TensorType
     public operator fun TensorType.plus(other: TensorType): TensorType
@@ -87,45 +84,4 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
     //https://pytorch.org/docs/stable/generated/torch.symeig.html
     public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
 
-}
-
-public inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkShapeCompatible(
-    a: TensorType, b: TensorType
-): Unit =
-    check(a.shape contentEquals b.shape) {
-        "Tensors must be of identical shape"
-    }
-
-public inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkDot(a: TensorType, b: TensorType): Unit {
-    val sa = a.shape
-    val sb = b.shape
-    val na = sa.size
-    val nb = sb.size
-    var status: Boolean
-    if (nb == 1) {
-        status = sa.last() == sb[0]
-    } else {
-        status = sa.last() == sb[nb - 2]
-        if ((na > 2) and (nb > 2)) {
-            status = status and
-                    (sa.take(nb - 2).toIntArray() contentEquals sb.take(nb - 2).toIntArray())
-        }
-    }
-    check(status) { "Incompatible shapes $sa and $sb for dot product" }
-}
-
-public inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkTranspose(dim: Int, i: Int, j: Int): Unit =
-    check((i < dim) and (j < dim)) {
-        "Cannot transpose $i to $j for a tensor of dim $dim"
-    }
-
-public inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkView(a: TensorType, shape: IntArray): Unit =
-    check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
\ No newline at end of file
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
index d6a6f5f16..d1d1204b4 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
@@ -5,7 +5,7 @@ import space.kscience.kmath.nd.offsetFromIndex
 import kotlin.math.max
 
 
-public inline fun stridesFromShape(shape: IntArray): IntArray {
+internal inline fun stridesFromShape(shape: IntArray): IntArray {
     val nDim = shape.size
     val res = IntArray(nDim)
     if (nDim == 0)
@@ -22,7 +22,7 @@ public inline fun stridesFromShape(shape: IntArray): IntArray {
 
 }
 
-public inline fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {
+internal inline fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {
     val res = IntArray(nDim)
     var current = offset
     var strideIndex = 0
@@ -35,7 +35,7 @@ public inline fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): In
     return res
 }
 
-public inline fun nextIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
+internal inline fun nextIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
     val res = index.copyOf()
     var current = nDim - 1
     var carry = 0
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
new file mode 100644
index 000000000..65409bb15
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
@@ -0,0 +1,96 @@
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.structures.array
+import kotlin.math.max
+
+
+internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
+    var totalDim = 0
+    for (shape in shapes) {
+        totalDim = max(totalDim, shape.size)
+    }
+
+    val totalShape = IntArray(totalDim) { 0 }
+    for (shape in shapes) {
+        for (i in shape.indices) {
+            val curDim = shape[i]
+            val offset = totalDim - shape.size
+            totalShape[i + offset] = max(totalShape[i + offset], curDim)
+        }
+    }
+
+    for (shape in shapes) {
+        for (i in shape.indices) {
+            val curDim = shape[i]
+            val offset = totalDim - shape.size
+            if (curDim != 1 && totalShape[i + offset] != curDim) {
+                throw RuntimeException("Shapes are not compatible and cannot be broadcast")
+            }
+        }
+    }
+
+    return totalShape
+}
+
+internal inline fun broadcastTensors(vararg tensors: RealTensor): List<RealTensor> {
+    val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
+    val n = totalShape.reduce { acc, i -> acc * i }
+
+    val res = ArrayList<RealTensor>(0)
+    for (tensor in tensors) {
+        val resTensor = RealTensor(totalShape, DoubleArray(n))
+
+        for (linearIndex in 0 until n) {
+            val totalMultiIndex = resTensor.strides.index(linearIndex)
+            val curMultiIndex = tensor.shape.copyOf()
+
+            val offset = totalMultiIndex.size - curMultiIndex.size
+
+            for (i in curMultiIndex.indices) {
+                if (curMultiIndex[i] != 1) {
+                    curMultiIndex[i] = totalMultiIndex[i + offset]
+                } else {
+                    curMultiIndex[i] = 0
+                }
+            }
+
+            val curLinearIndex = tensor.strides.offset(curMultiIndex)
+            resTensor.buffer.array[linearIndex] = tensor.buffer.array[curLinearIndex]
+        }
+        res.add(resTensor)
+    }
+
+    return res
+}
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkDot(a: TensorType, b: TensorType): Unit {
+    val sa = a.shape
+    val sb = b.shape
+    val na = sa.size
+    val nb = sb.size
+    var status: Boolean
+    if (nb == 1) {
+        status = sa.last() == sb[0]
+    } else {
+        status = sa.last() == sb[nb - 2]
+        if ((na > 2) and (nb > 2)) {
+            status = status and
+                    (sa.take(nb - 2).toIntArray() contentEquals sb.take(nb - 2).toIntArray())
+        }
+    }
+    check(status) { "Incompatible shapes $sa and $sb for dot product" }
+}
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkTranspose(dim: Int, i: Int, j: Int): Unit =
+    check((i < dim) and (j < dim)) {
+        "Cannot transpose $i to $j for a tensor of dim $dim"
+    }
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkView(a: TensorType, shape: IntArray): Unit =
+    check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
\ No newline at end of file
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
index c9fd6bb3a..7c27f5fa9 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
@@ -2,8 +2,6 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.structures.array
 import kotlin.test.Test
-import kotlin.test.assertFails
-import kotlin.test.assertFailsWith
 import kotlin.test.assertTrue
 
 class TestRealTensorAlgebra {
@@ -51,11 +49,11 @@ class TestRealTensorAlgebra {
 
     @Test
     fun broadcastShapes() = RealTensorAlgebra {
-        assertTrue(this.broadcastShapes(
+        assertTrue(broadcastShapes(
             intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
         ) contentEquals intArrayOf(1, 2, 3))
 
-        assertTrue(this.broadcastShapes(
+        assertTrue(broadcastShapes(
             intArrayOf(6, 7), intArrayOf(5, 6, 1), intArrayOf(7,), intArrayOf(5, 1, 7)
         ) contentEquals intArrayOf(5, 6, 7))
     }
@@ -66,7 +64,7 @@ class TestRealTensorAlgebra {
         val tensor2 = RealTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = RealTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
 
-        val res = this.broadcastTensors(tensor1, tensor2, tensor3)
+        val res = broadcastTensors(tensor1, tensor2, tensor3)
 
         assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3))
         assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))

From c02f71263ded2c41806c821c351c58fd40d868ec Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sun, 14 Mar 2021 21:18:20 +0000
Subject: [PATCH 022/178] Adding the interfaces for AnalyticTensorAlgebra and
 LinearOpsTensorAlgebra

---
 .../kmath/tensors/AnalyticTensorAlgebra.kt    | 112 +++++++++++
 .../kmath/tensors/LinearOpsTensorAlgebra.kt   |  43 ++++
 .../tensors/RealAnalyticTensorAlgebra.kt      | 148 ++++++++++++++
 .../tensors/RealLinearOpsTensorAlgebra.kt     | 133 +++++++++++++
 .../kmath/tensors/RealTensorAlgebra.kt        | 185 +++++++-----------
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  93 +++++----
 .../tensors/TensorPartialDivisionAlgebra.kt   |  27 +++
 7 files changed, 586 insertions(+), 155 deletions(-)
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
new file mode 100644
index 000000000..17a25b6b3
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
@@ -0,0 +1,112 @@
+package space.kscience.kmath.tensors
+
+
+public interface AnalyticTensorAlgebra<T, TensorType : TensorStructure<T>> :
+    TensorPartialDivisionAlgebra<T, TensorType> {
+
+    //https://pytorch.org/docs/stable/generated/torch.exp.html
+    public fun TensorType.exp(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.log.html
+    public fun TensorType.log(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.sqrt.html
+    public fun TensorType.sqrt(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.square.html
+    public fun TensorType.square(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.acos.html#torch.cos
+    public fun TensorType.cos(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.acos.html#torch.acos
+    public fun TensorType.acos(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.cosh
+    public fun TensorType.cosh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.acosh
+    public fun TensorType.acosh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sin
+    public fun TensorType.sin(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asin
+    public fun TensorType.asin(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sinh
+    public fun TensorType.sinh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asinh
+    public fun TensorType.asinh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.atan.html#torch.tan
+    public fun TensorType.tan(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.atan.html#torch.atan
+    public fun TensorType.atan(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.tanh
+    public fun TensorType.tanh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.atanh
+    public fun TensorType.atanh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.ceil.html#torch.ceil
+    public fun TensorType.ceil(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.floor.html#torch.floor
+    public fun TensorType.floor(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.clamp.html#torch.clamp
+    public fun TensorType.clamp(min: T, max: T): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.erf.html#torch.erf
+    public fun TensorType.erf(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.erfinv.html#torch.erfinv
+    public fun TensorType.erfinv(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.erfc.html#torch.erfc
+    public fun TensorType.erfc(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.lerp.html#torch.lerp
+    public fun TensorType.lerp(end: TensorType, weight: TensorType): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.lgamma.html#torch.lgamma
+    public fun TensorType.lgamma(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.logit.html#torch.logit
+    public fun TensorType.logit(eps: T): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.igamma.html#torch.igamma
+    public fun TensorType.igamma(other: TensorType): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.igammac.html#torch.igammac
+    public fun TensorType.igammac(other: TensorType): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.mvlgamma.html#torch.mvlgamma
+    public fun TensorType.mvlgamma(dimensions: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.polygamma.html#torch.polygamma
+    public fun TensorType.polygamma(order: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.pow.html#torch.pow
+    public fun TensorType.pow(exponent: T): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.round.html#torch.round
+    public fun TensorType.round(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.sigmoid.html#torch.sigmoid
+    public fun TensorType.sigmoid(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.sinc.html#torch.sinc
+    public fun TensorType.sinc(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.heaviside.html#torch.heaviside
+    public fun TensorType.heaviside(values: TensorType): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.trapz.html#torch.trapz
+    public fun TensorType.trapz(xValues: TensorType, dim: Int): TensorType
+
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
new file mode 100644
index 000000000..bd9cbfd45
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -0,0 +1,43 @@
+package space.kscience.kmath.tensors
+
+
+public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>> :
+    TensorPartialDivisionAlgebra<T, TensorType> {
+
+    //https://pytorch.org/docs/stable/generated/torch.eye.html
+    public fun eye(n: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.matmul.html
+    public infix fun TensorType.dot(other: TensorType): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
+    public fun diagonalEmbedding(
+        diagonalEntries: TensorType,
+        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+    ): TensorType
+
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
+    public fun TensorType.det(): TensorType
+
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
+    public fun TensorType.inv(): TensorType
+
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.cholesky
+    public fun TensorType.cholesky(): TensorType
+
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
+    public fun TensorType.qr(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.lu.html
+    public fun TensorType.lu(): Pair<TensorType, IntTensor>
+
+    //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
+    public fun luPivot(aLU: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
+
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
+    public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
+
+    //https://pytorch.org/docs/stable/generated/torch.symeig.html
+    public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
+
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
new file mode 100644
index 000000000..cfecac0f4
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
@@ -0,0 +1,148 @@
+package space.kscience.kmath.tensors
+
+public class RealAnalyticTensorAlgebra:
+    AnalyticTensorAlgebra<Double, RealTensor>,
+    RealTensorAlgebra()
+{
+    override fun RealTensor.exp(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.log(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sqrt(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.square(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cos(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.acos(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cosh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.acosh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sin(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.asin(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sinh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.asinh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.tan(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.atan(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.tanh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.atanh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.ceil(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.floor(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.clamp(min: Double, max: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.erf(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.erfinv(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.erfc(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.lerp(end: RealTensor, weight: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.lgamma(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.logit(eps: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.igamma(other: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.igammac(other: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.mvlgamma(dimensions: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.polygamma(order: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.pow(exponent: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.round(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sigmoid(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sinc(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.heaviside(values: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.trapz(xValues: RealTensor, dim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
new file mode 100644
index 000000000..18c2050c0
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
@@ -0,0 +1,133 @@
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.structures.array
+
+public class RealLinearOpsTensorAlgebra :
+    LinearOpsTensorAlgebra<Double, RealTensor>,
+    RealTensorAlgebra()
+{
+    override fun eye(n: Int): RealTensor {
+        val shape = intArrayOf(n, n)
+        val buffer = DoubleArray(n * n) { 0.0 }
+        val res = RealTensor(shape, buffer)
+        for (i in 0 until n) {
+            res[intArrayOf(i, i)] = 1.0
+        }
+        return res
+    }
+
+
+    override fun RealTensor.dot(other: RealTensor): RealTensor {
+        TODO("Alya")
+    }
+
+    override fun diagonalEmbedding(diagonalEntries: RealTensor, offset: Int, dim1: Int, dim2: Int): RealTensor {
+        TODO("Alya")
+    }
+
+
+    override fun RealTensor.lu(): Pair<RealTensor, IntTensor> {
+        // todo checks
+        val lu = this.copy()
+        val m = this.shape[0]
+        val pivot = IntArray(m)
+
+
+        // Initialize permutation array and parity
+        for (row in 0 until m) pivot[row] = row
+        var even = true
+
+        for (i in 0 until m) {
+            var maxA = -1.0
+            var iMax = i
+
+            for (k in i until m) {
+                val absA = kotlin.math.abs(lu[k, i])
+                if (absA > maxA) {
+                    maxA = absA
+                    iMax = k
+                }
+            }
+
+            //todo check singularity
+
+            if (iMax != i) {
+
+                val j = pivot[i]
+                pivot[i] = pivot[iMax]
+                pivot[iMax] = j
+                even != even
+
+                for (k in 0 until m) {
+                    val tmp = lu[i, k]
+                    lu[i, k] = lu[iMax, k]
+                    lu[iMax, k] = tmp
+                }
+
+            }
+
+            for (j in i + 1 until m) {
+                lu[j, i] /= lu[i, i]
+                for (k in i + 1 until m) {
+                    lu[j, k] -= lu[j, i] * lu[i, k]
+                }
+            }
+        }
+        return Pair(lu, IntTensor(intArrayOf(m), pivot))
+    }
+
+    override fun luPivot(lu: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
+        // todo checks
+        val n = lu.shape[0]
+        val p = lu.zeroesLike()
+        pivots.buffer.array.forEachIndexed { i, pivot ->
+            p[i, pivot] = 1.0
+        }
+        val l = lu.zeroesLike()
+        val u = lu.zeroesLike()
+
+        for (i in 0 until n) {
+            for (j in 0 until n) {
+                if (i == j) {
+                    l[i, j] = 1.0
+                }
+                if (j < i) {
+                    l[i, j] = lu[i, j]
+                }
+                if (j >= i) {
+                    u[i, j] = lu[i, j]
+                }
+            }
+        }
+        return Triple(p, l, u)
+    }
+
+    override fun RealTensor.det(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.inv(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cholesky(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.qr(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+
+    override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.symEig(eigenvectors: Boolean): Pair<RealTensor, RealTensor> {
+        TODO("Not yet implemented")
+    }
+
+}
+
+public inline fun <R> RealLinearOpsTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
+    RealTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index b3e54f077..05e2b57d2 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -1,12 +1,9 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.RealBuffer
 import space.kscience.kmath.structures.array
-import kotlin.math.abs
-import kotlin.math.max
 
 
-public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
+public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 
     override fun RealTensor.value(): Double {
         check(this.shape contentEquals intArrayOf(1)) {
@@ -15,24 +12,13 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         return this.buffer.array[0]
     }
 
-    override fun eye(n: Int): RealTensor {
-        val shape = intArrayOf(n, n)
-        val buffer = DoubleArray(n * n) { 0.0 }
-        val res = RealTensor(shape, buffer)
-        for (i in 0 until n) {
-            res[intArrayOf(i, i)] = 1.0
-        }
-        return res
-    }
-
     override fun zeros(shape: IntArray): RealTensor {
         TODO("Not yet implemented")
     }
 
-    override fun zeroesLike(other: RealTensor): RealTensor {
-        // TODO refactor very bad!!!
-        val shape = other.shape
-        val buffer = DoubleArray(other.buffer.size) { 0.0 }
+    override fun RealTensor.zeroesLike(): RealTensor {
+        val shape = this.shape
+        val buffer = DoubleArray(this.buffer.size) { 0.0 }
         return RealTensor(shape, buffer)
     }
 
@@ -40,7 +26,7 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
-    override fun onesLike(shape: IntArray): RealTensor {
+    override fun RealTensor.onesLike(): RealTensor {
         TODO("Not yet implemented")
     }
 
@@ -156,14 +142,6 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         return RealTensor(this.shape, resBuffer)
     }
 
-    override fun RealTensor.dot(other: RealTensor): RealTensor {
-        TODO("Alya")
-    }
-
-    override fun diagonalEmbedding(diagonalEntries: RealTensor, offset: Int, dim1: Int, dim2: Int): RealTensor {
-        TODO("Alya")
-    }
-
     override fun RealTensor.transpose(i: Int, j: Int): RealTensor {
         checkTranspose(this.dimension, i, j)
         val n = this.buffer.size
@@ -198,9 +176,64 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun full(shape: IntArray, value: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.fullLike(value: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
 
 
-    override fun RealTensor.sum(): RealTensor {
+    override fun RealTensor.sum(dim: Int, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cumsum(dim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.prod(dim: Int, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cumprod(dim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.max(dim: Int, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cummax(dim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.min(dim: Int, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cummin(dim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.median(dim: Int, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun maximum(lhs: RealTensor, rhs: RealTensor) {
+        TODO("Not yet implemented")
+    }
+
+    override fun minimum(lhs: RealTensor, rhs: RealTensor) {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sort(dim: Int, keepDim: Boolean, descending: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun cat(tensors: List<RealTensor>, dim: Int): RealTensor {
         TODO("Not yet implemented")
     }
 
@@ -213,6 +246,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun RealTensor.flatten(startDim: Int, endDim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.divAssign(value: Double) {
         TODO("Not yet implemented")
     }
@@ -221,99 +258,23 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.exp(): RealTensor {
+    override fun RealTensor.mean(dim: Int, keepDim: Boolean): RealTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.log(): RealTensor {
+    override fun RealTensor.quantile(q: Double, dim: Int, keepDim: Boolean): RealTensor {
         TODO("Not yet implemented")
     }
 
-
-    override fun RealTensor.lu(): Pair<RealTensor, IntTensor> {
-        // todo checks
-        val lu = this.copy()
-        val m = this.shape[0]
-        val pivot = IntArray(m)
-
-
-        // Initialize permutation array and parity
-        for (row in 0 until m) pivot[row] = row
-        var even = true
-
-        for (i in 0 until m) {
-            var maxA = -1.0
-            var iMax = i
-
-            for (k in i until m) {
-                val absA = abs(lu[k, i])
-                if (absA > maxA) {
-                    maxA = absA
-                    iMax = k
-                }
-            }
-
-            //todo check singularity
-
-            if (iMax != i) {
-
-                val j = pivot[i]
-                pivot[i] = pivot[iMax]
-                pivot[iMax] = j
-                even != even
-
-                for (k in 0 until m) {
-                    val tmp = lu[i, k]
-                    lu[i, k] = lu[iMax, k]
-                    lu[iMax, k] = tmp
-                }
-
-            }
-
-            for (j in i + 1 until m) {
-                lu[j, i] /= lu[i, i]
-                for (k in i + 1 until m) {
-                    lu[j, k] -= lu[j, i] * lu[i, k]
-                }
-            }
-        }
-        return Pair(lu, IntTensor(intArrayOf(m), pivot))
-    }
-
-    override fun luUnpack(lu: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
-        // todo checks
-        val n = lu.shape[0]
-        val p = zeroesLike(lu)
-        pivots.buffer.array.forEachIndexed { i, pivot ->
-            p[i, pivot] = 1.0
-        }
-        val l = zeroesLike(lu)
-        val u = zeroesLike(lu)
-
-        for (i in 0 until n){
-            for (j in 0 until n){
-                if (i == j) {
-                    l[i, j] = 1.0
-                }
-                if (j < i) {
-                    l[i, j] = lu[i, j]
-                }
-                if (j >= i) {
-                    u[i, j] = lu[i, j]
-                }
-            }
-        }
-        return Triple(p, l, u)
-    }
-
-    override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {
-        /**
-         * Main first task for @AlyaNovikova
-         */
+    override fun RealTensor.std(dim: Int, unbiased: Boolean, keepDim: Boolean): RealTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.symEig(eigenvectors: Boolean): Pair<RealTensor, RealTensor> {
+    override fun RealTensor.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.histc(bins: Int, min: Double, max: Double): RealTensor {
         TODO("Not yet implemented")
     }
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 280acf8ea..a53054e21 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -5,11 +5,17 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     public fun TensorType.value(): T
 
-    public fun eye(n: Int): TensorType
     public fun zeros(shape: IntArray): TensorType
-    public fun zeroesLike(other: TensorType): TensorType
+    public fun TensorType.zeroesLike(): TensorType
     public fun ones(shape: IntArray): TensorType
-    public fun onesLike(shape: IntArray): TensorType
+    public fun TensorType.onesLike(): TensorType
+
+
+    //https://pytorch.org/docs/stable/generated/torch.full.html
+    public fun full(shape: IntArray, value: T): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
+    public fun TensorType.fullLike(value: T): TensorType
 
     public fun TensorType.copy(): TensorType
 
@@ -33,15 +39,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public operator fun TensorType.unaryMinus(): TensorType
 
 
-    //https://pytorch.org/docs/stable/generated/torch.matmul.html
-    public infix fun TensorType.dot(other: TensorType): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
-    public fun diagonalEmbedding(
-        diagonalEntries: TensorType,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
-    ): TensorType
-
     //https://pytorch.org/docs/stable/generated/torch.transpose.html
     public fun TensorType.transpose(i: Int, j: Int): TensorType
 
@@ -53,35 +50,45 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public fun TensorType.abs(): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.sum.html
-    public fun TensorType.sum(): TensorType
+    public fun TensorType.sum(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.cumsum.html#torch.cumsum
+    public fun TensorType.cumsum(dim: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.prod.html#torch.prod
+    public fun TensorType.prod(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.cumprod.html#torch.cumprod
+    public fun TensorType.cumprod(dim: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.max.html#torch.max
+    public fun TensorType.max(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.cummax.html#torch.cummax
+    public fun TensorType.cummax(dim: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.min.html#torch.min
+    public fun TensorType.min(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.cummin.html#torch.cummin
+    public fun TensorType.cummin(dim: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.median.html#torch.median
+    public fun TensorType.median(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum
+    public fun maximum(lhs: TensorType, rhs: TensorType)
+
+    //https://pytorch.org/docs/stable/generated/torch.minimum.html#torch.minimum
+    public fun minimum(lhs: TensorType, rhs: TensorType)
+
+    //https://pytorch.org/docs/stable/generated/torch.sort.html#torch.sort
+    public fun TensorType.sort(dim: Int, keepDim: Boolean, descending: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat
+    public fun cat(tensors: List<TensorType>, dim: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.flatten.html#torch.flatten
+    public fun TensorType.flatten(startDim: Int, endDim: Int): TensorType
+
 }
-
-// https://proofwiki.org/wiki/Definition:Division_Algebra
-public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
-    TensorAlgebra<T, TensorType> {
-
-    public operator fun TensorType.div(value: T): TensorType
-    public operator fun TensorType.div(other: TensorType): TensorType
-    public operator fun TensorType.divAssign(value: T)
-    public operator fun TensorType.divAssign(other: TensorType)
-
-    //https://pytorch.org/docs/stable/generated/torch.exp.html
-    public fun TensorType.exp(): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.log.html
-    public fun TensorType.log(): TensorType
-
-    // todo change type of pivots
-    //https://pytorch.org/docs/stable/generated/torch.lu.html
-    public fun TensorType.lu(): Pair<TensorType, IntTensor>
-
-    //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
-    public fun luUnpack(A_LU: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
-
-    //https://pytorch.org/docs/stable/generated/torch.svd.html
-    public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
-
-    //https://pytorch.org/docs/stable/generated/torch.symeig.html
-    public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
-
-}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
new file mode 100644
index 000000000..2d448fa8c
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
@@ -0,0 +1,27 @@
+package space.kscience.kmath.tensors
+
+// https://proofwiki.org/wiki/Definition:Division_Algebra
+public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
+    TensorAlgebra<T, TensorType> {
+
+    public operator fun TensorType.div(value: T): TensorType
+    public operator fun TensorType.div(other: TensorType): TensorType
+    public operator fun TensorType.divAssign(value: T)
+    public operator fun TensorType.divAssign(other: TensorType)
+
+    //https://pytorch.org/docs/stable/generated/torch.mean.html#torch.mean
+    public fun TensorType.mean(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.quantile.html#torch.quantile
+    public fun TensorType.quantile(q: T, dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.std.html#torch.std
+    public fun TensorType.std(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.var.html#torch.var
+    public fun TensorType.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.histc.html#torch.histc
+    public fun TensorType.histc(bins: Int, min: T, max: T): TensorType
+
+}

From 39a088912346078f3c3647ac262b2cf8c490aa4a Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 07:50:20 +0000
Subject: [PATCH 023/178] Dev merge assemble fixed, tests still broken

---
 .../kmath/tensors/LinearOpsTensorAlgebra.kt   |  2 +-
 .../tensors/RealAnalyticTensorAlgebra.kt      |  4 +-
 .../tensors/RealLinearOpsTensorAlgebra.kt     |  7 +--
 .../kmath/tensors/RealTensorAlgebra.kt        | 46 +++++++++----------
 .../space/kscience/kmath/tensors/utils.kt     |  4 +-
 .../kscience/kmath/tensors/TestRealTensor.kt  |  4 +-
 .../kmath/tensors/TestRealTensorAlgebra.kt    | 26 +++++------
 7 files changed, 48 insertions(+), 45 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index bd9cbfd45..db1135a33 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -32,7 +32,7 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>> :
     public fun TensorType.lu(): Pair<TensorType, IntTensor>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
-    public fun luPivot(aLU: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
+    public fun luPivot(lu: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
     public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
index cfecac0f4..a93ebcb89 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
@@ -144,5 +144,7 @@ public class RealAnalyticTensorAlgebra:
         TODO("Not yet implemented")
     }
 
+}
 
-}
\ No newline at end of file
+public inline fun <R> RealAnalyticTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
+    RealAnalyticTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
index 18c2050c0..98a5e581f 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
@@ -1,6 +1,7 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.array
+import space.kscience.kmath.structures.toDoubleArray
+import space.kscience.kmath.structures.toIntArray
 
 public class RealLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, RealTensor>,
@@ -80,7 +81,7 @@ public class RealLinearOpsTensorAlgebra :
         // todo checks
         val n = lu.shape[0]
         val p = lu.zeroesLike()
-        pivots.buffer.array.forEachIndexed { i, pivot ->
+        pivots.buffer.toIntArray().forEachIndexed { i, pivot ->
             p[i, pivot] = 1.0
         }
         val l = lu.zeroesLike()
@@ -130,4 +131,4 @@ public class RealLinearOpsTensorAlgebra :
 }
 
 public inline fun <R> RealLinearOpsTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
-    RealTensorAlgebra().block()
\ No newline at end of file
+    RealLinearOpsTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index 05e2b57d2..10cc0edad 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -1,6 +1,6 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.array
+import space.kscience.kmath.structures.toDoubleArray
 
 
 public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
@@ -9,7 +9,7 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
         check(this.shape contentEquals intArrayOf(1)) {
             "Inconsistent value for tensor of shape ${shape.toList()}"
         }
-        return this.buffer.array[0]
+        return this.buffer.toDoubleArray()[0]
     }
 
     override fun zeros(shape: IntArray): RealTensor {
@@ -32,13 +32,13 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
 
     override fun RealTensor.copy(): RealTensor {
         // should be rework as soon as copy() method for NDBuffer will be available
-        return RealTensor(this.shape, this.buffer.array.copyOf())
+        return RealTensor(this.shape, this.buffer.toDoubleArray().copyOf())
     }
 
 
     override fun Double.plus(other: RealTensor): RealTensor {
         val resBuffer = DoubleArray(other.buffer.size) { i ->
-            other.buffer.array[i] + this
+            other.buffer.toDoubleArray()[i] + this
         }
         return RealTensor(other.shape, resBuffer)
     }
@@ -50,34 +50,34 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.buffer.size) { i ->
-            newThis.buffer.array[i] + newOther.buffer.array[i]
+            newThis.buffer.toDoubleArray()[i] + newOther.buffer.toDoubleArray()[i]
         }
         return RealTensor(newThis.shape, resBuffer)
     }
 
     override fun RealTensor.plusAssign(value: Double) {
-        for (i in this.buffer.array.indices) {
-            this.buffer.array[i] += value
+        for (i in this.buffer.toDoubleArray().indices) {
+            this.buffer.toDoubleArray()[i] += value
         }
     }
 
     override fun RealTensor.plusAssign(other: RealTensor) {
         //todo should be change with broadcasting
-        for (i in this.buffer.array.indices) {
-            this.buffer.array[i] += other.buffer.array[i]
+        for (i in this.buffer.toDoubleArray().indices) {
+            this.buffer.toDoubleArray()[i] += other.buffer.toDoubleArray()[i]
         }
     }
 
     override fun Double.minus(other: RealTensor): RealTensor {
         val resBuffer = DoubleArray(other.buffer.size) { i ->
-            this - other.buffer.array[i]
+            this - other.buffer.toDoubleArray()[i]
         }
         return RealTensor(other.shape, resBuffer)
     }
 
     override fun RealTensor.minus(value: Double): RealTensor {
         val resBuffer = DoubleArray(this.buffer.size) { i ->
-            this.buffer.array[i] - value
+            this.buffer.toDoubleArray()[i] - value
         }
         return RealTensor(this.shape, resBuffer)
     }
@@ -87,14 +87,14 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.buffer.size) { i ->
-            newThis.buffer.array[i] - newOther.buffer.array[i]
+            newThis.buffer.toDoubleArray()[i] - newOther.buffer.toDoubleArray()[i]
         }
         return RealTensor(newThis.shape, resBuffer)
     }
 
     override fun RealTensor.minusAssign(value: Double) {
-        for (i in this.buffer.array.indices) {
-            this.buffer.array[i] -= value
+        for (i in this.buffer.toDoubleArray().indices) {
+            this.buffer.toDoubleArray()[i] -= value
         }
     }
 
@@ -105,7 +105,7 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
     override fun Double.times(other: RealTensor): RealTensor {
         //todo should be change with broadcasting
         val resBuffer = DoubleArray(other.buffer.size) { i ->
-            other.buffer.array[i] * this
+            other.buffer.toDoubleArray()[i] * this
         }
         return RealTensor(other.shape, resBuffer)
     }
@@ -116,28 +116,28 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
     override fun RealTensor.times(other: RealTensor): RealTensor {
         //todo should be change with broadcasting
         val resBuffer = DoubleArray(this.buffer.size) { i ->
-            this.buffer.array[i] * other.buffer.array[i]
+            this.buffer.toDoubleArray()[i] * other.buffer.toDoubleArray()[i]
         }
         return RealTensor(this.shape, resBuffer)
     }
 
     override fun RealTensor.timesAssign(value: Double) {
         //todo should be change with broadcasting
-        for (i in this.buffer.array.indices) {
-            this.buffer.array[i] *= value
+        for (i in this.buffer.toDoubleArray().indices) {
+            this.buffer.toDoubleArray()[i] *= value
         }
     }
 
     override fun RealTensor.timesAssign(other: RealTensor) {
         //todo should be change with broadcasting
-        for (i in this.buffer.array.indices) {
-            this.buffer.array[i] *= other.buffer.array[i]
+        for (i in this.buffer.toDoubleArray().indices) {
+            this.buffer.toDoubleArray()[i] *= other.buffer.toDoubleArray()[i]
         }
     }
 
     override fun RealTensor.unaryMinus(): RealTensor {
         val resBuffer = DoubleArray(this.buffer.size) { i ->
-            this.buffer.array[i].unaryMinus()
+            this.buffer.toDoubleArray()[i].unaryMinus()
         }
         return RealTensor(this.shape, resBuffer)
     }
@@ -158,14 +158,14 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
             newMultiIndex[i] = newMultiIndex[j].also { newMultiIndex[j] = newMultiIndex[i] }
 
             val linearIndex = resTensor.strides.offset(newMultiIndex)
-            resTensor.buffer.array[linearIndex] = this.buffer.array[offset]
+            resTensor.buffer.toDoubleArray()[linearIndex] = this.buffer.toDoubleArray()[offset]
         }
         return resTensor
     }
 
 
     override fun RealTensor.view(shape: IntArray): RealTensor {
-        return RealTensor(shape, this.buffer.array)
+        return RealTensor(shape, this.buffer.toDoubleArray())
     }
 
     override fun RealTensor.viewAs(other: RealTensor): RealTensor {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
index 65409bb15..f61bdbd01 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
@@ -1,6 +1,6 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.array
+import space.kscience.kmath.structures.toDoubleArray
 import kotlin.math.max
 
 
@@ -55,7 +55,7 @@ internal inline fun broadcastTensors(vararg tensors: RealTensor): List<RealTenso
             }
 
             val curLinearIndex = tensor.strides.offset(curMultiIndex)
-            resTensor.buffer.array[linearIndex] = tensor.buffer.array[curLinearIndex]
+            resTensor.buffer.toDoubleArray()[linearIndex] = tensor.buffer.toDoubleArray()[curLinearIndex]
         }
         res.add(resTensor)
     }
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
index d96b00baf..473a3da8b 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
@@ -1,7 +1,7 @@
 package space.kscience.kmath.tensors
 
 
-import space.kscience.kmath.structures.array
+import space.kscience.kmath.structures.toDoubleArray
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
@@ -19,6 +19,6 @@ class TestRealTensor {
     fun stridesTest(){
         val tensor = RealTensor(intArrayOf(2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
         assertEquals(tensor[intArrayOf(0,1)], 5.8)
-        assertTrue(tensor.elements().map{ it.second }.toList().toDoubleArray() contentEquals tensor.buffer.array)
+        assertTrue(tensor.elements().map{ it.second }.toList().toDoubleArray() contentEquals tensor.buffer.toDoubleArray())
     }
 }
\ No newline at end of file
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
index 7c27f5fa9..80e3c3eab 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
@@ -1,6 +1,6 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.array
+import space.kscience.kmath.structures.toDoubleArray
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
@@ -10,7 +10,7 @@ class TestRealTensorAlgebra {
     fun doublePlus() = RealTensorAlgebra {
         val tensor = RealTensor(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
-        assertTrue(res.buffer.array contentEquals doubleArrayOf(11.0,12.0))
+        assertTrue(res.buffer.toDoubleArray() contentEquals doubleArrayOf(11.0,12.0))
     }
 
     @Test
@@ -18,7 +18,7 @@ class TestRealTensorAlgebra {
         val tensor = RealTensor(intArrayOf(1), doubleArrayOf(0.0))
         val res = tensor.transpose(0, 0)
 
-        assertTrue(res.buffer.array contentEquals doubleArrayOf(0.0))
+        assertTrue(res.buffer.toDoubleArray() contentEquals doubleArrayOf(0.0))
         assertTrue(res.shape contentEquals intArrayOf(1))
     }
 
@@ -27,7 +27,7 @@ class TestRealTensorAlgebra {
         val tensor = RealTensor(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res = tensor.transpose(1, 0)
 
-        assertTrue(res.buffer.array contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
+        assertTrue(res.buffer.toDoubleArray() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
         assertTrue(res.shape contentEquals intArrayOf(2, 3))
     }
 
@@ -42,9 +42,9 @@ class TestRealTensorAlgebra {
         assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
         assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2))
 
-        assertTrue(res01.buffer.array contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res02.buffer.array contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
-        assertTrue(res12.buffer.array contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res01.buffer.toDoubleArray() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res02.buffer.toDoubleArray() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res12.buffer.toDoubleArray() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
     }
 
     @Test
@@ -70,9 +70,9 @@ class TestRealTensorAlgebra {
         assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
         assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
 
-        assertTrue(res[0].buffer.array contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res[1].buffer.array contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
-        assertTrue(res[2].buffer.array contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
+        assertTrue(res[0].buffer.toDoubleArray() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res[1].buffer.toDoubleArray() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+        assertTrue(res[2].buffer.toDoubleArray() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
     }
 
     @Test
@@ -82,14 +82,14 @@ class TestRealTensorAlgebra {
         val tensor3 = RealTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
 
         assertTrue((tensor2 - tensor1).shape contentEquals intArrayOf(2, 3))
-        assertTrue((tensor2 - tensor1).buffer.array contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
+        assertTrue((tensor2 - tensor1).buffer.toDoubleArray() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
 
         assertTrue((tensor3 - tensor1).shape contentEquals intArrayOf(1, 2, 3))
-        assertTrue((tensor3 - tensor1).buffer.array
+        assertTrue((tensor3 - tensor1).buffer.toDoubleArray()
                 contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0))
 
         assertTrue((tensor3 - tensor2).shape contentEquals intArrayOf(1, 1, 3))
-        assertTrue((tensor3 - tensor2).buffer.array contentEquals doubleArrayOf(490.0, 480.0, 470.0))
+        assertTrue((tensor3 - tensor2).buffer.toDoubleArray() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
     }
 
 }
\ No newline at end of file

From 04f6ef1ed0c691d9510ce0c5ff87c16afe4894e6 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 08:31:19 +0000
Subject: [PATCH 024/178] Fixed tests with unsafe accessors

---
 .../kscience/kmath/structures/FloatBuffer.kt  |  8 ++++
 .../kscience/kmath/structures/IntBuffer.kt    |  8 ++++
 .../kscience/kmath/structures/LongBuffer.kt   |  8 ++++
 .../kscience/kmath/structures/RealBuffer.kt   |  8 ++++
 .../tensors/RealLinearOpsTensorAlgebra.kt     |  5 +-
 .../kmath/tensors/RealTensorAlgebra.kt        | 46 +++++++++----------
 .../space/kscience/kmath/tensors/utils.kt     |  4 +-
 7 files changed, 59 insertions(+), 28 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt
index 9fc7d55f3..dbcf35504 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt
@@ -43,6 +43,14 @@ public fun Buffer<Float>.toFloatArray(): FloatArray = when(this) {
     else -> FloatArray(size, ::get)
 }
 
+/**
+ * Returns a reference to [FloatArray] containing all of the elements of this [Buffer].
+ */
+public fun Buffer<Float>.unsafeToFloatArray(): FloatArray = when(this) {
+    is FloatBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to FloatArray")
+}
+
 /**
  * Returns [FloatBuffer] over this array.
  *
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt
index d3d0f79a5..d58451bc4 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt
@@ -42,6 +42,14 @@ public fun Buffer<Int>.toIntArray(): IntArray = when(this) {
     else -> IntArray(size, ::get)
 }
 
+/**
+ * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
+ */
+public fun Buffer<Int>.unsafeToIntArray(): IntArray = when(this) {
+    is IntBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to IntArray")
+}
+
 /**
  * Returns [IntBuffer] over this array.
  *
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt
index fec358421..3fa9bf861 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt
@@ -42,6 +42,14 @@ public fun Buffer<Long>.toLongArray(): LongArray = when(this) {
     else -> LongArray(size, ::get)
 }
 
+/**
+ * Returns a reference to [LongArray] containing all of the elements of this [Buffer].
+ */
+public fun Buffer<Long>.unsafeToLongArray(): LongArray = when(this) {
+    is LongBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to LongArray")
+}
+
 /**
  * Returns [LongBuffer] over this array.
  *
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/RealBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/RealBuffer.kt
index 01b533138..69e1ae9cd 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/RealBuffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/RealBuffer.kt
@@ -47,6 +47,14 @@ public fun Buffer<Double>.toDoubleArray(): DoubleArray = when(this) {
     else -> DoubleArray(size, ::get)
 }
 
+/**
+ * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer].
+ */
+public fun Buffer<Double>.unsafeToDoubleArray(): DoubleArray = when(this) {
+    is RealBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
+}
+
 /**
  * Returns [RealBuffer] over this array.
  *
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
index 98a5e581f..f73e4c4cd 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
@@ -1,7 +1,6 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.toDoubleArray
-import space.kscience.kmath.structures.toIntArray
+import space.kscience.kmath.structures.unsafeToIntArray
 
 public class RealLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, RealTensor>,
@@ -81,7 +80,7 @@ public class RealLinearOpsTensorAlgebra :
         // todo checks
         val n = lu.shape[0]
         val p = lu.zeroesLike()
-        pivots.buffer.toIntArray().forEachIndexed { i, pivot ->
+        pivots.buffer.unsafeToIntArray().forEachIndexed { i, pivot ->
             p[i, pivot] = 1.0
         }
         val l = lu.zeroesLike()
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index 10cc0edad..ee0733577 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -1,6 +1,6 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.toDoubleArray
+import space.kscience.kmath.structures.unsafeToDoubleArray
 
 
 public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
@@ -9,7 +9,7 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
         check(this.shape contentEquals intArrayOf(1)) {
             "Inconsistent value for tensor of shape ${shape.toList()}"
         }
-        return this.buffer.toDoubleArray()[0]
+        return this.buffer.unsafeToDoubleArray()[0]
     }
 
     override fun zeros(shape: IntArray): RealTensor {
@@ -32,13 +32,13 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
 
     override fun RealTensor.copy(): RealTensor {
         // should be rework as soon as copy() method for NDBuffer will be available
-        return RealTensor(this.shape, this.buffer.toDoubleArray().copyOf())
+        return RealTensor(this.shape, this.buffer.unsafeToDoubleArray().copyOf())
     }
 
 
     override fun Double.plus(other: RealTensor): RealTensor {
         val resBuffer = DoubleArray(other.buffer.size) { i ->
-            other.buffer.toDoubleArray()[i] + this
+            other.buffer.unsafeToDoubleArray()[i] + this
         }
         return RealTensor(other.shape, resBuffer)
     }
@@ -50,34 +50,34 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.buffer.size) { i ->
-            newThis.buffer.toDoubleArray()[i] + newOther.buffer.toDoubleArray()[i]
+            newThis.buffer.unsafeToDoubleArray()[i] + newOther.buffer.unsafeToDoubleArray()[i]
         }
         return RealTensor(newThis.shape, resBuffer)
     }
 
     override fun RealTensor.plusAssign(value: Double) {
-        for (i in this.buffer.toDoubleArray().indices) {
-            this.buffer.toDoubleArray()[i] += value
+        for (i in this.buffer.unsafeToDoubleArray().indices) {
+            this.buffer.unsafeToDoubleArray()[i] += value
         }
     }
 
     override fun RealTensor.plusAssign(other: RealTensor) {
         //todo should be change with broadcasting
-        for (i in this.buffer.toDoubleArray().indices) {
-            this.buffer.toDoubleArray()[i] += other.buffer.toDoubleArray()[i]
+        for (i in this.buffer.unsafeToDoubleArray().indices) {
+            this.buffer.unsafeToDoubleArray()[i] += other.buffer.unsafeToDoubleArray()[i]
         }
     }
 
     override fun Double.minus(other: RealTensor): RealTensor {
         val resBuffer = DoubleArray(other.buffer.size) { i ->
-            this - other.buffer.toDoubleArray()[i]
+            this - other.buffer.unsafeToDoubleArray()[i]
         }
         return RealTensor(other.shape, resBuffer)
     }
 
     override fun RealTensor.minus(value: Double): RealTensor {
         val resBuffer = DoubleArray(this.buffer.size) { i ->
-            this.buffer.toDoubleArray()[i] - value
+            this.buffer.unsafeToDoubleArray()[i] - value
         }
         return RealTensor(this.shape, resBuffer)
     }
@@ -87,14 +87,14 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.buffer.size) { i ->
-            newThis.buffer.toDoubleArray()[i] - newOther.buffer.toDoubleArray()[i]
+            newThis.buffer.unsafeToDoubleArray()[i] - newOther.buffer.unsafeToDoubleArray()[i]
         }
         return RealTensor(newThis.shape, resBuffer)
     }
 
     override fun RealTensor.minusAssign(value: Double) {
-        for (i in this.buffer.toDoubleArray().indices) {
-            this.buffer.toDoubleArray()[i] -= value
+        for (i in this.buffer.unsafeToDoubleArray().indices) {
+            this.buffer.unsafeToDoubleArray()[i] -= value
         }
     }
 
@@ -105,7 +105,7 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
     override fun Double.times(other: RealTensor): RealTensor {
         //todo should be change with broadcasting
         val resBuffer = DoubleArray(other.buffer.size) { i ->
-            other.buffer.toDoubleArray()[i] * this
+            other.buffer.unsafeToDoubleArray()[i] * this
         }
         return RealTensor(other.shape, resBuffer)
     }
@@ -116,28 +116,28 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
     override fun RealTensor.times(other: RealTensor): RealTensor {
         //todo should be change with broadcasting
         val resBuffer = DoubleArray(this.buffer.size) { i ->
-            this.buffer.toDoubleArray()[i] * other.buffer.toDoubleArray()[i]
+            this.buffer.unsafeToDoubleArray()[i] * other.buffer.unsafeToDoubleArray()[i]
         }
         return RealTensor(this.shape, resBuffer)
     }
 
     override fun RealTensor.timesAssign(value: Double) {
         //todo should be change with broadcasting
-        for (i in this.buffer.toDoubleArray().indices) {
-            this.buffer.toDoubleArray()[i] *= value
+        for (i in this.buffer.unsafeToDoubleArray().indices) {
+            this.buffer.unsafeToDoubleArray()[i] *= value
         }
     }
 
     override fun RealTensor.timesAssign(other: RealTensor) {
         //todo should be change with broadcasting
-        for (i in this.buffer.toDoubleArray().indices) {
-            this.buffer.toDoubleArray()[i] *= other.buffer.toDoubleArray()[i]
+        for (i in this.buffer.unsafeToDoubleArray().indices) {
+            this.buffer.unsafeToDoubleArray()[i] *= other.buffer.unsafeToDoubleArray()[i]
         }
     }
 
     override fun RealTensor.unaryMinus(): RealTensor {
         val resBuffer = DoubleArray(this.buffer.size) { i ->
-            this.buffer.toDoubleArray()[i].unaryMinus()
+            this.buffer.unsafeToDoubleArray()[i].unaryMinus()
         }
         return RealTensor(this.shape, resBuffer)
     }
@@ -158,14 +158,14 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
             newMultiIndex[i] = newMultiIndex[j].also { newMultiIndex[j] = newMultiIndex[i] }
 
             val linearIndex = resTensor.strides.offset(newMultiIndex)
-            resTensor.buffer.toDoubleArray()[linearIndex] = this.buffer.toDoubleArray()[offset]
+            resTensor.buffer.unsafeToDoubleArray()[linearIndex] = this.buffer.unsafeToDoubleArray()[offset]
         }
         return resTensor
     }
 
 
     override fun RealTensor.view(shape: IntArray): RealTensor {
-        return RealTensor(shape, this.buffer.toDoubleArray())
+        return RealTensor(shape, this.buffer.unsafeToDoubleArray())
     }
 
     override fun RealTensor.viewAs(other: RealTensor): RealTensor {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
index f61bdbd01..04b4ddcbf 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
@@ -1,6 +1,6 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.toDoubleArray
+import space.kscience.kmath.structures.unsafeToDoubleArray
 import kotlin.math.max
 
 
@@ -55,7 +55,7 @@ internal inline fun broadcastTensors(vararg tensors: RealTensor): List<RealTenso
             }
 
             val curLinearIndex = tensor.strides.offset(curMultiIndex)
-            resTensor.buffer.toDoubleArray()[linearIndex] = tensor.buffer.toDoubleArray()[curLinearIndex]
+            resTensor.buffer.unsafeToDoubleArray()[linearIndex] = tensor.buffer.unsafeToDoubleArray()[curLinearIndex]
         }
         res.add(resTensor)
     }

From 4e4690e5104af58d48fd8f08d2fdbbb132f26751 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 08:48:31 +0000
Subject: [PATCH 025/178] unsafe buffer casts moved to internal utils

---
 .../kscience/kmath/structures/FloatBuffer.kt  |  8 -----
 .../kscience/kmath/structures/IntBuffer.kt    |  8 -----
 .../kscience/kmath/structures/LongBuffer.kt   |  8 -----
 .../kscience/kmath/structures/RealBuffer.kt   |  8 -----
 .../tensors/RealLinearOpsTensorAlgebra.kt     |  2 --
 .../kmath/tensors/RealTensorAlgebra.kt        |  2 --
 .../space/kscience/kmath/tensors/utils.kt     | 36 +++++++++++++++++--
 .../kmath/tensors/TestRealTensorAlgebra.kt    | 26 +++++++-------
 8 files changed, 47 insertions(+), 51 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt
index dbcf35504..9fc7d55f3 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt
@@ -43,14 +43,6 @@ public fun Buffer<Float>.toFloatArray(): FloatArray = when(this) {
     else -> FloatArray(size, ::get)
 }
 
-/**
- * Returns a reference to [FloatArray] containing all of the elements of this [Buffer].
- */
-public fun Buffer<Float>.unsafeToFloatArray(): FloatArray = when(this) {
-    is FloatBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to FloatArray")
-}
-
 /**
  * Returns [FloatBuffer] over this array.
  *
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt
index d58451bc4..d3d0f79a5 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt
@@ -42,14 +42,6 @@ public fun Buffer<Int>.toIntArray(): IntArray = when(this) {
     else -> IntArray(size, ::get)
 }
 
-/**
- * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
- */
-public fun Buffer<Int>.unsafeToIntArray(): IntArray = when(this) {
-    is IntBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to IntArray")
-}
-
 /**
  * Returns [IntBuffer] over this array.
  *
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt
index 3fa9bf861..fec358421 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt
@@ -42,14 +42,6 @@ public fun Buffer<Long>.toLongArray(): LongArray = when(this) {
     else -> LongArray(size, ::get)
 }
 
-/**
- * Returns a reference to [LongArray] containing all of the elements of this [Buffer].
- */
-public fun Buffer<Long>.unsafeToLongArray(): LongArray = when(this) {
-    is LongBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to LongArray")
-}
-
 /**
  * Returns [LongBuffer] over this array.
  *
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/RealBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/RealBuffer.kt
index 69e1ae9cd..01b533138 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/RealBuffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/RealBuffer.kt
@@ -47,14 +47,6 @@ public fun Buffer<Double>.toDoubleArray(): DoubleArray = when(this) {
     else -> DoubleArray(size, ::get)
 }
 
-/**
- * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer].
- */
-public fun Buffer<Double>.unsafeToDoubleArray(): DoubleArray = when(this) {
-    is RealBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
-}
-
 /**
  * Returns [RealBuffer] over this array.
  *
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
index f73e4c4cd..dcd740356 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
@@ -1,7 +1,5 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.unsafeToIntArray
-
 public class RealLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, RealTensor>,
     RealTensorAlgebra()
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
index ee0733577..83a513a7a 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
@@ -1,7 +1,5 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.unsafeToDoubleArray
-
 
 public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
index 04b4ddcbf..b03fb7dd7 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
@@ -1,6 +1,6 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.unsafeToDoubleArray
+import space.kscience.kmath.structures.*
 import kotlin.math.max
 
 
@@ -93,4 +93,36 @@ internal inline fun <T, TensorType : TensorStructure<T>,
 internal inline fun <T, TensorType : TensorStructure<T>,
         TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
         TorchTensorAlgebraType.checkView(a: TensorType, shape: IntArray): Unit =
-    check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
\ No newline at end of file
+    check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
+
+/**
+ * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Int>.unsafeToIntArray(): IntArray = when(this) {
+    is IntBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to IntArray")
+}
+
+/**
+ * Returns a reference to [LongArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Long>.unsafeToLongArray(): LongArray = when(this) {
+    is LongBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to LongArray")
+}
+
+/**
+ * Returns a reference to [FloatArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Float>.unsafeToFloatArray(): FloatArray = when(this) {
+    is FloatBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to FloatArray")
+}
+
+/**
+ * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Double>.unsafeToDoubleArray(): DoubleArray = when(this) {
+    is RealBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
+}
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
index 80e3c3eab..72bbf1787 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
@@ -1,6 +1,6 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.toDoubleArray
+
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
@@ -10,7 +10,7 @@ class TestRealTensorAlgebra {
     fun doublePlus() = RealTensorAlgebra {
         val tensor = RealTensor(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
-        assertTrue(res.buffer.toDoubleArray() contentEquals doubleArrayOf(11.0,12.0))
+        assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(11.0,12.0))
     }
 
     @Test
@@ -18,7 +18,7 @@ class TestRealTensorAlgebra {
         val tensor = RealTensor(intArrayOf(1), doubleArrayOf(0.0))
         val res = tensor.transpose(0, 0)
 
-        assertTrue(res.buffer.toDoubleArray() contentEquals doubleArrayOf(0.0))
+        assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(0.0))
         assertTrue(res.shape contentEquals intArrayOf(1))
     }
 
@@ -27,7 +27,7 @@ class TestRealTensorAlgebra {
         val tensor = RealTensor(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res = tensor.transpose(1, 0)
 
-        assertTrue(res.buffer.toDoubleArray() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
+        assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
         assertTrue(res.shape contentEquals intArrayOf(2, 3))
     }
 
@@ -42,9 +42,9 @@ class TestRealTensorAlgebra {
         assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
         assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2))
 
-        assertTrue(res01.buffer.toDoubleArray() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res02.buffer.toDoubleArray() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
-        assertTrue(res12.buffer.toDoubleArray() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res01.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res02.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res12.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
     }
 
     @Test
@@ -70,9 +70,9 @@ class TestRealTensorAlgebra {
         assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
         assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
 
-        assertTrue(res[0].buffer.toDoubleArray() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res[1].buffer.toDoubleArray() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
-        assertTrue(res[2].buffer.toDoubleArray() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
+        assertTrue(res[0].buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res[1].buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+        assertTrue(res[2].buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
     }
 
     @Test
@@ -82,14 +82,14 @@ class TestRealTensorAlgebra {
         val tensor3 = RealTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
 
         assertTrue((tensor2 - tensor1).shape contentEquals intArrayOf(2, 3))
-        assertTrue((tensor2 - tensor1).buffer.toDoubleArray() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
+        assertTrue((tensor2 - tensor1).buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
 
         assertTrue((tensor3 - tensor1).shape contentEquals intArrayOf(1, 2, 3))
-        assertTrue((tensor3 - tensor1).buffer.toDoubleArray()
+        assertTrue((tensor3 - tensor1).buffer.unsafeToDoubleArray()
                 contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0))
 
         assertTrue((tensor3 - tensor2).shape contentEquals intArrayOf(1, 1, 3))
-        assertTrue((tensor3 - tensor2).buffer.toDoubleArray() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
+        assertTrue((tensor3 - tensor2).buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
     }
 
 }
\ No newline at end of file

From 2d2c5aa6840902db6c5688d19e2960b124b5e0d3 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Mon, 15 Mar 2021 15:18:21 +0300
Subject: [PATCH 026/178] matrixhelper

---
 .../kscience/kmath/tensors/BufferedTensor.kt  |  53 ++++++++
 .../tensors/RealLinearOpsTensorAlgebra.kt     | 116 ++++++++++--------
 2 files changed, 120 insertions(+), 49 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
index 29605024d..4d89996a0 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
@@ -13,6 +13,7 @@ public open class BufferedTensor<T>(
         TensorStrides(shape),
         buffer
     ) {
+    /*
 
     public operator fun get(i: Int, j: Int): T {
         check(this.dimension == 2) { "Not matrix" }
@@ -24,8 +25,60 @@ public open class BufferedTensor<T>(
         this[intArrayOf(i, j)] = value
     }
 
+     */
 }
 
+//todo make generator mb nextMatrixIndex?
+public class InnerMatrix<T>(private val tensor: BufferedTensor<T>){
+    private var offset: Int = 0
+    private val n : Int = tensor.shape.size
+    //stride?
+    private val step = tensor.shape[n - 1] * tensor.shape[n - 2]
+
+    public operator fun get(i: Int, j: Int): T {
+        val index = tensor.strides.index(offset)
+        index[n - 2] = i
+        index[n - 1] = j
+        return tensor[index]
+    }
+
+    public operator fun set(i: Int, j: Int, value: T): Unit {
+        val index = tensor.strides.index(offset)
+        index[n - 2] = i
+        index[n - 1] = j
+        tensor[index] = value
+    }
+
+    public fun makeStep(){
+        offset += step
+    }
+}
+
+public class InnerVector<T>(private val tensor: BufferedTensor<T>){
+    private var offset: Int = 0
+    private val n : Int = tensor.shape.size
+    //stride?
+    private val step = tensor.shape[n - 1]
+
+    public operator fun get(i: Int): T {
+        val index = tensor.strides.index(offset)
+        index[n - 1] = i
+        return tensor[index]
+    }
+
+    public operator fun set(i: Int, value: T): Unit {
+        val index = tensor.strides.index(offset)
+        index[n - 1] = i
+        tensor[index] = value
+    }
+
+    public fun makeStep(){
+        offset += step
+    }
+}
+
+
+//todo default buffer = arrayOf(0)???
 public class IntTensor(
     shape: IntArray,
     buffer: IntArray
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
index dcd740356..ab39fc6ac 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
@@ -2,8 +2,7 @@ package space.kscience.kmath.tensors
 
 public class RealLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, RealTensor>,
-    RealTensorAlgebra()
-{
+    RealTensorAlgebra() {
     override fun eye(n: Int): RealTensor {
         val shape = intArrayOf(n, n)
         val buffer = DoubleArray(n * n) { 0.0 }
@@ -26,55 +25,71 @@ public class RealLinearOpsTensorAlgebra :
 
     override fun RealTensor.lu(): Pair<RealTensor, IntTensor> {
         // todo checks
-        val lu = this.copy()
-        val m = this.shape[0]
-        val pivot = IntArray(m)
-
-
-        // Initialize permutation array and parity
-        for (row in 0 until m) pivot[row] = row
-        var even = true
-
-        for (i in 0 until m) {
-            var maxA = -1.0
-            var iMax = i
-
-            for (k in i until m) {
-                val absA = kotlin.math.abs(lu[k, i])
-                if (absA > maxA) {
-                    maxA = absA
-                    iMax = k
-                }
-            }
-
-            //todo check singularity
-
-            if (iMax != i) {
-
-                val j = pivot[i]
-                pivot[i] = pivot[iMax]
-                pivot[iMax] = j
-                even != even
-
-                for (k in 0 until m) {
-                    val tmp = lu[i, k]
-                    lu[i, k] = lu[iMax, k]
-                    lu[iMax, k] = tmp
-                }
-
-            }
-
-            for (j in i + 1 until m) {
-                lu[j, i] /= lu[i, i]
-                for (k in i + 1 until m) {
-                    lu[j, k] -= lu[j, i] * lu[i, k]
-                }
-            }
+        val luTensor = this.copy()
+        val lu = InnerMatrix(luTensor)
+        //stride TODO!!! move to generator?
+        var matCnt = 1
+        for (i in 0 until this.shape.size - 2) {
+            matCnt *= this.shape[i]
         }
-        return Pair(lu, IntTensor(intArrayOf(m), pivot))
+        val n = this.shape.size
+        val m = this.shape[n - 1]
+        val pivotsShape = IntArray(n - 1) { i ->
+            this.shape[i]
+        }
+        val pivotsTensor = IntTensor(
+            pivotsShape,
+            IntArray(matCnt * m) { 0 }
+        )
+        val pivot = InnerVector(pivotsTensor)
+        for (i in 0 until matCnt) {
+            for (row in 0 until m) pivot[row] = row
+
+            for (i in 0 until m) {
+                var maxA = -1.0
+                var iMax = i
+
+                for (k in i until m) {
+                    val absA = kotlin.math.abs(lu[k, i])
+                    if (absA > maxA) {
+                        maxA = absA
+                        iMax = k
+                    }
+                }
+
+                //todo check singularity
+
+                if (iMax != i) {
+
+                    val j = pivot[i]
+                    pivot[i] = pivot[iMax]
+                    pivot[iMax] = j
+
+                    for (k in 0 until m) {
+                        val tmp = lu[i, k]
+                        lu[i, k] = lu[iMax, k]
+                        lu[iMax, k] = tmp
+                    }
+
+                }
+
+                for (j in i + 1 until m) {
+                    lu[j, i] /= lu[i, i]
+                    for (k in i + 1 until m) {
+                        lu[j, k] -= lu[j, i] * lu[i, k]
+                    }
+                }
+            }
+            lu.makeStep()
+            pivot.makeStep()
+        }
+
+        return Pair(luTensor, pivotsTensor)
     }
 
     override fun luPivot(lu: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
+        TODO()
+        /*
         // todo checks
         val n = lu.shape[0]
         val p = lu.zeroesLike()
@@ -97,9 +112,12 @@ public class RealLinearOpsTensorAlgebra :
                 }
             }
         }
-        return Triple(p, l, u)
+
+        return Triple(p, l, u)*/
     }
 
+
+
     override fun RealTensor.det(): RealTensor {
         TODO("Not yet implemented")
     }
@@ -127,5 +145,5 @@ public class RealLinearOpsTensorAlgebra :
 
 }
 
-public inline fun <R> RealLinearOpsTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
+public inline fun <R> RealLinearOpsTensorAlgebra(block: RealLinearOpsTensorAlgebra.() -> R): R =
     RealLinearOpsTensorAlgebra().block()
\ No newline at end of file

From 50ed7ce28bb072f33a3527879fe8d9453cba6ac4 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 12:54:46 +0000
Subject: [PATCH 027/178] ComplexTensorAlgebra interface

---
 .../kmath/tensors/ComplexTensorAlgebra.kt     | 47 +++++++++++++++++++
 .../kmath/tensors/ComplexTensorStructure.kt   | 14 ++++++
 .../tensors/RealAnalyticTensorAlgebra.kt      |  2 +-
 .../tensors/RealLinearOpsTensorAlgebra.kt     |  2 +-
 4 files changed, 63 insertions(+), 2 deletions(-)
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorStructure.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
new file mode 100644
index 000000000..3f5b7d667
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
@@ -0,0 +1,47 @@
+package space.kscience.kmath.tensors
+
+public interface ComplexTensorAlgebra<T,
+        RealTensorType : TensorStructure<T>,
+        ComplexTensorType : ComplexTensorStructure<T, RealTensorType>>
+    : TensorPartialDivisionAlgebra<T, ComplexTensorType>{
+
+    //https://pytorch.org/docs/stable/generated/torch.view_as_complex.html
+    public fun RealTensorType.viewAsComplex(): ComplexTensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.angle.html
+    public fun ComplexTensorType.angle(): RealTensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.stft.html#torch.stft
+    public fun ComplexTensorType.stft(
+        nFFT: Int,
+        hopLength: Int,
+        winLength: Int,
+        window: RealTensorType,
+        normalised: Boolean,
+        oneSided: Boolean
+    )
+
+    //https://pytorch.org/docs/stable/generated/torch.istft.html#torch.istft
+    public fun ComplexTensorType.istft(
+        nFFT: Int,
+        hopLength: Int,
+        winLength: Int,
+        window: RealTensorType,
+        center: Boolean,
+        normalised: Boolean,
+        oneSided: Boolean,
+        length: Int
+    )
+
+    //https://pytorch.org/docs/stable/generated/torch.bartlett_window.html#torch.bartlett_window
+    public fun bartlettWindow(windowLength: Int, periodic: Boolean): RealTensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.blackman_window.html#torch.blackman_window
+    public fun blackmanWindow(windowLength: Int, periodic: Boolean): RealTensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.hamming_window.html#torch.hamming_window
+    public fun hammingWindow(windowLength: Int, periodic: Boolean, alpha: T, beta: T): RealTensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.kaiser_window.html#torch.kaiser_window
+    public fun kaiserWindow(windowLength: Int, periodic: Boolean, beta: T): RealTensorType
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorStructure.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorStructure.kt
new file mode 100644
index 000000000..0e0975830
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorStructure.kt
@@ -0,0 +1,14 @@
+package space.kscience.kmath.tensors
+
+public interface ComplexTensorStructure<T, RealTensorType: TensorStructure<T>> : TensorStructure<T> {
+
+    //https://pytorch.org/docs/master/generated/torch.view_as_real.html
+    public fun viewAsReal(): RealTensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.real.html
+    public fun realPart(): RealTensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.imag.html
+    public fun imaginaryPart(): RealTensorType
+
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
index a93ebcb89..f610361fc 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
@@ -146,5 +146,5 @@ public class RealAnalyticTensorAlgebra:
 
 }
 
-public inline fun <R> RealAnalyticTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
+public inline fun <R> RealAnalyticTensorAlgebra(block: RealAnalyticTensorAlgebra.() -> R): R =
     RealAnalyticTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
index dcd740356..6b1957b8c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
@@ -127,5 +127,5 @@ public class RealLinearOpsTensorAlgebra :
 
 }
 
-public inline fun <R> RealLinearOpsTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
+public inline fun <R> RealLinearOpsTensorAlgebra(block: RealLinearOpsTensorAlgebra.() -> R): R =
     RealLinearOpsTensorAlgebra().block()
\ No newline at end of file

From a3ca861ebe034fc1a8b08f18c2613df3ef81c785 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 13:05:45 +0000
Subject: [PATCH 028/178] Rename FFTs

---
 .../space/kscience/kmath/tensors/ComplexTensorAlgebra.kt      | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
index 3f5b7d667..8399511a5 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
@@ -12,7 +12,7 @@ public interface ComplexTensorAlgebra<T,
     public fun ComplexTensorType.angle(): RealTensorType
 
     //https://pytorch.org/docs/stable/generated/torch.stft.html#torch.stft
-    public fun ComplexTensorType.stft(
+    public fun ComplexTensorType.shortTimeFourierTransform(
         nFFT: Int,
         hopLength: Int,
         winLength: Int,
@@ -22,7 +22,7 @@ public interface ComplexTensorAlgebra<T,
     )
 
     //https://pytorch.org/docs/stable/generated/torch.istft.html#torch.istft
-    public fun ComplexTensorType.istft(
+    public fun ComplexTensorType.inverseShortTimeFourierTransform(
         nFFT: Int,
         hopLength: Int,
         winLength: Int,

From f66ac653e0264881d1197a395be84bdb37902b23 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 16:59:50 +0000
Subject: [PATCH 029/178] RealTensor to DoubleTensor rename

---
 .../kscience/kmath/tensors/BufferedTensor.kt  |   2 +-
 .../tensors/DoubleAnalyticTensorAlgebra.kt    | 150 ++++++++++++++++++
 ...bra.kt => DoubleLinearOpsTensorAlgebra.kt} |  26 +--
 ...ensorAlgebra.kt => DoubleTensorAlgebra.kt} | 128 +++++++--------
 .../tensors/RealAnalyticTensorAlgebra.kt      | 150 ------------------
 .../space/kscience/kmath/tensors/utils.kt     |   6 +-
 .../kscience/kmath/tensors/TestRealTensor.kt  |   4 +-
 .../kmath/tensors/TestRealTensorAlgebra.kt    |  20 +--
 8 files changed, 243 insertions(+), 243 deletions(-)
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
 rename kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/{RealLinearOpsTensorAlgebra.kt => DoubleLinearOpsTensorAlgebra.kt} (73%)
 rename kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/{RealTensorAlgebra.kt => DoubleTensorAlgebra.kt} (54%)
 delete mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
index 29605024d..066fb8708 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
@@ -41,7 +41,7 @@ public class FloatTensor(
     buffer: FloatArray
 ) : BufferedTensor<Float>(shape, FloatBuffer(buffer))
 
-public class RealTensor(
+public class DoubleTensor(
     shape: IntArray,
     buffer: DoubleArray
 ) : BufferedTensor<Double>(shape, RealBuffer(buffer))
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
new file mode 100644
index 000000000..702049a20
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
@@ -0,0 +1,150 @@
+package space.kscience.kmath.tensors
+
+public class RealAnalyticTensorAlgebra:
+    AnalyticTensorAlgebra<Double, DoubleTensor>,
+    RealTensorAlgebra()
+{
+    override fun DoubleTensor.exp(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.log(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.sqrt(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.square(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.cos(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.acos(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.cosh(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.acosh(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.sin(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.asin(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.sinh(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.asinh(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.tan(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.atan(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.tanh(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.atanh(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.ceil(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.floor(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.clamp(min: Double, max: Double): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.erf(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.erfinv(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.erfc(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.lerp(end: DoubleTensor, weight: DoubleTensor): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.lgamma(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.logit(eps: Double): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.igamma(other: DoubleTensor): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.igammac(other: DoubleTensor): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.mvlgamma(dimensions: Int): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.polygamma(order: Int): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.pow(exponent: Double): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.round(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.sigmoid(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.sinc(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.heaviside(values: DoubleTensor): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.trapz(xValues: DoubleTensor, dim: Int): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+}
+
+public inline fun <R> RealAnalyticTensorAlgebra(block: RealAnalyticTensorAlgebra.() -> R): R =
+    RealAnalyticTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
similarity index 73%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
rename to kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
index 6b1957b8c..5fd8c4151 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
@@ -1,13 +1,13 @@
 package space.kscience.kmath.tensors
 
 public class RealLinearOpsTensorAlgebra :
-    LinearOpsTensorAlgebra<Double, RealTensor>,
+    LinearOpsTensorAlgebra<Double, DoubleTensor>,
     RealTensorAlgebra()
 {
-    override fun eye(n: Int): RealTensor {
+    override fun eye(n: Int): DoubleTensor {
         val shape = intArrayOf(n, n)
         val buffer = DoubleArray(n * n) { 0.0 }
-        val res = RealTensor(shape, buffer)
+        val res = DoubleTensor(shape, buffer)
         for (i in 0 until n) {
             res[intArrayOf(i, i)] = 1.0
         }
@@ -15,16 +15,16 @@ public class RealLinearOpsTensorAlgebra :
     }
 
 
-    override fun RealTensor.dot(other: RealTensor): RealTensor {
+    override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
         TODO("Alya")
     }
 
-    override fun diagonalEmbedding(diagonalEntries: RealTensor, offset: Int, dim1: Int, dim2: Int): RealTensor {
+    override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
         TODO("Alya")
     }
 
 
-    override fun RealTensor.lu(): Pair<RealTensor, IntTensor> {
+    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
         // todo checks
         val lu = this.copy()
         val m = this.shape[0]
@@ -74,7 +74,7 @@ public class RealLinearOpsTensorAlgebra :
         return Pair(lu, IntTensor(intArrayOf(m), pivot))
     }
 
-    override fun luPivot(lu: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
+    override fun luPivot(lu: DoubleTensor, pivots: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         // todo checks
         val n = lu.shape[0]
         val p = lu.zeroesLike()
@@ -100,28 +100,28 @@ public class RealLinearOpsTensorAlgebra :
         return Triple(p, l, u)
     }
 
-    override fun RealTensor.det(): RealTensor {
+    override fun DoubleTensor.det(): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.inv(): RealTensor {
+    override fun DoubleTensor.inv(): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.cholesky(): RealTensor {
+    override fun DoubleTensor.cholesky(): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.qr(): RealTensor {
+    override fun DoubleTensor.qr(): DoubleTensor {
         TODO("Not yet implemented")
     }
 
 
-    override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {
+    override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.symEig(eigenvectors: Boolean): Pair<RealTensor, RealTensor> {
+    override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
         TODO("Not yet implemented")
     }
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
similarity index 54%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
rename to kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
index 83a513a7a..dd15a257c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
@@ -1,146 +1,146 @@
 package space.kscience.kmath.tensors
 
 
-public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
+public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
 
-    override fun RealTensor.value(): Double {
+    override fun DoubleTensor.value(): Double {
         check(this.shape contentEquals intArrayOf(1)) {
             "Inconsistent value for tensor of shape ${shape.toList()}"
         }
         return this.buffer.unsafeToDoubleArray()[0]
     }
 
-    override fun zeros(shape: IntArray): RealTensor {
+    override fun zeros(shape: IntArray): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.zeroesLike(): RealTensor {
+    override fun DoubleTensor.zeroesLike(): DoubleTensor {
         val shape = this.shape
         val buffer = DoubleArray(this.buffer.size) { 0.0 }
-        return RealTensor(shape, buffer)
+        return DoubleTensor(shape, buffer)
     }
 
-    override fun ones(shape: IntArray): RealTensor {
+    override fun ones(shape: IntArray): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.onesLike(): RealTensor {
+    override fun DoubleTensor.onesLike(): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.copy(): RealTensor {
+    override fun DoubleTensor.copy(): DoubleTensor {
         // should be rework as soon as copy() method for NDBuffer will be available
-        return RealTensor(this.shape, this.buffer.unsafeToDoubleArray().copyOf())
+        return DoubleTensor(this.shape, this.buffer.unsafeToDoubleArray().copyOf())
     }
 
 
-    override fun Double.plus(other: RealTensor): RealTensor {
+    override fun Double.plus(other: DoubleTensor): DoubleTensor {
         val resBuffer = DoubleArray(other.buffer.size) { i ->
             other.buffer.unsafeToDoubleArray()[i] + this
         }
-        return RealTensor(other.shape, resBuffer)
+        return DoubleTensor(other.shape, resBuffer)
     }
 
-    override fun RealTensor.plus(value: Double): RealTensor = value + this
+    override fun DoubleTensor.plus(value: Double): DoubleTensor = value + this
 
-    override fun RealTensor.plus(other: RealTensor): RealTensor {
+    override fun DoubleTensor.plus(other: DoubleTensor): DoubleTensor {
         val broadcast = broadcastTensors(this, other)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.buffer.size) { i ->
             newThis.buffer.unsafeToDoubleArray()[i] + newOther.buffer.unsafeToDoubleArray()[i]
         }
-        return RealTensor(newThis.shape, resBuffer)
+        return DoubleTensor(newThis.shape, resBuffer)
     }
 
-    override fun RealTensor.plusAssign(value: Double) {
+    override fun DoubleTensor.plusAssign(value: Double) {
         for (i in this.buffer.unsafeToDoubleArray().indices) {
             this.buffer.unsafeToDoubleArray()[i] += value
         }
     }
 
-    override fun RealTensor.plusAssign(other: RealTensor) {
+    override fun DoubleTensor.plusAssign(other: DoubleTensor) {
         //todo should be change with broadcasting
         for (i in this.buffer.unsafeToDoubleArray().indices) {
             this.buffer.unsafeToDoubleArray()[i] += other.buffer.unsafeToDoubleArray()[i]
         }
     }
 
-    override fun Double.minus(other: RealTensor): RealTensor {
+    override fun Double.minus(other: DoubleTensor): DoubleTensor {
         val resBuffer = DoubleArray(other.buffer.size) { i ->
             this - other.buffer.unsafeToDoubleArray()[i]
         }
-        return RealTensor(other.shape, resBuffer)
+        return DoubleTensor(other.shape, resBuffer)
     }
 
-    override fun RealTensor.minus(value: Double): RealTensor {
+    override fun DoubleTensor.minus(value: Double): DoubleTensor {
         val resBuffer = DoubleArray(this.buffer.size) { i ->
             this.buffer.unsafeToDoubleArray()[i] - value
         }
-        return RealTensor(this.shape, resBuffer)
+        return DoubleTensor(this.shape, resBuffer)
     }
 
-    override fun RealTensor.minus(other: RealTensor): RealTensor {
+    override fun DoubleTensor.minus(other: DoubleTensor): DoubleTensor {
         val broadcast = broadcastTensors(this, other)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.buffer.size) { i ->
             newThis.buffer.unsafeToDoubleArray()[i] - newOther.buffer.unsafeToDoubleArray()[i]
         }
-        return RealTensor(newThis.shape, resBuffer)
+        return DoubleTensor(newThis.shape, resBuffer)
     }
 
-    override fun RealTensor.minusAssign(value: Double) {
+    override fun DoubleTensor.minusAssign(value: Double) {
         for (i in this.buffer.unsafeToDoubleArray().indices) {
             this.buffer.unsafeToDoubleArray()[i] -= value
         }
     }
 
-    override fun RealTensor.minusAssign(other: RealTensor) {
+    override fun DoubleTensor.minusAssign(other: DoubleTensor) {
         TODO("Alya")
     }
 
-    override fun Double.times(other: RealTensor): RealTensor {
+    override fun Double.times(other: DoubleTensor): DoubleTensor {
         //todo should be change with broadcasting
         val resBuffer = DoubleArray(other.buffer.size) { i ->
             other.buffer.unsafeToDoubleArray()[i] * this
         }
-        return RealTensor(other.shape, resBuffer)
+        return DoubleTensor(other.shape, resBuffer)
     }
 
     //todo should be change with broadcasting
-    override fun RealTensor.times(value: Double): RealTensor = value * this
+    override fun DoubleTensor.times(value: Double): DoubleTensor = value * this
 
-    override fun RealTensor.times(other: RealTensor): RealTensor {
+    override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
         //todo should be change with broadcasting
         val resBuffer = DoubleArray(this.buffer.size) { i ->
             this.buffer.unsafeToDoubleArray()[i] * other.buffer.unsafeToDoubleArray()[i]
         }
-        return RealTensor(this.shape, resBuffer)
+        return DoubleTensor(this.shape, resBuffer)
     }
 
-    override fun RealTensor.timesAssign(value: Double) {
+    override fun DoubleTensor.timesAssign(value: Double) {
         //todo should be change with broadcasting
         for (i in this.buffer.unsafeToDoubleArray().indices) {
             this.buffer.unsafeToDoubleArray()[i] *= value
         }
     }
 
-    override fun RealTensor.timesAssign(other: RealTensor) {
+    override fun DoubleTensor.timesAssign(other: DoubleTensor) {
         //todo should be change with broadcasting
         for (i in this.buffer.unsafeToDoubleArray().indices) {
             this.buffer.unsafeToDoubleArray()[i] *= other.buffer.unsafeToDoubleArray()[i]
         }
     }
 
-    override fun RealTensor.unaryMinus(): RealTensor {
+    override fun DoubleTensor.unaryMinus(): DoubleTensor {
         val resBuffer = DoubleArray(this.buffer.size) { i ->
             this.buffer.unsafeToDoubleArray()[i].unaryMinus()
         }
-        return RealTensor(this.shape, resBuffer)
+        return DoubleTensor(this.shape, resBuffer)
     }
 
-    override fun RealTensor.transpose(i: Int, j: Int): RealTensor {
+    override fun DoubleTensor.transpose(i: Int, j: Int): DoubleTensor {
         checkTranspose(this.dimension, i, j)
         val n = this.buffer.size
         val resBuffer = DoubleArray(n)
@@ -148,7 +148,7 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
         val resShape = this.shape.copyOf()
         resShape[i] = resShape[j].also { resShape[j] = resShape[i] }
 
-        val resTensor = RealTensor(resShape, resBuffer)
+        val resTensor = DoubleTensor(resShape, resBuffer)
 
         for (offset in 0 until n) {
             val oldMultiIndex = this.strides.index(offset)
@@ -162,117 +162,117 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealT
     }
 
 
-    override fun RealTensor.view(shape: IntArray): RealTensor {
-        return RealTensor(shape, this.buffer.unsafeToDoubleArray())
+    override fun DoubleTensor.view(shape: IntArray): DoubleTensor {
+        return DoubleTensor(shape, this.buffer.unsafeToDoubleArray())
     }
 
-    override fun RealTensor.viewAs(other: RealTensor): RealTensor {
+    override fun DoubleTensor.viewAs(other: DoubleTensor): DoubleTensor {
         return this.view(other.shape)
     }
 
-    override fun RealTensor.abs(): RealTensor {
+    override fun DoubleTensor.abs(): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun full(shape: IntArray, value: Double): RealTensor {
+    override fun full(shape: IntArray, value: Double): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.fullLike(value: Double): RealTensor {
+    override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
         TODO("Not yet implemented")
     }
 
 
-    override fun RealTensor.sum(dim: Int, keepDim: Boolean): RealTensor {
+    override fun DoubleTensor.sum(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.cumsum(dim: Int): RealTensor {
+    override fun DoubleTensor.cumsum(dim: Int): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.prod(dim: Int, keepDim: Boolean): RealTensor {
+    override fun DoubleTensor.prod(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.cumprod(dim: Int): RealTensor {
+    override fun DoubleTensor.cumprod(dim: Int): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.max(dim: Int, keepDim: Boolean): RealTensor {
+    override fun DoubleTensor.max(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.cummax(dim: Int): RealTensor {
+    override fun DoubleTensor.cummax(dim: Int): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.min(dim: Int, keepDim: Boolean): RealTensor {
+    override fun DoubleTensor.min(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.cummin(dim: Int): RealTensor {
+    override fun DoubleTensor.cummin(dim: Int): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.median(dim: Int, keepDim: Boolean): RealTensor {
+    override fun DoubleTensor.median(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun maximum(lhs: RealTensor, rhs: RealTensor) {
+    override fun maximum(lhs: DoubleTensor, rhs: DoubleTensor) {
         TODO("Not yet implemented")
     }
 
-    override fun minimum(lhs: RealTensor, rhs: RealTensor) {
+    override fun minimum(lhs: DoubleTensor, rhs: DoubleTensor) {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.sort(dim: Int, keepDim: Boolean, descending: Boolean): RealTensor {
+    override fun DoubleTensor.sort(dim: Int, keepDim: Boolean, descending: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun cat(tensors: List<RealTensor>, dim: Int): RealTensor {
+    override fun cat(tensors: List<DoubleTensor>, dim: Int): DoubleTensor {
         TODO("Not yet implemented")
     }
 
 
-    override fun RealTensor.div(value: Double): RealTensor {
+    override fun DoubleTensor.div(value: Double): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.div(other: RealTensor): RealTensor {
+    override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.flatten(startDim: Int, endDim: Int): RealTensor {
+    override fun DoubleTensor.flatten(startDim: Int, endDim: Int): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.divAssign(value: Double) {
+    override fun DoubleTensor.divAssign(value: Double) {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.divAssign(other: RealTensor) {
+    override fun DoubleTensor.divAssign(other: DoubleTensor) {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.mean(dim: Int, keepDim: Boolean): RealTensor {
+    override fun DoubleTensor.mean(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.quantile(q: Double, dim: Int, keepDim: Boolean): RealTensor {
+    override fun DoubleTensor.quantile(q: Double, dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.std(dim: Int, unbiased: Boolean, keepDim: Boolean): RealTensor {
+    override fun DoubleTensor.std(dim: Int, unbiased: Boolean, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): RealTensor {
+    override fun DoubleTensor.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.histc(bins: Int, min: Double, max: Double): RealTensor {
+    override fun DoubleTensor.histc(bins: Int, min: Double, max: Double): DoubleTensor {
         TODO("Not yet implemented")
     }
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
deleted file mode 100644
index f610361fc..000000000
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt
+++ /dev/null
@@ -1,150 +0,0 @@
-package space.kscience.kmath.tensors
-
-public class RealAnalyticTensorAlgebra:
-    AnalyticTensorAlgebra<Double, RealTensor>,
-    RealTensorAlgebra()
-{
-    override fun RealTensor.exp(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.log(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.sqrt(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.square(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.cos(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.acos(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.cosh(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.acosh(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.sin(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.asin(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.sinh(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.asinh(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.tan(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.atan(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.tanh(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.atanh(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.ceil(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.floor(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.clamp(min: Double, max: Double): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.erf(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.erfinv(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.erfc(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.lerp(end: RealTensor, weight: RealTensor): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.lgamma(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.logit(eps: Double): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.igamma(other: RealTensor): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.igammac(other: RealTensor): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.mvlgamma(dimensions: Int): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.polygamma(order: Int): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.pow(exponent: Double): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.round(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.sigmoid(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.sinc(): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.heaviside(values: RealTensor): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun RealTensor.trapz(xValues: RealTensor, dim: Int): RealTensor {
-        TODO("Not yet implemented")
-    }
-
-}
-
-public inline fun <R> RealAnalyticTensorAlgebra(block: RealAnalyticTensorAlgebra.() -> R): R =
-    RealAnalyticTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
index b03fb7dd7..9f7e811db 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
@@ -32,13 +32,13 @@ internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
     return totalShape
 }
 
-internal inline fun broadcastTensors(vararg tensors: RealTensor): List<RealTensor> {
+internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
     val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
     val n = totalShape.reduce { acc, i -> acc * i }
 
-    val res = ArrayList<RealTensor>(0)
+    val res = ArrayList<DoubleTensor>(0)
     for (tensor in tensors) {
-        val resTensor = RealTensor(totalShape, DoubleArray(n))
+        val resTensor = DoubleTensor(totalShape, DoubleArray(n))
 
         for (linearIndex in 0 until n) {
             val totalMultiIndex = resTensor.strides.index(linearIndex)
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
index 473a3da8b..5d42e6d0f 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
@@ -11,13 +11,13 @@ class TestRealTensor {
     @Test
     fun valueTest() = RealTensorAlgebra {
         val value = 12.5
-        val tensor = RealTensor(intArrayOf(1), doubleArrayOf(value))
+        val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
     }
 
     @Test
     fun stridesTest(){
-        val tensor = RealTensor(intArrayOf(2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
+        val tensor = DoubleTensor(intArrayOf(2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
         assertEquals(tensor[intArrayOf(0,1)], 5.8)
         assertTrue(tensor.elements().map{ it.second }.toList().toDoubleArray() contentEquals tensor.buffer.toDoubleArray())
     }
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
index 72bbf1787..3e27a1cc2 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
@@ -8,14 +8,14 @@ class TestRealTensorAlgebra {
 
     @Test
     fun doublePlus() = RealTensorAlgebra {
-        val tensor = RealTensor(intArrayOf(2), doubleArrayOf(1.0, 2.0))
+        val tensor = DoubleTensor(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
         assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(11.0,12.0))
     }
 
     @Test
     fun transpose1x1() = RealTensorAlgebra {
-        val tensor = RealTensor(intArrayOf(1), doubleArrayOf(0.0))
+        val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(0.0))
         val res = tensor.transpose(0, 0)
 
         assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(0.0))
@@ -24,7 +24,7 @@ class TestRealTensorAlgebra {
 
     @Test
     fun transpose3x2() = RealTensorAlgebra {
-        val tensor = RealTensor(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor = DoubleTensor(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res = tensor.transpose(1, 0)
 
         assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
@@ -33,7 +33,7 @@ class TestRealTensorAlgebra {
 
     @Test
     fun transpose1x2x3() = RealTensorAlgebra {
-        val tensor = RealTensor(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor = DoubleTensor(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res01 = tensor.transpose(0, 1)
         val res02 = tensor.transpose(0, 2)
         val res12 = tensor.transpose(1, 2)
@@ -60,9 +60,9 @@ class TestRealTensorAlgebra {
 
     @Test
     fun broadcastTensors() = RealTensorAlgebra {
-        val tensor1 = RealTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val tensor2 = RealTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
-        val tensor3 = RealTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
+        val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor3 = DoubleTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
 
         val res = broadcastTensors(tensor1, tensor2, tensor3)
 
@@ -77,9 +77,9 @@ class TestRealTensorAlgebra {
 
     @Test
     fun minusTensor() = RealTensorAlgebra {
-        val tensor1 = RealTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val tensor2 = RealTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
-        val tensor3 = RealTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
+        val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor3 = DoubleTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
 
         assertTrue((tensor2 - tensor1).shape contentEquals intArrayOf(2, 3))
         assertTrue((tensor2 - tensor1).buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))

From b227a82a80d406fa3c4b4e50373a02363f161954 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 19:06:33 +0000
Subject: [PATCH 030/178] OrderedTensorAlgebra

---
 .../kmath/tensors/ComplexTensorAlgebra.kt     |  6 +++
 .../tensors/DoubleAnalyticTensorAlgebra.kt    |  8 +--
 .../tensors/DoubleLinearOpsTensorAlgebra.kt   | 32 ++----------
 .../tensors/DoubleOrderedTensorAlgebra.kt     | 41 +++++++++++++++
 .../kmath/tensors/DoubleTensorAlgebra.kt      | 51 ++++++++-----------
 .../kmath/tensors/LinearOpsTensorAlgebra.kt   | 15 ------
 .../kmath/tensors/OrderedTensorAlgebra.kt     | 29 +++++++++++
 .../kscience/kmath/tensors/TensorAlgebra.kt   | 35 +++++--------
 .../kscience/kmath/tensors/TestRealTensor.kt  |  2 +-
 .../kmath/tensors/TestRealTensorAlgebra.kt    | 14 ++---
 10 files changed, 125 insertions(+), 108 deletions(-)
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/OrderedTensorAlgebra.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
index 8399511a5..e56920916 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
@@ -8,6 +8,12 @@ public interface ComplexTensorAlgebra<T,
     //https://pytorch.org/docs/stable/generated/torch.view_as_complex.html
     public fun RealTensorType.viewAsComplex(): ComplexTensorType
 
+    // Embed a real tensor as real + i * imaginary
+    public fun RealTensorType.cartesianEmbedding(imaginary: RealTensorType): ComplexTensorType
+
+    // Embed a real tensor as real * exp(i * angle)
+    public fun RealTensorType.polarEmbedding(angle: RealTensorType): ComplexTensorType
+
     //https://pytorch.org/docs/stable/generated/torch.angle.html
     public fun ComplexTensorType.angle(): RealTensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
index 702049a20..00e7a7fbd 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
@@ -1,8 +1,8 @@
 package space.kscience.kmath.tensors
 
-public class RealAnalyticTensorAlgebra:
+public class DoubleAnalyticTensorAlgebra:
     AnalyticTensorAlgebra<Double, DoubleTensor>,
-    RealTensorAlgebra()
+    DoubleTensorAlgebra()
 {
     override fun DoubleTensor.exp(): DoubleTensor {
         TODO("Not yet implemented")
@@ -146,5 +146,5 @@ public class RealAnalyticTensorAlgebra:
 
 }
 
-public inline fun <R> RealAnalyticTensorAlgebra(block: RealAnalyticTensorAlgebra.() -> R): R =
-    RealAnalyticTensorAlgebra().block()
\ No newline at end of file
+public inline fun <R> DoubleAnalyticTensorAlgebra(block: DoubleAnalyticTensorAlgebra.() -> R): R =
+    DoubleAnalyticTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
index ddb3dfa12..1aaf8df9d 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
@@ -2,27 +2,12 @@ package space.kscience.kmath.tensors
 
 public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor>,
-    RealTensorAlgebra() {
-    override fun eye(n: Int): DoubleTensor {
-        val shape = intArrayOf(n, n)
-        val buffer = DoubleArray(n * n) { 0.0 }
-        val res = DoubleTensor(shape, buffer)
-        for (i in 0 until n) {
-            res[intArrayOf(i, i)] = 1.0
-        }
-        return res
+    DoubleTensorAlgebra() {
+
+    override fun DoubleTensor.inv(): DoubleTensor {
+        TODO("Not yet implemented")
     }
 
-
-    override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
-        TODO("Alya")
-    }
-
-    override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
-        TODO("Alya")
-    }
-
-
     override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
         // todo checks
         val luTensor = this.copy()
@@ -115,15 +100,6 @@ public class DoubleLinearOpsTensorAlgebra :
         return Triple(p, l, u)
     }
 
-    override fun DoubleTensor.det(): DoubleTensor {
-
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.inv(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
     override fun DoubleTensor.cholesky(): DoubleTensor {
         TODO("Not yet implemented")
     }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt
new file mode 100644
index 000000000..e7ebf6c56
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt
@@ -0,0 +1,41 @@
+package space.kscience.kmath.tensors
+
+public class DoubleOrderedTensorAlgebra:
+    OrderedTensorAlgebra<Double, DoubleTensor>,
+    DoubleTensorAlgebra()
+{
+    override fun DoubleTensor.max(dim: Int, keepDim: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.cummax(dim: Int): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.min(dim: Int, keepDim: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.cummin(dim: Int): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.median(dim: Int, keepDim: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun maximum(lhs: DoubleTensor, rhs: DoubleTensor) {
+        TODO("Not yet implemented")
+    }
+
+    override fun minimum(lhs: DoubleTensor, rhs: DoubleTensor) {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.sort(dim: Int, keepDim: Boolean, descending: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+}
+
+public inline fun <R> DoubleOrderedTensorAlgebra(block: DoubleOrderedTensorAlgebra.() -> R): R =
+    DoubleOrderedTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
index dd15a257c..8b9701127 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
@@ -1,7 +1,7 @@
 package space.kscience.kmath.tensors
 
 
-public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
+public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
 
     override fun DoubleTensor.value(): Double {
         check(this.shape contentEquals intArrayOf(1)) {
@@ -27,6 +27,15 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, Doubl
     override fun DoubleTensor.onesLike(): DoubleTensor {
         TODO("Not yet implemented")
     }
+    override fun eye(n: Int): DoubleTensor {
+        val shape = intArrayOf(n, n)
+        val buffer = DoubleArray(n * n) { 0.0 }
+        val res = DoubleTensor(shape, buffer)
+        for (i in 0 until n) {
+            res[intArrayOf(i, i)] = 1.0
+        }
+        return res
+    }
 
     override fun DoubleTensor.copy(): DoubleTensor {
         // should be rework as soon as copy() method for NDBuffer will be available
@@ -199,36 +208,12 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, Doubl
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.max(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
+    override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
+        TODO("Alya")
     }
 
-    override fun DoubleTensor.cummax(dim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.min(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.cummin(dim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.median(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun maximum(lhs: DoubleTensor, rhs: DoubleTensor) {
-        TODO("Not yet implemented")
-    }
-
-    override fun minimum(lhs: DoubleTensor, rhs: DoubleTensor) {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.sort(dim: Int, keepDim: Boolean, descending: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
+    override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
+        TODO("Alya")
     }
 
     override fun cat(tensors: List<DoubleTensor>, dim: Int): DoubleTensor {
@@ -276,7 +261,11 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, Doubl
         TODO("Not yet implemented")
     }
 
+    override fun DoubleTensor.det(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
 }
 
-public inline fun <R> RealTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
-    RealTensorAlgebra().block()
\ No newline at end of file
+public inline fun <R> DoubleTensorAlgebra(block: DoubleTensorAlgebra.() -> R): R =
+    DoubleTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index db1135a33..7450c09c1 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -4,21 +4,6 @@ package space.kscience.kmath.tensors
 public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>> :
     TensorPartialDivisionAlgebra<T, TensorType> {
 
-    //https://pytorch.org/docs/stable/generated/torch.eye.html
-    public fun eye(n: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.matmul.html
-    public infix fun TensorType.dot(other: TensorType): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
-    public fun diagonalEmbedding(
-        diagonalEntries: TensorType,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
-    ): TensorType
-
-    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
-    public fun TensorType.det(): TensorType
-
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
     public fun TensorType.inv(): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/OrderedTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/OrderedTensorAlgebra.kt
new file mode 100644
index 000000000..3320c3a1e
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/OrderedTensorAlgebra.kt
@@ -0,0 +1,29 @@
+package space.kscience.kmath.tensors
+
+public interface OrderedTensorAlgebra<T, TensorType : TensorStructure<T>> :
+    TensorAlgebra<T, TensorType> {
+
+    //https://pytorch.org/docs/stable/generated/torch.max.html#torch.max
+    public fun TensorType.max(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.cummax.html#torch.cummax
+    public fun TensorType.cummax(dim: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.min.html#torch.min
+    public fun TensorType.min(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.cummin.html#torch.cummin
+    public fun TensorType.cummin(dim: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.median.html#torch.median
+    public fun TensorType.median(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum
+    public fun maximum(lhs: TensorType, rhs: TensorType)
+
+    //https://pytorch.org/docs/stable/generated/torch.minimum.html#torch.minimum
+    public fun minimum(lhs: TensorType, rhs: TensorType)
+
+    //https://pytorch.org/docs/stable/generated/torch.sort.html#torch.sort
+    public fun TensorType.sort(dim: Int, keepDim: Boolean, descending: Boolean): TensorType
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index a53054e21..6f368f332 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -17,6 +17,9 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     //https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
     public fun TensorType.fullLike(value: T): TensorType
 
+    //https://pytorch.org/docs/stable/generated/torch.eye.html
+    public fun eye(n: Int): TensorType
+
     public fun TensorType.copy(): TensorType
 
     public operator fun T.plus(other: TensorType): TensorType
@@ -46,6 +49,9 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public fun TensorType.view(shape: IntArray): TensorType
     public fun TensorType.viewAs(other: TensorType): TensorType
 
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
+    public fun TensorType.det(): TensorType
+
     //https://pytorch.org/docs/stable/generated/torch.abs.html
     public fun TensorType.abs(): TensorType
 
@@ -61,29 +67,14 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     //https://pytorch.org/docs/stable/generated/torch.cumprod.html#torch.cumprod
     public fun TensorType.cumprod(dim: Int): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.max.html#torch.max
-    public fun TensorType.max(dim: Int, keepDim: Boolean): TensorType
+    //https://pytorch.org/docs/stable/generated/torch.matmul.html
+    public infix fun TensorType.dot(other: TensorType): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.cummax.html#torch.cummax
-    public fun TensorType.cummax(dim: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.min.html#torch.min
-    public fun TensorType.min(dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.cummin.html#torch.cummin
-    public fun TensorType.cummin(dim: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.median.html#torch.median
-    public fun TensorType.median(dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum
-    public fun maximum(lhs: TensorType, rhs: TensorType)
-
-    //https://pytorch.org/docs/stable/generated/torch.minimum.html#torch.minimum
-    public fun minimum(lhs: TensorType, rhs: TensorType)
-
-    //https://pytorch.org/docs/stable/generated/torch.sort.html#torch.sort
-    public fun TensorType.sort(dim: Int, keepDim: Boolean, descending: Boolean): TensorType
+    //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
+    public fun diagonalEmbedding(
+        diagonalEntries: TensorType,
+        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+    ): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat
     public fun cat(tensors: List<TensorType>, dim: Int): TensorType
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
index 5d42e6d0f..72179ecbc 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
@@ -9,7 +9,7 @@ import kotlin.test.assertTrue
 class TestRealTensor {
 
     @Test
-    fun valueTest() = RealTensorAlgebra {
+    fun valueTest() = DoubleTensorAlgebra {
         val value = 12.5
         val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
index 3e27a1cc2..9dddcf59e 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
@@ -7,14 +7,14 @@ import kotlin.test.assertTrue
 class TestRealTensorAlgebra {
 
     @Test
-    fun doublePlus() = RealTensorAlgebra {
+    fun doublePlus() = DoubleTensorAlgebra {
         val tensor = DoubleTensor(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
         assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(11.0,12.0))
     }
 
     @Test
-    fun transpose1x1() = RealTensorAlgebra {
+    fun transpose1x1() = DoubleTensorAlgebra {
         val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(0.0))
         val res = tensor.transpose(0, 0)
 
@@ -23,7 +23,7 @@ class TestRealTensorAlgebra {
     }
 
     @Test
-    fun transpose3x2() = RealTensorAlgebra {
+    fun transpose3x2() = DoubleTensorAlgebra {
         val tensor = DoubleTensor(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res = tensor.transpose(1, 0)
 
@@ -32,7 +32,7 @@ class TestRealTensorAlgebra {
     }
 
     @Test
-    fun transpose1x2x3() = RealTensorAlgebra {
+    fun transpose1x2x3() = DoubleTensorAlgebra {
         val tensor = DoubleTensor(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res01 = tensor.transpose(0, 1)
         val res02 = tensor.transpose(0, 2)
@@ -48,7 +48,7 @@ class TestRealTensorAlgebra {
     }
 
     @Test
-    fun broadcastShapes() = RealTensorAlgebra {
+    fun broadcastShapes() = DoubleTensorAlgebra {
         assertTrue(broadcastShapes(
             intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
         ) contentEquals intArrayOf(1, 2, 3))
@@ -59,7 +59,7 @@ class TestRealTensorAlgebra {
     }
 
     @Test
-    fun broadcastTensors() = RealTensorAlgebra {
+    fun broadcastTensors() = DoubleTensorAlgebra {
         val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = DoubleTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
@@ -76,7 +76,7 @@ class TestRealTensorAlgebra {
     }
 
     @Test
-    fun minusTensor() = RealTensorAlgebra {
+    fun minusTensor() = DoubleTensorAlgebra {
         val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = DoubleTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))

From f8e0d4be17baac7e2f5930083b2bfeb7338f99e8 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 21:18:15 +0000
Subject: [PATCH 031/178] MutableStructure 2D & 1D

---
 .../space/kscience/kmath/nd/Structure1D.kt    | 56 +++++++++++++++++++
 .../space/kscience/kmath/nd/Structure2D.kt    | 53 +++++++++++++++++-
 .../space/kscience/kmath/structures/Buffer.kt |  5 ++
 .../kscience/kmath/tensors/BufferedTensor.kt  |  1 -
 .../kmath/tensors/DoubleTensorAlgebra.kt      |  4 ++
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  2 +
 .../kscience/kmath/tensors/TensorStructure.kt |  1 -
 7 files changed, 118 insertions(+), 4 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
index 1335a4933..2926b3d1b 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
@@ -1,6 +1,8 @@
 package space.kscience.kmath.nd
 
 import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.MutableBuffer
+import space.kscience.kmath.structures.asMutableBuffer
 import space.kscience.kmath.structures.asSequence
 
 /**
@@ -17,6 +19,16 @@ public interface Structure1D<T> : NDStructure<T>, Buffer<T> {
     public override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map(::get).iterator()
 }
 
+/**
+ * A mutable structure that is guaranteed to be one-dimensional
+ */
+public interface MutableStructure1D<T> : Structure1D<T>, MutableNDStructure<T>, MutableBuffer<T> {
+    public override operator fun set(index: IntArray, value: T) {
+        require(index.size == 1) { "Index dimension mismatch. Expected 1 but found ${index.size}" }
+        set(index[0], value)
+    }
+}
+
 /**
  * A 1D wrapper for nd-structure
  */
@@ -28,6 +40,25 @@ private inline class Structure1DWrapper<T>(val structure: NDStructure<T>) : Stru
     override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
 }
 
+/**
+ * A 1D wrapper for a mutable nd-structure
+ */
+private inline class MutableStructure1DWrapper<T>(val structure: MutableNDStructure<T>) : MutableStructure1D<T> {
+    override val shape: IntArray get() = structure.shape
+    override val size: Int get() = structure.shape[0]
+    override fun elements(): Sequence<Pair<IntArray, T>> {
+        TODO("Not yet implemented")
+    }
+
+    override fun get(index: Int): T = structure[index]
+    override fun set(index: Int, value: T) {
+        set(index, value)
+    }
+
+    override fun copy(): MutableBuffer<T> =
+        structure.elements().map { it.second }.toMutableList().asMutableBuffer()
+}
+
 
 /**
  * A structure wrapper for buffer
@@ -42,6 +73,21 @@ private inline class Buffer1DWrapper<T>(val buffer: Buffer<T>) : Structure1D<T>
     override operator fun get(index: Int): T = buffer[index]
 }
 
+private inline class MutableBuffer1DWrapper<T>(val buffer: MutableBuffer<T>) : MutableStructure1D<T> {
+    override val shape: IntArray get() = intArrayOf(buffer.size)
+    override val size: Int get() = buffer.size
+
+    override fun elements(): Sequence<Pair<IntArray, T>> =
+        buffer.asSequence().mapIndexed { index, value -> intArrayOf(index) to value }
+
+    override operator fun get(index: Int): T = buffer[index]
+    override fun set(index: Int, value: T) {
+        buffer[index] = value
+    }
+
+    override fun copy(): MutableBuffer<T> = buffer.copy()
+}
+
 /**
  * Represent a [NDStructure] as [Structure1D]. Throw error in case of dimension mismatch
  */
@@ -52,7 +98,17 @@ public fun <T> NDStructure<T>.as1D(): Structure1D<T> = this as? Structure1D<T> ?
     }
 } else error("Can't create 1d-structure from ${shape.size}d-structure")
 
+public fun <T> MutableNDStructure<T>.as1D(): MutableStructure1D<T> =
+    this as? MutableStructure1D<T> ?: if (shape.size == 1) {
+        when (this) {
+            is MutableNDBuffer -> MutableBuffer1DWrapper(this.buffer)
+            else -> MutableStructure1DWrapper(this)
+        }
+    } else error("Can't create 1d-structure from ${shape.size}d-structure")
+
 /**
  * Represent this buffer as 1D structure
  */
 public fun <T> Buffer<T>.asND(): Structure1D<T> = Buffer1DWrapper(this)
+
+public fun <T> MutableBuffer<T>.asND(): MutableStructure1D<T> = MutableBuffer1DWrapper(this)
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
index e9f8234e5..2f2fd653e 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
@@ -57,6 +57,20 @@ public interface Structure2D<T> : NDStructure<T> {
     public companion object
 }
 
+/**
+ * Represents mutable [Structure2D].
+ */
+public interface MutableStructure2D<T> : Structure2D<T>, MutableNDStructure<T> {
+    /**
+     * Inserts an item at the specified indices.
+     *
+     * @param i the first index.
+     * @param j the second index.
+     * @param value the value.
+     */
+    public operator fun set(i: Int, j: Int, value: T)
+}
+
 /**
  * A 2D wrapper for nd-structure
  */
@@ -79,11 +93,46 @@ private class Structure2DWrapper<T>(val structure: NDStructure<T>) : Structure2D
 }
 
 /**
- * Represent a [NDStructure] as [Structure1D]. Throw error in case of dimension mismatch
+ * A 2D wrapper for a mutable nd-structure
+ */
+private class MutableStructure2DWrapper<T>(val structure: MutableNDStructure<T>): MutableStructure2D<T>
+{
+    override val shape: IntArray get() = structure.shape
+
+    override val rowNum: Int get() = shape[0]
+    override val colNum: Int get() = shape[1]
+
+    override operator fun get(i: Int, j: Int): T = structure[i, j]
+
+    override fun set(index: IntArray, value: T) {
+        structure[index] = value
+    }
+
+    override operator fun set(i: Int, j: Int, value: T){
+        structure[intArrayOf(i, j)] = value
+    }
+
+    override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
+
+    override fun equals(other: Any?): Boolean = false
+
+    override fun hashCode(): Int = 0
+}
+
+/**
+ * Represent a [NDStructure] as [Structure2D]. Throw error in case of dimension mismatch
  */
 public fun <T> NDStructure<T>.as2D(): Structure2D<T> = this as? Structure2D<T> ?: when (shape.size) {
     2 -> Structure2DWrapper(this)
     else -> error("Can't create 2d-structure from ${shape.size}d-structure")
 }
 
-internal fun <T> Structure2D<T>.unwrap(): NDStructure<T> = if (this is Structure2DWrapper) structure else this
\ No newline at end of file
+internal fun <T> Structure2D<T>.unwrap(): NDStructure<T> = if (this is Structure2DWrapper) structure else this
+
+public fun <T> MutableNDStructure<T>.as2D(): MutableStructure2D<T> = this as? MutableStructure2D<T> ?: when (shape.size) {
+    2 -> MutableStructure2DWrapper(this)
+    else -> error("Can't create 2d-structure from ${shape.size}d-structure")
+}
+
+internal fun <T> MutableStructure2D<T>.unwrap(): MutableNDStructure<T> =
+    if (this is MutableStructure2DWrapper) structure else this
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
index 2bde18fce..c62fa30ba 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
@@ -236,6 +236,11 @@ public inline class MutableListBuffer<T>(public val list: MutableList<T>) : Muta
     override fun copy(): MutableBuffer<T> = MutableListBuffer(ArrayList(list))
 }
 
+/**
+ * Returns an [MutableListBuffer] that wraps the original list.
+ */
+public fun <T> MutableList<T>.asMutableBuffer(): MutableListBuffer<T> = MutableListBuffer(this)
+
 /**
  * [MutableBuffer] implementation over [Array].
  *
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
index d5adf380c..68fc0412e 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
@@ -25,7 +25,6 @@ public open class BufferedTensor<T>(
         this[intArrayOf(i, j)] = value
     }
 
-
 }
 
 //todo make generator mb nextMatrixIndex?
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
index 8b9701127..76a3c4c9c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
@@ -10,6 +10,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return this.buffer.unsafeToDoubleArray()[0]
     }
 
+    override fun DoubleTensor.get(i: Int): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
     override fun zeros(shape: IntArray): DoubleTensor {
         TODO("Not yet implemented")
     }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 6f368f332..02e1a0742 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -41,6 +41,8 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public operator fun TensorType.timesAssign(other: TensorType): Unit
     public operator fun TensorType.unaryMinus(): TensorType
 
+    //https://pytorch.org/cppdocs/notes/tensor_indexing.html
+    public fun TensorType.get(i: Int): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.transpose.html
     public fun TensorType.transpose(i: Int, j: Int): TensorType
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
index 5463877ce..f5ea39d1b 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
@@ -3,4 +3,3 @@ package space.kscience.kmath.tensors
 import space.kscience.kmath.nd.MutableNDStructure
 
 public typealias TensorStructure<T> = MutableNDStructure<T>
-

From 7cb5cd8f71721e5eb5e77e0849311a3fd3c2374f Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 22:11:15 +0000
Subject: [PATCH 032/178] BufferedTensor revisited

---
 .../kscience/kmath/tensors/BufferedTensor.kt  | 54 ++++++++-------
 .../tensors/DoubleLinearOpsTensorAlgebra.kt   | 10 ++-
 .../kmath/tensors/DoubleTensorAlgebra.kt      | 69 ++++++++++---------
 .../space/kscience/kmath/tensors/utils.kt     |  3 +-
 ...{TestRealTensor.kt => TestDoubleTensor.kt} |  2 +-
 ...rAlgebra.kt => TestDoubleTensorAlgebra.kt} |  2 +-
 6 files changed, 78 insertions(+), 62 deletions(-)
 rename kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/{TestRealTensor.kt => TestDoubleTensor.kt} (96%)
 rename kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/{TestRealTensorAlgebra.kt => TestDoubleTensorAlgebra.kt} (99%)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
index 68fc0412e..264692d0c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
@@ -1,32 +1,34 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.nd.MutableNDBuffer
 import space.kscience.kmath.structures.*
 
 
 public open class BufferedTensor<T>(
     override val shape: IntArray,
-    buffer: MutableBuffer<T>
-) :
-    TensorStructure<T>,
-    MutableNDBuffer<T>(
-        TensorStrides(shape),
-        buffer
-    ) {
+    public val buffer: MutableBuffer<T>,
+    internal val bufferStart: Int
+) : TensorStructure<T>
+{
+    public val strides: TensorStrides
+        get() = TensorStrides(shape)
 
+    override fun get(index: IntArray): T = buffer[bufferStart + strides.offset(index)]
 
-    public operator fun get(i: Int, j: Int): T {
-        check(this.dimension == 2) { "Not matrix" }
-        return this[intArrayOf(i, j)]
+    override fun set(index: IntArray, value: T) {
+        buffer[bufferStart + strides.offset(index)] = value
     }
 
-    public operator fun set(i: Int, j: Int, value: T): Unit {
-        check(this.dimension == 2) { "Not matrix" }
-        this[intArrayOf(i, j)] = value
+    override fun elements(): Sequence<Pair<IntArray, T>> = strides.indices().map {
+        it to this[it]
     }
 
+    override fun equals(other: Any?): Boolean = false
+
+    override fun hashCode(): Int = 0
+
 }
 
+/*
 //todo make generator mb nextMatrixIndex?
 public class InnerMatrix<T>(private val tensor: BufferedTensor<T>){
     private var offset: Int = 0
@@ -75,25 +77,29 @@ public class InnerVector<T>(private val tensor: BufferedTensor<T>){
         offset += step
     }
 }
-
-
 //todo default buffer = arrayOf(0)???
+ */
+
 public class IntTensor(
     shape: IntArray,
-    buffer: IntArray
-) : BufferedTensor<Int>(shape, IntBuffer(buffer))
+    buffer: IntArray,
+    offset: Int = 0
+) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset)
 
 public class LongTensor(
     shape: IntArray,
-    buffer: LongArray
-) : BufferedTensor<Long>(shape, LongBuffer(buffer))
+    buffer: LongArray,
+    offset: Int = 0
+) : BufferedTensor<Long>(shape, LongBuffer(buffer), offset)
 
 public class FloatTensor(
     shape: IntArray,
-    buffer: FloatArray
-) : BufferedTensor<Float>(shape, FloatBuffer(buffer))
+    buffer: FloatArray,
+    offset: Int = 0
+) : BufferedTensor<Float>(shape, FloatBuffer(buffer), offset)
 
 public class DoubleTensor(
     shape: IntArray,
-    buffer: DoubleArray
-) : BufferedTensor<Double>(shape, RealBuffer(buffer))
\ No newline at end of file
+    buffer: DoubleArray,
+    offset: Int = 0
+) : BufferedTensor<Double>(shape, RealBuffer(buffer), offset)
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
index 1aaf8df9d..eceb28459 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
@@ -9,6 +9,7 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
+        /*
         // todo checks
         val luTensor = this.copy()
         val lu = InnerMatrix(luTensor)
@@ -69,11 +70,13 @@ public class DoubleLinearOpsTensorAlgebra :
             pivot.makeStep()
         }
 
-        return Pair(luTensor, pivotsTensor)
+        return Pair(luTensor, pivotsTensor)*/
+
+        TODO("Andrei, first we need to view and get(Int)")
     }
 
     override fun luPivot(lu: DoubleTensor, pivots: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-
+        /*
         // todo checks
         val n = lu.shape[0]
         val p = lu.zeroesLike()
@@ -97,7 +100,8 @@ public class DoubleLinearOpsTensorAlgebra :
             }
         }
 
-        return Triple(p, l, u)
+        return Triple(p, l, u)*/
+        TODO("Andrei, first we need implement get(Int)")
     }
 
     override fun DoubleTensor.cholesky(): DoubleTensor {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
index 76a3c4c9c..391c2895f 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
@@ -7,11 +7,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         check(this.shape contentEquals intArrayOf(1)) {
             "Inconsistent value for tensor of shape ${shape.toList()}"
         }
-        return this.buffer.unsafeToDoubleArray()[0]
+        return this.buffer.unsafeToDoubleArray()[this.bufferStart]
     }
 
     override fun DoubleTensor.get(i: Int): DoubleTensor {
-        TODO("Not yet implemented")
+        TODO("TOP PRIORITY")
     }
 
     override fun zeros(shape: IntArray): DoubleTensor {
@@ -20,7 +20,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun DoubleTensor.zeroesLike(): DoubleTensor {
         val shape = this.shape
-        val buffer = DoubleArray(this.buffer.size) { 0.0 }
+        val buffer = DoubleArray(this.strides.linearSize) { 0.0 }
         return DoubleTensor(shape, buffer)
     }
 
@@ -31,6 +31,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     override fun DoubleTensor.onesLike(): DoubleTensor {
         TODO("Not yet implemented")
     }
+
     override fun eye(n: Int): DoubleTensor {
         val shape = intArrayOf(n, n)
         val buffer = DoubleArray(n * n) { 0.0 }
@@ -42,14 +43,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.copy(): DoubleTensor {
-        // should be rework as soon as copy() method for NDBuffer will be available
-        return DoubleTensor(this.shape, this.buffer.unsafeToDoubleArray().copyOf())
+        return DoubleTensor(this.shape, this.buffer.unsafeToDoubleArray().copyOf(), this.bufferStart)
     }
 
 
     override fun Double.plus(other: DoubleTensor): DoubleTensor {
-        val resBuffer = DoubleArray(other.buffer.size) { i ->
-            other.buffer.unsafeToDoubleArray()[i] + this
+        val resBuffer = DoubleArray(other.strides.linearSize) { i ->
+            other.buffer.unsafeToDoubleArray()[other.bufferStart + i] + this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
@@ -60,35 +60,36 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         val broadcast = broadcastTensors(this, other)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.buffer.size) { i ->
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
             newThis.buffer.unsafeToDoubleArray()[i] + newOther.buffer.unsafeToDoubleArray()[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
     override fun DoubleTensor.plusAssign(value: Double) {
-        for (i in this.buffer.unsafeToDoubleArray().indices) {
-            this.buffer.unsafeToDoubleArray()[i] += value
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] += value
         }
     }
 
     override fun DoubleTensor.plusAssign(other: DoubleTensor) {
         //todo should be change with broadcasting
-        for (i in this.buffer.unsafeToDoubleArray().indices) {
-            this.buffer.unsafeToDoubleArray()[i] += other.buffer.unsafeToDoubleArray()[i]
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] +=
+                other.buffer.unsafeToDoubleArray()[this.bufferStart + i]
         }
     }
 
     override fun Double.minus(other: DoubleTensor): DoubleTensor {
-        val resBuffer = DoubleArray(other.buffer.size) { i ->
-            this - other.buffer.unsafeToDoubleArray()[i]
+        val resBuffer = DoubleArray(other.strides.linearSize) { i ->
+            this - other.buffer.unsafeToDoubleArray()[other.bufferStart + i]
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
     override fun DoubleTensor.minus(value: Double): DoubleTensor {
-        val resBuffer = DoubleArray(this.buffer.size) { i ->
-            this.buffer.unsafeToDoubleArray()[i] - value
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] - value
         }
         return DoubleTensor(this.shape, resBuffer)
     }
@@ -97,15 +98,15 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         val broadcast = broadcastTensors(this, other)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.buffer.size) { i ->
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
             newThis.buffer.unsafeToDoubleArray()[i] - newOther.buffer.unsafeToDoubleArray()[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
     override fun DoubleTensor.minusAssign(value: Double) {
-        for (i in this.buffer.unsafeToDoubleArray().indices) {
-            this.buffer.unsafeToDoubleArray()[i] -= value
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] -= value
         }
     }
 
@@ -115,8 +116,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun Double.times(other: DoubleTensor): DoubleTensor {
         //todo should be change with broadcasting
-        val resBuffer = DoubleArray(other.buffer.size) { i ->
-            other.buffer.unsafeToDoubleArray()[i] * this
+        val resBuffer = DoubleArray(other.strides.linearSize) { i ->
+            other.buffer.unsafeToDoubleArray()[other.bufferStart + i] * this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
@@ -126,36 +127,38 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
         //todo should be change with broadcasting
-        val resBuffer = DoubleArray(this.buffer.size) { i ->
-            this.buffer.unsafeToDoubleArray()[i] * other.buffer.unsafeToDoubleArray()[i]
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+            this.buffer.unsafeToDoubleArray()[other.bufferStart + i] *
+                    other.buffer.unsafeToDoubleArray()[other.bufferStart + i]
         }
         return DoubleTensor(this.shape, resBuffer)
     }
 
     override fun DoubleTensor.timesAssign(value: Double) {
         //todo should be change with broadcasting
-        for (i in this.buffer.unsafeToDoubleArray().indices) {
-            this.buffer.unsafeToDoubleArray()[i] *= value
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] *= value
         }
     }
 
     override fun DoubleTensor.timesAssign(other: DoubleTensor) {
         //todo should be change with broadcasting
-        for (i in this.buffer.unsafeToDoubleArray().indices) {
-            this.buffer.unsafeToDoubleArray()[i] *= other.buffer.unsafeToDoubleArray()[i]
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] *=
+                other.buffer.unsafeToDoubleArray()[this.bufferStart + i]
         }
     }
 
     override fun DoubleTensor.unaryMinus(): DoubleTensor {
-        val resBuffer = DoubleArray(this.buffer.size) { i ->
-            this.buffer.unsafeToDoubleArray()[i].unaryMinus()
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+            this.buffer.unsafeToDoubleArray()[this.bufferStart + i].unaryMinus()
         }
         return DoubleTensor(this.shape, resBuffer)
     }
 
     override fun DoubleTensor.transpose(i: Int, j: Int): DoubleTensor {
         checkTranspose(this.dimension, i, j)
-        val n = this.buffer.size
+        val n = this.strides.linearSize
         val resBuffer = DoubleArray(n)
 
         val resShape = this.shape.copyOf()
@@ -169,14 +172,16 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
             newMultiIndex[i] = newMultiIndex[j].also { newMultiIndex[j] = newMultiIndex[i] }
 
             val linearIndex = resTensor.strides.offset(newMultiIndex)
-            resTensor.buffer.unsafeToDoubleArray()[linearIndex] = this.buffer.unsafeToDoubleArray()[offset]
+            resTensor.buffer.unsafeToDoubleArray()[linearIndex] =
+                this.buffer.unsafeToDoubleArray()[this.bufferStart + offset]
         }
         return resTensor
     }
 
 
     override fun DoubleTensor.view(shape: IntArray): DoubleTensor {
-        return DoubleTensor(shape, this.buffer.unsafeToDoubleArray())
+        checkView(this, shape)
+        return DoubleTensor(shape, this.buffer.unsafeToDoubleArray(), this.bufferStart)
     }
 
     override fun DoubleTensor.viewAs(other: DoubleTensor): DoubleTensor {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
index 9f7e811db..74a774f45 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
@@ -55,7 +55,8 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
             }
 
             val curLinearIndex = tensor.strides.offset(curMultiIndex)
-            resTensor.buffer.unsafeToDoubleArray()[linearIndex] = tensor.buffer.unsafeToDoubleArray()[curLinearIndex]
+            resTensor.buffer.unsafeToDoubleArray()[linearIndex] =
+                tensor.buffer.unsafeToDoubleArray()[tensor.bufferStart + curLinearIndex]
         }
         res.add(resTensor)
     }
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
similarity index 96%
rename from kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
rename to kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
index 72179ecbc..31c6ccbbf 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
@@ -6,7 +6,7 @@ import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
 
-class TestRealTensor {
+class TestDoubleTensor {
 
     @Test
     fun valueTest() = DoubleTensorAlgebra {
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
similarity index 99%
rename from kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
rename to kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
index 9dddcf59e..91181484c 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestRealTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
@@ -4,7 +4,7 @@ package space.kscience.kmath.tensors
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
-class TestRealTensorAlgebra {
+class TestDoubleTensorAlgebra {
 
     @Test
     fun doublePlus() = DoubleTensorAlgebra {

From 791f55ee8a87f2822769099b22e70e04f4daa2f5 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 22:39:29 +0000
Subject: [PATCH 033/178] get dim 0 operator for tensors

---
 .../tensors/DoubleLinearOpsTensorAlgebra.kt   |  2 +-
 .../kmath/tensors/DoubleTensorAlgebra.kt      | 49 ++++++++++---------
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  2 +-
 .../space/kscience/kmath/tensors/utils.kt     | 12 ++---
 .../kmath/tensors/TestDoubleTensor.kt         |  6 +++
 .../kmath/tensors/TestDoubleTensorAlgebra.kt  | 24 ++++-----
 6 files changed, 52 insertions(+), 43 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
index eceb28459..d6b202556 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
@@ -72,7 +72,7 @@ public class DoubleLinearOpsTensorAlgebra :
 
         return Pair(luTensor, pivotsTensor)*/
 
-        TODO("Andrei, first we need to view and get(Int)")
+        TODO("Andrei, use view, get, as2D, as1D")
     }
 
     override fun luPivot(lu: DoubleTensor, pivots: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
index 391c2895f..3b65e89da 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
@@ -7,11 +7,14 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         check(this.shape contentEquals intArrayOf(1)) {
             "Inconsistent value for tensor of shape ${shape.toList()}"
         }
-        return this.buffer.unsafeToDoubleArray()[this.bufferStart]
+        return this.buffer.array()[this.bufferStart]
     }
 
-    override fun DoubleTensor.get(i: Int): DoubleTensor {
-        TODO("TOP PRIORITY")
+    override operator fun DoubleTensor.get(i: Int): DoubleTensor {
+        val lastShape = this.shape.drop(1).toIntArray()
+        val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
+        val newStart = newShape.reduce(Int::times) * i + this.bufferStart
+        return DoubleTensor(newShape, this.buffer.array(), newStart)
     }
 
     override fun zeros(shape: IntArray): DoubleTensor {
@@ -43,13 +46,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.copy(): DoubleTensor {
-        return DoubleTensor(this.shape, this.buffer.unsafeToDoubleArray().copyOf(), this.bufferStart)
+        return DoubleTensor(this.shape, this.buffer.array().copyOf(), this.bufferStart)
     }
 
 
     override fun Double.plus(other: DoubleTensor): DoubleTensor {
         val resBuffer = DoubleArray(other.strides.linearSize) { i ->
-            other.buffer.unsafeToDoubleArray()[other.bufferStart + i] + this
+            other.buffer.array()[other.bufferStart + i] + this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
@@ -61,35 +64,35 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
-            newThis.buffer.unsafeToDoubleArray()[i] + newOther.buffer.unsafeToDoubleArray()[i]
+            newThis.buffer.array()[i] + newOther.buffer.array()[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
     override fun DoubleTensor.plusAssign(value: Double) {
         for (i in 0 until this.strides.linearSize) {
-            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] += value
+            this.buffer.array()[this.bufferStart + i] += value
         }
     }
 
     override fun DoubleTensor.plusAssign(other: DoubleTensor) {
         //todo should be change with broadcasting
         for (i in 0 until this.strides.linearSize) {
-            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] +=
-                other.buffer.unsafeToDoubleArray()[this.bufferStart + i]
+            this.buffer.array()[this.bufferStart + i] +=
+                other.buffer.array()[this.bufferStart + i]
         }
     }
 
     override fun Double.minus(other: DoubleTensor): DoubleTensor {
         val resBuffer = DoubleArray(other.strides.linearSize) { i ->
-            this - other.buffer.unsafeToDoubleArray()[other.bufferStart + i]
+            this - other.buffer.array()[other.bufferStart + i]
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
     override fun DoubleTensor.minus(value: Double): DoubleTensor {
         val resBuffer = DoubleArray(this.strides.linearSize) { i ->
-            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] - value
+            this.buffer.array()[this.bufferStart + i] - value
         }
         return DoubleTensor(this.shape, resBuffer)
     }
@@ -99,14 +102,14 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
-            newThis.buffer.unsafeToDoubleArray()[i] - newOther.buffer.unsafeToDoubleArray()[i]
+            newThis.buffer.array()[i] - newOther.buffer.array()[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
     override fun DoubleTensor.minusAssign(value: Double) {
         for (i in 0 until this.strides.linearSize) {
-            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] -= value
+            this.buffer.array()[this.bufferStart + i] -= value
         }
     }
 
@@ -117,7 +120,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     override fun Double.times(other: DoubleTensor): DoubleTensor {
         //todo should be change with broadcasting
         val resBuffer = DoubleArray(other.strides.linearSize) { i ->
-            other.buffer.unsafeToDoubleArray()[other.bufferStart + i] * this
+            other.buffer.array()[other.bufferStart + i] * this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
@@ -128,8 +131,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
         //todo should be change with broadcasting
         val resBuffer = DoubleArray(this.strides.linearSize) { i ->
-            this.buffer.unsafeToDoubleArray()[other.bufferStart + i] *
-                    other.buffer.unsafeToDoubleArray()[other.bufferStart + i]
+            this.buffer.array()[other.bufferStart + i] *
+                    other.buffer.array()[other.bufferStart + i]
         }
         return DoubleTensor(this.shape, resBuffer)
     }
@@ -137,21 +140,21 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     override fun DoubleTensor.timesAssign(value: Double) {
         //todo should be change with broadcasting
         for (i in 0 until this.strides.linearSize) {
-            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] *= value
+            this.buffer.array()[this.bufferStart + i] *= value
         }
     }
 
     override fun DoubleTensor.timesAssign(other: DoubleTensor) {
         //todo should be change with broadcasting
         for (i in 0 until this.strides.linearSize) {
-            this.buffer.unsafeToDoubleArray()[this.bufferStart + i] *=
-                other.buffer.unsafeToDoubleArray()[this.bufferStart + i]
+            this.buffer.array()[this.bufferStart + i] *=
+                other.buffer.array()[this.bufferStart + i]
         }
     }
 
     override fun DoubleTensor.unaryMinus(): DoubleTensor {
         val resBuffer = DoubleArray(this.strides.linearSize) { i ->
-            this.buffer.unsafeToDoubleArray()[this.bufferStart + i].unaryMinus()
+            this.buffer.array()[this.bufferStart + i].unaryMinus()
         }
         return DoubleTensor(this.shape, resBuffer)
     }
@@ -172,8 +175,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
             newMultiIndex[i] = newMultiIndex[j].also { newMultiIndex[j] = newMultiIndex[i] }
 
             val linearIndex = resTensor.strides.offset(newMultiIndex)
-            resTensor.buffer.unsafeToDoubleArray()[linearIndex] =
-                this.buffer.unsafeToDoubleArray()[this.bufferStart + offset]
+            resTensor.buffer.array()[linearIndex] =
+                this.buffer.array()[this.bufferStart + offset]
         }
         return resTensor
     }
@@ -181,7 +184,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun DoubleTensor.view(shape: IntArray): DoubleTensor {
         checkView(this, shape)
-        return DoubleTensor(shape, this.buffer.unsafeToDoubleArray(), this.bufferStart)
+        return DoubleTensor(shape, this.buffer.array(), this.bufferStart)
     }
 
     override fun DoubleTensor.viewAs(other: DoubleTensor): DoubleTensor {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 02e1a0742..e56abfaaa 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -42,7 +42,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public operator fun TensorType.unaryMinus(): TensorType
 
     //https://pytorch.org/cppdocs/notes/tensor_indexing.html
-    public fun TensorType.get(i: Int): TensorType
+    public operator fun TensorType.get(i: Int): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.transpose.html
     public fun TensorType.transpose(i: Int, j: Int): TensorType
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
index 74a774f45..3e32e2b72 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
@@ -55,8 +55,8 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
             }
 
             val curLinearIndex = tensor.strides.offset(curMultiIndex)
-            resTensor.buffer.unsafeToDoubleArray()[linearIndex] =
-                tensor.buffer.unsafeToDoubleArray()[tensor.bufferStart + curLinearIndex]
+            resTensor.buffer.array()[linearIndex] =
+                tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
         }
         res.add(resTensor)
     }
@@ -99,7 +99,7 @@ internal inline fun <T, TensorType : TensorStructure<T>,
 /**
  * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
  */
-internal fun Buffer<Int>.unsafeToIntArray(): IntArray = when(this) {
+internal fun Buffer<Int>.array(): IntArray = when(this) {
     is IntBuffer -> array
     else -> throw RuntimeException("Failed to cast Buffer to IntArray")
 }
@@ -107,7 +107,7 @@ internal fun Buffer<Int>.unsafeToIntArray(): IntArray = when(this) {
 /**
  * Returns a reference to [LongArray] containing all of the elements of this [Buffer].
  */
-internal fun Buffer<Long>.unsafeToLongArray(): LongArray = when(this) {
+internal fun Buffer<Long>.array(): LongArray = when(this) {
     is LongBuffer -> array
     else -> throw RuntimeException("Failed to cast Buffer to LongArray")
 }
@@ -115,7 +115,7 @@ internal fun Buffer<Long>.unsafeToLongArray(): LongArray = when(this) {
 /**
  * Returns a reference to [FloatArray] containing all of the elements of this [Buffer].
  */
-internal fun Buffer<Float>.unsafeToFloatArray(): FloatArray = when(this) {
+internal fun Buffer<Float>.array(): FloatArray = when(this) {
     is FloatBuffer -> array
     else -> throw RuntimeException("Failed to cast Buffer to FloatArray")
 }
@@ -123,7 +123,7 @@ internal fun Buffer<Float>.unsafeToFloatArray(): FloatArray = when(this) {
 /**
  * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer].
  */
-internal fun Buffer<Double>.unsafeToDoubleArray(): DoubleArray = when(this) {
+internal fun Buffer<Double>.array(): DoubleArray = when(this) {
     is RealBuffer -> array
     else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
 }
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
index 31c6ccbbf..b1c8cd6dd 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
@@ -21,4 +21,10 @@ class TestDoubleTensor {
         assertEquals(tensor[intArrayOf(0,1)], 5.8)
         assertTrue(tensor.elements().map{ it.second }.toList().toDoubleArray() contentEquals tensor.buffer.toDoubleArray())
     }
+
+    @Test
+    fun getTest() = DoubleTensorAlgebra {
+        val tensor = DoubleTensor(intArrayOf(2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
+        assertTrue(tensor[0].elements().map{ it.second }.toList().toDoubleArray() contentEquals doubleArrayOf(3.5,5.8))
+    }
 }
\ No newline at end of file
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
index 91181484c..226454bf4 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
@@ -10,7 +10,7 @@ class TestDoubleTensorAlgebra {
     fun doublePlus() = DoubleTensorAlgebra {
         val tensor = DoubleTensor(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
-        assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(11.0,12.0))
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(11.0,12.0))
     }
 
     @Test
@@ -18,7 +18,7 @@ class TestDoubleTensorAlgebra {
         val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(0.0))
         val res = tensor.transpose(0, 0)
 
-        assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(0.0))
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(0.0))
         assertTrue(res.shape contentEquals intArrayOf(1))
     }
 
@@ -27,7 +27,7 @@ class TestDoubleTensorAlgebra {
         val tensor = DoubleTensor(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res = tensor.transpose(1, 0)
 
-        assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
         assertTrue(res.shape contentEquals intArrayOf(2, 3))
     }
 
@@ -42,9 +42,9 @@ class TestDoubleTensorAlgebra {
         assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
         assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2))
 
-        assertTrue(res01.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res02.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
-        assertTrue(res12.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res01.buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res02.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res12.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
     }
 
     @Test
@@ -70,9 +70,9 @@ class TestDoubleTensorAlgebra {
         assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
         assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
 
-        assertTrue(res[0].buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res[1].buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
-        assertTrue(res[2].buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
+        assertTrue(res[0].buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res[1].buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+        assertTrue(res[2].buffer.array() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
     }
 
     @Test
@@ -82,14 +82,14 @@ class TestDoubleTensorAlgebra {
         val tensor3 = DoubleTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
 
         assertTrue((tensor2 - tensor1).shape contentEquals intArrayOf(2, 3))
-        assertTrue((tensor2 - tensor1).buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
+        assertTrue((tensor2 - tensor1).buffer.array() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
 
         assertTrue((tensor3 - tensor1).shape contentEquals intArrayOf(1, 2, 3))
-        assertTrue((tensor3 - tensor1).buffer.unsafeToDoubleArray()
+        assertTrue((tensor3 - tensor1).buffer.array()
                 contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0))
 
         assertTrue((tensor3 - tensor2).shape contentEquals intArrayOf(1, 1, 3))
-        assertTrue((tensor3 - tensor2).buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
+        assertTrue((tensor3 - tensor2).buffer.array() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
     }
 
 }
\ No newline at end of file

From f4454a6cf6b8d54b1ecd818e2c39e6287b382189 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 15 Mar 2021 22:45:55 +0000
Subject: [PATCH 034/178] Matrices from tensors test

---
 .../kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
index b1c8cd6dd..3050f064a 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
@@ -1,6 +1,7 @@
 package space.kscience.kmath.tensors
 
 
+import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.structures.toDoubleArray
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -24,7 +25,8 @@ class TestDoubleTensor {
 
     @Test
     fun getTest() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
-        assertTrue(tensor[0].elements().map{ it.second }.toList().toDoubleArray() contentEquals doubleArrayOf(3.5,5.8))
+        val tensor = DoubleTensor(intArrayOf(1,2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
+        val matrix = tensor[0].as2D()
+        assertEquals(matrix[0,1], 5.8)
     }
 }
\ No newline at end of file

From 0553a28ee89e0d55f498c578882fece47bc620fe Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 16 Mar 2021 07:47:02 +0000
Subject: [PATCH 035/178] ReduceOpsTensorAlgebra

---
 .../kmath/tensors/AnalyticTensorAlgebra.kt    | 15 +++++++++++-
 .../tensors/DoubleAnalyticTensorAlgebra.kt    | 18 ++++++++++++++-
 .../tensors/DoubleOrderedTensorAlgebra.kt     |  2 +-
 .../tensors/DoubleReduceOpsTensorAlgebra.kt   | 16 +++++++++++++
 .../kmath/tensors/DoubleTensorAlgebra.kt      | 23 -------------------
 .../kmath/tensors/ReduceOpsTensorAlgebra.kt   |  7 ++++++
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  1 -
 .../tensors/TensorPartialDivisionAlgebra.kt   | 14 -----------
 .../kmath/tensors/TestDoubleTensor.kt         | 12 +++++++++-
 9 files changed, 66 insertions(+), 42 deletions(-)
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleReduceOpsTensorAlgebra.kt
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ReduceOpsTensorAlgebra.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
index 17a25b6b3..41772da44 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
@@ -2,7 +2,17 @@ package space.kscience.kmath.tensors
 
 
 public interface AnalyticTensorAlgebra<T, TensorType : TensorStructure<T>> :
-    TensorPartialDivisionAlgebra<T, TensorType> {
+    TensorPartialDivisionAlgebra<T, TensorType>,
+    OrderedTensorAlgebra<T, TensorType>{
+
+    //https://pytorch.org/docs/stable/generated/torch.quantile.html#torch.quantile
+    public fun TensorType.quantile(q: T, dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.std.html#torch.std
+    public fun TensorType.std(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.var.html#torch.var
+    public fun TensorType.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.exp.html
     public fun TensorType.exp(): TensorType
@@ -109,4 +119,7 @@ public interface AnalyticTensorAlgebra<T, TensorType : TensorStructure<T>> :
     //https://pytorch.org/docs/stable/generated/torch.trapz.html#torch.trapz
     public fun TensorType.trapz(xValues: TensorType, dim: Int): TensorType
 
+    //https://pytorch.org/docs/stable/generated/torch.histc.html#torch.histc
+    public fun TensorType.histc(bins: Int, min: T, max: T): TensorType
+
 }
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
index 00e7a7fbd..cccc02789 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
@@ -2,7 +2,7 @@ package space.kscience.kmath.tensors
 
 public class DoubleAnalyticTensorAlgebra:
     AnalyticTensorAlgebra<Double, DoubleTensor>,
-    DoubleTensorAlgebra()
+    DoubleOrderedTensorAlgebra()
 {
     override fun DoubleTensor.exp(): DoubleTensor {
         TODO("Not yet implemented")
@@ -144,6 +144,22 @@ public class DoubleAnalyticTensorAlgebra:
         TODO("Not yet implemented")
     }
 
+    override fun DoubleTensor.quantile(q: Double, dim: Int, keepDim: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.std(dim: Int, unbiased: Boolean, keepDim: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.histc(bins: Int, min: Double, max: Double): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
 }
 
 public inline fun <R> DoubleAnalyticTensorAlgebra(block: DoubleAnalyticTensorAlgebra.() -> R): R =
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt
index e7ebf6c56..bd6bcfe8f 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt
@@ -1,6 +1,6 @@
 package space.kscience.kmath.tensors
 
-public class DoubleOrderedTensorAlgebra:
+public open class DoubleOrderedTensorAlgebra:
     OrderedTensorAlgebra<Double, DoubleTensor>,
     DoubleTensorAlgebra()
 {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleReduceOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleReduceOpsTensorAlgebra.kt
new file mode 100644
index 000000000..00d9b3ff8
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleReduceOpsTensorAlgebra.kt
@@ -0,0 +1,16 @@
+package space.kscience.kmath.tensors
+
+public class DoubleReduceOpsTensorAlgebra:
+    DoubleTensorAlgebra(),
+    ReduceOpsTensorAlgebra<Double, DoubleTensor>  {
+
+    override fun DoubleTensor.value(): Double {
+        check(this.shape contentEquals intArrayOf(1)) {
+            "Inconsistent value for tensor of shape ${shape.toList()}"
+        }
+        return this.buffer.array()[this.bufferStart]
+    }
+}
+
+public inline fun <R> DoubleReduceOpsTensorAlgebra(block: DoubleReduceOpsTensorAlgebra.() -> R): R =
+    DoubleReduceOpsTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
index 3b65e89da..c383387ef 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
@@ -3,13 +3,6 @@ package space.kscience.kmath.tensors
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
 
-    override fun DoubleTensor.value(): Double {
-        check(this.shape contentEquals intArrayOf(1)) {
-            "Inconsistent value for tensor of shape ${shape.toList()}"
-        }
-        return this.buffer.array()[this.bufferStart]
-    }
-
     override operator fun DoubleTensor.get(i: Int): DoubleTensor {
         val lastShape = this.shape.drop(1).toIntArray()
         val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
@@ -257,22 +250,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.quantile(q: Double, dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.std(dim: Int, unbiased: Boolean, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.histc(bins: Int, min: Double, max: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
     override fun DoubleTensor.det(): DoubleTensor {
         TODO("Not yet implemented")
     }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ReduceOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ReduceOpsTensorAlgebra.kt
new file mode 100644
index 000000000..820b9b25b
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ReduceOpsTensorAlgebra.kt
@@ -0,0 +1,7 @@
+package space.kscience.kmath.tensors
+
+public interface ReduceOpsTensorAlgebra<T, TensorType : TensorStructure<T>> :
+    TensorAlgebra<T, TensorType> {
+    public fun TensorType.value(): T
+
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index e56abfaaa..60f0b3379 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -3,7 +3,6 @@ package space.kscience.kmath.tensors
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
-    public fun TensorType.value(): T
 
     public fun zeros(shape: IntArray): TensorType
     public fun TensorType.zeroesLike(): TensorType
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
index 2d448fa8c..ca3876e2c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
@@ -3,7 +3,6 @@ package space.kscience.kmath.tensors
 // https://proofwiki.org/wiki/Definition:Division_Algebra
 public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
     TensorAlgebra<T, TensorType> {
-
     public operator fun TensorType.div(value: T): TensorType
     public operator fun TensorType.div(other: TensorType): TensorType
     public operator fun TensorType.divAssign(value: T)
@@ -11,17 +10,4 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.mean.html#torch.mean
     public fun TensorType.mean(dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.quantile.html#torch.quantile
-    public fun TensorType.quantile(q: T, dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.std.html#torch.std
-    public fun TensorType.std(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.var.html#torch.var
-    public fun TensorType.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.histc.html#torch.histc
-    public fun TensorType.histc(bins: Int, min: T, max: T): TensorType
-
 }
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
index 3050f064a..006b0273a 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
@@ -1,6 +1,7 @@
 package space.kscience.kmath.tensors
 
 
+import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.structures.toDoubleArray
 import kotlin.test.Test
@@ -10,7 +11,7 @@ import kotlin.test.assertTrue
 class TestDoubleTensor {
 
     @Test
-    fun valueTest() = DoubleTensorAlgebra {
+    fun valueTest() = DoubleReduceOpsTensorAlgebra {
         val value = 12.5
         val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
@@ -28,5 +29,14 @@ class TestDoubleTensor {
         val tensor = DoubleTensor(intArrayOf(1,2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
         val matrix = tensor[0].as2D()
         assertEquals(matrix[0,1], 5.8)
+
+        val vector = tensor[0][1].as1D()
+        assertEquals(vector[0], 58.4)
+
+        matrix[0,1] = 77.89
+        assertEquals(tensor[intArrayOf(0,0,1)], 77.89)
+
+        //vector[0] = 109.56
+        //println(tensor[intArrayOf(0,1,0)])
     }
 }
\ No newline at end of file

From 99ee5aa54a043427d8ade94c82107cdc18e48819 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Tue, 16 Mar 2021 14:57:19 +0300
Subject: [PATCH 036/178] add broadcast to functions

---
 .../kmath/tensors/DoubleTensorAlgebra.kt      | 30 ++++++++-------
 .../space/kscience/kmath/tensors/utils.kt     | 37 +++++++++++++++++++
 .../kmath/tensors/TestDoubleTensorAlgebra.kt  | 10 +++++
 3 files changed, 64 insertions(+), 13 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
index c383387ef..794a06102 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
@@ -69,10 +69,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.plusAssign(other: DoubleTensor) {
-        //todo should be change with broadcasting
+        val newOther = broadcastTo(other, this.shape)
         for (i in 0 until this.strides.linearSize) {
             this.buffer.array()[this.bufferStart + i] +=
-                other.buffer.array()[this.bufferStart + i]
+                newOther.buffer.array()[this.bufferStart + i]
         }
     }
 
@@ -107,41 +107,45 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.minusAssign(other: DoubleTensor) {
-        TODO("Alya")
+        val newOther = broadcastTo(other, this.shape)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] -=
+                newOther.buffer.array()[this.bufferStart + i]
+        }
     }
 
     override fun Double.times(other: DoubleTensor): DoubleTensor {
-        //todo should be change with broadcasting
         val resBuffer = DoubleArray(other.strides.linearSize) { i ->
             other.buffer.array()[other.bufferStart + i] * this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
-    //todo should be change with broadcasting
     override fun DoubleTensor.times(value: Double): DoubleTensor = value * this
 
     override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
-        //todo should be change with broadcasting
-        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
-            this.buffer.array()[other.bufferStart + i] *
-                    other.buffer.array()[other.bufferStart + i]
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+            newThis.buffer.array()[newOther.bufferStart + i] *
+                    newOther.buffer.array()[newOther.bufferStart + i]
         }
-        return DoubleTensor(this.shape, resBuffer)
+        return DoubleTensor(newThis.shape, resBuffer)
     }
 
     override fun DoubleTensor.timesAssign(value: Double) {
-        //todo should be change with broadcasting
         for (i in 0 until this.strides.linearSize) {
             this.buffer.array()[this.bufferStart + i] *= value
         }
     }
 
     override fun DoubleTensor.timesAssign(other: DoubleTensor) {
-        //todo should be change with broadcasting
+        val newOther = broadcastTo(other, this.shape)
         for (i in 0 until this.strides.linearSize) {
             this.buffer.array()[this.bufferStart + i] *=
-                other.buffer.array()[this.bufferStart + i]
+                newOther.buffer.array()[this.bufferStart + i]
         }
     }
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
index 3e32e2b72..e7e043463 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
@@ -32,6 +32,43 @@ internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
     return totalShape
 }
 
+internal inline fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor {
+    if (tensor.shape.size > newShape.size) {
+        throw RuntimeException("Tensor is not compatible with the new shape")
+    }
+
+    val n = newShape.reduce { acc, i -> acc * i }
+    val resTensor = DoubleTensor(newShape, DoubleArray(n))
+
+    for (i in tensor.shape.indices) {
+        val curDim = tensor.shape[i]
+        val offset = newShape.size - tensor.shape.size
+        if (curDim != 1 && newShape[i + offset] != curDim) {
+            throw RuntimeException("Tensor is not compatible with the new shape and cannot be broadcast")
+        }
+    }
+
+    for (linearIndex in 0 until n) {
+        val totalMultiIndex = resTensor.strides.index(linearIndex)
+        val curMultiIndex = tensor.shape.copyOf()
+
+        val offset = totalMultiIndex.size - curMultiIndex.size
+
+        for (i in curMultiIndex.indices) {
+            if (curMultiIndex[i] != 1) {
+                curMultiIndex[i] = totalMultiIndex[i + offset]
+            } else {
+                curMultiIndex[i] = 0
+            }
+        }
+
+        val curLinearIndex = tensor.strides.offset(curMultiIndex)
+        resTensor.buffer.array()[linearIndex] =
+            tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
+    }
+    return resTensor
+}
+
 internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
     val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
     val n = totalShape.reduce { acc, i -> acc * i }
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
index 226454bf4..a060a970f 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
@@ -58,6 +58,16 @@ class TestDoubleTensorAlgebra {
         ) contentEquals intArrayOf(5, 6, 7))
     }
 
+    @Test
+    fun broadcastTo() = DoubleTensorAlgebra {
+        val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+
+        val res = broadcastTo(tensor2, tensor1.shape)
+        assertTrue(res.shape contentEquals intArrayOf(2, 3))
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+    }
+
     @Test
     fun broadcastTensors() = DoubleTensorAlgebra {
         val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))

From 70bebbe8488840a38e84855ca3d3eea1ddb4eebb Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 16 Mar 2021 12:12:28 +0000
Subject: [PATCH 037/178] 1D mutable structure setter fixed

---
 .../kotlin/space/kscience/kmath/nd/Structure1D.kt         | 2 +-
 .../space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt | 6 ------
 .../kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt | 8 --------
 .../space/kscience/kmath/tensors/DoubleTensorAlgebra.kt   | 8 ++++++++
 .../kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt  | 3 +++
 .../kmath/tensors/TensorPartialDivisionAlgebra.kt         | 3 +++
 .../space/kscience/kmath/tensors/TestDoubleTensor.kt      | 4 ++--
 7 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
index 2926b3d1b..fd965a668 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
@@ -52,7 +52,7 @@ private inline class MutableStructure1DWrapper<T>(val structure: MutableNDStruct
 
     override fun get(index: Int): T = structure[index]
     override fun set(index: Int, value: T) {
-        set(index, value)
+        structure[intArrayOf(index)] = value
     }
 
     override fun copy(): MutableBuffer<T> =
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
index 41772da44..c3a558298 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
@@ -11,9 +11,6 @@ public interface AnalyticTensorAlgebra<T, TensorType : TensorStructure<T>> :
     //https://pytorch.org/docs/stable/generated/torch.std.html#torch.std
     public fun TensorType.std(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.var.html#torch.var
-    public fun TensorType.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
-
     //https://pytorch.org/docs/stable/generated/torch.exp.html
     public fun TensorType.exp(): TensorType
 
@@ -23,9 +20,6 @@ public interface AnalyticTensorAlgebra<T, TensorType : TensorStructure<T>> :
     //https://pytorch.org/docs/stable/generated/torch.sqrt.html
     public fun TensorType.sqrt(): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.square.html
-    public fun TensorType.square(): TensorType
-
     //https://pytorch.org/docs/stable/generated/torch.acos.html#torch.cos
     public fun TensorType.cos(): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
index cccc02789..5349a9923 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
@@ -16,10 +16,6 @@ public class DoubleAnalyticTensorAlgebra:
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.square(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
     override fun DoubleTensor.cos(): DoubleTensor {
         TODO("Not yet implemented")
     }
@@ -152,10 +148,6 @@ public class DoubleAnalyticTensorAlgebra:
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
     override fun DoubleTensor.histc(bins: Int, min: Double, max: Double): DoubleTensor {
         TODO("Not yet implemented")
     }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
index c383387ef..bafbd9a96 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
@@ -254,6 +254,14 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
+    override fun DoubleTensor.square(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
 }
 
 public inline fun <R> DoubleTensorAlgebra(block: DoubleTensorAlgebra.() -> R): R =
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 60f0b3379..0af757d1a 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -40,6 +40,9 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public operator fun TensorType.timesAssign(other: TensorType): Unit
     public operator fun TensorType.unaryMinus(): TensorType
 
+    //https://pytorch.org/docs/stable/generated/torch.square.html
+    public fun TensorType.square(): TensorType
+
     //https://pytorch.org/cppdocs/notes/tensor_indexing.html
     public operator fun TensorType.get(i: Int): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
index ca3876e2c..9f70f9621 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
@@ -10,4 +10,7 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.mean.html#torch.mean
     public fun TensorType.mean(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.var.html#torch.var
+    public fun TensorType.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
 }
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
index 006b0273a..6b20027c7 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
@@ -36,7 +36,7 @@ class TestDoubleTensor {
         matrix[0,1] = 77.89
         assertEquals(tensor[intArrayOf(0,0,1)], 77.89)
 
-        //vector[0] = 109.56
-        //println(tensor[intArrayOf(0,1,0)])
+        vector[0] = 109.56
+        assertEquals(tensor[intArrayOf(0,1,0)], 109.56)
     }
 }
\ No newline at end of file

From bd3425e7a545622c117740bba178ec1b1b531eab Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 16 Mar 2021 14:43:20 +0000
Subject: [PATCH 038/178] IndexTensor type added to LinearOps

---
 .../kotlin/space/kscience/kmath/tensors/BufferedTensor.kt     | 3 +++
 .../kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt    | 2 +-
 .../space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt    | 4 ++--
 3 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
index 264692d0c..c9a401ad5 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
@@ -12,6 +12,9 @@ public open class BufferedTensor<T>(
     public val strides: TensorStrides
         get() = TensorStrides(shape)
 
+    public val numel: Int
+        get() = strides.linearSize
+
     override fun get(index: IntArray): T = buffer[bufferStart + strides.offset(index)]
 
     override fun set(index: IntArray, value: T) {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
index d6b202556..689eda9e0 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
@@ -1,7 +1,7 @@
 package space.kscience.kmath.tensors
 
 public class DoubleLinearOpsTensorAlgebra :
-    LinearOpsTensorAlgebra<Double, DoubleTensor>,
+    LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
     DoubleTensorAlgebra() {
 
     override fun DoubleTensor.inv(): DoubleTensor {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index 7450c09c1..2e1c4a92c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -1,7 +1,7 @@
 package space.kscience.kmath.tensors
 
 
-public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>> :
+public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, IndexTensorType: TensorStructure<Int>> :
     TensorPartialDivisionAlgebra<T, TensorType> {
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
@@ -14,7 +14,7 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>> :
     public fun TensorType.qr(): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.lu.html
-    public fun TensorType.lu(): Pair<TensorType, IntTensor>
+    public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
     public fun luPivot(lu: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>

From efb23591a9efebe3bf86b2757ac482032b5047a7 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 17 Mar 2021 07:36:35 +0000
Subject: [PATCH 039/178] Added squeeze

---
 .../space/kscience/kmath/tensors/DoubleTensorAlgebra.kt       | 4 ++++
 .../kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt      | 2 ++
 2 files changed, 6 insertions(+)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
index 888f12923..675be2f33 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
@@ -266,6 +266,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
+    override fun DoubleTensor.squeeze(dim: Int): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
 }
 
 public inline fun <R> DoubleTensorAlgebra(block: DoubleTensorAlgebra.() -> R): R =
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 0af757d1a..b1adf2962 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -86,4 +86,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     //https://pytorch.org/docs/stable/generated/torch.flatten.html#torch.flatten
     public fun TensorType.flatten(startDim: Int, endDim: Int): TensorType
 
+    //https://pytorch.org/docs/stable/generated/torch.squeeze.html
+    public fun TensorType.squeeze(dim: Int): TensorType
 }

From 1fa0da2810d69aa6d9dac669b4ebc353dd5265d1 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Wed, 17 Mar 2021 17:53:14 +0300
Subject: [PATCH 040/178] complete lu and matrix mapping

---
 .../kscience/kmath/tensors/BufferedTensor.kt  | 95 +++++++++----------
 .../tensors/DoubleLinearOpsTensorAlgebra.kt   | 83 ++++++++--------
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  2 +-
 3 files changed, 85 insertions(+), 95 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
index c9a401ad5..c48e47f4c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
@@ -1,5 +1,7 @@
 package space.kscience.kmath.tensors
 
+import space.kscience.kmath.linear.Matrix
+import space.kscience.kmath.nd.*
 import space.kscience.kmath.structures.*
 
 
@@ -29,59 +31,50 @@ public open class BufferedTensor<T>(
 
     override fun hashCode(): Int = 0
 
+    // todo rename to vector
+    public inline fun forEachVector(vectorAction : (MutableStructure1D<T>) -> Unit): Unit {
+        check(shape.size >= 1) {"todo"}
+        val vectorOffset = strides.strides[0]
+        val vectorShape = intArrayOf(shape.last())
+        for (offset in 0 until numel step vectorOffset) {
+            val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
+            vectorAction(vector)
+        }
+    }
+
+    public inline fun forEachMatrix(matrixAction : (MutableStructure2D<T>) -> Unit): Unit {
+        check(shape.size >= 2) {"todo"}
+        val matrixOffset = strides.strides[1]
+        val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
+        for (offset in 0 until numel step matrixOffset) {
+            val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
+            matrixAction(matrix)
+        }
+    }
+    // todo remove code copy-pasting
+
+    public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
+        check(shape.size >= 1) {"todo"}
+        val vectorOffset = strides.strides[0]
+        val vectorShape = intArrayOf(shape.last())
+        for (offset in 0 until numel step vectorOffset) {
+            val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
+            yield(vector)
+        }
+    }
+
+    public fun matrixSequence(): Sequence<MutableStructure2D<T>> = sequence {
+        check(shape.size >= 2) {"todo"}
+        val matrixOffset = strides.strides[1]
+        val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
+        for (offset in 0 until numel step matrixOffset) {
+            val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
+            yield(matrix)
+        }
+    }
+
 }
 
-/*
-//todo make generator mb nextMatrixIndex?
-public class InnerMatrix<T>(private val tensor: BufferedTensor<T>){
-    private var offset: Int = 0
-    private val n : Int = tensor.shape.size
-    //stride?
-    private val step = tensor.shape[n - 1] * tensor.shape[n - 2]
-
-    public operator fun get(i: Int, j: Int): T {
-        val index = tensor.strides.index(offset)
-        index[n - 2] = i
-        index[n - 1] = j
-        return tensor[index]
-    }
-
-    public operator fun set(i: Int, j: Int, value: T): Unit {
-        val index = tensor.strides.index(offset)
-        index[n - 2] = i
-        index[n - 1] = j
-        tensor[index] = value
-    }
-
-    public fun makeStep(){
-        offset += step
-    }
-}
-
-public class InnerVector<T>(private val tensor: BufferedTensor<T>){
-    private var offset: Int = 0
-    private val n : Int = tensor.shape.size
-    //stride?
-    private val step = tensor.shape[n - 1]
-
-    public operator fun get(i: Int): T {
-        val index = tensor.strides.index(offset)
-        index[n - 1] = i
-        return tensor[index]
-    }
-
-    public operator fun set(i: Int, value: T): Unit {
-        val index = tensor.strides.index(offset)
-        index[n - 1] = i
-        tensor[index] = value
-    }
-
-    public fun makeStep(){
-        offset += step
-    }
-}
-//todo default buffer = arrayOf(0)???
- */
 
 public class IntTensor(
     shape: IntArray,
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
index 689eda9e0..3f44305b1 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
@@ -9,27 +9,21 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
-        /*
+
         // todo checks
+
         val luTensor = this.copy()
-        val lu = InnerMatrix(luTensor)
-        //stride TODO!!! move to generator?
-        var matCnt = 1
-        for (i in 0 until this.shape.size - 2) {
-            matCnt *= this.shape[i]
-        }
+
         val n = this.shape.size
-        val m = this.shape[n - 1]
-        val pivotsShape = IntArray(n - 1) { i ->
-            this.shape[i]
-        }
+        val m = this.shape.last()
+        val pivotsShape = IntArray(n - 1) { i -> this.shape[i] }
         val pivotsTensor = IntTensor(
             pivotsShape,
-            IntArray(matCnt * m) { 0 }
+            IntArray(pivotsShape.reduce(Int::times)) { 0 } //todo default???
         )
-        val pivot = InnerVector(pivotsTensor)
-        for (i in 0 until matCnt) {
-            for (row in 0 until m) pivot[row] = row
+
+        for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
+            for (row in 0 until m) pivots[row] = row
 
             for (i in 0 until m) {
                 var maxA = -1.0
@@ -47,9 +41,9 @@ public class DoubleLinearOpsTensorAlgebra :
 
                 if (iMax != i) {
 
-                    val j = pivot[i]
-                    pivot[i] = pivot[iMax]
-                    pivot[iMax] = j
+                    val j = pivots[i]
+                    pivots[i] = pivots[iMax]
+                    pivots[iMax] = j
 
                     for (k in 0 until m) {
                         val tmp = lu[i, k]
@@ -66,42 +60,45 @@ public class DoubleLinearOpsTensorAlgebra :
                     }
                 }
             }
-            lu.makeStep()
-            pivot.makeStep()
         }
 
-        return Pair(luTensor, pivotsTensor)*/
 
-        TODO("Andrei, use view, get, as2D, as1D")
+        return Pair(luTensor, pivotsTensor)
+
     }
 
-    override fun luPivot(lu: DoubleTensor, pivots: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        /*
-        // todo checks
-        val n = lu.shape[0]
-        val p = lu.zeroesLike()
-        pivots.buffer.unsafeToIntArray().forEachIndexed { i, pivot ->
-            p[i, pivot] = 1.0
+    override fun luPivot(luTensor: DoubleTensor, pivotsTensor: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+        //todo checks
+        val n = luTensor.shape.last()
+        val pTensor = luTensor.zeroesLike()
+        for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
+            for (i in 0 until n){
+                p[i, pivot[i]] = 1.0
+            }
         }
-        val l = lu.zeroesLike()
-        val u = lu.zeroesLike()
 
-        for (i in 0 until n) {
-            for (j in 0 until n) {
-                if (i == j) {
-                    l[i, j] = 1.0
-                }
-                if (j < i) {
-                    l[i, j] = lu[i, j]
-                }
-                if (j >= i) {
-                    u[i, j] = lu[i, j]
+        val lTensor = luTensor.zeroesLike()
+        val uTensor = luTensor.zeroesLike()
+
+        for ((pairLU, lu) in lTensor.matrixSequence().zip(uTensor.matrixSequence()).zip(luTensor.matrixSequence())){
+            val (l, u) = pairLU
+            for (i in 0 until n) {
+                for (j in 0 until n) {
+                    if (i == j) {
+                        l[i, j] = 1.0
+                    }
+                    if (j < i) {
+                        l[i, j] = lu[i, j]
+                    }
+                    if (j >= i) {
+                        u[i, j] = lu[i, j]
+                    }
                 }
             }
         }
 
-        return Triple(p, l, u)*/
-        TODO("Andrei, first we need implement get(Int)")
+        return Triple(pTensor, lTensor, uTensor)
+
     }
 
     override fun DoubleTensor.cholesky(): DoubleTensor {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index b1adf2962..7ec920d88 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -5,7 +5,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
 
     public fun zeros(shape: IntArray): TensorType
-    public fun TensorType.zeroesLike(): TensorType
+    public fun TensorType.zeroesLike(): TensorType // mb it shouldn't be tensor but algebra method (like in numpy/torch) ?
     public fun ones(shape: IntArray): TensorType
     public fun TensorType.onesLike(): TensorType
 

From 5e94610e2862fc2ecfe5e4f9e709ecc2ab3f428a Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 19 Mar 2021 17:51:30 +0300
Subject: [PATCH 041/178] imp full and remove copypaste in geners

---
 .../kscience/kmath/tensors/BufferedTensor.kt  | 34 +++++++------------
 .../kmath/tensors/DoubleTensorAlgebra.kt      | 34 +++++++------------
 .../kscience/kmath/tensors/TensorAlgebra.kt   | 12 +++----
 3 files changed, 31 insertions(+), 49 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
index c48e47f4c..b9ebf578a 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
@@ -31,28 +31,6 @@ public open class BufferedTensor<T>(
 
     override fun hashCode(): Int = 0
 
-    // todo rename to vector
-    public inline fun forEachVector(vectorAction : (MutableStructure1D<T>) -> Unit): Unit {
-        check(shape.size >= 1) {"todo"}
-        val vectorOffset = strides.strides[0]
-        val vectorShape = intArrayOf(shape.last())
-        for (offset in 0 until numel step vectorOffset) {
-            val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
-            vectorAction(vector)
-        }
-    }
-
-    public inline fun forEachMatrix(matrixAction : (MutableStructure2D<T>) -> Unit): Unit {
-        check(shape.size >= 2) {"todo"}
-        val matrixOffset = strides.strides[1]
-        val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
-        for (offset in 0 until numel step matrixOffset) {
-            val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
-            matrixAction(matrix)
-        }
-    }
-    // todo remove code copy-pasting
-
     public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
         check(shape.size >= 1) {"todo"}
         val vectorOffset = strides.strides[0]
@@ -73,6 +51,18 @@ public open class BufferedTensor<T>(
         }
     }
 
+    public inline fun forEachVector(vectorAction : (MutableStructure1D<T>) -> Unit): Unit {
+        for (vector in vectorSequence()){
+            vectorAction(vector)
+        }
+    }
+
+    public inline fun forEachMatrix(matrixAction : (MutableStructure2D<T>) -> Unit): Unit {
+        for (matrix in matrixSequence()){
+            matrixAction(matrix)
+        }
+    }
+
 }
 
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
index 675be2f33..6459f4510 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
@@ -10,23 +10,24 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return DoubleTensor(newShape, this.buffer.array(), newStart)
     }
 
-    override fun zeros(shape: IntArray): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.zeroesLike(): DoubleTensor {
-        val shape = this.shape
-        val buffer = DoubleArray(this.strides.linearSize) { 0.0 }
+    override fun full(shape: IntArray, value: Double): DoubleTensor {
+        val buffer = DoubleArray(TensorStrides(shape).linearSize) { value }
         return DoubleTensor(shape, buffer)
     }
 
-    override fun ones(shape: IntArray): DoubleTensor {
-        TODO("Not yet implemented")
+    override fun zeros(shape: IntArray): DoubleTensor = full(shape, 0.0)
+
+    override fun ones(shape: IntArray): DoubleTensor = full(shape, 1.0)
+
+    override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
+        val shape = this.shape
+        val buffer = DoubleArray(this.strides.linearSize) { value }
+        return DoubleTensor(shape, buffer)
     }
 
-    override fun DoubleTensor.onesLike(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
+
+    override fun DoubleTensor.onesLike(): DoubleTensor = this.fullLike(1.0)
 
     override fun eye(n: Int): DoubleTensor {
         val shape = intArrayOf(n, n)
@@ -192,15 +193,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
-    override fun full(shape: IntArray, value: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-
     override fun DoubleTensor.sum(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 7ec920d88..7172acfaf 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -4,18 +4,18 @@ package space.kscience.kmath.tensors
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
 
-    public fun zeros(shape: IntArray): TensorType
-    public fun TensorType.zeroesLike(): TensorType // mb it shouldn't be tensor but algebra method (like in numpy/torch) ?
-    public fun ones(shape: IntArray): TensorType
-    public fun TensorType.onesLike(): TensorType
-
-
     //https://pytorch.org/docs/stable/generated/torch.full.html
     public fun full(shape: IntArray, value: T): TensorType
 
+    public fun ones(shape: IntArray): TensorType
+    public fun zeros(shape: IntArray): TensorType
+
     //https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
     public fun TensorType.fullLike(value: T): TensorType
 
+    public fun TensorType.zeroesLike(): TensorType
+    public fun TensorType.onesLike(): TensorType
+
     //https://pytorch.org/docs/stable/generated/torch.eye.html
     public fun eye(n: Int): TensorType
 

From 274be613302d8cdef5f8a81fc8aeee5c88bf8ccd Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 19 Mar 2021 19:40:17 +0000
Subject: [PATCH 042/178] Explicit broadcasting enforced

---
 .../kmath/tensors/LinearOpsTensorAlgebra.kt   |   2 +-
 .../BroadcastDoubleTensorAlgebra.kt}          | 151 ++++++++++--------
 .../tensors/{ => core}/BufferedTensor.kt      |  12 +-
 .../{ => core}/DoubleAnalyticTensorAlgebra.kt |   4 +-
 .../DoubleLinearOpsTensorAlgebra.kt           |   4 +-
 .../{ => core}/DoubleOrderedTensorAlgebra.kt  |   4 +-
 .../DoubleReduceOpsTensorAlgebra.kt           |   6 +-
 .../tensors/{ => core}/DoubleTensorAlgebra.kt | 107 +++++++------
 .../kscience/kmath/tensors/core/checks.kt     |  67 ++++++++
 .../kscience/kmath/tensors/core/utils.kt      |  36 +++++
 .../kmath/tensors/TestDoubleTensorAlgebra.kt  | 105 ------------
 .../kmath/tensors/core/TestBroadcasting.kt    |  82 ++++++++++
 .../tensors/{ => core}/TestDoubleTensor.kt    |  10 +-
 .../tensors/core/TestDoubleTensorAlgebra.kt   |  50 ++++++
 14 files changed, 407 insertions(+), 233 deletions(-)
 rename kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/{utils.kt => core/BroadcastDoubleTensorAlgebra.kt} (50%)
 rename kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => core}/BufferedTensor.kt (88%)
 rename kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => core}/DoubleAnalyticTensorAlgebra.kt (96%)
 rename kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => core}/DoubleLinearOpsTensorAlgebra.kt (96%)
 rename kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => core}/DoubleOrderedTensorAlgebra.kt (89%)
 rename kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => core}/DoubleReduceOpsTensorAlgebra.kt (68%)
 rename kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => core}/DoubleTensorAlgebra.kt (77%)
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
 delete mode 100644 kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
 create mode 100644 kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
 rename kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/{ => core}/TestDoubleTensor.kt (74%)
 create mode 100644 kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index 2e1c4a92c..94176564c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -17,7 +17,7 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, Inde
     public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
-    public fun luPivot(lu: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
+    public fun luPivot(lu: TensorType, pivots: IndexTensorType): Triple<TensorType, TensorType, TensorType>
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
     public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
similarity index 50%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
rename to kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
index e7e043463..a4767a612 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/utils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
@@ -1,10 +1,91 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.structures.*
 import kotlin.math.max
 
+public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
+
+    override fun DoubleTensor.plus(other: DoubleTensor): DoubleTensor {
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+            newThis.buffer.array()[i] + newOther.buffer.array()[i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.plusAssign(other: DoubleTensor) {
+        val newOther = broadcastTo(other, this.shape)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] +=
+                newOther.buffer.array()[this.bufferStart + i]
+        }
+    }
+
+    override fun DoubleTensor.minus(other: DoubleTensor): DoubleTensor {
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+            newThis.buffer.array()[i] - newOther.buffer.array()[i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.minusAssign(other: DoubleTensor) {
+        val newOther = broadcastTo(other, this.shape)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] -=
+                newOther.buffer.array()[this.bufferStart + i]
+        }
+    }
+
+    override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+            newThis.buffer.array()[newOther.bufferStart + i] *
+                    newOther.buffer.array()[newOther.bufferStart + i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.timesAssign(other: DoubleTensor) {
+        val newOther = broadcastTo(other, this.shape)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] *=
+                newOther.buffer.array()[this.bufferStart + i]
+        }
+    }
+
+    override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+            newThis.buffer.array()[newOther.bufferStart + i] /
+                    newOther.buffer.array()[newOther.bufferStart + i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.divAssign(other: DoubleTensor) {
+        val newOther = broadcastTo(other, this.shape)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] /=
+                newOther.buffer.array()[this.bufferStart + i]
+        }
+    }
+
+}
+
+public inline fun <R> broadcastDoubleTensorAlgebra(block: BroadcastDoubleTensorAlgebra.() -> R): R =
+    BroadcastDoubleTensorAlgebra().block()
+
 
 internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
+    println(shapes)
     var totalDim = 0
     for (shape in shapes) {
         totalDim = max(totalDim, shape.size)
@@ -99,68 +180,4 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
     }
 
     return res
-}
-
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkDot(a: TensorType, b: TensorType): Unit {
-    val sa = a.shape
-    val sb = b.shape
-    val na = sa.size
-    val nb = sb.size
-    var status: Boolean
-    if (nb == 1) {
-        status = sa.last() == sb[0]
-    } else {
-        status = sa.last() == sb[nb - 2]
-        if ((na > 2) and (nb > 2)) {
-            status = status and
-                    (sa.take(nb - 2).toIntArray() contentEquals sb.take(nb - 2).toIntArray())
-        }
-    }
-    check(status) { "Incompatible shapes $sa and $sb for dot product" }
-}
-
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkTranspose(dim: Int, i: Int, j: Int): Unit =
-    check((i < dim) and (j < dim)) {
-        "Cannot transpose $i to $j for a tensor of dim $dim"
-    }
-
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkView(a: TensorType, shape: IntArray): Unit =
-    check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
-
-/**
- * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
- */
-internal fun Buffer<Int>.array(): IntArray = when(this) {
-    is IntBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to IntArray")
-}
-
-/**
- * Returns a reference to [LongArray] containing all of the elements of this [Buffer].
- */
-internal fun Buffer<Long>.array(): LongArray = when(this) {
-    is LongBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to LongArray")
-}
-
-/**
- * Returns a reference to [FloatArray] containing all of the elements of this [Buffer].
- */
-internal fun Buffer<Float>.array(): FloatArray = when(this) {
-    is FloatBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to FloatArray")
-}
-
-/**
- * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer].
- */
-internal fun Buffer<Double>.array(): DoubleArray = when(this) {
-    is RealBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
-}
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
similarity index 88%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
rename to kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index c48e47f4c..cbfb15be0 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -1,8 +1,10 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.linear.Matrix
 import space.kscience.kmath.nd.*
 import space.kscience.kmath.structures.*
+import space.kscience.kmath.tensors.TensorStrides
+import space.kscience.kmath.tensors.TensorStructure
 
 
 public open class BufferedTensor<T>(
@@ -76,25 +78,25 @@ public open class BufferedTensor<T>(
 }
 
 
-public class IntTensor(
+public class IntTensor internal constructor(
     shape: IntArray,
     buffer: IntArray,
     offset: Int = 0
 ) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset)
 
-public class LongTensor(
+public class LongTensor internal constructor(
     shape: IntArray,
     buffer: LongArray,
     offset: Int = 0
 ) : BufferedTensor<Long>(shape, LongBuffer(buffer), offset)
 
-public class FloatTensor(
+public class FloatTensor internal constructor(
     shape: IntArray,
     buffer: FloatArray,
     offset: Int = 0
 ) : BufferedTensor<Float>(shape, FloatBuffer(buffer), offset)
 
-public class DoubleTensor(
+public class DoubleTensor internal constructor(
     shape: IntArray,
     buffer: DoubleArray,
     offset: Int = 0
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
similarity index 96%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
rename to kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
index 5349a9923..a34fe4b6c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
@@ -1,4 +1,6 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.AnalyticTensorAlgebra
 
 public class DoubleAnalyticTensorAlgebra:
     AnalyticTensorAlgebra<Double, DoubleTensor>,
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
similarity index 96%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
rename to kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 3f44305b1..8a16dc3ed 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -1,4 +1,6 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 
 public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra.kt
similarity index 89%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt
rename to kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra.kt
index bd6bcfe8f..a6bea59f4 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleOrderedTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra.kt
@@ -1,4 +1,6 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.OrderedTensorAlgebra
 
 public open class DoubleOrderedTensorAlgebra:
     OrderedTensorAlgebra<Double, DoubleTensor>,
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleReduceOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebra.kt
similarity index 68%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleReduceOpsTensorAlgebra.kt
rename to kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebra.kt
index 00d9b3ff8..9a8aa9ebf 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleReduceOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebra.kt
@@ -1,8 +1,10 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.ReduceOpsTensorAlgebra
 
 public class DoubleReduceOpsTensorAlgebra:
     DoubleTensorAlgebra(),
-    ReduceOpsTensorAlgebra<Double, DoubleTensor>  {
+    ReduceOpsTensorAlgebra<Double, DoubleTensor> {
 
     override fun DoubleTensor.value(): Double {
         check(this.shape contentEquals intArrayOf(1)) {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
similarity index 77%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
rename to kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 675be2f33..9decc0e6a 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -1,8 +1,18 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
 
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
 
+    public fun fromArray(shape: IntArray, buffer: DoubleArray): DoubleTensor {
+        checkEmptyShape(shape)
+        checkEmptyDoubleBuffer(buffer)
+        checkBufferShapeConsistency(shape, buffer)
+        return DoubleTensor(shape, buffer, 0)
+    }
+
+
     override operator fun DoubleTensor.get(i: Int): DoubleTensor {
         val lastShape = this.shape.drop(1).toIntArray()
         val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
@@ -53,13 +63,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     override fun DoubleTensor.plus(value: Double): DoubleTensor = value + this
 
     override fun DoubleTensor.plus(other: DoubleTensor): DoubleTensor {
-        val broadcast = broadcastTensors(this, other)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
-            newThis.buffer.array()[i] + newOther.buffer.array()[i]
+        checkShapesCompatible(this, other)
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+            this.buffer.array()[i] + other.buffer.array()[i]
         }
-        return DoubleTensor(newThis.shape, resBuffer)
+        return DoubleTensor(this.shape, resBuffer)
     }
 
     override fun DoubleTensor.plusAssign(value: Double) {
@@ -69,10 +77,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.plusAssign(other: DoubleTensor) {
-        val newOther = broadcastTo(other, this.shape)
+        checkShapesCompatible(this, other)
         for (i in 0 until this.strides.linearSize) {
             this.buffer.array()[this.bufferStart + i] +=
-                newOther.buffer.array()[this.bufferStart + i]
+                other.buffer.array()[this.bufferStart + i]
         }
     }
 
@@ -91,13 +99,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.minus(other: DoubleTensor): DoubleTensor {
-        val broadcast = broadcastTensors(this, other)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
-            newThis.buffer.array()[i] - newOther.buffer.array()[i]
+        checkShapesCompatible(this, other)
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+            this.buffer.array()[i] - other.buffer.array()[i]
         }
-        return DoubleTensor(newThis.shape, resBuffer)
+        return DoubleTensor(this.shape, resBuffer)
     }
 
     override fun DoubleTensor.minusAssign(value: Double) {
@@ -107,10 +113,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.minusAssign(other: DoubleTensor) {
-        val newOther = broadcastTo(other, this.shape)
+        checkShapesCompatible(this, other)
         for (i in 0 until this.strides.linearSize) {
             this.buffer.array()[this.bufferStart + i] -=
-                newOther.buffer.array()[this.bufferStart + i]
+                other.buffer.array()[this.bufferStart + i]
         }
     }
 
@@ -124,15 +130,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     override fun DoubleTensor.times(value: Double): DoubleTensor = value * this
 
     override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
-        val broadcast = broadcastTensors(this, other)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-
-        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
-            newThis.buffer.array()[newOther.bufferStart + i] *
-                    newOther.buffer.array()[newOther.bufferStart + i]
+        checkShapesCompatible(this, other)
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+            this.buffer.array()[other.bufferStart + i] *
+                    other.buffer.array()[other.bufferStart + i]
         }
-        return DoubleTensor(newThis.shape, resBuffer)
+        return DoubleTensor(this.shape, resBuffer)
     }
 
     override fun DoubleTensor.timesAssign(value: Double) {
@@ -142,10 +145,40 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.timesAssign(other: DoubleTensor) {
-        val newOther = broadcastTo(other, this.shape)
+        checkShapesCompatible(this, other)
         for (i in 0 until this.strides.linearSize) {
             this.buffer.array()[this.bufferStart + i] *=
-                newOther.buffer.array()[this.bufferStart + i]
+                other.buffer.array()[this.bufferStart + i]
+        }
+    }
+
+    override fun DoubleTensor.div(value: Double): DoubleTensor {
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+            this.buffer.array()[this.bufferStart + i] / value
+        }
+        return DoubleTensor(this.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
+        checkShapesCompatible(this, other)
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+            this.buffer.array()[other.bufferStart + i] /
+                    other.buffer.array()[other.bufferStart + i]
+        }
+        return DoubleTensor(this.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.divAssign(value: Double) {
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] /= value
+        }
+    }
+
+    override fun DoubleTensor.divAssign(other: DoubleTensor) {
+        checkShapesCompatible(this, other)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] /=
+                other.buffer.array()[this.bufferStart + i]
         }
     }
 
@@ -229,27 +262,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
-
-    override fun DoubleTensor.div(value: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
     override fun DoubleTensor.flatten(startDim: Int, endDim: Int): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.divAssign(value: Double) {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.divAssign(other: DoubleTensor) {
-        TODO("Not yet implemented")
-    }
-
     override fun DoubleTensor.mean(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
@@ -272,5 +288,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
 }
 
+
 public inline fun <R> DoubleTensorAlgebra(block: DoubleTensorAlgebra.() -> R): R =
-    DoubleTensorAlgebra().block()
\ No newline at end of file
+    DoubleTensorAlgebra().block()
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
new file mode 100644
index 000000000..f1ae89490
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -0,0 +1,67 @@
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.TensorAlgebra
+import space.kscience.kmath.tensors.TensorStructure
+
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkEmptyShape(shape: IntArray): Unit =
+    check(shape.isNotEmpty()) {
+        "Illegal empty shape provided"
+    }
+
+internal inline fun < TensorType : TensorStructure<Double>,
+        TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
+        TorchTensorAlgebraType.checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
+    check(buffer.isNotEmpty()) {
+        "Illegal empty buffer provided"
+    }
+
+internal inline fun < TensorType : TensorStructure<Double>,
+        TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
+        TorchTensorAlgebraType.checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
+    check(buffer.size == shape.reduce(Int::times)) {
+        "Inconsistent shape ${shape.toList()} for buffer of size ${buffer.size} provided"
+    }
+
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkShapesCompatible(a: TensorType, b: TensorType): Unit =
+    check(a.shape contentEquals b.shape) {
+        "Incompatible shapes ${a.shape.toList()} and ${b.shape.toList()} "
+    }
+
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkDot(a: TensorType, b: TensorType): Unit {
+    val sa = a.shape
+    val sb = b.shape
+    val na = sa.size
+    val nb = sb.size
+    var status: Boolean
+    if (nb == 1) {
+        status = sa.last() == sb[0]
+    } else {
+        status = sa.last() == sb[nb - 2]
+        if ((na > 2) and (nb > 2)) {
+            status = status and
+                    (sa.take(nb - 2).toIntArray() contentEquals sb.take(nb - 2).toIntArray())
+        }
+    }
+    check(status) { "Incompatible shapes ${sa.toList()} and ${sb.toList()} provided for dot product" }
+}
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkTranspose(dim: Int, i: Int, j: Int): Unit =
+    check((i < dim) and (j < dim)) {
+        "Cannot transpose $i to $j for a tensor of dim $dim"
+    }
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkView(a: TensorType, shape: IntArray): Unit =
+    check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
new file mode 100644
index 000000000..69c8afde5
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -0,0 +1,36 @@
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.structures.*
+
+
+/**
+ * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Int>.array(): IntArray = when (this) {
+    is IntBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to IntArray")
+}
+
+/**
+ * Returns a reference to [LongArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Long>.array(): LongArray = when (this) {
+    is LongBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to LongArray")
+}
+
+/**
+ * Returns a reference to [FloatArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Float>.array(): FloatArray = when (this) {
+    is FloatBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to FloatArray")
+}
+
+/**
+ * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Double>.array(): DoubleArray = when (this) {
+    is RealBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
+}
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
deleted file mode 100644
index a060a970f..000000000
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensorAlgebra.kt
+++ /dev/null
@@ -1,105 +0,0 @@
-package space.kscience.kmath.tensors
-
-
-import kotlin.test.Test
-import kotlin.test.assertTrue
-
-class TestDoubleTensorAlgebra {
-
-    @Test
-    fun doublePlus() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(2), doubleArrayOf(1.0, 2.0))
-        val res = 10.0 + tensor
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(11.0,12.0))
-    }
-
-    @Test
-    fun transpose1x1() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(0.0))
-        val res = tensor.transpose(0, 0)
-
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(0.0))
-        assertTrue(res.shape contentEquals intArrayOf(1))
-    }
-
-    @Test
-    fun transpose3x2() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val res = tensor.transpose(1, 0)
-
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
-        assertTrue(res.shape contentEquals intArrayOf(2, 3))
-    }
-
-    @Test
-    fun transpose1x2x3() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val res01 = tensor.transpose(0, 1)
-        val res02 = tensor.transpose(0, 2)
-        val res12 = tensor.transpose(1, 2)
-
-        assertTrue(res01.shape contentEquals intArrayOf(2, 1, 3))
-        assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
-        assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2))
-
-        assertTrue(res01.buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res02.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
-        assertTrue(res12.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
-    }
-
-    @Test
-    fun broadcastShapes() = DoubleTensorAlgebra {
-        assertTrue(broadcastShapes(
-            intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
-        ) contentEquals intArrayOf(1, 2, 3))
-
-        assertTrue(broadcastShapes(
-            intArrayOf(6, 7), intArrayOf(5, 6, 1), intArrayOf(7,), intArrayOf(5, 1, 7)
-        ) contentEquals intArrayOf(5, 6, 7))
-    }
-
-    @Test
-    fun broadcastTo() = DoubleTensorAlgebra {
-        val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
-
-        val res = broadcastTo(tensor2, tensor1.shape)
-        assertTrue(res.shape contentEquals intArrayOf(2, 3))
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
-    }
-
-    @Test
-    fun broadcastTensors() = DoubleTensorAlgebra {
-        val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
-        val tensor3 = DoubleTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
-
-        val res = broadcastTensors(tensor1, tensor2, tensor3)
-
-        assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3))
-        assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
-        assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
-
-        assertTrue(res[0].buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res[1].buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
-        assertTrue(res[2].buffer.array() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
-    }
-
-    @Test
-    fun minusTensor() = DoubleTensorAlgebra {
-        val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
-        val tensor3 = DoubleTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
-
-        assertTrue((tensor2 - tensor1).shape contentEquals intArrayOf(2, 3))
-        assertTrue((tensor2 - tensor1).buffer.array() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
-
-        assertTrue((tensor3 - tensor1).shape contentEquals intArrayOf(1, 2, 3))
-        assertTrue((tensor3 - tensor1).buffer.array()
-                contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0))
-
-        assertTrue((tensor3 - tensor2).shape contentEquals intArrayOf(1, 1, 3))
-        assertTrue((tensor3 - tensor2).buffer.array() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
-    }
-
-}
\ No newline at end of file
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
new file mode 100644
index 000000000..2633229ea
--- /dev/null
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -0,0 +1,82 @@
+package space.kscience.kmath.tensors.core
+
+import kotlin.test.Test
+import kotlin.test.assertTrue
+
+class TestBroadcasting {
+
+    @Test
+    fun broadcastShapes() = DoubleTensorAlgebra {
+        assertTrue(
+            broadcastShapes(
+                intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
+            ) contentEquals intArrayOf(1, 2, 3)
+        )
+
+        assertTrue(
+            broadcastShapes(
+                intArrayOf(6, 7), intArrayOf(5, 6, 1), intArrayOf(7), intArrayOf(5, 1, 7)
+            ) contentEquals intArrayOf(5, 6, 7)
+        )
+    }
+
+    @Test
+    fun broadcastTo() = DoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+
+        val res = broadcastTo(tensor2, tensor1.shape)
+        assertTrue(res.shape contentEquals intArrayOf(2, 3))
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+    }
+
+    @Test
+    fun broadcastTensors() = DoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
+
+        val res = broadcastTensors(tensor1, tensor2, tensor3)
+
+        assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3))
+        assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
+        assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
+
+        assertTrue(res[0].buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res[1].buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+        assertTrue(res[2].buffer.array() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
+    }
+
+    @Test
+    fun minusTensor() = DoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
+
+        val tensor21 = broadcastDoubleTensorAlgebra {
+            tensor2 - tensor1
+        }
+
+        val tensor31 = broadcastDoubleTensorAlgebra {
+            tensor3 - tensor1
+        }
+
+        val tensor32 = broadcastDoubleTensorAlgebra {
+            tensor3 - tensor2
+        }
+
+
+        assertTrue(tensor21.shape contentEquals intArrayOf(2, 3))
+        assertTrue(tensor21.buffer.array() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
+
+        assertTrue(tensor31.shape contentEquals intArrayOf(1, 2, 3))
+        assertTrue(
+            tensor31.buffer.array()
+                    contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0)
+        )
+
+        assertTrue(tensor32.shape contentEquals intArrayOf(1, 1, 3))
+        assertTrue(tensor32.buffer.array() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
+    }
+
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
similarity index 74%
rename from kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
rename to kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 6b20027c7..b12b08b52 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/TestDoubleTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -1,4 +1,4 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
 
 
 import space.kscience.kmath.nd.as1D
@@ -13,20 +13,20 @@ class TestDoubleTensor {
     @Test
     fun valueTest() = DoubleReduceOpsTensorAlgebra {
         val value = 12.5
-        val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(value))
+        val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
     }
 
     @Test
-    fun stridesTest(){
-        val tensor = DoubleTensor(intArrayOf(2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
+    fun stridesTest() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
         assertEquals(tensor[intArrayOf(0,1)], 5.8)
         assertTrue(tensor.elements().map{ it.second }.toList().toDoubleArray() contentEquals tensor.buffer.toDoubleArray())
     }
 
     @Test
     fun getTest() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(1,2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
+        val tensor = fromArray(intArrayOf(1,2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
         val matrix = tensor[0].as2D()
         assertEquals(matrix[0,1], 5.8)
 
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
new file mode 100644
index 000000000..8b4d5ca16
--- /dev/null
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -0,0 +1,50 @@
+package space.kscience.kmath.tensors.core
+
+
+import kotlin.test.Test
+import kotlin.test.assertTrue
+
+class TestDoubleTensorAlgebra {
+
+    @Test
+    fun doublePlus() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(2), doubleArrayOf(1.0, 2.0))
+        val res = 10.0 + tensor
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(11.0, 12.0))
+    }
+
+    @Test
+    fun transpose1x1() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(1), doubleArrayOf(0.0))
+        val res = tensor.transpose(0, 0)
+
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(0.0))
+        assertTrue(res.shape contentEquals intArrayOf(1))
+    }
+
+    @Test
+    fun transpose3x2() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val res = tensor.transpose(1, 0)
+
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
+        assertTrue(res.shape contentEquals intArrayOf(2, 3))
+    }
+
+    @Test
+    fun transpose1x2x3() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val res01 = tensor.transpose(0, 1)
+        val res02 = tensor.transpose(0, 2)
+        val res12 = tensor.transpose(1, 2)
+
+        assertTrue(res01.shape contentEquals intArrayOf(2, 1, 3))
+        assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
+        assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2))
+
+        assertTrue(res01.buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res02.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res12.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+    }
+
+}
\ No newline at end of file

From 3535e512483b74f00867858b18e38a2150f3451c Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 19 Mar 2021 19:52:58 +0000
Subject: [PATCH 043/178] Broadcasting as its own algebra

---
 .../core/BroadcastDoubleTensorAlgebra.kt        |  2 +-
 .../kmath/tensors/core/TestBroadcasting.kt      | 17 ++++-------------
 2 files changed, 5 insertions(+), 14 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
index a4767a612..20f64f469 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
@@ -80,7 +80,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
 }
 
-public inline fun <R> broadcastDoubleTensorAlgebra(block: BroadcastDoubleTensorAlgebra.() -> R): R =
+public inline fun <R> BroadcastDoubleTensorAlgebra(block: BroadcastDoubleTensorAlgebra.() -> R): R =
     BroadcastDoubleTensorAlgebra().block()
 
 
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
index 2633229ea..73e3993a1 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -48,23 +48,14 @@ class TestBroadcasting {
     }
 
     @Test
-    fun minusTensor() = DoubleTensorAlgebra {
+    fun minusTensor() = BroadcastDoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
 
-        val tensor21 = broadcastDoubleTensorAlgebra {
-            tensor2 - tensor1
-        }
-
-        val tensor31 = broadcastDoubleTensorAlgebra {
-            tensor3 - tensor1
-        }
-
-        val tensor32 = broadcastDoubleTensorAlgebra {
-            tensor3 - tensor2
-        }
-
+        val tensor21 = tensor2 - tensor1
+        val tensor31 = tensor3 - tensor1
+        val tensor32 = tensor3 - tensor2
 
         assertTrue(tensor21.shape contentEquals intArrayOf(2, 3))
         assertTrue(tensor21.buffer.array() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))

From 93d3cb47bed67d95c75d6e9f54283f7428dd150e Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 19 Mar 2021 20:10:08 +0000
Subject: [PATCH 044/178] Testing linear structure

---
 .../kscience/kmath/tensors/TensorAlgebra.kt   | 12 +++----
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 35 ++++++++-----------
 .../tensors/core/TestDoubleTensorAlgebra.kt   | 32 +++++++++++++++++
 3 files changed, 51 insertions(+), 28 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 7ec920d88..1673657d3 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -3,19 +3,17 @@ package space.kscience.kmath.tensors
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
+    //https://pytorch.org/docs/stable/generated/torch.full.html
+    public fun full(value: T, shape: IntArray): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
+    public fun TensorType.fullLike(value: T): TensorType
 
     public fun zeros(shape: IntArray): TensorType
     public fun TensorType.zeroesLike(): TensorType // mb it shouldn't be tensor but algebra method (like in numpy/torch) ?
     public fun ones(shape: IntArray): TensorType
     public fun TensorType.onesLike(): TensorType
 
-
-    //https://pytorch.org/docs/stable/generated/torch.full.html
-    public fun full(shape: IntArray, value: T): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
-    public fun TensorType.fullLike(value: T): TensorType
-
     //https://pytorch.org/docs/stable/generated/torch.eye.html
     public fun eye(n: Int): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 9decc0e6a..1c156b4e3 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -20,23 +20,25 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return DoubleTensor(newShape, this.buffer.array(), newStart)
     }
 
-    override fun zeros(shape: IntArray): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.zeroesLike(): DoubleTensor {
-        val shape = this.shape
-        val buffer = DoubleArray(this.strides.linearSize) { 0.0 }
+    override fun full(value: Double, shape: IntArray): DoubleTensor {
+        checkEmptyShape(shape)
+        val buffer = DoubleArray(shape.reduce(Int::times)) { value }
         return DoubleTensor(shape, buffer)
     }
 
-    override fun ones(shape: IntArray): DoubleTensor {
-        TODO("Not yet implemented")
+    override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
+        val shape = this.shape
+        val buffer = DoubleArray(this.strides.linearSize) { value }
+        return DoubleTensor(shape, buffer)
     }
 
-    override fun DoubleTensor.onesLike(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
+
+    override fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
+
+    override fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
+
+    override fun DoubleTensor.onesLike(): DoubleTensor = this.fullLike(1.0)
 
     override fun eye(n: Int): DoubleTensor {
         val shape = intArrayOf(n, n)
@@ -225,15 +227,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
-    override fun full(shape: IntArray, value: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-
     override fun DoubleTensor.sum(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index 8b4d5ca16..ddfba0d59 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -47,4 +47,36 @@ class TestDoubleTensorAlgebra {
         assertTrue(res12.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
     }
 
+    @Test
+    fun linearStructure() = DoubleTensorAlgebra {
+        val shape = intArrayOf(3)
+        val tensorA = full(value = -4.5, shape = shape)
+        val tensorB = full(value = 10.9, shape = shape)
+        val tensorC = full(value = 789.3, shape = shape)
+        val tensorD = full(value = -72.9, shape = shape)
+        val tensorE = full(value = 553.1, shape = shape)
+        val result = 15.8 * tensorA - 1.5 * tensorB * (-tensorD) + 0.02 * tensorC / tensorE - 39.4
+
+        val expected = fromArray(
+            shape,
+            (1..3).map {
+                15.8 * (-4.5) - 1.5 * 10.9 * 72.9 + 0.02 * 789.3 / 553.1 - 39.4
+            }.toDoubleArray()
+        )
+
+        val assignResult = zeros(shape)
+        tensorA *= 15.8
+        tensorB *= 1.5
+        tensorB *= -tensorD
+        tensorC *= 0.02
+        tensorC /= tensorE
+        assignResult += tensorA
+        assignResult -= tensorB
+        assignResult += tensorC
+        assignResult += -39.4
+
+        assertTrue(expected.buffer.array() contentEquals result.buffer.array())
+        assertTrue(expected.buffer.array() contentEquals assignResult.buffer.array())
+    }
+
 }
\ No newline at end of file

From 8933c820b371b9b03d096c016bb55c104d3b2a9d Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 19 Mar 2021 20:32:57 +0000
Subject: [PATCH 045/178] merge PR

---
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 19 -------------------
 1 file changed, 19 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 465c2ddc1..875f21687 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -12,7 +12,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return DoubleTensor(shape, buffer, 0)
     }
 
-
     override operator fun DoubleTensor.get(i: Int): DoubleTensor {
         val lastShape = this.shape.drop(1).toIntArray()
         val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
@@ -20,41 +19,24 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return DoubleTensor(newShape, this.buffer.array(), newStart)
     }
 
-<<<<<<< HEAD:kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
-    override fun full(shape: IntArray, value: Double): DoubleTensor {
-        val buffer = DoubleArray(TensorStrides(shape).linearSize) { value }
-        return DoubleTensor(shape, buffer)
-    }
-
-    override fun zeros(shape: IntArray): DoubleTensor = full(shape, 0.0)
-
-    override fun ones(shape: IntArray): DoubleTensor = full(shape, 1.0)
-
-=======
     override fun full(value: Double, shape: IntArray): DoubleTensor {
         checkEmptyShape(shape)
         val buffer = DoubleArray(shape.reduce(Int::times)) { value }
         return DoubleTensor(shape, buffer)
     }
 
->>>>>>> ups/feature/tensor-algebra:kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
     override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
         val shape = this.shape
         val buffer = DoubleArray(this.strides.linearSize) { value }
         return DoubleTensor(shape, buffer)
     }
 
-<<<<<<< HEAD:kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
-    override fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
-
-=======
     override fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
 
     override fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
 
     override fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
 
->>>>>>> ups/feature/tensor-algebra:kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
     override fun DoubleTensor.onesLike(): DoubleTensor = this.fullLike(1.0)
 
     override fun eye(n: Int): DoubleTensor {
@@ -71,7 +53,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return DoubleTensor(this.shape, this.buffer.array().copyOf(), this.bufferStart)
     }
 
-
     override fun Double.plus(other: DoubleTensor): DoubleTensor {
         val resBuffer = DoubleArray(other.strides.linearSize) { i ->
             other.buffer.array()[other.bufferStart + i] + this

From b7e1349eadc0cfe9b573006c79e714333655a2d1 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sat, 20 Mar 2021 18:59:54 +0000
Subject: [PATCH 046/178] atanh as example

---
 .../kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt      | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
index a34fe4b6c..8a579b7da 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
@@ -1,6 +1,7 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.AnalyticTensorAlgebra
+import kotlin.math.*
 
 public class DoubleAnalyticTensorAlgebra:
     AnalyticTensorAlgebra<Double, DoubleTensor>,
@@ -63,7 +64,11 @@ public class DoubleAnalyticTensorAlgebra:
     }
 
     override fun DoubleTensor.atanh(): DoubleTensor {
-        TODO("Not yet implemented")
+        return DoubleTensor(
+            this.shape,
+            this.buffer.array().map(::atanh).toDoubleArray(),
+            this.bufferStart
+        )
     }
 
     override fun DoubleTensor.ceil(): DoubleTensor {

From 94e5ee4a6de020d77c49ce57e7795d3a33abe80e Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sun, 21 Mar 2021 08:01:52 +0000
Subject: [PATCH 047/178] TensorLinearStructure introduced

---
 .../space/kscience/kmath/nd/StructureND.kt    | 17 +++----
 .../core/BroadcastDoubleTensorAlgebra.kt      | 24 +++++-----
 .../kmath/tensors/core/BufferedTensor.kt      | 18 ++++----
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 44 +++++++++----------
 .../TensorLinearStructure.kt}                 | 33 ++++++++------
 5 files changed, 69 insertions(+), 67 deletions(-)
 rename kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/{TensorStrides.kt => core/TensorLinearStructure.kt} (55%)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
index 86b867130..f28d29e47 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
@@ -177,7 +177,7 @@ public interface Strides {
     /**
      * Array strides
      */
-    public val strides: IntArray
+    public val strides: List<Int>
 
     /**
      * Get linear index from multidimensional index
@@ -209,12 +209,6 @@ public interface Strides {
     }
 }
 
-internal inline fun offsetFromIndex(index: IntArray, shape: IntArray, strides: IntArray): Int =
-    index.mapIndexed { i, value ->
-        if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${shape[i]})")
-        value * strides[i]
-    }.sum()
-
 /**
  * Simple implementation of [Strides].
  */
@@ -225,7 +219,7 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
     /**
      * Strides for memory access
      */
-    override val strides: IntArray by lazy {
+    override val strides: List<Int> by lazy {
         sequence {
             var current = 1
             yield(1)
@@ -234,10 +228,13 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
                 current *= it
                 yield(current)
             }
-        }.toList().toIntArray()
+        }.toList()
     }
 
-    override fun offset(index: IntArray): Int = offsetFromIndex(index, shape, strides)
+    override fun offset(index: IntArray): Int = index.mapIndexed { i, value ->
+        if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${shape[i]})")
+        value * strides[i]
+    }.sum()
 
     override fun index(offset: Int): IntArray {
         val res = IntArray(shape.size)
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
index 20f64f469..4bfbe5863 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
@@ -8,7 +8,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val broadcast = broadcastTensors(this, other)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
             newThis.buffer.array()[i] + newOther.buffer.array()[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
@@ -16,7 +16,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun DoubleTensor.plusAssign(other: DoubleTensor) {
         val newOther = broadcastTo(other, this.shape)
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] +=
                 newOther.buffer.array()[this.bufferStart + i]
         }
@@ -26,7 +26,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val broadcast = broadcastTensors(this, other)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
             newThis.buffer.array()[i] - newOther.buffer.array()[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
@@ -34,7 +34,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun DoubleTensor.minusAssign(other: DoubleTensor) {
         val newOther = broadcastTo(other, this.shape)
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] -=
                 newOther.buffer.array()[this.bufferStart + i]
         }
@@ -44,7 +44,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val broadcast = broadcastTensors(this, other)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
             newThis.buffer.array()[newOther.bufferStart + i] *
                     newOther.buffer.array()[newOther.bufferStart + i]
         }
@@ -53,7 +53,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun DoubleTensor.timesAssign(other: DoubleTensor) {
         val newOther = broadcastTo(other, this.shape)
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] *=
                 newOther.buffer.array()[this.bufferStart + i]
         }
@@ -63,7 +63,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val broadcast = broadcastTensors(this, other)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
             newThis.buffer.array()[newOther.bufferStart + i] /
                     newOther.buffer.array()[newOther.bufferStart + i]
         }
@@ -72,7 +72,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun DoubleTensor.divAssign(other: DoubleTensor) {
         val newOther = broadcastTo(other, this.shape)
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] /=
                 newOther.buffer.array()[this.bufferStart + i]
         }
@@ -130,7 +130,7 @@ internal inline fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): Doubl
     }
 
     for (linearIndex in 0 until n) {
-        val totalMultiIndex = resTensor.strides.index(linearIndex)
+        val totalMultiIndex = resTensor.linearStructure.index(linearIndex)
         val curMultiIndex = tensor.shape.copyOf()
 
         val offset = totalMultiIndex.size - curMultiIndex.size
@@ -143,7 +143,7 @@ internal inline fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): Doubl
             }
         }
 
-        val curLinearIndex = tensor.strides.offset(curMultiIndex)
+        val curLinearIndex = tensor.linearStructure.offset(curMultiIndex)
         resTensor.buffer.array()[linearIndex] =
             tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
     }
@@ -159,7 +159,7 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
         val resTensor = DoubleTensor(totalShape, DoubleArray(n))
 
         for (linearIndex in 0 until n) {
-            val totalMultiIndex = resTensor.strides.index(linearIndex)
+            val totalMultiIndex = resTensor.linearStructure.index(linearIndex)
             val curMultiIndex = tensor.shape.copyOf()
 
             val offset = totalMultiIndex.size - curMultiIndex.size
@@ -172,7 +172,7 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
                 }
             }
 
-            val curLinearIndex = tensor.strides.offset(curMultiIndex)
+            val curLinearIndex = tensor.linearStructure.offset(curMultiIndex)
             resTensor.buffer.array()[linearIndex] =
                 tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
         }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 8717e0f7e..1893ea3da 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -1,9 +1,7 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.linear.Matrix
 import space.kscience.kmath.nd.*
 import space.kscience.kmath.structures.*
-import space.kscience.kmath.tensors.TensorStrides
 import space.kscience.kmath.tensors.TensorStructure
 
 
@@ -13,19 +11,19 @@ public open class BufferedTensor<T>(
     internal val bufferStart: Int
 ) : TensorStructure<T>
 {
-    public val strides: TensorStrides
-        get() = TensorStrides(shape)
+    public val linearStructure: TensorLinearStructure
+        get() = TensorLinearStructure(shape)
 
     public val numel: Int
-        get() = strides.linearSize
+        get() = linearStructure.size
 
-    override fun get(index: IntArray): T = buffer[bufferStart + strides.offset(index)]
+    override fun get(index: IntArray): T = buffer[bufferStart + linearStructure.offset(index)]
 
     override fun set(index: IntArray, value: T) {
-        buffer[bufferStart + strides.offset(index)] = value
+        buffer[bufferStart + linearStructure.offset(index)] = value
     }
 
-    override fun elements(): Sequence<Pair<IntArray, T>> = strides.indices().map {
+    override fun elements(): Sequence<Pair<IntArray, T>> = linearStructure.indices().map {
         it to this[it]
     }
 
@@ -35,7 +33,7 @@ public open class BufferedTensor<T>(
 
     public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
         check(shape.size >= 1) {"todo"}
-        val vectorOffset = strides.strides[0]
+        val vectorOffset = linearStructure.strides[0]
         val vectorShape = intArrayOf(shape.last())
         for (offset in 0 until numel step vectorOffset) {
             val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
@@ -45,7 +43,7 @@ public open class BufferedTensor<T>(
 
     public fun matrixSequence(): Sequence<MutableStructure2D<T>> = sequence {
         check(shape.size >= 2) {"todo"}
-        val matrixOffset = strides.strides[1]
+        val matrixOffset = linearStructure.strides[1]
         val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
         for (offset in 0 until numel step matrixOffset) {
             val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 875f21687..385118d97 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -27,7 +27,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
         val shape = this.shape
-        val buffer = DoubleArray(this.strides.linearSize) { value }
+        val buffer = DoubleArray(this.linearStructure.size) { value }
         return DoubleTensor(shape, buffer)
     }
 
@@ -54,7 +54,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun Double.plus(other: DoubleTensor): DoubleTensor {
-        val resBuffer = DoubleArray(other.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(other.linearStructure.size) { i ->
             other.buffer.array()[other.bufferStart + i] + this
         }
         return DoubleTensor(other.shape, resBuffer)
@@ -64,35 +64,35 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun DoubleTensor.plus(other: DoubleTensor): DoubleTensor {
         checkShapesCompatible(this, other)
-        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
             this.buffer.array()[i] + other.buffer.array()[i]
         }
         return DoubleTensor(this.shape, resBuffer)
     }
 
     override fun DoubleTensor.plusAssign(value: Double) {
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] += value
         }
     }
 
     override fun DoubleTensor.plusAssign(other: DoubleTensor) {
         checkShapesCompatible(this, other)
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] +=
                 other.buffer.array()[this.bufferStart + i]
         }
     }
 
     override fun Double.minus(other: DoubleTensor): DoubleTensor {
-        val resBuffer = DoubleArray(other.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(other.linearStructure.size) { i ->
             this - other.buffer.array()[other.bufferStart + i]
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
     override fun DoubleTensor.minus(value: Double): DoubleTensor {
-        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
             this.buffer.array()[this.bufferStart + i] - value
         }
         return DoubleTensor(this.shape, resBuffer)
@@ -100,28 +100,28 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun DoubleTensor.minus(other: DoubleTensor): DoubleTensor {
         checkShapesCompatible(this, other)
-        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
             this.buffer.array()[i] - other.buffer.array()[i]
         }
         return DoubleTensor(this.shape, resBuffer)
     }
 
     override fun DoubleTensor.minusAssign(value: Double) {
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] -= value
         }
     }
 
     override fun DoubleTensor.minusAssign(other: DoubleTensor) {
         checkShapesCompatible(this, other)
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] -=
                 other.buffer.array()[this.bufferStart + i]
         }
     }
 
     override fun Double.times(other: DoubleTensor): DoubleTensor {
-        val resBuffer = DoubleArray(other.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(other.linearStructure.size) { i ->
             other.buffer.array()[other.bufferStart + i] * this
         }
         return DoubleTensor(other.shape, resBuffer)
@@ -131,7 +131,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
         checkShapesCompatible(this, other)
-        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
             this.buffer.array()[other.bufferStart + i] *
                     other.buffer.array()[other.bufferStart + i]
         }
@@ -139,21 +139,21 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.timesAssign(value: Double) {
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] *= value
         }
     }
 
     override fun DoubleTensor.timesAssign(other: DoubleTensor) {
         checkShapesCompatible(this, other)
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] *=
                 other.buffer.array()[this.bufferStart + i]
         }
     }
 
     override fun DoubleTensor.div(value: Double): DoubleTensor {
-        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
             this.buffer.array()[this.bufferStart + i] / value
         }
         return DoubleTensor(this.shape, resBuffer)
@@ -161,7 +161,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
         checkShapesCompatible(this, other)
-        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
             this.buffer.array()[other.bufferStart + i] /
                     other.buffer.array()[other.bufferStart + i]
         }
@@ -169,21 +169,21 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.divAssign(value: Double) {
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] /= value
         }
     }
 
     override fun DoubleTensor.divAssign(other: DoubleTensor) {
         checkShapesCompatible(this, other)
-        for (i in 0 until this.strides.linearSize) {
+        for (i in 0 until this.linearStructure.size) {
             this.buffer.array()[this.bufferStart + i] /=
                 other.buffer.array()[this.bufferStart + i]
         }
     }
 
     override fun DoubleTensor.unaryMinus(): DoubleTensor {
-        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
             this.buffer.array()[this.bufferStart + i].unaryMinus()
         }
         return DoubleTensor(this.shape, resBuffer)
@@ -191,7 +191,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun DoubleTensor.transpose(i: Int, j: Int): DoubleTensor {
         checkTranspose(this.dimension, i, j)
-        val n = this.strides.linearSize
+        val n = this.linearStructure.size
         val resBuffer = DoubleArray(n)
 
         val resShape = this.shape.copyOf()
@@ -200,11 +200,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         val resTensor = DoubleTensor(resShape, resBuffer)
 
         for (offset in 0 until n) {
-            val oldMultiIndex = this.strides.index(offset)
+            val oldMultiIndex = this.linearStructure.index(offset)
             val newMultiIndex = oldMultiIndex.copyOf()
             newMultiIndex[i] = newMultiIndex[j].also { newMultiIndex[j] = newMultiIndex[i] }
 
-            val linearIndex = resTensor.strides.offset(newMultiIndex)
+            val linearIndex = resTensor.linearStructure.offset(newMultiIndex)
             resTensor.buffer.array()[linearIndex] =
                 this.buffer.array()[this.bufferStart + offset]
         }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
similarity index 55%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
rename to kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
index d1d1204b4..97ce29657 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStrides.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
@@ -1,10 +1,14 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.nd.Strides
-import space.kscience.kmath.nd.offsetFromIndex
 import kotlin.math.max
 
 
+internal inline fun offsetFromIndex(index: IntArray, shape: IntArray, strides: IntArray): Int =
+    index.mapIndexed { i, value ->
+        if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${shape[i]})")
+        value * strides[i]
+    }.sum()
+
 internal inline fun stridesFromShape(shape: IntArray): IntArray {
     val nDim = shape.size
     val res = IntArray(nDim)
@@ -35,7 +39,7 @@ internal inline fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int):
     return res
 }
 
-internal inline fun nextIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
+internal inline fun stepIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
     val res = index.copyOf()
     var current = nDim - 1
     var carry = 0
@@ -47,26 +51,29 @@ internal inline fun nextIndex(index: IntArray, shape: IntArray, nDim: Int): IntA
             res[current] = 0
         }
         current--
-    } while(carry != 0 && current >= 0)
+    } while (carry != 0 && current >= 0)
 
     return res
 }
 
 
-
-public class TensorStrides(override val shape: IntArray): Strides
+public class TensorLinearStructure(public val shape: IntArray)
 {
-    override val strides: IntArray
+    public val strides: IntArray
         get() = stridesFromShape(shape)
 
-    override fun offset(index: IntArray): Int  = offsetFromIndex(index, shape, strides)
+    public fun offset(index: IntArray): Int = offsetFromIndex(index, shape, strides)
 
-    override fun index(offset: Int): IntArray =
+    public fun index(offset: Int): IntArray =
         indexFromOffset(offset, strides, shape.size)
 
-    override fun nextIndex(index: IntArray): IntArray =
-        nextIndex(index, shape, shape.size)
+    public fun stepIndex(index: IntArray): IntArray =
+        stepIndex(index, shape, shape.size)
 
-    override val linearSize: Int
+    public val size: Int
         get() = shape.reduce(Int::times)
+
+    public fun indices(): Sequence<IntArray> = (0 until size).asSequence().map {
+        index(it)
+    }
 }
\ No newline at end of file

From d8ef190ed8912476aaf7901431cc7e24d6449d94 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sun, 21 Mar 2021 08:07:17 +0000
Subject: [PATCH 048/178] UnaryPlus fails API check

---
 .../kotlin/space/kscience/kmath/nd/StructureND.kt    | 12 +-----------
 1 file changed, 1 insertion(+), 11 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
index f28d29e47..78eac1809 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
@@ -189,11 +189,6 @@ public interface Strides {
      */
     public fun index(offset: Int): IntArray
 
-    /**
-     * Get next multidimensional index from the current multidimensional index
-     */
-    public fun nextIndex(index: IntArray): IntArray
-
     /**
      * The size of linear buffer to accommodate all elements of ND-structure corresponding to strides
      */
@@ -232,7 +227,7 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
     }
 
     override fun offset(index: IntArray): Int = index.mapIndexed { i, value ->
-        if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${shape[i]})")
+        if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${this.shape[i]})")
         value * strides[i]
     }.sum()
 
@@ -250,10 +245,6 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
         return res
     }
 
-    override fun nextIndex(index: IntArray): IntArray {
-        TODO("Not yet implemented")
-    }
-
     override fun equals(other: Any?): Boolean {
         if (this === other) return true
         if (other !is DefaultStrides) return false
@@ -275,7 +266,6 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
     }
 }
 
-
 public inline fun <reified T : Any> StructureND<T>.combine(
     struct: StructureND<T>,
     crossinline block: (T, T) -> T,

From fa78ed1f45f3598e26d74d21d6f79c623c9e8e8e Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Sun, 21 Mar 2021 19:05:11 +0300
Subject: [PATCH 049/178] map and analytic funcions

---
 .../kmath/tensors/LinearOpsTensorAlgebra.kt   |  4 +-
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  5 ++
 .../kmath/tensors/core/BufferedTensor.kt      |  1 +
 .../core/DoubleAnalyticTensorAlgebra.kt       | 73 +++++--------------
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 63 +++++++++++-----
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 39 +++++++++-
 .../kscience/kmath/tensors/core/checks.kt     | 14 +++-
 7 files changed, 122 insertions(+), 77 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index 94176564c..c4fa3e0a8 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -14,10 +14,10 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, Inde
     public fun TensorType.qr(): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.lu.html
-    public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
+    public fun TensorType.lu(tol: T): Pair<TensorType, IndexTensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
-    public fun luPivot(lu: TensorType, pivots: IndexTensorType): Triple<TensorType, TensorType, TensorType>
+    public fun luPivot(luTensor: TensorType, pivotsTensor: IndexTensorType): Triple<TensorType, TensorType, TensorType>
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
     public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index e41b7546c..8be0dc149 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -3,6 +3,11 @@ package space.kscience.kmath.tensors
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
+    public fun TensorType.map(transform: (T) -> T): TensorType
+
+    public fun TensorType.eq(other: TensorType, eqFunction: (T, T) -> Boolean): Boolean
+    public fun TensorType.contentEquals(other: TensorType, eqFunction: (T, T) -> Boolean): Boolean
+
     //https://pytorch.org/docs/stable/generated/torch.full.html
     public fun full(value: T, shape: IntArray): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 5a0fc6c75..ca9168c91 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -5,6 +5,7 @@ import space.kscience.kmath.nd.*
 import space.kscience.kmath.structures.*
 import space.kscience.kmath.tensors.TensorStrides
 import space.kscience.kmath.tensors.TensorStructure
+import kotlin.math.atanh
 
 
 public open class BufferedTensor<T>(
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
index 8a579b7da..1eb4c3b63 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
@@ -7,77 +7,40 @@ public class DoubleAnalyticTensorAlgebra:
     AnalyticTensorAlgebra<Double, DoubleTensor>,
     DoubleOrderedTensorAlgebra()
 {
-    override fun DoubleTensor.exp(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.exp(): DoubleTensor = this.map(::exp)
 
-    override fun DoubleTensor.log(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    // todo log with other base????
+    override fun DoubleTensor.log(): DoubleTensor = this.map(::ln)
 
-    override fun DoubleTensor.sqrt(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.sqrt(): DoubleTensor = this.map(::sqrt)
 
-    override fun DoubleTensor.cos(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.cos(): DoubleTensor = this.map(::cos)
 
-    override fun DoubleTensor.acos(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.acos(): DoubleTensor = this.map(::acos)
 
-    override fun DoubleTensor.cosh(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.cosh(): DoubleTensor = this.map(::cosh)
 
-    override fun DoubleTensor.acosh(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.acosh(): DoubleTensor = this.map(::acosh)
 
-    override fun DoubleTensor.sin(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.sin(): DoubleTensor = this.map(::sin)
 
-    override fun DoubleTensor.asin(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.asin(): DoubleTensor = this.map(::asin)
 
-    override fun DoubleTensor.sinh(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.sinh(): DoubleTensor = this.map(::sinh)
 
-    override fun DoubleTensor.asinh(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.asinh(): DoubleTensor = this.map(::asinh)
 
-    override fun DoubleTensor.tan(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.tan(): DoubleTensor = this.map(::tan)
 
-    override fun DoubleTensor.atan(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.atan(): DoubleTensor = this.map(::atan)
 
-    override fun DoubleTensor.tanh(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.tanh(): DoubleTensor = this.map(::tanh)
 
-    override fun DoubleTensor.atanh(): DoubleTensor {
-        return DoubleTensor(
-            this.shape,
-            this.buffer.array().map(::atanh).toDoubleArray(),
-            this.bufferStart
-        )
-    }
+    override fun DoubleTensor.atanh(): DoubleTensor = this.map(::atanh)
 
-    override fun DoubleTensor.ceil(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.ceil(): DoubleTensor = this.map(::ceil)
 
-    override fun DoubleTensor.floor(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
+    override fun DoubleTensor.floor(): DoubleTensor = this.map(::floor)
 
     override fun DoubleTensor.clamp(min: Double, max: Double): DoubleTensor {
         TODO("Not yet implemented")
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 8a16dc3ed..e0abc49b7 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -1,6 +1,7 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
+import kotlin.math.sqrt
 
 public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
@@ -10,47 +11,47 @@ public class DoubleLinearOpsTensorAlgebra :
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
+    override fun DoubleTensor.lu(tol: Double): Pair<DoubleTensor, IntTensor> {
 
-        // todo checks
+        checkSquareMatrix(shape)
 
-        val luTensor = this.copy()
+        val luTensor = copy()
 
-        val n = this.shape.size
-        val m = this.shape.last()
-        val pivotsShape = IntArray(n - 1) { i -> this.shape[i] }
+        val n = shape.size
+        val m = shape.last()
+        val pivotsShape = IntArray(n - 1) { i -> shape[i] }
         val pivotsTensor = IntTensor(
             pivotsShape,
-            IntArray(pivotsShape.reduce(Int::times)) { 0 } //todo default???
+            IntArray(pivotsShape.reduce(Int::times)) { 0 }
         )
 
         for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
             for (row in 0 until m) pivots[row] = row
 
             for (i in 0 until m) {
-                var maxA = -1.0
-                var iMax = i
+                var maxVal = -1.0
+                var maxInd = i
 
                 for (k in i until m) {
                     val absA = kotlin.math.abs(lu[k, i])
-                    if (absA > maxA) {
-                        maxA = absA
-                        iMax = k
+                    if (absA > maxVal) {
+                        maxVal = absA
+                        maxInd = k
                     }
                 }
 
                 //todo check singularity
 
-                if (iMax != i) {
+                if (maxInd != i) {
 
                     val j = pivots[i]
-                    pivots[i] = pivots[iMax]
-                    pivots[iMax] = j
+                    pivots[i] = pivots[maxInd]
+                    pivots[maxInd] = j
 
                     for (k in 0 until m) {
                         val tmp = lu[i, k]
-                        lu[i, k] = lu[iMax, k]
-                        lu[iMax, k] = tmp
+                        lu[i, k] = lu[maxInd, k]
+                        lu[maxInd, k] = tmp
                     }
 
                 }
@@ -71,6 +72,9 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun luPivot(luTensor: DoubleTensor, pivotsTensor: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         //todo checks
+        checkSquareMatrix(luTensor.shape)
+        check(luTensor.shape.dropLast(1).toIntArray() contentEquals pivotsTensor.shape) { "Bed shapes (("} //todo rewrite
+
         val n = luTensor.shape.last()
         val pTensor = luTensor.zeroesLike()
         for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
@@ -104,7 +108,30 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     override fun DoubleTensor.cholesky(): DoubleTensor {
-        TODO("Not yet implemented")
+        // todo checks
+        checkSquareMatrix(shape)
+
+        val n = shape.last()
+        val lTensor = zeroesLike()
+
+        for ((a, l) in this.matrixSequence().zip(lTensor.matrixSequence())) {
+            for (i in 0 until n) {
+                for (j in 0 until i) {
+                    var h = a[i, j]
+                    for (k in 0 until j) {
+                        h -= l[i, k] * l[j, k]
+                    }
+                    l[i, j] = h / l[j, j]
+                }
+                var h = a[i, i]
+                for (j in 0 until i) {
+                    h -= l[i, j] * l[i, j]
+                }
+                l[i, i] = sqrt(h)
+            }
+        }
+
+        return lTensor
     }
 
     override fun DoubleTensor.qr(): DoubleTensor {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 875f21687..daa8b6d74 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -1,7 +1,7 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
-
+import kotlin.math.abs
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
 
@@ -277,6 +277,43 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
+    override fun DoubleTensor.map(transform: (Double) -> Double): DoubleTensor {
+        return DoubleTensor(
+            this.shape,
+            this.buffer.array().map { transform(it) }.toDoubleArray(),
+            this.bufferStart
+        )
+    }
+
+    public fun DoubleTensor.contentEquals(other: DoubleTensor, delta: Double = 1e-5): Boolean {
+        return this.contentEquals(other) { x, y -> abs(x - y) < delta }
+    }
+
+    public fun DoubleTensor.eq(other: DoubleTensor, delta: Double = 1e-5): Boolean {
+        return this.eq(other) { x, y -> abs(x - y) < delta }
+    }
+
+    override fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
+        if (!(this.shape contentEquals other.shape)){
+            return false
+        }
+        return this.eq(other, eqFunction)
+    }
+
+    override fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
+        // todo broadcasting checking
+        val n = this.strides.linearSize
+        if (n != other.strides.linearSize){
+            return false
+        }
+        for (i in 0 until n){
+            if (!eqFunction(this.buffer[this.bufferStart + i], other.buffer[other.bufferStart + i])) {
+                return false
+            }
+        }
+        return true
+    }
+
 }
 
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index f1ae89490..ec7a123a9 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -64,4 +64,16 @@ internal inline fun <T, TensorType : TensorStructure<T>,
 internal inline fun <T, TensorType : TensorStructure<T>,
         TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
         TorchTensorAlgebraType.checkView(a: TensorType, shape: IntArray): Unit =
-    check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
\ No newline at end of file
+    check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkSquareMatrix(shape: IntArray): Unit {
+    val n = shape.size
+    check(n >= 2) {
+        "Expected tensor with 2 or more dimensions, got size $n instead"
+    }
+    check(shape[n - 1] == shape[n - 2]) {
+        "Tensor must be batches of square matrices, but they are ${shape[n - 1]} by ${shape[n - 1]} matrices"
+    }
+}

From df402086daeb0b15c78a1a0d987cd46dd5122801 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Sun, 21 Mar 2021 19:08:30 +0300
Subject: [PATCH 050/178] init test for analytic algebra

---
 .../core/TestDoubleAnalyticTensorAlgebra.kt   | 35 +++++++++++++++++++
 .../tensors/core/TestDoubleTensorAlgebra.kt   |  4 +++
 2 files changed, 39 insertions(+)
 create mode 100644 kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt

diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
new file mode 100644
index 000000000..8028ce175
--- /dev/null
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -0,0 +1,35 @@
+package space.kscience.kmath.tensors.core
+
+import kotlin.math.abs
+import kotlin.math.exp
+import kotlin.test.Test
+import kotlin.test.assertEquals
+import kotlin.test.assertTrue
+
+class TestDoubleAnalyticTensorAlgebra {
+
+    val shape = intArrayOf(2, 1, 3, 2)
+    val buffer = doubleArrayOf(27.1, 20.0, 19.84, 23.123, 0.0, 1.0, 3.23, 133.7, 25.3, 100.3, 11.0, 12.012)
+    val tensor = DoubleTensor(shape, buffer)
+
+    fun DoubleArray.fmap(transform: (Double) -> Double): DoubleArray {
+        return this.map(transform).toDoubleArray()
+    }
+
+    fun DoubleArray.deltaEqual(other: DoubleArray, delta: Double = 1e-5): Boolean {
+        for ((elem1, elem2) in this.asSequence().zip(other.asSequence())) {
+            if (abs(elem1 - elem2) > delta) {
+                return false
+            }
+        }
+        return true
+    }
+
+    @Test
+    fun testExp() = DoubleAnalyticTensorAlgebra {
+        tensor.exp().let {
+            assertTrue { shape contentEquals it.shape }
+            assertTrue { buffer.fmap(::exp).deltaEqual(it.buffer.array())}
+        }
+    }
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index ddfba0d59..06aa3ebf7 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -79,4 +79,8 @@ class TestDoubleTensorAlgebra {
         assertTrue(expected.buffer.array() contentEquals assignResult.buffer.array())
     }
 
+    @Test
+    fun testContentEqual() = DoubleTensorAlgebra {
+        //TODO()
+    }
 }
\ No newline at end of file

From 0365d41f317af785f4963611f4c3899171618720 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sun, 21 Mar 2021 17:57:19 +0000
Subject: [PATCH 051/178] Merged PR

---
 kmath-core/api/kmath-core.api                 | 485 ++++++++++++++++++
 .../kscience/kmath/tensors/TensorAlgebra.kt   |   5 -
 .../kmath/tensors/core/DoubleTensorAlgebra.kt |  10 +-
 3 files changed, 490 insertions(+), 10 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 04325379e..70bec600d 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -972,6 +972,30 @@ public final class space/kscience/kmath/nd/GroupND$DefaultImpls {
 	public static fun unaryPlus (Lspace/kscience/kmath/nd/GroupND;Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/nd/StructureND;
 }
 
+public abstract interface class space/kscience/kmath/nd/MutableStructure1D : space/kscience/kmath/nd/MutableStructureND, space/kscience/kmath/nd/Structure1D, space/kscience/kmath/structures/MutableBuffer {
+	public abstract fun set ([ILjava/lang/Object;)V
+}
+
+public final class space/kscience/kmath/nd/MutableStructure1D$DefaultImpls {
+	public static fun get (Lspace/kscience/kmath/nd/MutableStructure1D;[I)Ljava/lang/Object;
+	public static fun getDimension (Lspace/kscience/kmath/nd/MutableStructure1D;)I
+	public static fun iterator (Lspace/kscience/kmath/nd/MutableStructure1D;)Ljava/util/Iterator;
+	public static fun set (Lspace/kscience/kmath/nd/MutableStructure1D;[ILjava/lang/Object;)V
+}
+
+public abstract interface class space/kscience/kmath/nd/MutableStructure2D : space/kscience/kmath/nd/MutableStructureND, space/kscience/kmath/nd/Structure2D {
+	public abstract fun set (IILjava/lang/Object;)V
+}
+
+public final class space/kscience/kmath/nd/MutableStructure2D$DefaultImpls {
+	public static fun elements (Lspace/kscience/kmath/nd/MutableStructure2D;)Lkotlin/sequences/Sequence;
+	public static fun get (Lspace/kscience/kmath/nd/MutableStructure2D;[I)Ljava/lang/Object;
+	public static fun getColumns (Lspace/kscience/kmath/nd/MutableStructure2D;)Ljava/util/List;
+	public static fun getDimension (Lspace/kscience/kmath/nd/MutableStructure2D;)I
+	public static fun getRows (Lspace/kscience/kmath/nd/MutableStructure2D;)Ljava/util/List;
+	public static fun getShape (Lspace/kscience/kmath/nd/MutableStructure2D;)[I
+}
+
 public abstract interface class space/kscience/kmath/nd/MutableStructureND : space/kscience/kmath/nd/StructureND {
 	public abstract fun set ([ILjava/lang/Object;)V
 }
@@ -1072,6 +1096,7 @@ public final class space/kscience/kmath/nd/Structure1D$DefaultImpls {
 }
 
 public final class space/kscience/kmath/nd/Structure1DKt {
+	public static final fun as1D (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructure1D;
 	public static final fun as1D (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/nd/Structure1D;
 	public static final fun asND (Lspace/kscience/kmath/structures/Buffer;)Lspace/kscience/kmath/nd/Structure1D;
 }
@@ -1101,6 +1126,7 @@ public final class space/kscience/kmath/nd/Structure2D$DefaultImpls {
 }
 
 public final class space/kscience/kmath/nd/Structure2DKt {
+	public static final fun as2D (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructure2D;
 	public static final fun as2D (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/nd/Structure2D;
 }
 
@@ -2582,3 +2608,462 @@ public final class space/kscience/kmath/structures/VirtualBuffer : space/kscienc
 	public fun iterator ()Ljava/util/Iterator;
 }
 
+public abstract interface class space/kscience/kmath/tensors/AnalyticTensorAlgebra : space/kscience/kmath/tensors/OrderedTensorAlgebra, space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
+	public abstract fun acos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun acosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun asin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun asinh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun atan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun atanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun ceil (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun clamp (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun cos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun cosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun erf (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun erfc (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun erfinv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun exp (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun floor (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun heaviside (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun histc (Lspace/kscience/kmath/nd/MutableStructureND;ILjava/lang/Object;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun igamma (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun igammac (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun lerp (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun lgamma (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun log (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun logit (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun mvlgamma (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun polygamma (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun pow (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun quantile (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun round (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun sigmoid (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun sin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun sinc (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun sinh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun sqrt (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun std (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun tan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun tanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun trapz (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+}
+
+public abstract interface class space/kscience/kmath/tensors/ComplexTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
+	public abstract fun angle (Lspace/kscience/kmath/tensors/ComplexTensorStructure;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun bartlettWindow (IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun blackmanWindow (IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun cartesianEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/tensors/ComplexTensorStructure;
+	public abstract fun hammingWindow (IZLjava/lang/Object;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun inverseShortTimeFourierTransform (Lspace/kscience/kmath/tensors/ComplexTensorStructure;IIILspace/kscience/kmath/nd/MutableStructureND;ZZZI)V
+	public abstract fun kaiserWindow (IZLjava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun polarEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/tensors/ComplexTensorStructure;
+	public abstract fun shortTimeFourierTransform (Lspace/kscience/kmath/tensors/ComplexTensorStructure;IIILspace/kscience/kmath/nd/MutableStructureND;ZZ)V
+	public abstract fun viewAsComplex (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/tensors/ComplexTensorStructure;
+}
+
+public abstract interface class space/kscience/kmath/tensors/ComplexTensorStructure : space/kscience/kmath/nd/MutableStructureND {
+	public abstract fun imaginaryPart ()Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun realPart ()Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun viewAsReal ()Lspace/kscience/kmath/nd/MutableStructureND;
+}
+
+public final class space/kscience/kmath/tensors/ComplexTensorStructure$DefaultImpls {
+	public static fun getDimension (Lspace/kscience/kmath/tensors/ComplexTensorStructure;)I
+}
+
+public abstract interface class space/kscience/kmath/tensors/LinearOpsTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
+	public abstract fun cholesky (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun inv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun lu (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lkotlin/Pair;
+	public abstract fun luPivot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
+	public abstract fun qr (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun svd (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
+	public abstract fun symEig (Lspace/kscience/kmath/nd/MutableStructureND;Z)Lkotlin/Pair;
+}
+
+public final class space/kscience/kmath/tensors/LinearOpsTensorAlgebra$DefaultImpls {
+	public static synthetic fun symEig$default (Lspace/kscience/kmath/tensors/LinearOpsTensorAlgebra;Lspace/kscience/kmath/nd/MutableStructureND;ZILjava/lang/Object;)Lkotlin/Pair;
+}
+
+public abstract interface class space/kscience/kmath/tensors/OrderedTensorAlgebra : space/kscience/kmath/tensors/TensorAlgebra {
+	public abstract fun cummax (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun cummin (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun max (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun maximum (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public abstract fun median (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun min (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun minimum (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public abstract fun sort (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
+}
+
+public abstract interface class space/kscience/kmath/tensors/ReduceOpsTensorAlgebra : space/kscience/kmath/tensors/TensorAlgebra {
+	public abstract fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
+}
+
+public abstract interface class space/kscience/kmath/tensors/TensorAlgebra {
+	public abstract fun abs (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun cat (Ljava/util/List;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun copy (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun cumprod (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun cumsum (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun det (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun dot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun flatten (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun full (Ljava/lang/Object;[I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun fullLike (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun get (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun minus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
+	public abstract fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public abstract fun ones ([I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun onesLike (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun plus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
+	public abstract fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public abstract fun prod (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun square (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun squeeze (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun sum (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun times (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun times (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun times (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
+	public abstract fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public abstract fun transpose (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun unaryMinus (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun view (Lspace/kscience/kmath/nd/MutableStructureND;[I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun viewAs (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun zeroesLike (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun zeros ([I)Lspace/kscience/kmath/nd/MutableStructureND;
+}
+
+public final class space/kscience/kmath/tensors/TensorAlgebra$DefaultImpls {
+	public static synthetic fun diagonalEmbedding$default (Lspace/kscience/kmath/tensors/TensorAlgebra;Lspace/kscience/kmath/nd/MutableStructureND;IIIILjava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+}
+
+public abstract interface class space/kscience/kmath/tensors/TensorPartialDivisionAlgebra : space/kscience/kmath/tensors/TensorAlgebra {
+	public abstract fun div (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun div (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
+	public abstract fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public abstract fun mean (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun variance (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
+}
+
+public final class space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra {
+	public fun <init> ()V
+	public synthetic fun div (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun div (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public fun divAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
+	public synthetic fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun minus (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public fun minusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
+	public synthetic fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun plus (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public fun plusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
+	public synthetic fun times (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun times (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public fun timesAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
+}
+
+public final class space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebraKt {
+	public static final fun BroadcastDoubleTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
+}
+
+public class space/kscience/kmath/tensors/core/BufferedTensor : space/kscience/kmath/nd/MutableStructureND {
+	public fun <init> ([ILspace/kscience/kmath/structures/MutableBuffer;I)V
+	public fun elements ()Lkotlin/sequences/Sequence;
+	public fun equals (Ljava/lang/Object;)Z
+	public final fun forEachMatrix (Lkotlin/jvm/functions/Function1;)V
+	public final fun forEachVector (Lkotlin/jvm/functions/Function1;)V
+	public fun get ([I)Ljava/lang/Object;
+	public final fun getBuffer ()Lspace/kscience/kmath/structures/MutableBuffer;
+	public fun getDimension ()I
+	public final fun getLinearStructure ()Lspace/kscience/kmath/tensors/core/TensorLinearStructure;
+	public final fun getNumel ()I
+	public fun getShape ()[I
+	public fun hashCode ()I
+	public final fun matrixSequence ()Lkotlin/sequences/Sequence;
+	public fun set ([ILjava/lang/Object;)V
+	public final fun vectorSequence ()Lkotlin/sequences/Sequence;
+}
+
+public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra : space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra, space/kscience/kmath/tensors/AnalyticTensorAlgebra {
+	public fun <init> ()V
+	public synthetic fun acos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun acos (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun acosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun acosh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun asin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun asin (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun asinh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun asinh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun atan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun atan (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun atanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun atanh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun ceil (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun ceil (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun clamp (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun clamp (Lspace/kscience/kmath/tensors/core/DoubleTensor;DD)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun cos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun cos (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun cosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun cosh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun erf (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun erf (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun erfc (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun erfc (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun erfinv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun erfinv (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun exp (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun exp (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun floor (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun floor (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun heaviside (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun heaviside (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun histc (Lspace/kscience/kmath/nd/MutableStructureND;ILjava/lang/Object;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun histc (Lspace/kscience/kmath/tensors/core/DoubleTensor;IDD)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun igamma (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun igamma (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun igammac (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun igammac (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun lerp (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun lerp (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun lgamma (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun lgamma (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun log (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun log (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun logit (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun logit (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun mvlgamma (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun mvlgamma (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun polygamma (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun polygamma (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun pow (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun pow (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun quantile (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun quantile (Lspace/kscience/kmath/tensors/core/DoubleTensor;DIZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun round (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun round (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun sigmoid (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun sigmoid (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun sin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun sin (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun sinc (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun sinc (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun sinh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun sinh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun sqrt (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun sqrt (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun std (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun std (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun tan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun tan (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun tanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun tanh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun trapz (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun trapz (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+}
+
+public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebraKt {
+	public static final fun DoubleAnalyticTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
+}
+
+public final class space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/LinearOpsTensorAlgebra {
+	public fun <init> ()V
+	public synthetic fun cholesky (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun cholesky (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun inv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun inv (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun lu (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lkotlin/Pair;
+	public fun lu (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lkotlin/Pair;
+	public synthetic fun luPivot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
+	public fun luPivot (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/IntTensor;)Lkotlin/Triple;
+	public synthetic fun qr (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun qr (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun svd (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
+	public fun svd (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lkotlin/Triple;
+	public synthetic fun symEig (Lspace/kscience/kmath/nd/MutableStructureND;Z)Lkotlin/Pair;
+	public fun symEig (Lspace/kscience/kmath/tensors/core/DoubleTensor;Z)Lkotlin/Pair;
+}
+
+public final class space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebraKt {
+	public static final fun DoubleLinearOpsTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
+}
+
+public class space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/OrderedTensorAlgebra {
+	public fun <init> ()V
+	public synthetic fun cummax (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun cummax (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun cummin (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun cummin (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun max (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun max (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun maximum (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public fun maximum (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
+	public synthetic fun median (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun median (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun min (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun min (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun minimum (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public fun minimum (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
+	public synthetic fun sort (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun sort (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+}
+
+public final class space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebraKt {
+	public static final fun DoubleOrderedTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
+}
+
+public final class space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/ReduceOpsTensorAlgebra {
+	public fun <init> ()V
+	public synthetic fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
+	public fun value (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Ljava/lang/Double;
+}
+
+public final class space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebraKt {
+	public static final fun DoubleReduceOpsTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
+}
+
+public final class space/kscience/kmath/tensors/core/DoubleTensor : space/kscience/kmath/tensors/core/BufferedTensor {
+}
+
+public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
+	public fun <init> ()V
+	public synthetic fun abs (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun abs (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun cat (Ljava/util/List;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun cat (Ljava/util/List;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun contentEquals (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
+	public final fun contentEquals (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function2;)Z
+	public static synthetic fun contentEquals$default (Lspace/kscience/kmath/tensors/core/DoubleTensorAlgebra;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;DILjava/lang/Object;)Z
+	public synthetic fun copy (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun copy (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun cumprod (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun cumprod (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun cumsum (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun cumsum (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun det (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun det (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun diagonalEmbedding (Lspace/kscience/kmath/tensors/core/DoubleTensor;III)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun div (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public synthetic fun div (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun div (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public fun div (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
+	public synthetic fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public fun divAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)V
+	public fun divAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
+	public synthetic fun dot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun dot (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
+	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function2;)Z
+	public static synthetic fun eq$default (Lspace/kscience/kmath/tensors/core/DoubleTensorAlgebra;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;DILjava/lang/Object;)Z
+	public synthetic fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun eye (I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun flatten (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun flatten (Lspace/kscience/kmath/tensors/core/DoubleTensor;II)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun fromArray ([I[D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public fun full (D[I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun full (Ljava/lang/Object;[I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public synthetic fun fullLike (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun fullLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun get (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun get (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun map (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function1;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun mean (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun mean (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public fun minus (DLspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun minus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public synthetic fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public synthetic fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun minus (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public fun minus (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
+	public synthetic fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public fun minusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)V
+	public fun minusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
+	public synthetic fun ones ([I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun ones ([I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun onesLike (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun onesLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public fun plus (DLspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun plus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public synthetic fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public synthetic fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun plus (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public fun plus (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
+	public synthetic fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public fun plusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)V
+	public fun plusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
+	public synthetic fun prod (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun prod (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun square (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun square (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun squeeze (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun squeeze (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun sum (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun sum (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public fun times (DLspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun times (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public synthetic fun times (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public synthetic fun times (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun times (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public fun times (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
+	public synthetic fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
+	public fun timesAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)V
+	public fun timesAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
+	public synthetic fun transpose (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun transpose (Lspace/kscience/kmath/tensors/core/DoubleTensor;II)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun unaryMinus (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun unaryMinus (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun variance (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun variance (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun view (Lspace/kscience/kmath/nd/MutableStructureND;[I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun view (Lspace/kscience/kmath/tensors/core/DoubleTensor;[I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun viewAs (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun viewAs (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun zeroesLike (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun zeroesLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun zeros ([I)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun zeros ([I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+}
+
+public final class space/kscience/kmath/tensors/core/DoubleTensorAlgebraKt {
+	public static final fun DoubleTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
+}
+
+public final class space/kscience/kmath/tensors/core/FloatTensor : space/kscience/kmath/tensors/core/BufferedTensor {
+}
+
+public final class space/kscience/kmath/tensors/core/IntTensor : space/kscience/kmath/tensors/core/BufferedTensor {
+}
+
+public final class space/kscience/kmath/tensors/core/LongTensor : space/kscience/kmath/tensors/core/BufferedTensor {
+}
+
+public final class space/kscience/kmath/tensors/core/TensorLinearStructure {
+	public fun <init> ([I)V
+	public final fun getShape ()[I
+	public final fun getSize ()I
+	public final fun getStrides ()[I
+	public final fun index (I)[I
+	public final fun indices ()Lkotlin/sequences/Sequence;
+	public final fun offset ([I)I
+	public final fun stepIndex ([I)[I
+}
+
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 8be0dc149..e41b7546c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -3,11 +3,6 @@ package space.kscience.kmath.tensors
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
-    public fun TensorType.map(transform: (T) -> T): TensorType
-
-    public fun TensorType.eq(other: TensorType, eqFunction: (T, T) -> Boolean): Boolean
-    public fun TensorType.contentEquals(other: TensorType, eqFunction: (T, T) -> Boolean): Boolean
-
     //https://pytorch.org/docs/stable/generated/torch.full.html
     public fun full(value: T, shape: IntArray): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 57339adfa..67b50970b 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -277,7 +277,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.map(transform: (Double) -> Double): DoubleTensor {
+    public fun DoubleTensor.map(transform: (Double) -> Double): DoubleTensor {
         return DoubleTensor(
             this.shape,
             this.buffer.array().map { transform(it) }.toDoubleArray(),
@@ -293,17 +293,17 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return this.eq(other) { x, y -> abs(x - y) < delta }
     }
 
-    override fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
+    public fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
         if (!(this.shape contentEquals other.shape)){
             return false
         }
         return this.eq(other, eqFunction)
     }
 
-    override fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
+    public fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
         // todo broadcasting checking
-        val n = this.strides.linearSize
-        if (n != other.strides.linearSize){
+        val n = this.linearStructure.size
+        if (n != other.linearStructure.size){
             return false
         }
         for (i in 0 until n){

From 2d2c4bd4744af9f3d841571d213170b1d661fa8f Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Tue, 23 Mar 2021 14:53:54 +0300
Subject: [PATCH 052/178] add broadcast of all dims except the last 2, add
 tensors dot, fix bug in function times

---
 .../core/BroadcastDoubleTensorAlgebra.kt      | 59 +++++++++++++-
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 80 ++++++++++++++++++-
 .../kmath/tensors/core/TestBroadcasting.kt    | 30 +++++++
 .../tensors/core/TestDoubleTensorAlgebra.kt   | 14 ++++
 4 files changed, 180 insertions(+), 3 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
index 4bfbe5863..425178fc1 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
@@ -85,7 +85,6 @@ public inline fun <R> BroadcastDoubleTensorAlgebra(block: BroadcastDoubleTensorA
 
 
 internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
-    println(shapes)
     var totalDim = 0
     for (shape in shapes) {
         totalDim = max(totalDim, shape.size)
@@ -179,5 +178,63 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
         res.add(resTensor)
     }
 
+    return res
+}
+
+internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
+    var onlyTwoDims = true
+    for (tensor in tensors) {
+        if (tensor.shape.size < 2) {
+            throw RuntimeException("Tensors must have at least 2 dimensions")
+        }
+        if (tensor.shape.size != 2) {
+            onlyTwoDims = false
+        }
+    }
+
+    if (onlyTwoDims) {
+        return tensors.asList()
+    }
+
+    val totalShape = broadcastShapes(*(tensors.map { it.shape.sliceArray(0..it.shape.size - 3) }).toTypedArray())
+    val n = totalShape.reduce { acc, i -> acc * i }
+
+    val res = ArrayList<DoubleTensor>(0)
+    for (tensor in tensors) {
+        val matrixShape = tensor.shape.sliceArray(tensor.shape.size - 2 until tensor.shape.size).copyOf()
+        val matrixSize = matrixShape[0] * matrixShape[1]
+        val matrix = DoubleTensor(matrixShape, DoubleArray(matrixSize))
+
+        val outerTensor = DoubleTensor(totalShape, DoubleArray(n))
+        val resTensor = DoubleTensor(totalShape + matrixShape, DoubleArray(n * matrixSize))
+
+        for (linearIndex in 0 until n) {
+            val totalMultiIndex = outerTensor.linearStructure.index(linearIndex)
+            var curMultiIndex = tensor.shape.sliceArray(0..tensor.shape.size - 3).copyOf()
+            curMultiIndex = IntArray(totalMultiIndex.size - curMultiIndex.size) {1} + curMultiIndex
+
+            val newTensor = DoubleTensor(curMultiIndex + matrixShape, tensor.buffer.array())
+
+            for (i in curMultiIndex.indices) {
+                if (curMultiIndex[i] != 1) {
+                    curMultiIndex[i] = totalMultiIndex[i]
+                } else {
+                    curMultiIndex[i] = 0
+                }
+            }
+
+            for (i in 0 until matrixSize) {
+                val curLinearIndex = newTensor.linearStructure.offset(curMultiIndex +
+                        matrix.linearStructure.index(i))
+                val newLinearIndex = resTensor.linearStructure.offset(totalMultiIndex +
+                        matrix.linearStructure.index(i))
+
+                resTensor.buffer.array()[resTensor.bufferStart + newLinearIndex] =
+                    newTensor.buffer.array()[newTensor.bufferStart + curLinearIndex]
+            }
+        }
+        res.add(resTensor)
+    }
+
     return res
 }
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 67b50970b..9cdc6c130 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -132,7 +132,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
         checkShapesCompatible(this, other)
         val resBuffer = DoubleArray(this.linearStructure.size) { i ->
-            this.buffer.array()[other.bufferStart + i] *
+            this.buffer.array()[this.bufferStart + i] *
                     other.buffer.array()[other.bufferStart + i]
         }
         return DoubleTensor(this.shape, resBuffer)
@@ -241,8 +241,84 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
+    private fun DoubleTensor.dotTwoDimensionalTensors(other: DoubleTensor): DoubleTensor {
+        if (this.shape.size > 2 || other.shape.size > 2) {
+            throw RuntimeException("Both tensors must have a maximum of 2 dimensions")
+        }
+
+        if (this.shape[1] != other.shape[0]) {
+            throw RuntimeException("Tensors dot operation dimension mismatch: " +
+                    "(${this.shape[0]}, ${this.shape[1]}) x (${other.shape[0]}, ${other.shape[1]})")
+        }
+
+        val l = this.shape[0]
+        val m = this.shape[1]
+        val n = other.shape[1]
+
+        val res = DoubleTensor(intArrayOf(l, n), DoubleArray(l * n))
+
+        for (i in 0 until l) {
+            for (j in 0 until n) {
+                var curr = 0.0
+                for (k in 0 until m) {
+                    val ik = this.linearStructure.offset(intArrayOf(i, k))
+                    val kj = other.linearStructure.offset(intArrayOf(k, j))
+                    curr += this.buffer.array()[ik] * other.buffer.array()[kj]
+                }
+                val linearIndex = res.linearStructure.offset(intArrayOf(i, j))
+                res.buffer.array()[linearIndex] = curr
+            }
+        }
+        return res
+    }
+
     override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
-        TODO("Alya")
+        if (this.shape.size == 1 && other.shape.size == 1) {
+            return DoubleTensor(intArrayOf(1), doubleArrayOf(this.times(other).buffer.array().sum()))
+        }
+
+        var newThis = this.copy()
+        var newOther = other.copy()
+        if (this.shape.size == 1) {
+            newThis = this.view(intArrayOf(1) + this.shape)
+        }
+        if (other.shape.size == 1) {
+            newOther = other.view(other.shape + intArrayOf(1) )
+        }
+
+        val broadcastTensors = broadcastOuterTensors(newThis, newOther)
+        newThis = broadcastTensors[0]
+        newOther = broadcastTensors[1]
+
+        val l = newThis.shape[newThis.shape.size - 2]
+        val m1= newThis.shape[newThis.shape.size - 1]
+        val m2 = newOther.shape[newOther.shape.size - 2]
+        val n = newOther.shape[newOther.shape.size - 1]
+        if (m1 != m2) {
+            throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
+        }
+        val m = m1
+
+        var resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
+        val resSize = resShape.reduce { acc, i -> acc * i }
+        val resTensor = DoubleTensor(resShape, DoubleArray(resSize))
+
+        for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
+            val a = ab.first
+            val b = ab.second
+
+            for (i in 0 until l) {
+                for (j in 0 until n) {
+                    var curr = 0.0
+                    for (k in 0 until m) {
+                        curr += a[i, k] * b[k, j]
+                    }
+                   res[i, j] = curr
+                }
+            }
+        }
+
+        return resTensor
     }
 
     override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
index 73e3993a1..41c9b72f7 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -47,6 +47,36 @@ class TestBroadcasting {
         assertTrue(res[2].buffer.array() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
     }
 
+    @Test
+    fun broadcastOuterTensors() = DoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
+
+        val res = broadcastOuterTensors(tensor1, tensor2, tensor3)
+
+        assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3))
+        assertTrue(res[1].shape contentEquals intArrayOf(1, 1, 3))
+        assertTrue(res[2].shape contentEquals intArrayOf(1, 1, 1))
+
+        assertTrue(res[0].buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res[1].buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0))
+        assertTrue(res[2].buffer.array() contentEquals doubleArrayOf(500.0))
+    }
+
+    @Test
+    fun broadcastOuterTensorsShapes() = DoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(2, 1, 3, 2, 3), DoubleArray(2 * 1 * 3 * 2 * 3) {0.0})
+        val tensor2 = fromArray(intArrayOf(4, 2, 5, 1, 3, 3), DoubleArray(4 * 2 * 5 * 1 * 3 * 3) {0.0})
+        val tensor3 = fromArray(intArrayOf(1, 1), doubleArrayOf(500.0))
+
+        val res = broadcastOuterTensors(tensor1, tensor2, tensor3)
+
+        assertTrue(res[0].shape contentEquals intArrayOf(4, 2, 5, 3, 2, 3))
+        assertTrue(res[1].shape contentEquals intArrayOf(4, 2, 5, 3, 3, 3))
+        assertTrue(res[2].shape contentEquals intArrayOf(4, 2, 5, 3, 1, 1))
+    }
+
     @Test
     fun minusTensor() = BroadcastDoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index 06aa3ebf7..aa3c14412 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -79,6 +79,20 @@ class TestDoubleTensorAlgebra {
         assertTrue(expected.buffer.array() contentEquals assignResult.buffer.array())
     }
 
+    @Test
+    fun dot() = DoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
+        val res12 = tensor1.dot(tensor2)
+
+        assertTrue(res12.buffer.array() contentEquals doubleArrayOf(140.0, 320.0))
+        assertTrue(res12.shape contentEquals intArrayOf(2, 1))
+
+        val tensor4 = fromArray(intArrayOf(10, 3, 4), DoubleArray(10 * 3 * 4) {0.0})
+        val tensor5 = fromArray(intArrayOf(10, 4, 5), DoubleArray(10 * 4 * 5) {0.0})
+        assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(10, 3, 5))
+    }
+
     @Test
     fun testContentEqual() = DoubleTensorAlgebra {
         //TODO()

From 078686a04660e78b9929d2ed9a3c61f3fa888e62 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Tue, 23 Mar 2021 15:59:55 +0300
Subject: [PATCH 053/178] hotfix sequence

---
 .../space/kscience/kmath/tensors/core/BufferedTensor.kt   | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 4f5e1f9f0..160c61260 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -34,7 +34,8 @@ public open class BufferedTensor<T>(
 
     public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
         check(shape.size >= 1) {"todo"}
-        val vectorOffset = linearStructure.strides[0]
+        val n = shape.size
+        val vectorOffset = shape[n - 1]
         val vectorShape = intArrayOf(shape.last())
         for (offset in 0 until numel step vectorOffset) {
             val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
@@ -44,8 +45,9 @@ public open class BufferedTensor<T>(
 
     public fun matrixSequence(): Sequence<MutableStructure2D<T>> = sequence {
         check(shape.size >= 2) {"todo"}
-        val matrixOffset = linearStructure.strides[1]
-        val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
+        val n = shape.size
+        val matrixOffset = shape[n - 1] * shape[n - 2]
+        val matrixShape = intArrayOf(shape[n - 2], shape[n - 1]) //todo better way?
         for (offset in 0 until numel step matrixOffset) {
             val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
             yield(matrix)

From e01ca38fb3a7b05037582f561298928be552100e Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 23 Mar 2021 13:51:52 +0000
Subject: [PATCH 054/178] Tasks for Andrei and Alya

---
 .../kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt    | 8 ++++----
 .../kscience/kmath/tensors/core/DoubleTensorAlgebra.kt    | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index e0abc49b7..8a16a991d 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -8,7 +8,7 @@ public class DoubleLinearOpsTensorAlgebra :
     DoubleTensorAlgebra() {
 
     override fun DoubleTensor.inv(): DoubleTensor {
-        TODO("Not yet implemented")
+        TODO("ANDREI")
     }
 
     override fun DoubleTensor.lu(tol: Double): Pair<DoubleTensor, IntTensor> {
@@ -135,16 +135,16 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     override fun DoubleTensor.qr(): DoubleTensor {
-        TODO("Not yet implemented")
+        TODO("ANDREI")
     }
 
 
     override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        TODO("Not yet implemented")
+        TODO("ALYA")
     }
 
     override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
-        TODO("Not yet implemented")
+        TODO("ANDREI")
     }
 
 }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 67b50970b..c67687a09 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -262,7 +262,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.det(): DoubleTensor {
-        TODO("Not yet implemented")
+        TODO("ANDREI")
     }
 
     override fun DoubleTensor.square(): DoubleTensor {

From fea53af0eeaa3522a5754bfe83688f010a37a5f4 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Wed, 24 Mar 2021 14:00:47 +0300
Subject: [PATCH 055/178] fix

---
 .../space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt     | 2 +-
 .../kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt         | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index c4fa3e0a8..e412af7c6 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -14,7 +14,7 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, Inde
     public fun TensorType.qr(): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.lu.html
-    public fun TensorType.lu(tol: T): Pair<TensorType, IndexTensorType>
+    public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
     public fun luPivot(luTensor: TensorType, pivotsTensor: IndexTensorType): Triple<TensorType, TensorType, TensorType>
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index e0abc49b7..d090ce79c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -11,7 +11,7 @@ public class DoubleLinearOpsTensorAlgebra :
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.lu(tol: Double): Pair<DoubleTensor, IntTensor> {
+    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
 
         checkSquareMatrix(shape)
 
@@ -138,7 +138,6 @@ public class DoubleLinearOpsTensorAlgebra :
         TODO("Not yet implemented")
     }
 
-
     override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         TODO("Not yet implemented")
     }

From 206bcfc909d56a859ec783a30cc75a8ade178786 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Wed, 24 Mar 2021 18:08:36 +0300
Subject: [PATCH 056/178] lu inv and det complete + tests

---
 .../kmath/tensors/LinearOpsTensorAlgebra.kt   |  2 +-
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 79 +++++++++++++++++++
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 35 +++++++-
 .../core/TestDoubleAnalyticTensorAlgebra.kt   |  7 +-
 .../core/TestDoubleLinearOpsAlgebra.kt        | 73 +++++++++++++++++
 5 files changed, 187 insertions(+), 9 deletions(-)
 create mode 100644 kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index e412af7c6..f551d524a 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -13,7 +13,7 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, Inde
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
     public fun TensorType.qr(): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.lu.html
+    //htt   ps://pytorch.org/docs/stable/generated/torch.lu.html
     public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 7779ad029..c8c4c413f 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -1,5 +1,8 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.nd.MutableStructure2D
+import space.kscience.kmath.nd.Structure1D
+import space.kscience.kmath.nd.Structure2D
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 import kotlin.math.sqrt
 
@@ -20,6 +23,8 @@ public class DoubleLinearOpsTensorAlgebra :
         val n = shape.size
         val m = shape.last()
         val pivotsShape = IntArray(n - 1) { i -> shape[i] }
+        pivotsShape[n - 2] = m + 1
+
         val pivotsTensor = IntTensor(
             pivotsShape,
             IntArray(pivotsShape.reduce(Int::times)) { 0 }
@@ -54,6 +59,8 @@ public class DoubleLinearOpsTensorAlgebra :
                         lu[maxInd, k] = tmp
                     }
 
+                    pivots[m] += 1
+
                 }
 
                 for (j in i + 1 until m) {
@@ -146,6 +153,78 @@ public class DoubleLinearOpsTensorAlgebra :
         TODO("ANDREI")
     }
 
+    private fun luMatrixDet(lu: Structure2D<Double>, pivots: Structure1D<Int>): Double {
+        val m = lu.shape[0]
+        val sign = if((pivots[m] - m) % 2 == 0) 1.0 else -1.0
+        var det = sign
+        for (i in 0 until m){
+            det *= lu[i, i]
+        }
+        return det
+    }
+
+    public fun DoubleTensor.detLU(): DoubleTensor {
+        val (luTensor, pivotsTensor) = this.lu()
+        val n = shape.size
+
+        val detTensorShape = IntArray(n - 1) { i -> shape[i] }
+        detTensorShape[n - 2] = 1
+        val resBuffer =  DoubleArray(detTensorShape.reduce(Int::times)) { 0.0 }
+
+        val detTensor = DoubleTensor(
+            detTensorShape,
+            resBuffer
+        )
+
+        luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).forEachIndexed { index, (luMatrix, pivots) ->
+            resBuffer[index] = luMatrixDet(luMatrix, pivots)
+        }
+
+        return detTensor
+    }
+
+    private fun luMatrixInv(
+        lu: Structure2D<Double>,
+        pivots: Structure1D<Int>,
+        invMatrix : MutableStructure2D<Double>
+    ): Unit {
+        val m = lu.shape[0]
+
+        for (j in 0 until m) {
+            for (i in 0 until m) {
+                if (pivots[i] == j){
+                    invMatrix[i, j] = 1.0
+                }
+
+                for (k in 0 until i){
+                    invMatrix[i, j] -= lu[i, k] * invMatrix[k, j]
+                }
+            }
+
+            for (i in m - 1 downTo 0) {
+                for (k in i + 1 until m) {
+                    invMatrix[i, j] -= lu[i, k] * invMatrix[k, j]
+                }
+                invMatrix[i, j] /= lu[i, i]
+            }
+        }
+    }
+
+    public fun DoubleTensor.invLU(): DoubleTensor {
+        val (luTensor, pivotsTensor) = this.lu()
+        val n = shape.size
+        val invTensor = luTensor.zeroesLike()
+
+        for (
+        (luP, invMatrix) in
+        luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
+        ) {
+            val (lu, pivots) = luP
+            luMatrixInv(lu, pivots, invMatrix)
+        }
+
+        return invTensor
+    }
 }
 
 public inline fun <R> DoubleLinearOpsTensorAlgebra(block: DoubleLinearOpsTensorAlgebra.() -> R): R =
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index c67687a09..ff65ca027 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -1,5 +1,8 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.linear.Matrix
+import space.kscience.kmath.nd.MutableStructure2D
+import space.kscience.kmath.nd.Structure2D
 import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
 import kotlin.math.abs
 
@@ -262,7 +265,31 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.det(): DoubleTensor {
-        TODO("ANDREI")
+        TODO()
+        /*
+        checkSquareMatrix(shape)
+
+        val n = shape.size
+        val m = shape.last()
+
+        val detTensorShape = IntArray(n - 1) { i -> shape[i] }
+        detTensorShape[n - 1] = 1
+        val resBuffer =  DoubleArray(detTensorShape.reduce(Int::times)) { 0.0 }
+
+        val detTensor = DoubleTensor(
+            detTensorShape,
+            resBuffer
+        )
+
+        this.matrixSequence().forEachIndexed{i, matrix ->
+            // todo need Matrix determinant algo
+            // todo resBuffer[i] = matrix.det()
+        }
+
+
+        return detTensor
+
+         */
     }
 
     override fun DoubleTensor.square(): DoubleTensor {
@@ -294,7 +321,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     public fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
-        if (!(this.shape contentEquals other.shape)){
+        if (!(this.shape contentEquals other.shape)) {
             return false
         }
         return this.eq(other, eqFunction)
@@ -303,10 +330,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     public fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
         // todo broadcasting checking
         val n = this.linearStructure.size
-        if (n != other.linearStructure.size){
+        if (n != other.linearStructure.size) {
             return false
         }
-        for (i in 0 until n){
+        for (i in 0 until n) {
             if (!eqFunction(this.buffer[this.bufferStart + i], other.buffer[other.bufferStart + i])) {
                 return false
             }
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
index 8028ce175..4dcdb7848 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -3,7 +3,6 @@ package space.kscience.kmath.tensors.core
 import kotlin.math.abs
 import kotlin.math.exp
 import kotlin.test.Test
-import kotlin.test.assertEquals
 import kotlin.test.assertTrue
 
 class TestDoubleAnalyticTensorAlgebra {
@@ -16,9 +15,9 @@ class TestDoubleAnalyticTensorAlgebra {
         return this.map(transform).toDoubleArray()
     }
 
-    fun DoubleArray.deltaEqual(other: DoubleArray, delta: Double = 1e-5): Boolean {
+    fun DoubleArray.epsEqual(other: DoubleArray, eps: Double = 1e-5): Boolean {
         for ((elem1, elem2) in this.asSequence().zip(other.asSequence())) {
-            if (abs(elem1 - elem2) > delta) {
+            if (abs(elem1 - elem2) > eps) {
                 return false
             }
         }
@@ -29,7 +28,7 @@ class TestDoubleAnalyticTensorAlgebra {
     fun testExp() = DoubleAnalyticTensorAlgebra {
         tensor.exp().let {
             assertTrue { shape contentEquals it.shape }
-            assertTrue { buffer.fmap(::exp).deltaEqual(it.buffer.array())}
+            assertTrue { buffer.fmap(::exp).epsEqual(it.buffer.array())}
         }
     }
 }
\ No newline at end of file
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
new file mode 100644
index 000000000..2da79382f
--- /dev/null
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -0,0 +1,73 @@
+package space.kscience.kmath.tensors.core
+
+import kotlin.math.abs
+import kotlin.math.exp
+import kotlin.test.Test
+import kotlin.test.assertEquals
+import kotlin.test.assertTrue
+
+class TestDoubleLinearOpsTensorAlgebra {
+
+    private val eps = 1e-5
+
+    private fun Double.epsEqual(other: Double): Boolean {
+        return abs(this - other) < eps
+    }
+
+    fun DoubleArray.epsEqual(other: DoubleArray, eps: Double = 1e-5): Boolean {
+        for ((elem1, elem2) in this.asSequence().zip(other.asSequence())) {
+            if (abs(elem1 - elem2) > eps) {
+                return false
+            }
+        }
+        return true
+    }
+
+    @Test
+    fun testDetLU() = DoubleLinearOpsTensorAlgebra {
+        val tensor = fromArray(
+            intArrayOf(2, 2, 2),
+            doubleArrayOf(
+                1.0, 3.0,
+                1.0, 2.0,
+                1.5, 1.0,
+                10.0, 2.0
+            )
+        )
+
+        val expectedShape = intArrayOf(2, 1)
+        val expectedBuffer = doubleArrayOf(
+            -1.0,
+            -7.0
+        )
+        val detTensor = tensor.detLU()
+
+        assertTrue { detTensor.shape contentEquals expectedShape }
+        assertTrue { detTensor.buffer.array().epsEqual(expectedBuffer) }
+    }
+
+    @Test
+    fun testInvLU() = DoubleLinearOpsTensorAlgebra {
+        val tensor = fromArray(
+            intArrayOf(2, 2, 2),
+            doubleArrayOf(
+                1.0, 0.0,
+                0.0, 2.0,
+                1.0, 1.0,
+                1.0, 0.0
+            )
+        )
+
+        val expectedShape = intArrayOf(2, 2, 2)
+        val expectedBuffer = doubleArrayOf(
+            1.0, 0.0,
+            0.0, 0.5,
+            0.0, 1.0,
+            1.0, -1.0
+        )
+
+        val invTensor = tensor.invLU()
+        assertTrue { invTensor.shape contentEquals expectedShape }
+        assertTrue { invTensor.buffer.array().epsEqual(expectedBuffer) }
+    }
+}

From ab8137000146c52f10b3213c960b03917a0ada4f Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Wed, 24 Mar 2021 18:42:41 +0300
Subject: [PATCH 057/178] fixes

---
 .../kmath/tensors/LinearOpsTensorAlgebra.kt   |  2 +-
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 22 +++++++------------
 2 files changed, 9 insertions(+), 15 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index f551d524a..e412af7c6 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -13,7 +13,7 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, Inde
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
     public fun TensorType.qr(): TensorType
 
-    //htt   ps://pytorch.org/docs/stable/generated/torch.lu.html
+    //https://pytorch.org/docs/stable/generated/torch.lu.html
     public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index c8c4c413f..0b9d824f0 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -10,9 +10,9 @@ public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
     DoubleTensorAlgebra() {
 
-    override fun DoubleTensor.inv(): DoubleTensor {
-        TODO("ANDREI")
-    }
+    override fun DoubleTensor.inv(): DoubleTensor = invLU()
+
+    override fun DoubleTensor.det(): DoubleTensor = detLU()
 
     override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
 
@@ -156,15 +156,11 @@ public class DoubleLinearOpsTensorAlgebra :
     private fun luMatrixDet(lu: Structure2D<Double>, pivots: Structure1D<Int>): Double {
         val m = lu.shape[0]
         val sign = if((pivots[m] - m) % 2 == 0) 1.0 else -1.0
-        var det = sign
-        for (i in 0 until m){
-            det *= lu[i, i]
-        }
-        return det
+        return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
     }
 
     public fun DoubleTensor.detLU(): DoubleTensor {
-        val (luTensor, pivotsTensor) = this.lu()
+        val (luTensor, pivotsTensor) = lu()
         val n = shape.size
 
         val detTensorShape = IntArray(n - 1) { i -> shape[i] }
@@ -211,14 +207,12 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     public fun DoubleTensor.invLU(): DoubleTensor {
-        val (luTensor, pivotsTensor) = this.lu()
+        val (luTensor, pivotsTensor) = lu()
         val n = shape.size
         val invTensor = luTensor.zeroesLike()
 
-        for (
-        (luP, invMatrix) in
-        luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
-        ) {
+        val seq = luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
+        for ((luP, invMatrix) in seq) {
             val (lu, pivots) = luP
             luMatrixInv(lu, pivots, invMatrix)
         }

From a4aa4b80d27cecef092a1a411163e24ff70d08ec Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 24 Mar 2021 15:51:58 +0000
Subject: [PATCH 058/178] Updated API

---
 kmath-core/api/kmath-core.api | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 70bec600d..295df69e3 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -2674,7 +2674,7 @@ public final class space/kscience/kmath/tensors/ComplexTensorStructure$DefaultIm
 public abstract interface class space/kscience/kmath/tensors/LinearOpsTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
 	public abstract fun cholesky (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun inv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun lu (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lkotlin/Pair;
+	public abstract fun lu (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Pair;
 	public abstract fun luPivot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
 	public abstract fun qr (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun svd (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
@@ -2884,10 +2884,14 @@ public final class space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebr
 	public fun <init> ()V
 	public synthetic fun cholesky (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun cholesky (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun det (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public fun det (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun detLU (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun inv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun inv (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun lu (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lkotlin/Pair;
-	public fun lu (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lkotlin/Pair;
+	public final fun invLU (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun lu (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Pair;
+	public fun lu (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lkotlin/Pair;
 	public synthetic fun luPivot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
 	public fun luPivot (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/IntTensor;)Lkotlin/Triple;
 	public synthetic fun qr (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;

From f70f60c0e8d5a3c830bc1ce577a63fa5ddfad290 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 24 Mar 2021 15:58:25 +0000
Subject: [PATCH 059/178] Moved det to LinearOps

---
 kmath-core/api/kmath-core.api                 |  4 +--
 .../kmath/tensors/LinearOpsTensorAlgebra.kt   |  3 ++
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  3 --
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 28 -------------------
 4 files changed, 4 insertions(+), 34 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 295df69e3..502628920 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -2673,6 +2673,7 @@ public final class space/kscience/kmath/tensors/ComplexTensorStructure$DefaultIm
 
 public abstract interface class space/kscience/kmath/tensors/LinearOpsTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
 	public abstract fun cholesky (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun det (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun inv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun lu (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Pair;
 	public abstract fun luPivot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
@@ -2706,7 +2707,6 @@ public abstract interface class space/kscience/kmath/tensors/TensorAlgebra {
 	public abstract fun copy (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun cumprod (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun cumsum (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun det (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun dot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2958,8 +2958,6 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public fun cumprod (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun cumsum (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun cumsum (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun det (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun det (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun diagonalEmbedding (Lspace/kscience/kmath/tensors/core/DoubleTensor;III)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun div (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index e412af7c6..3d1625a50 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -4,6 +4,9 @@ package space.kscience.kmath.tensors
 public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, IndexTensorType: TensorStructure<Int>> :
     TensorPartialDivisionAlgebra<T, TensorType> {
 
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
+    public fun TensorType.det(): TensorType
+
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
     public fun TensorType.inv(): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index e41b7546c..702dc15e2 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -52,9 +52,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public fun TensorType.view(shape: IntArray): TensorType
     public fun TensorType.viewAs(other: TensorType): TensorType
 
-    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
-    public fun TensorType.det(): TensorType
-
     //https://pytorch.org/docs/stable/generated/torch.abs.html
     public fun TensorType.abs(): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 3ac09c8c7..df4a32bd8 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -320,34 +320,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.det(): DoubleTensor {
-        TODO()
-        /*
-        checkSquareMatrix(shape)
-
-        val n = shape.size
-        val m = shape.last()
-
-        val detTensorShape = IntArray(n - 1) { i -> shape[i] }
-        detTensorShape[n - 1] = 1
-        val resBuffer =  DoubleArray(detTensorShape.reduce(Int::times)) { 0.0 }
-
-        val detTensor = DoubleTensor(
-            detTensorShape,
-            resBuffer
-        )
-
-        this.matrixSequence().forEachIndexed{i, matrix ->
-            // todo need Matrix determinant algo
-            // todo resBuffer[i] = matrix.det()
-        }
-
-
-        return detTensor
-
-         */
-    }
-
     override fun DoubleTensor.square(): DoubleTensor {
         TODO("Not yet implemented")
     }

From 94b5afa6c475c4595ecb602f3ac1b66938ab04e6 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 24 Mar 2021 18:39:40 +0000
Subject: [PATCH 060/178] eq moved to interface

---
 .../kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt      | 4 ++++
 .../space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt  | 4 +++-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 702dc15e2..37c6a34f8 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -1,5 +1,7 @@
 package space.kscience.kmath.tensors
 
+import space.kscience.kmath.tensors.core.DoubleTensor
+
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
@@ -52,6 +54,8 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public fun TensorType.view(shape: IntArray): TensorType
     public fun TensorType.viewAs(other: TensorType): TensorType
 
+    public fun TensorType.eq(other: TensorType, delta: T): Boolean
+
     //https://pytorch.org/docs/stable/generated/torch.abs.html
     public fun TensorType.abs(): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index df4a32bd8..0c82ec9c8 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -344,10 +344,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return this.contentEquals(other) { x, y -> abs(x - y) < delta }
     }
 
-    public fun DoubleTensor.eq(other: DoubleTensor, delta: Double = 1e-5): Boolean {
+    override fun DoubleTensor.eq(other: DoubleTensor, delta: Double): Boolean {
         return this.eq(other) { x, y -> abs(x - y) < delta }
     }
 
+    public fun DoubleTensor.eq(other: DoubleTensor): Boolean = this.eq(other, 1e-5)
+
     public fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
         if (!(this.shape contentEquals other.shape)) {
             return false

From daa077718299839d80256ef1705cb48f31d36c9f Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 24 Mar 2021 18:43:03 +0000
Subject: [PATCH 061/178] removed unused code

---
 .../kscience/kmath/tensors/core/checks.kt     | 20 -------------------
 1 file changed, 20 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index ec7a123a9..c644a295c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -34,26 +34,6 @@ internal inline fun <T, TensorType : TensorStructure<T>,
     }
 
 
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkDot(a: TensorType, b: TensorType): Unit {
-    val sa = a.shape
-    val sb = b.shape
-    val na = sa.size
-    val nb = sb.size
-    var status: Boolean
-    if (nb == 1) {
-        status = sa.last() == sb[0]
-    } else {
-        status = sa.last() == sb[nb - 2]
-        if ((na > 2) and (nb > 2)) {
-            status = status and
-                    (sa.take(nb - 2).toIntArray() contentEquals sb.take(nb - 2).toIntArray())
-        }
-    }
-    check(status) { "Incompatible shapes ${sa.toList()} and ${sb.toList()} provided for dot product" }
-}
-
 internal inline fun <T, TensorType : TensorStructure<T>,
         TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
         TorchTensorAlgebraType.checkTranspose(dim: Int, i: Int, j: Int): Unit =

From 1056fc7200b5fbbb03b75c66ca09c30483f12e5c Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 26 Mar 2021 13:48:59 +0000
Subject: [PATCH 062/178] Test for scalar product

---
 .../kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt   | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 2da79382f..d77f33f9d 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -70,4 +70,14 @@ class TestDoubleLinearOpsTensorAlgebra {
         assertTrue { invTensor.shape contentEquals expectedShape }
         assertTrue { invTensor.buffer.array().epsEqual(expectedBuffer) }
     }
+
+    @Test
+    fun testScalarProduct() = DoubleLinearOpsTensorAlgebra {
+        val a = fromArray(intArrayOf(3), doubleArrayOf(1.8,2.5, 6.8))
+        val b = fromArray(intArrayOf(3), doubleArrayOf(5.5,2.6, 6.4))
+        DoubleReduceOpsTensorAlgebra {
+            assertEquals(a.dot(b).value(), 59.92)
+        }
+
+    }
 }

From 9162867fc28a4e948b0a520c67fca6cdf20ce53e Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 26 Mar 2021 14:37:27 +0000
Subject: [PATCH 063/178] Mutable structures 2D functionality added

---
 kmath-core/api/kmath-core.api                 | 17 ++++++++++++--
 .../kscience/kmath/linear/LinearSpace.kt      |  1 +
 .../space/kscience/kmath/nd/Structure2D.kt    | 14 ++++++++++++
 .../space/kscience/kmath/structures/Buffer.kt | 22 +++++++++++++++----
 .../tensors/TensorPartialDivisionAlgebra.kt   |  4 ++--
 5 files changed, 50 insertions(+), 8 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 502628920..a109aaab5 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -984,6 +984,8 @@ public final class space/kscience/kmath/nd/MutableStructure1D$DefaultImpls {
 }
 
 public abstract interface class space/kscience/kmath/nd/MutableStructure2D : space/kscience/kmath/nd/MutableStructureND, space/kscience/kmath/nd/Structure2D {
+	public abstract fun getColumns ()Ljava/util/List;
+	public abstract fun getRows ()Ljava/util/List;
 	public abstract fun set (IILjava/lang/Object;)V
 }
 
@@ -2608,6 +2610,15 @@ public final class space/kscience/kmath/structures/VirtualBuffer : space/kscienc
 	public fun iterator ()Ljava/util/Iterator;
 }
 
+public final class space/kscience/kmath/structures/VirtualMutableBuffer : space/kscience/kmath/structures/MutableBuffer {
+	public fun <init> (ILkotlin/jvm/functions/Function1;)V
+	public fun copy ()Lspace/kscience/kmath/structures/MutableBuffer;
+	public fun get (I)Ljava/lang/Object;
+	public fun getSize ()I
+	public fun iterator ()Ljava/util/Iterator;
+	public fun set (ILjava/lang/Object;)V
+}
+
 public abstract interface class space/kscience/kmath/tensors/AnalyticTensorAlgebra : space/kscience/kmath/tensors/OrderedTensorAlgebra, space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
 	public abstract fun acos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun acosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2709,6 +2720,7 @@ public abstract interface class space/kscience/kmath/tensors/TensorAlgebra {
 	public abstract fun cumsum (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun dot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun eq (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Z
 	public abstract fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun flatten (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun full (Ljava/lang/Object;[I)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2970,9 +2982,10 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public fun divAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
 	public synthetic fun dot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun dot (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
+	public synthetic fun eq (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Z
+	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Z
+	public fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
 	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function2;)Z
-	public static synthetic fun eq$default (Lspace/kscience/kmath/tensors/core/DoubleTensorAlgebra;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;DILjava/lang/Object;)Z
 	public synthetic fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun eye (I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun flatten (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt
index 6a587270b..4f2afc6fa 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt
@@ -14,6 +14,7 @@ import kotlin.reflect.KClass
  * @param T the type of items.
  */
 public typealias Matrix<T> = Structure2D<T>
+public typealias MutableMatrix<T> = MutableStructure2D<T>
 
 /**
  * Alias or using [Buffer] as a point/vector in a many-dimensional space.
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
index d49438b2c..762b59b28 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
@@ -2,7 +2,9 @@ package space.kscience.kmath.nd
 
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.MutableBuffer
 import space.kscience.kmath.structures.VirtualBuffer
+import space.kscience.kmath.structures.VirtualMutableBuffer
 import kotlin.reflect.KClass
 
 /**
@@ -69,6 +71,18 @@ public interface MutableStructure2D<T> : Structure2D<T>, MutableStructureND<T> {
      * @param value the value.
      */
     public operator fun set(i: Int, j: Int, value: T)
+
+    /**
+     * The buffer of rows of this structure. It gets elements from the structure dynamically.
+     */
+    override val rows: List<MutableBuffer<T>>
+        get() = List(rowNum) { i -> VirtualMutableBuffer(colNum) { j -> get(i, j) } }
+
+    /**
+     * The buffer of columns of this structure. It gets elements from the structure dynamically.
+     */
+    override val columns: List<MutableBuffer<T>>
+        get() = List(colNum) { j -> VirtualMutableBuffer(rowNum) { i -> get(i, j) } }
 }
 
 /**
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
index c85c6c5e3..72a6e3cae 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
@@ -223,11 +223,7 @@ public inline class MutableListBuffer<T>(public val list: MutableList<T>) : Muta
 }
 
 /**
-<<<<<<< HEAD
  * Returns an [MutableListBuffer] that wraps the original list.
-=======
- * Returns an [ListBuffer] that wraps the original list.
->>>>>>> dev
  */
 public fun <T> MutableList<T>.asMutableBuffer(): MutableListBuffer<T> = MutableListBuffer(this)
 
@@ -286,6 +282,24 @@ public class VirtualBuffer<T>(override val size: Int, private val generator: (In
     override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map(generator).iterator()
 }
 
+public class VirtualMutableBuffer<T>(override val size: Int, private val generator: (Int) -> T) : MutableBuffer<T> {
+
+    private val bufferHolder: MutableListBuffer<T> = (0 until size).map(generator).toMutableList().asMutableBuffer()
+
+    override operator fun get(index: Int): T {
+        if (index < 0 || index >= size) throw IndexOutOfBoundsException("Expected index from 0 to ${size - 1}, but found $index")
+        return bufferHolder[index]
+    }
+
+    override operator fun iterator(): Iterator<T> = bufferHolder.iterator()
+
+    override fun set(index: Int, value: T) {
+        bufferHolder[index] = value
+    }
+
+    override fun copy(): MutableBuffer<T> = bufferHolder.copy()
+}
+
 /**
  * Convert this buffer to read-only buffer.
  */
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
index 9f70f9621..54417e842 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
@@ -9,8 +9,8 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
     public operator fun TensorType.divAssign(other: TensorType)
 
     //https://pytorch.org/docs/stable/generated/torch.mean.html#torch.mean
-    public fun TensorType.mean(dim: Int, keepDim: Boolean): TensorType
+    public fun TensorType.mean(dim: Int = 0, keepDim: Boolean = false): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.var.html#torch.var
-    public fun TensorType.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
+    public fun TensorType.variance(dim: Int = 0, unbiased: Boolean = true, keepDim: Boolean = false): TensorType
 }

From 1f19ac88aee34ed5b790356a81f762b21c1752f3 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 26 Mar 2021 17:43:08 +0300
Subject: [PATCH 064/178] qr fix

---
 .../kmath/tensors/LinearOpsTensorAlgebra.kt        |  2 +-
 .../tensors/core/DoubleLinearOpsTensorAlgebra.kt   | 14 +++++++++++++-
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index 3d1625a50..d980c510f 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -14,7 +14,7 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, Inde
     public fun TensorType.cholesky(): TensorType
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
-    public fun TensorType.qr(): TensorType
+    public fun TensorType.qr(): Pair<TensorType, TensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.lu.html
     public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 0b9d824f0..d49fc941a 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -1,8 +1,10 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.linear.Matrix
 import space.kscience.kmath.nd.MutableStructure2D
 import space.kscience.kmath.nd.Structure1D
 import space.kscience.kmath.nd.Structure2D
+import space.kscience.kmath.nd.asND
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 import kotlin.math.sqrt
 
@@ -141,7 +143,15 @@ public class DoubleLinearOpsTensorAlgebra :
         return lTensor
     }
 
-    override fun DoubleTensor.qr(): DoubleTensor {
+    private fun matrixQR(
+        matrix: Structure2D<Double>,
+        q: MutableStructure2D<Double>,
+        r: MutableStructure2D<Double>
+    ) {
+
+    }
+
+    override fun DoubleTensor.qr(): Pair<DoubleTensor, DoubleTensor> {
         TODO("ANDREI")
     }
 
@@ -154,6 +164,7 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     private fun luMatrixDet(lu: Structure2D<Double>, pivots: Structure1D<Int>): Double {
+        // todo check
         val m = lu.shape[0]
         val sign = if((pivots[m] - m) % 2 == 0) 1.0 else -1.0
         return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
@@ -184,6 +195,7 @@ public class DoubleLinearOpsTensorAlgebra :
         pivots: Structure1D<Int>,
         invMatrix : MutableStructure2D<Double>
     ): Unit {
+        //todo check
         val m = lu.shape[0]
 
         for (j in 0 until m) {

From a6354623ec08bb0c66d1e2a45eacde3b08e29c57 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 26 Mar 2021 15:06:05 +0000
Subject: [PATCH 065/178] returning cols/rows as vectors

---
 kmath-core/api/kmath-core.api                     |  5 +++++
 .../kotlin/space/kscience/kmath/nd/Structure1D.kt | 15 +++++++++++++++
 .../kotlin/space/kscience/kmath/nd/Structure2D.kt |  8 ++++----
 3 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index a109aaab5..0a3e3721d 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -2768,6 +2768,11 @@ public abstract interface class space/kscience/kmath/tensors/TensorPartialDivisi
 	public abstract fun variance (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
 }
 
+public final class space/kscience/kmath/tensors/TensorPartialDivisionAlgebra$DefaultImpls {
+	public static synthetic fun mean$default (Lspace/kscience/kmath/tensors/TensorPartialDivisionAlgebra;Lspace/kscience/kmath/nd/MutableStructureND;IZILjava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public static synthetic fun variance$default (Lspace/kscience/kmath/tensors/TensorPartialDivisionAlgebra;Lspace/kscience/kmath/nd/MutableStructureND;IZZILjava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
+}
+
 public final class space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra {
 	public fun <init> ()V
 	public synthetic fun div (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
index c157f9a2f..354f3d802 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
@@ -71,6 +71,21 @@ private inline class Buffer1DWrapper<T>(val buffer: Buffer<T>) : Structure1D<T>
     override operator fun get(index: Int): T = buffer[index]
 }
 
+internal inline class MutableBuffer1DWrapper<T>(val buffer: MutableBuffer<T>) : MutableStructure1D<T> {
+    override val shape: IntArray get() = intArrayOf(buffer.size)
+    override val size: Int get() = buffer.size
+
+    override fun elements(): Sequence<Pair<IntArray, T>> =
+        buffer.asSequence().mapIndexed { index, value -> intArrayOf(index) to value }
+
+    override operator fun get(index: Int): T = buffer[index]
+    override fun set(index: Int, value: T) {
+        buffer[index] = value
+    }
+
+    override fun copy(): MutableBuffer<T> = buffer.copy()
+}
+
 /**
  * Represent a [StructureND] as [Structure1D]. Throw error in case of dimension mismatch
  */
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
index 762b59b28..e1a1d37de 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
@@ -75,14 +75,14 @@ public interface MutableStructure2D<T> : Structure2D<T>, MutableStructureND<T> {
     /**
      * The buffer of rows of this structure. It gets elements from the structure dynamically.
      */
-    override val rows: List<MutableBuffer<T>>
-        get() = List(rowNum) { i -> VirtualMutableBuffer(colNum) { j -> get(i, j) } }
+    override val rows: List<MutableStructure1D<T>>
+        get() = List(rowNum) { i -> MutableBuffer1DWrapper(VirtualMutableBuffer(colNum) { j -> get(i, j) })}
 
     /**
      * The buffer of columns of this structure. It gets elements from the structure dynamically.
      */
-    override val columns: List<MutableBuffer<T>>
-        get() = List(colNum) { j -> VirtualMutableBuffer(rowNum) { i -> get(i, j) } }
+    override val columns: List<MutableStructure1D<T>>
+        get() = List(colNum) { j -> MutableBuffer1DWrapper(VirtualMutableBuffer(rowNum) { i -> get(i, j) }) }
 }
 
 /**

From 1588b5d94ff751fdf2d1d91686173068430723cd Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 26 Mar 2021 15:09:18 +0000
Subject: [PATCH 066/178] once mutable stay it and enjoy

---
 .../kotlin/space/kscience/kmath/nd/Structure2D.kt         | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
index e1a1d37de..8e41110fe 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
@@ -75,14 +75,14 @@ public interface MutableStructure2D<T> : Structure2D<T>, MutableStructureND<T> {
     /**
      * The buffer of rows of this structure. It gets elements from the structure dynamically.
      */
-    override val rows: List<MutableStructure1D<T>>
-        get() = List(rowNum) { i -> MutableBuffer1DWrapper(VirtualMutableBuffer(colNum) { j -> get(i, j) })}
+    override val rows: MutableList<MutableStructure1D<T>>
+        get() = MutableList(rowNum) { i -> MutableBuffer1DWrapper(VirtualMutableBuffer(colNum) { j -> get(i, j) })}
 
     /**
      * The buffer of columns of this structure. It gets elements from the structure dynamically.
      */
-    override val columns: List<MutableStructure1D<T>>
-        get() = List(colNum) { j -> MutableBuffer1DWrapper(VirtualMutableBuffer(rowNum) { i -> get(i, j) }) }
+    override val columns: MutableList<MutableStructure1D<T>>
+        get() = MutableList(colNum) { j -> MutableBuffer1DWrapper(VirtualMutableBuffer(rowNum) { i -> get(i, j) }) }
 }
 
 /**

From e03910354e5ba02b6ec5551357ba629fa8cd8cca Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 26 Mar 2021 15:14:23 +0000
Subject: [PATCH 067/178] ok reverting

---
 .../kotlin/space/kscience/kmath/nd/Structure2D.kt         | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
index 8e41110fe..e1a1d37de 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
@@ -75,14 +75,14 @@ public interface MutableStructure2D<T> : Structure2D<T>, MutableStructureND<T> {
     /**
      * The buffer of rows of this structure. It gets elements from the structure dynamically.
      */
-    override val rows: MutableList<MutableStructure1D<T>>
-        get() = MutableList(rowNum) { i -> MutableBuffer1DWrapper(VirtualMutableBuffer(colNum) { j -> get(i, j) })}
+    override val rows: List<MutableStructure1D<T>>
+        get() = List(rowNum) { i -> MutableBuffer1DWrapper(VirtualMutableBuffer(colNum) { j -> get(i, j) })}
 
     /**
      * The buffer of columns of this structure. It gets elements from the structure dynamically.
      */
-    override val columns: MutableList<MutableStructure1D<T>>
-        get() = MutableList(colNum) { j -> MutableBuffer1DWrapper(VirtualMutableBuffer(rowNum) { i -> get(i, j) }) }
+    override val columns: List<MutableStructure1D<T>>
+        get() = List(colNum) { j -> MutableBuffer1DWrapper(VirtualMutableBuffer(rowNum) { i -> get(i, j) }) }
 }
 
 /**

From 516cd90677700d3f49096194bffa2cfd17ea78eb Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 26 Mar 2021 15:36:53 +0000
Subject: [PATCH 068/178] Moved out to archive unimplemented API

---
 kmath-core/api/kmath-core.api                 | 160 +-----------------
 .../kmath/tensors/AnalyticTensorAlgebra.kt    |  63 +------
 .../kmath/tensors/ComplexTensorAlgebra.kt     |  53 ------
 .../kmath/tensors/ComplexTensorStructure.kt   |  14 --
 .../kmath/tensors/OrderedTensorAlgebra.kt     |  29 ----
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  26 ---
 .../tensors/TensorPartialDivisionAlgebra.kt   |   6 -
 .../core/DoubleAnalyticTensorAlgebra.kt       |  83 +--------
 .../core/DoubleOrderedTensorAlgebra.kt        |  43 -----
 .../kmath/tensors/core/DoubleTensorAlgebra.kt |  43 -----
 10 files changed, 4 insertions(+), 516 deletions(-)
 delete mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
 delete mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorStructure.kt
 delete mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/OrderedTensorAlgebra.kt
 delete mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra.kt

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 0a3e3721d..a45f00a55 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -2619,7 +2619,7 @@ public final class space/kscience/kmath/structures/VirtualMutableBuffer : space/
 	public fun set (ILjava/lang/Object;)V
 }
 
-public abstract interface class space/kscience/kmath/tensors/AnalyticTensorAlgebra : space/kscience/kmath/tensors/OrderedTensorAlgebra, space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
+public abstract interface class space/kscience/kmath/tensors/AnalyticTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
 	public abstract fun acos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun acosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun asin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2627,59 +2627,16 @@ public abstract interface class space/kscience/kmath/tensors/AnalyticTensorAlgeb
 	public abstract fun atan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun atanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun ceil (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun clamp (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun cos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun cosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun erf (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun erfc (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun erfinv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun exp (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun floor (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun heaviside (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun histc (Lspace/kscience/kmath/nd/MutableStructureND;ILjava/lang/Object;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun igamma (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun igammac (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun lerp (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun lgamma (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun log (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun logit (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun mvlgamma (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun polygamma (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun pow (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun quantile (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun round (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun sigmoid (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun sin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun sinc (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun sinh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun sqrt (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun std (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun tan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun tanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun trapz (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-}
-
-public abstract interface class space/kscience/kmath/tensors/ComplexTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
-	public abstract fun angle (Lspace/kscience/kmath/tensors/ComplexTensorStructure;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun bartlettWindow (IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun blackmanWindow (IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun cartesianEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/tensors/ComplexTensorStructure;
-	public abstract fun hammingWindow (IZLjava/lang/Object;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun inverseShortTimeFourierTransform (Lspace/kscience/kmath/tensors/ComplexTensorStructure;IIILspace/kscience/kmath/nd/MutableStructureND;ZZZI)V
-	public abstract fun kaiserWindow (IZLjava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun polarEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/tensors/ComplexTensorStructure;
-	public abstract fun shortTimeFourierTransform (Lspace/kscience/kmath/tensors/ComplexTensorStructure;IIILspace/kscience/kmath/nd/MutableStructureND;ZZ)V
-	public abstract fun viewAsComplex (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/tensors/ComplexTensorStructure;
-}
-
-public abstract interface class space/kscience/kmath/tensors/ComplexTensorStructure : space/kscience/kmath/nd/MutableStructureND {
-	public abstract fun imaginaryPart ()Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun realPart ()Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun viewAsReal ()Lspace/kscience/kmath/nd/MutableStructureND;
-}
-
-public final class space/kscience/kmath/tensors/ComplexTensorStructure$DefaultImpls {
-	public static fun getDimension (Lspace/kscience/kmath/tensors/ComplexTensorStructure;)I
 }
 
 public abstract interface class space/kscience/kmath/tensors/LinearOpsTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
@@ -2697,32 +2654,16 @@ public final class space/kscience/kmath/tensors/LinearOpsTensorAlgebra$DefaultIm
 	public static synthetic fun symEig$default (Lspace/kscience/kmath/tensors/LinearOpsTensorAlgebra;Lspace/kscience/kmath/nd/MutableStructureND;ZILjava/lang/Object;)Lkotlin/Pair;
 }
 
-public abstract interface class space/kscience/kmath/tensors/OrderedTensorAlgebra : space/kscience/kmath/tensors/TensorAlgebra {
-	public abstract fun cummax (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun cummin (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun max (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun maximum (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public abstract fun median (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun min (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun minimum (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public abstract fun sort (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
-}
-
 public abstract interface class space/kscience/kmath/tensors/ReduceOpsTensorAlgebra : space/kscience/kmath/tensors/TensorAlgebra {
 	public abstract fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
 }
 
 public abstract interface class space/kscience/kmath/tensors/TensorAlgebra {
-	public abstract fun abs (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun cat (Ljava/util/List;I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun copy (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun cumprod (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun cumsum (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun dot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun eq (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Z
 	public abstract fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun flatten (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun full (Ljava/lang/Object;[I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun fullLike (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun get (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2738,10 +2679,6 @@ public abstract interface class space/kscience/kmath/tensors/TensorAlgebra {
 	public abstract fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
 	public abstract fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public abstract fun prod (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun square (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun squeeze (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun sum (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun times (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun times (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun times (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2764,13 +2701,6 @@ public abstract interface class space/kscience/kmath/tensors/TensorPartialDivisi
 	public abstract fun div (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
 	public abstract fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public abstract fun mean (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun variance (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
-}
-
-public final class space/kscience/kmath/tensors/TensorPartialDivisionAlgebra$DefaultImpls {
-	public static synthetic fun mean$default (Lspace/kscience/kmath/tensors/TensorPartialDivisionAlgebra;Lspace/kscience/kmath/nd/MutableStructureND;IZILjava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public static synthetic fun variance$default (Lspace/kscience/kmath/tensors/TensorPartialDivisionAlgebra;Lspace/kscience/kmath/nd/MutableStructureND;IZZILjava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
 }
 
 public final class space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra {
@@ -2815,7 +2745,7 @@ public class space/kscience/kmath/tensors/core/BufferedTensor : space/kscience/k
 	public final fun vectorSequence ()Lkotlin/sequences/Sequence;
 }
 
-public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra : space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra, space/kscience/kmath/tensors/AnalyticTensorAlgebra {
+public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/AnalyticTensorAlgebra {
 	public fun <init> ()V
 	public synthetic fun acos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun acos (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
@@ -2831,66 +2761,26 @@ public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra
 	public fun atanh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun ceil (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun ceil (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun clamp (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun clamp (Lspace/kscience/kmath/tensors/core/DoubleTensor;DD)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun cos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun cos (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun cosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun cosh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun erf (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun erf (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun erfc (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun erfc (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun erfinv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun erfinv (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun exp (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun exp (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun floor (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun floor (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun heaviside (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun heaviside (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun histc (Lspace/kscience/kmath/nd/MutableStructureND;ILjava/lang/Object;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun histc (Lspace/kscience/kmath/tensors/core/DoubleTensor;IDD)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun igamma (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun igamma (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun igammac (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun igammac (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun lerp (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun lerp (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun lgamma (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun lgamma (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun log (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun log (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun logit (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun logit (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun mvlgamma (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun mvlgamma (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun polygamma (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun polygamma (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun pow (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun pow (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun quantile (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun quantile (Lspace/kscience/kmath/tensors/core/DoubleTensor;DIZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun round (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun round (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun sigmoid (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun sigmoid (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun sin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun sin (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun sinc (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun sinc (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun sinh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun sinh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun sqrt (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun sqrt (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun std (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun std (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun tan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun tan (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun tanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun tanh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun trapz (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun trapz (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 }
 
 public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebraKt {
@@ -2923,30 +2813,6 @@ public final class space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebr
 	public static final fun DoubleLinearOpsTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
 }
 
-public class space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/OrderedTensorAlgebra {
-	public fun <init> ()V
-	public synthetic fun cummax (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun cummax (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun cummin (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun cummin (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun max (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun max (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun maximum (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public fun maximum (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public synthetic fun median (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun median (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun min (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun min (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun minimum (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public fun minimum (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public synthetic fun sort (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun sort (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-}
-
-public final class space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebraKt {
-	public static final fun DoubleOrderedTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
-}
-
 public final class space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/ReduceOpsTensorAlgebra {
 	public fun <init> ()V
 	public synthetic fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
@@ -2962,19 +2828,11 @@ public final class space/kscience/kmath/tensors/core/DoubleTensor : space/kscien
 
 public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
 	public fun <init> ()V
-	public synthetic fun abs (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun abs (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun cat (Ljava/util/List;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun cat (Ljava/util/List;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public final fun contentEquals (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
 	public final fun contentEquals (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function2;)Z
 	public static synthetic fun contentEquals$default (Lspace/kscience/kmath/tensors/core/DoubleTensorAlgebra;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;DILjava/lang/Object;)Z
 	public synthetic fun copy (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun copy (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun cumprod (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun cumprod (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun cumsum (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun cumsum (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun diagonalEmbedding (Lspace/kscience/kmath/tensors/core/DoubleTensor;III)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun div (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2993,8 +2851,6 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function2;)Z
 	public synthetic fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun eye (I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun flatten (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun flatten (Lspace/kscience/kmath/tensors/core/DoubleTensor;II)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public final fun fromArray ([I[D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public fun full (D[I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun full (Ljava/lang/Object;[I)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -3003,8 +2859,6 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public synthetic fun get (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun get (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public final fun map (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function1;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun mean (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun mean (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public fun minus (DLspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun minus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public synthetic fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -3029,14 +2883,6 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public synthetic fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
 	public fun plusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)V
 	public fun plusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public synthetic fun prod (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun prod (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun square (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun square (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun squeeze (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun squeeze (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun sum (Lspace/kscience/kmath/nd/MutableStructureND;IZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun sum (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public fun times (DLspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun times (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public synthetic fun times (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -3051,8 +2897,6 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public fun transpose (Lspace/kscience/kmath/tensors/core/DoubleTensor;II)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun unaryMinus (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun unaryMinus (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun variance (Lspace/kscience/kmath/nd/MutableStructureND;IZZ)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun variance (Lspace/kscience/kmath/tensors/core/DoubleTensor;IZZ)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun view (Lspace/kscience/kmath/nd/MutableStructureND;[I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun view (Lspace/kscience/kmath/tensors/core/DoubleTensor;[I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun viewAs (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
index c3a558298..fa1de81fa 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
@@ -2,14 +2,7 @@ package space.kscience.kmath.tensors
 
 
 public interface AnalyticTensorAlgebra<T, TensorType : TensorStructure<T>> :
-    TensorPartialDivisionAlgebra<T, TensorType>,
-    OrderedTensorAlgebra<T, TensorType>{
-
-    //https://pytorch.org/docs/stable/generated/torch.quantile.html#torch.quantile
-    public fun TensorType.quantile(q: T, dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.std.html#torch.std
-    public fun TensorType.std(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
+    TensorPartialDivisionAlgebra<T, TensorType> {
 
     //https://pytorch.org/docs/stable/generated/torch.exp.html
     public fun TensorType.exp(): TensorType
@@ -62,58 +55,4 @@ public interface AnalyticTensorAlgebra<T, TensorType : TensorStructure<T>> :
     //https://pytorch.org/docs/stable/generated/torch.floor.html#torch.floor
     public fun TensorType.floor(): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.clamp.html#torch.clamp
-    public fun TensorType.clamp(min: T, max: T): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.erf.html#torch.erf
-    public fun TensorType.erf(): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.erfinv.html#torch.erfinv
-    public fun TensorType.erfinv(): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.erfc.html#torch.erfc
-    public fun TensorType.erfc(): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.lerp.html#torch.lerp
-    public fun TensorType.lerp(end: TensorType, weight: TensorType): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.lgamma.html#torch.lgamma
-    public fun TensorType.lgamma(): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.logit.html#torch.logit
-    public fun TensorType.logit(eps: T): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.igamma.html#torch.igamma
-    public fun TensorType.igamma(other: TensorType): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.igammac.html#torch.igammac
-    public fun TensorType.igammac(other: TensorType): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.mvlgamma.html#torch.mvlgamma
-    public fun TensorType.mvlgamma(dimensions: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.polygamma.html#torch.polygamma
-    public fun TensorType.polygamma(order: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.pow.html#torch.pow
-    public fun TensorType.pow(exponent: T): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.round.html#torch.round
-    public fun TensorType.round(): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.sigmoid.html#torch.sigmoid
-    public fun TensorType.sigmoid(): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.sinc.html#torch.sinc
-    public fun TensorType.sinc(): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.heaviside.html#torch.heaviside
-    public fun TensorType.heaviside(values: TensorType): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.trapz.html#torch.trapz
-    public fun TensorType.trapz(xValues: TensorType, dim: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.histc.html#torch.histc
-    public fun TensorType.histc(bins: Int, min: T, max: T): TensorType
-
 }
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
deleted file mode 100644
index e56920916..000000000
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorAlgebra.kt
+++ /dev/null
@@ -1,53 +0,0 @@
-package space.kscience.kmath.tensors
-
-public interface ComplexTensorAlgebra<T,
-        RealTensorType : TensorStructure<T>,
-        ComplexTensorType : ComplexTensorStructure<T, RealTensorType>>
-    : TensorPartialDivisionAlgebra<T, ComplexTensorType>{
-
-    //https://pytorch.org/docs/stable/generated/torch.view_as_complex.html
-    public fun RealTensorType.viewAsComplex(): ComplexTensorType
-
-    // Embed a real tensor as real + i * imaginary
-    public fun RealTensorType.cartesianEmbedding(imaginary: RealTensorType): ComplexTensorType
-
-    // Embed a real tensor as real * exp(i * angle)
-    public fun RealTensorType.polarEmbedding(angle: RealTensorType): ComplexTensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.angle.html
-    public fun ComplexTensorType.angle(): RealTensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.stft.html#torch.stft
-    public fun ComplexTensorType.shortTimeFourierTransform(
-        nFFT: Int,
-        hopLength: Int,
-        winLength: Int,
-        window: RealTensorType,
-        normalised: Boolean,
-        oneSided: Boolean
-    )
-
-    //https://pytorch.org/docs/stable/generated/torch.istft.html#torch.istft
-    public fun ComplexTensorType.inverseShortTimeFourierTransform(
-        nFFT: Int,
-        hopLength: Int,
-        winLength: Int,
-        window: RealTensorType,
-        center: Boolean,
-        normalised: Boolean,
-        oneSided: Boolean,
-        length: Int
-    )
-
-    //https://pytorch.org/docs/stable/generated/torch.bartlett_window.html#torch.bartlett_window
-    public fun bartlettWindow(windowLength: Int, periodic: Boolean): RealTensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.blackman_window.html#torch.blackman_window
-    public fun blackmanWindow(windowLength: Int, periodic: Boolean): RealTensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.hamming_window.html#torch.hamming_window
-    public fun hammingWindow(windowLength: Int, periodic: Boolean, alpha: T, beta: T): RealTensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.kaiser_window.html#torch.kaiser_window
-    public fun kaiserWindow(windowLength: Int, periodic: Boolean, beta: T): RealTensorType
-}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorStructure.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorStructure.kt
deleted file mode 100644
index 0e0975830..000000000
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ComplexTensorStructure.kt
+++ /dev/null
@@ -1,14 +0,0 @@
-package space.kscience.kmath.tensors
-
-public interface ComplexTensorStructure<T, RealTensorType: TensorStructure<T>> : TensorStructure<T> {
-
-    //https://pytorch.org/docs/master/generated/torch.view_as_real.html
-    public fun viewAsReal(): RealTensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.real.html
-    public fun realPart(): RealTensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.imag.html
-    public fun imaginaryPart(): RealTensorType
-
-}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/OrderedTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/OrderedTensorAlgebra.kt
deleted file mode 100644
index 3320c3a1e..000000000
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/OrderedTensorAlgebra.kt
+++ /dev/null
@@ -1,29 +0,0 @@
-package space.kscience.kmath.tensors
-
-public interface OrderedTensorAlgebra<T, TensorType : TensorStructure<T>> :
-    TensorAlgebra<T, TensorType> {
-
-    //https://pytorch.org/docs/stable/generated/torch.max.html#torch.max
-    public fun TensorType.max(dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.cummax.html#torch.cummax
-    public fun TensorType.cummax(dim: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.min.html#torch.min
-    public fun TensorType.min(dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.cummin.html#torch.cummin
-    public fun TensorType.cummin(dim: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.median.html#torch.median
-    public fun TensorType.median(dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum
-    public fun maximum(lhs: TensorType, rhs: TensorType)
-
-    //https://pytorch.org/docs/stable/generated/torch.minimum.html#torch.minimum
-    public fun minimum(lhs: TensorType, rhs: TensorType)
-
-    //https://pytorch.org/docs/stable/generated/torch.sort.html#torch.sort
-    public fun TensorType.sort(dim: Int, keepDim: Boolean, descending: Boolean): TensorType
-}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 37c6a34f8..b05105ed8 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -41,9 +41,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public operator fun TensorType.timesAssign(other: TensorType): Unit
     public operator fun TensorType.unaryMinus(): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.square.html
-    public fun TensorType.square(): TensorType
-
     //https://pytorch.org/cppdocs/notes/tensor_indexing.html
     public operator fun TensorType.get(i: Int): TensorType
 
@@ -56,21 +53,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     public fun TensorType.eq(other: TensorType, delta: T): Boolean
 
-    //https://pytorch.org/docs/stable/generated/torch.abs.html
-    public fun TensorType.abs(): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.sum.html
-    public fun TensorType.sum(dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.cumsum.html#torch.cumsum
-    public fun TensorType.cumsum(dim: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.prod.html#torch.prod
-    public fun TensorType.prod(dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.cumprod.html#torch.cumprod
-    public fun TensorType.cumprod(dim: Int): TensorType
-
     //https://pytorch.org/docs/stable/generated/torch.matmul.html
     public infix fun TensorType.dot(other: TensorType): TensorType
 
@@ -80,12 +62,4 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
         offset: Int = 0, dim1: Int = -2, dim2: Int = -1
     ): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat
-    public fun cat(tensors: List<TensorType>, dim: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.flatten.html#torch.flatten
-    public fun TensorType.flatten(startDim: Int, endDim: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.squeeze.html
-    public fun TensorType.squeeze(dim: Int): TensorType
 }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
index 54417e842..67b9c9d73 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
@@ -7,10 +7,4 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
     public operator fun TensorType.div(other: TensorType): TensorType
     public operator fun TensorType.divAssign(value: T)
     public operator fun TensorType.divAssign(other: TensorType)
-
-    //https://pytorch.org/docs/stable/generated/torch.mean.html#torch.mean
-    public fun TensorType.mean(dim: Int = 0, keepDim: Boolean = false): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.var.html#torch.var
-    public fun TensorType.variance(dim: Int = 0, unbiased: Boolean = true, keepDim: Boolean = false): TensorType
 }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
index 1eb4c3b63..b46c7fa9c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
@@ -5,11 +5,10 @@ import kotlin.math.*
 
 public class DoubleAnalyticTensorAlgebra:
     AnalyticTensorAlgebra<Double, DoubleTensor>,
-    DoubleOrderedTensorAlgebra()
+        DoubleTensorAlgebra()
 {
     override fun DoubleTensor.exp(): DoubleTensor = this.map(::exp)
 
-    // todo log with other base????
     override fun DoubleTensor.log(): DoubleTensor = this.map(::ln)
 
     override fun DoubleTensor.sqrt(): DoubleTensor = this.map(::sqrt)
@@ -42,86 +41,6 @@ public class DoubleAnalyticTensorAlgebra:
 
     override fun DoubleTensor.floor(): DoubleTensor = this.map(::floor)
 
-    override fun DoubleTensor.clamp(min: Double, max: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.erf(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.erfinv(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.erfc(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.lerp(end: DoubleTensor, weight: DoubleTensor): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.lgamma(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.logit(eps: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.igamma(other: DoubleTensor): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.igammac(other: DoubleTensor): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.mvlgamma(dimensions: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.polygamma(order: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.pow(exponent: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.round(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.sigmoid(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.sinc(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.heaviside(values: DoubleTensor): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.trapz(xValues: DoubleTensor, dim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.quantile(q: Double, dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.std(dim: Int, unbiased: Boolean, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.histc(bins: Int, min: Double, max: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
 }
 
 public inline fun <R> DoubleAnalyticTensorAlgebra(block: DoubleAnalyticTensorAlgebra.() -> R): R =
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra.kt
deleted file mode 100644
index a6bea59f4..000000000
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleOrderedTensorAlgebra.kt
+++ /dev/null
@@ -1,43 +0,0 @@
-package space.kscience.kmath.tensors.core
-
-import space.kscience.kmath.tensors.OrderedTensorAlgebra
-
-public open class DoubleOrderedTensorAlgebra:
-    OrderedTensorAlgebra<Double, DoubleTensor>,
-    DoubleTensorAlgebra()
-{
-    override fun DoubleTensor.max(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.cummax(dim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.min(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.cummin(dim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.median(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun maximum(lhs: DoubleTensor, rhs: DoubleTensor) {
-        TODO("Not yet implemented")
-    }
-
-    override fun minimum(lhs: DoubleTensor, rhs: DoubleTensor) {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.sort(dim: Int, keepDim: Boolean, descending: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-}
-
-public inline fun <R> DoubleOrderedTensorAlgebra(block: DoubleOrderedTensorAlgebra.() -> R): R =
-    DoubleOrderedTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 0c82ec9c8..0a2e8c86c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -224,26 +224,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return this.view(other.shape)
     }
 
-    override fun DoubleTensor.abs(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.sum(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.cumsum(dim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.prod(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.cumprod(dim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
     override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
         if (this.shape.size == 1 && other.shape.size == 1) {
             return DoubleTensor(intArrayOf(1), doubleArrayOf(this.times(other).buffer.array().sum()))
@@ -308,29 +288,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Alya")
     }
 
-    override fun cat(tensors: List<DoubleTensor>, dim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.flatten(startDim: Int, endDim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.mean(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.square(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.squeeze(dim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
 
     public fun DoubleTensor.map(transform: (Double) -> Double): DoubleTensor {
         return DoubleTensor(

From 22b68e5ca469038e8ee6c2083aafa2f54d51d032 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 26 Mar 2021 20:36:21 +0000
Subject: [PATCH 069/178] BufferedTensor2D & BufferedTensor1D

---
 kmath-core/api/kmath-core.api                 |  28 ++++
 .../kmath/tensors/core/BufferedTensor.kt      | 133 ++++++++++++++++--
 .../core/DoubleLinearOpsTensorAlgebra.kt      |  11 +-
 .../core/TestDoubleLinearOpsAlgebra.kt        |   1 -
 .../kmath/tensors/core/TestDoubleTensor.kt    |   3 -
 5 files changed, 152 insertions(+), 24 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index a45f00a55..fc0905bd0 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -2745,6 +2745,34 @@ public class space/kscience/kmath/tensors/core/BufferedTensor : space/kscience/k
 	public final fun vectorSequence ()Lkotlin/sequences/Sequence;
 }
 
+public final class space/kscience/kmath/tensors/core/BufferedTensor1D : space/kscience/kmath/tensors/core/BufferedTensor, space/kscience/kmath/nd/MutableStructure1D {
+	public fun copy ()Lspace/kscience/kmath/structures/MutableBuffer;
+	public fun get (I)Ljava/lang/Object;
+	public fun get ([I)Ljava/lang/Object;
+	public fun getDimension ()I
+	public fun getSize ()I
+	public fun iterator ()Ljava/util/Iterator;
+	public fun set (ILjava/lang/Object;)V
+	public fun set ([ILjava/lang/Object;)V
+}
+
+public final class space/kscience/kmath/tensors/core/BufferedTensor2D : space/kscience/kmath/tensors/core/BufferedTensor, space/kscience/kmath/nd/MutableStructure2D {
+	public fun elements ()Lkotlin/sequences/Sequence;
+	public fun get (II)Ljava/lang/Object;
+	public fun get ([I)Ljava/lang/Object;
+	public fun getColNum ()I
+	public fun getColumns ()Ljava/util/List;
+	public fun getRowNum ()I
+	public fun getRows ()Ljava/util/List;
+	public fun getShape ()[I
+	public fun set (IILjava/lang/Object;)V
+}
+
+public final class space/kscience/kmath/tensors/core/BufferedTensorKt {
+	public static final fun as1D (Lspace/kscience/kmath/tensors/core/BufferedTensor;)Lspace/kscience/kmath/tensors/core/BufferedTensor1D;
+	public static final fun as2D (Lspace/kscience/kmath/tensors/core/BufferedTensor;)Lspace/kscience/kmath/tensors/core/BufferedTensor2D;
+}
+
 public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/AnalyticTensorAlgebra {
 	public fun <init> ()V
 	public synthetic fun acos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 160c61260..aa6e58ef6 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -1,23 +1,26 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.nd.*
+import space.kscience.kmath.nd.MutableStructure1D
+import space.kscience.kmath.nd.MutableStructure2D
 import space.kscience.kmath.structures.*
 import space.kscience.kmath.tensors.TensorStructure
-import kotlin.math.atanh
+
 
 
 public open class BufferedTensor<T>(
     override val shape: IntArray,
     public val buffer: MutableBuffer<T>,
     internal val bufferStart: Int
-) : TensorStructure<T>
-{
+) : TensorStructure<T> {
     public val linearStructure: TensorLinearStructure
         get() = TensorLinearStructure(shape)
 
     public val numel: Int
         get() = linearStructure.size
 
+    internal constructor(tensor: BufferedTensor<T>) :
+            this(tensor.shape, tensor.buffer, tensor.bufferStart)
+
     override fun get(index: IntArray): T = buffer[bufferStart + linearStructure.offset(index)]
 
     override fun set(index: IntArray, value: T) {
@@ -32,8 +35,8 @@ public open class BufferedTensor<T>(
 
     override fun hashCode(): Int = 0
 
-    public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
-        check(shape.size >= 1) {"todo"}
+    public fun vectorSequence(): Sequence<BufferedTensor1D<T>> = sequence {
+        check(shape.size >= 1) { "todo" }
         val n = shape.size
         val vectorOffset = shape[n - 1]
         val vectorShape = intArrayOf(shape.last())
@@ -43,8 +46,8 @@ public open class BufferedTensor<T>(
         }
     }
 
-    public fun matrixSequence(): Sequence<MutableStructure2D<T>> = sequence {
-        check(shape.size >= 2) {"todo"}
+    public fun matrixSequence(): Sequence<BufferedTensor2D<T>> = sequence {
+        check(shape.size >= 2) { "todo" }
         val n = shape.size
         val matrixOffset = shape[n - 1] * shape[n - 2]
         val matrixShape = intArrayOf(shape[n - 2], shape[n - 1]) //todo better way?
@@ -54,14 +57,14 @@ public open class BufferedTensor<T>(
         }
     }
 
-    public inline fun forEachVector(vectorAction : (MutableStructure1D<T>) -> Unit): Unit {
-        for (vector in vectorSequence()){
+    public inline fun forEachVector(vectorAction: (BufferedTensor1D<T>) -> Unit): Unit {
+        for (vector in vectorSequence()) {
             vectorAction(vector)
         }
     }
 
-    public inline fun forEachMatrix(matrixAction : (MutableStructure2D<T>) -> Unit): Unit {
-        for (matrix in matrixSequence()){
+    public inline fun forEachMatrix(matrixAction: (BufferedTensor2D<T>) -> Unit): Unit {
+        for (matrix in matrixSequence()) {
             matrixAction(matrix)
         }
     }
@@ -74,21 +77,125 @@ public class IntTensor internal constructor(
     buffer: IntArray,
     offset: Int = 0
 ) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset)
+{
+    internal constructor(bufferedTensor: BufferedTensor<Int>):
+            this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
+}
 
 public class LongTensor internal constructor(
     shape: IntArray,
     buffer: LongArray,
     offset: Int = 0
 ) : BufferedTensor<Long>(shape, LongBuffer(buffer), offset)
+{
+    internal constructor(bufferedTensor: BufferedTensor<Long>):
+            this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
+}
 
 public class FloatTensor internal constructor(
     shape: IntArray,
     buffer: FloatArray,
     offset: Int = 0
 ) : BufferedTensor<Float>(shape, FloatBuffer(buffer), offset)
+{
+    internal constructor(bufferedTensor: BufferedTensor<Float>):
+            this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
+}
 
 public class DoubleTensor internal constructor(
     shape: IntArray,
     buffer: DoubleArray,
     offset: Int = 0
-) : BufferedTensor<Double>(shape, DoubleBuffer(buffer), offset)
\ No newline at end of file
+) : BufferedTensor<Double>(shape, DoubleBuffer(buffer), offset)
+{
+    internal constructor(bufferedTensor: BufferedTensor<Double>):
+            this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
+}
+
+
+public class BufferedTensor2D<T> internal constructor(
+    private val tensor: BufferedTensor<T>,
+) : BufferedTensor<T>(tensor), MutableStructure2D<T> {
+    init {
+        check(shape.size == 2) {
+            "Shape ${shape.toList()} not compatible with DoubleTensor2D"
+        }
+    }
+
+    override val shape: IntArray
+        get() = tensor.shape
+
+    override val rowNum: Int
+        get() = shape[0]
+    override val colNum: Int
+        get() = shape[1]
+
+    override fun get(i: Int, j: Int): T = tensor[intArrayOf(i, j)]
+
+    override fun get(index: IntArray): T = tensor[index]
+
+    override fun elements(): Sequence<Pair<IntArray, T>> = tensor.elements()
+
+    override fun set(i: Int, j: Int, value: T) {
+        tensor[intArrayOf(i, j)] = value
+    }
+
+    override val rows: List<BufferedTensor1D<T>>
+        get() = List(rowNum) { i ->
+            BufferedTensor1D(
+                BufferedTensor(
+                    shape = intArrayOf(colNum),
+                    buffer = VirtualMutableBuffer(colNum) { j -> get(i, j) },
+                    bufferStart = 0
+                )
+            )
+        }
+
+    override val columns: List<BufferedTensor1D<T>>
+        get() = List(colNum) { j ->
+            BufferedTensor1D(
+                BufferedTensor(
+                    shape = intArrayOf(rowNum),
+                    buffer = VirtualMutableBuffer(rowNum) { i -> get(i, j) },
+                    bufferStart = 0
+                )
+            )
+        }
+}
+
+public class BufferedTensor1D<T> internal constructor(
+    private val tensor: BufferedTensor<T>
+) : BufferedTensor<T>(tensor), MutableStructure1D<T> {
+    init {
+        check(shape.size == 1) {
+            "Shape ${shape.toList()} not compatible with DoubleTensor1D"
+        }
+    }
+
+    override fun get(index: IntArray): T = tensor[index]
+
+    override fun set(index: IntArray, value: T) {
+        tensor[index] = value
+    }
+
+    override val size: Int
+        get() = tensor.linearStructure.size
+
+    override fun get(index: Int): T = tensor[intArrayOf(index)]
+
+    override fun set(index: Int, value: T) {
+        tensor[intArrayOf(index)] = value
+    }
+
+    override fun copy(): MutableBuffer<T> = tensor.buffer.copy()
+
+}
+
+internal fun BufferedTensor<Int>.asIntTensor(): IntTensor = IntTensor(this)
+internal fun BufferedTensor<Long>.asLongTensor(): LongTensor = LongTensor(this)
+internal fun BufferedTensor<Float>.asFloatTensor(): FloatTensor = FloatTensor(this)
+internal fun BufferedTensor<Double>.asDoubleTensor(): DoubleTensor = DoubleTensor(this)
+
+
+public fun <T> BufferedTensor<T>.as2D(): BufferedTensor2D<T> = BufferedTensor2D(this)
+public fun <T> BufferedTensor<T>.as1D(): BufferedTensor1D<T> = BufferedTensor1D(this)
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 0b9d824f0..70fe8a1cc 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -1,8 +1,5 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.nd.MutableStructure2D
-import space.kscience.kmath.nd.Structure1D
-import space.kscience.kmath.nd.Structure2D
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 import kotlin.math.sqrt
 
@@ -153,7 +150,7 @@ public class DoubleLinearOpsTensorAlgebra :
         TODO("ANDREI")
     }
 
-    private fun luMatrixDet(lu: Structure2D<Double>, pivots: Structure1D<Int>): Double {
+    private fun luMatrixDet(lu: BufferedTensor2D<Double>, pivots: BufferedTensor1D<Int>): Double {
         val m = lu.shape[0]
         val sign = if((pivots[m] - m) % 2 == 0) 1.0 else -1.0
         return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
@@ -180,9 +177,9 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     private fun luMatrixInv(
-        lu: Structure2D<Double>,
-        pivots: Structure1D<Int>,
-        invMatrix : MutableStructure2D<Double>
+        lu: BufferedTensor2D<Double>,
+        pivots: BufferedTensor1D<Int>,
+        invMatrix : BufferedTensor2D<Double>
     ): Unit {
         val m = lu.shape[0]
 
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index d77f33f9d..3a129b03c 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -1,7 +1,6 @@
 package space.kscience.kmath.tensors.core
 
 import kotlin.math.abs
-import kotlin.math.exp
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index b12b08b52..40597f539 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -1,8 +1,5 @@
 package space.kscience.kmath.tensors.core
 
-
-import space.kscience.kmath.nd.as1D
-import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.structures.toDoubleArray
 import kotlin.test.Test
 import kotlin.test.assertEquals

From 92710097f06d0ec6161e4f3518a2fd0815ff157a Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 29 Mar 2021 21:58:56 +0100
Subject: [PATCH 070/178] Fixing 2D and 1D casts

---
 kmath-core/api/kmath-core.api                 |  33 ---
 .../kmath/tensors/core/BufferedTensor.kt      | 107 +--------
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 220 ++++++++++--------
 .../kmath/tensors/core/DoubleTensorAlgebra.kt |  53 +++--
 .../kscience/kmath/tensors/core/checks.kt     |   6 +-
 .../kmath/tensors/core/TestDoubleTensor.kt    |   2 +
 6 files changed, 173 insertions(+), 248 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index fc0905bd0..786b81d19 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -2731,8 +2731,6 @@ public class space/kscience/kmath/tensors/core/BufferedTensor : space/kscience/k
 	public fun <init> ([ILspace/kscience/kmath/structures/MutableBuffer;I)V
 	public fun elements ()Lkotlin/sequences/Sequence;
 	public fun equals (Ljava/lang/Object;)Z
-	public final fun forEachMatrix (Lkotlin/jvm/functions/Function1;)V
-	public final fun forEachVector (Lkotlin/jvm/functions/Function1;)V
 	public fun get ([I)Ljava/lang/Object;
 	public final fun getBuffer ()Lspace/kscience/kmath/structures/MutableBuffer;
 	public fun getDimension ()I
@@ -2740,37 +2738,7 @@ public class space/kscience/kmath/tensors/core/BufferedTensor : space/kscience/k
 	public final fun getNumel ()I
 	public fun getShape ()[I
 	public fun hashCode ()I
-	public final fun matrixSequence ()Lkotlin/sequences/Sequence;
 	public fun set ([ILjava/lang/Object;)V
-	public final fun vectorSequence ()Lkotlin/sequences/Sequence;
-}
-
-public final class space/kscience/kmath/tensors/core/BufferedTensor1D : space/kscience/kmath/tensors/core/BufferedTensor, space/kscience/kmath/nd/MutableStructure1D {
-	public fun copy ()Lspace/kscience/kmath/structures/MutableBuffer;
-	public fun get (I)Ljava/lang/Object;
-	public fun get ([I)Ljava/lang/Object;
-	public fun getDimension ()I
-	public fun getSize ()I
-	public fun iterator ()Ljava/util/Iterator;
-	public fun set (ILjava/lang/Object;)V
-	public fun set ([ILjava/lang/Object;)V
-}
-
-public final class space/kscience/kmath/tensors/core/BufferedTensor2D : space/kscience/kmath/tensors/core/BufferedTensor, space/kscience/kmath/nd/MutableStructure2D {
-	public fun elements ()Lkotlin/sequences/Sequence;
-	public fun get (II)Ljava/lang/Object;
-	public fun get ([I)Ljava/lang/Object;
-	public fun getColNum ()I
-	public fun getColumns ()Ljava/util/List;
-	public fun getRowNum ()I
-	public fun getRows ()Ljava/util/List;
-	public fun getShape ()[I
-	public fun set (IILjava/lang/Object;)V
-}
-
-public final class space/kscience/kmath/tensors/core/BufferedTensorKt {
-	public static final fun as1D (Lspace/kscience/kmath/tensors/core/BufferedTensor;)Lspace/kscience/kmath/tensors/core/BufferedTensor1D;
-	public static final fun as2D (Lspace/kscience/kmath/tensors/core/BufferedTensor;)Lspace/kscience/kmath/tensors/core/BufferedTensor2D;
 }
 
 public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/AnalyticTensorAlgebra {
@@ -2876,7 +2844,6 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public synthetic fun eq (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Z
 	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Z
 	public fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
-	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function2;)Z
 	public synthetic fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun eye (I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public final fun fromArray ([I[D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index aa6e58ef6..d99c35569 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -35,35 +35,34 @@ public open class BufferedTensor<T>(
 
     override fun hashCode(): Int = 0
 
-    public fun vectorSequence(): Sequence<BufferedTensor1D<T>> = sequence {
-        check(shape.size >= 1) { "todo" }
+    internal fun vectorSequence(): Sequence<BufferedTensor<T>> = sequence {
         val n = shape.size
         val vectorOffset = shape[n - 1]
         val vectorShape = intArrayOf(shape.last())
         for (offset in 0 until numel step vectorOffset) {
-            val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
+            val vector = BufferedTensor(vectorShape, buffer, offset)
             yield(vector)
         }
     }
 
-    public fun matrixSequence(): Sequence<BufferedTensor2D<T>> = sequence {
+    internal fun matrixSequence(): Sequence<BufferedTensor<T>> = sequence {
         check(shape.size >= 2) { "todo" }
         val n = shape.size
         val matrixOffset = shape[n - 1] * shape[n - 2]
-        val matrixShape = intArrayOf(shape[n - 2], shape[n - 1]) //todo better way?
+        val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
         for (offset in 0 until numel step matrixOffset) {
-            val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
+            val matrix = BufferedTensor(matrixShape, buffer, offset)
             yield(matrix)
         }
     }
 
-    public inline fun forEachVector(vectorAction: (BufferedTensor1D<T>) -> Unit): Unit {
+    internal inline fun forEachVector(vectorAction: (BufferedTensor<T>) -> Unit): Unit {
         for (vector in vectorSequence()) {
             vectorAction(vector)
         }
     }
 
-    public inline fun forEachMatrix(matrixAction: (BufferedTensor2D<T>) -> Unit): Unit {
+    internal inline fun forEachMatrix(matrixAction: (BufferedTensor<T>) -> Unit): Unit {
         for (matrix in matrixSequence()) {
             matrixAction(matrix)
         }
@@ -71,7 +70,6 @@ public open class BufferedTensor<T>(
 
 }
 
-
 public class IntTensor internal constructor(
     shape: IntArray,
     buffer: IntArray,
@@ -112,90 +110,7 @@ public class DoubleTensor internal constructor(
             this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
 }
 
-
-public class BufferedTensor2D<T> internal constructor(
-    private val tensor: BufferedTensor<T>,
-) : BufferedTensor<T>(tensor), MutableStructure2D<T> {
-    init {
-        check(shape.size == 2) {
-            "Shape ${shape.toList()} not compatible with DoubleTensor2D"
-        }
-    }
-
-    override val shape: IntArray
-        get() = tensor.shape
-
-    override val rowNum: Int
-        get() = shape[0]
-    override val colNum: Int
-        get() = shape[1]
-
-    override fun get(i: Int, j: Int): T = tensor[intArrayOf(i, j)]
-
-    override fun get(index: IntArray): T = tensor[index]
-
-    override fun elements(): Sequence<Pair<IntArray, T>> = tensor.elements()
-
-    override fun set(i: Int, j: Int, value: T) {
-        tensor[intArrayOf(i, j)] = value
-    }
-
-    override val rows: List<BufferedTensor1D<T>>
-        get() = List(rowNum) { i ->
-            BufferedTensor1D(
-                BufferedTensor(
-                    shape = intArrayOf(colNum),
-                    buffer = VirtualMutableBuffer(colNum) { j -> get(i, j) },
-                    bufferStart = 0
-                )
-            )
-        }
-
-    override val columns: List<BufferedTensor1D<T>>
-        get() = List(colNum) { j ->
-            BufferedTensor1D(
-                BufferedTensor(
-                    shape = intArrayOf(rowNum),
-                    buffer = VirtualMutableBuffer(rowNum) { i -> get(i, j) },
-                    bufferStart = 0
-                )
-            )
-        }
-}
-
-public class BufferedTensor1D<T> internal constructor(
-    private val tensor: BufferedTensor<T>
-) : BufferedTensor<T>(tensor), MutableStructure1D<T> {
-    init {
-        check(shape.size == 1) {
-            "Shape ${shape.toList()} not compatible with DoubleTensor1D"
-        }
-    }
-
-    override fun get(index: IntArray): T = tensor[index]
-
-    override fun set(index: IntArray, value: T) {
-        tensor[index] = value
-    }
-
-    override val size: Int
-        get() = tensor.linearStructure.size
-
-    override fun get(index: Int): T = tensor[intArrayOf(index)]
-
-    override fun set(index: Int, value: T) {
-        tensor[intArrayOf(index)] = value
-    }
-
-    override fun copy(): MutableBuffer<T> = tensor.buffer.copy()
-
-}
-
-internal fun BufferedTensor<Int>.asIntTensor(): IntTensor = IntTensor(this)
-internal fun BufferedTensor<Long>.asLongTensor(): LongTensor = LongTensor(this)
-internal fun BufferedTensor<Float>.asFloatTensor(): FloatTensor = FloatTensor(this)
-internal fun BufferedTensor<Double>.asDoubleTensor(): DoubleTensor = DoubleTensor(this)
-
-
-public fun <T> BufferedTensor<T>.as2D(): BufferedTensor2D<T> = BufferedTensor2D(this)
-public fun <T> BufferedTensor<T>.as1D(): BufferedTensor1D<T> = BufferedTensor1D(this)
\ No newline at end of file
+internal fun BufferedTensor<Int>.asTensor(): IntTensor = IntTensor(this)
+internal fun BufferedTensor<Long>.asTensor(): LongTensor = LongTensor(this)
+internal fun BufferedTensor<Float>.asTensor(): FloatTensor = FloatTensor(this)
+internal fun BufferedTensor<Double>.asTensor(): DoubleTensor = DoubleTensor(this)
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 70fe8a1cc..0d19fd3f6 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -1,5 +1,9 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.nd.MutableStructure1D
+import space.kscience.kmath.nd.MutableStructure2D
+import space.kscience.kmath.nd.as1D
+import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 import kotlin.math.sqrt
 
@@ -11,6 +15,48 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun DoubleTensor.det(): DoubleTensor = detLU()
 
+    private inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStructure1D<Int>, m: Int) {
+        for (row in 0 until m) pivots[row] = row
+
+        for (i in 0 until m) {
+            var maxVal = -1.0
+            var maxInd = i
+
+            for (k in i until m) {
+                val absA = kotlin.math.abs(lu[k, i])
+                if (absA > maxVal) {
+                    maxVal = absA
+                    maxInd = k
+                }
+            }
+
+            //todo check singularity
+
+            if (maxInd != i) {
+
+                val j = pivots[i]
+                pivots[i] = pivots[maxInd]
+                pivots[maxInd] = j
+
+                for (k in 0 until m) {
+                    val tmp = lu[i, k]
+                    lu[i, k] = lu[maxInd, k]
+                    lu[maxInd, k] = tmp
+                }
+
+                pivots[m] += 1
+
+            }
+
+            for (j in i + 1 until m) {
+                lu[j, i] /= lu[i, i]
+                for (k in i + 1 until m) {
+                    lu[j, k] -= lu[j, i] * lu[i, k]
+                }
+            }
+        }
+    }
+
     override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
 
         checkSquareMatrix(shape)
@@ -27,90 +73,93 @@ public class DoubleLinearOpsTensorAlgebra :
             IntArray(pivotsShape.reduce(Int::times)) { 0 }
         )
 
-        for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
-            for (row in 0 until m) pivots[row] = row
-
-            for (i in 0 until m) {
-                var maxVal = -1.0
-                var maxInd = i
-
-                for (k in i until m) {
-                    val absA = kotlin.math.abs(lu[k, i])
-                    if (absA > maxVal) {
-                        maxVal = absA
-                        maxInd = k
-                    }
-                }
-
-                //todo check singularity
-
-                if (maxInd != i) {
-
-                    val j = pivots[i]
-                    pivots[i] = pivots[maxInd]
-                    pivots[maxInd] = j
-
-                    for (k in 0 until m) {
-                        val tmp = lu[i, k]
-                        lu[i, k] = lu[maxInd, k]
-                        lu[maxInd, k] = tmp
-                    }
-
-                    pivots[m] += 1
-
-                }
-
-                for (j in i + 1 until m) {
-                    lu[j, i] /= lu[i, i]
-                    for (k in i + 1 until m) {
-                        lu[j, k] -= lu[j, i] * lu[i, k]
-                    }
-                }
-            }
-        }
-
+        for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
+            luHelper(lu.as2D(), pivots.as1D(), m)
 
         return Pair(luTensor, pivotsTensor)
 
     }
 
-    override fun luPivot(luTensor: DoubleTensor, pivotsTensor: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+    private inline fun pivInit(
+        p: MutableStructure2D<Double>,
+        pivot: MutableStructure1D<Int>,
+        n: Int
+    ) {
+        for (i in 0 until n) {
+            p[i, pivot[i]] = 1.0
+        }
+    }
+
+    private inline fun luPivotHelper(
+        l: MutableStructure2D<Double>,
+        u: MutableStructure2D<Double>,
+        lu: MutableStructure2D<Double>,
+        n: Int
+    ) {
+        for (i in 0 until n) {
+            for (j in 0 until n) {
+                if (i == j) {
+                    l[i, j] = 1.0
+                }
+                if (j < i) {
+                    l[i, j] = lu[i, j]
+                }
+                if (j >= i) {
+                    u[i, j] = lu[i, j]
+                }
+            }
+        }
+    }
+
+    override fun luPivot(
+        luTensor: DoubleTensor,
+        pivotsTensor: IntTensor
+    ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         //todo checks
         checkSquareMatrix(luTensor.shape)
-        check(luTensor.shape.dropLast(1).toIntArray() contentEquals pivotsTensor.shape) { "Bed shapes (("} //todo rewrite
+        check(
+            luTensor.shape.dropLast(1).toIntArray() contentEquals pivotsTensor.shape
+        ) { "Bed shapes ((" } //todo rewrite
 
         val n = luTensor.shape.last()
         val pTensor = luTensor.zeroesLike()
-        for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
-            for (i in 0 until n){
-                p[i, pivot[i]] = 1.0
-            }
-        }
+        for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
+            pivInit(p.as2D(), pivot.as1D(), n)
 
         val lTensor = luTensor.zeroesLike()
         val uTensor = luTensor.zeroesLike()
 
-        for ((pairLU, lu) in lTensor.matrixSequence().zip(uTensor.matrixSequence()).zip(luTensor.matrixSequence())){
+        for ((pairLU, lu) in lTensor.matrixSequence().zip(uTensor.matrixSequence())
+            .zip(luTensor.matrixSequence())) {
             val (l, u) = pairLU
-            for (i in 0 until n) {
-                for (j in 0 until n) {
-                    if (i == j) {
-                        l[i, j] = 1.0
-                    }
-                    if (j < i) {
-                        l[i, j] = lu[i, j]
-                    }
-                    if (j >= i) {
-                        u[i, j] = lu[i, j]
-                    }
-                }
-            }
+            luPivotHelper(l.as2D(), u.as2D(), lu.as2D(), n)
         }
 
         return Triple(pTensor, lTensor, uTensor)
 
     }
 
+    private inline fun choleskyHelper(
+        a: MutableStructure2D<Double>,
+        l: MutableStructure2D<Double>,
+        n: Int
+    ) {
+        for (i in 0 until n) {
+            for (j in 0 until i) {
+                var h = a[i, j]
+                for (k in 0 until j) {
+                    h -= l[i, k] * l[j, k]
+                }
+                l[i, j] = h / l[j, j]
+            }
+            var h = a[i, i]
+            for (j in 0 until i) {
+                h -= l[i, j] * l[i, j]
+            }
+            l[i, i] = sqrt(h)
+        }
+    }
+
     override fun DoubleTensor.cholesky(): DoubleTensor {
         // todo checks
         checkSquareMatrix(shape)
@@ -118,22 +167,8 @@ public class DoubleLinearOpsTensorAlgebra :
         val n = shape.last()
         val lTensor = zeroesLike()
 
-        for ((a, l) in this.matrixSequence().zip(lTensor.matrixSequence())) {
-            for (i in 0 until n) {
-                for (j in 0 until i) {
-                    var h = a[i, j]
-                    for (k in 0 until j) {
-                        h -= l[i, k] * l[j, k]
-                    }
-                    l[i, j] = h / l[j, j]
-                }
-                var h = a[i, i]
-                for (j in 0 until i) {
-                    h -= l[i, j] * l[i, j]
-                }
-                l[i, i] = sqrt(h)
-            }
-        }
+        for ((a, l) in this.matrixSequence().zip(lTensor.matrixSequence()))
+            for (i in 0 until n) choleskyHelper(a.as2D(), l.as2D(), n)
 
         return lTensor
     }
@@ -150,9 +185,11 @@ public class DoubleLinearOpsTensorAlgebra :
         TODO("ANDREI")
     }
 
-    private fun luMatrixDet(lu: BufferedTensor2D<Double>, pivots: BufferedTensor1D<Int>): Double {
+    private fun luMatrixDet(luTensor: MutableStructure2D<Double>, pivotsTensor: MutableStructure1D<Int>): Double {
+        val lu = luTensor.as2D()
+        val pivots = pivotsTensor.as1D()
         val m = lu.shape[0]
-        val sign = if((pivots[m] - m) % 2 == 0) 1.0 else -1.0
+        val sign = if ((pivots[m] - m) % 2 == 0) 1.0 else -1.0
         return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
     }
 
@@ -162,34 +199,34 @@ public class DoubleLinearOpsTensorAlgebra :
 
         val detTensorShape = IntArray(n - 1) { i -> shape[i] }
         detTensorShape[n - 2] = 1
-        val resBuffer =  DoubleArray(detTensorShape.reduce(Int::times)) { 0.0 }
+        val resBuffer = DoubleArray(detTensorShape.reduce(Int::times)) { 0.0 }
 
         val detTensor = DoubleTensor(
             detTensorShape,
             resBuffer
         )
 
-        luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).forEachIndexed { index, (luMatrix, pivots) ->
-            resBuffer[index] = luMatrixDet(luMatrix, pivots)
+        luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).forEachIndexed { index, (lu, pivots) ->
+            resBuffer[index] = luMatrixDet(lu.as2D(), pivots.as1D())
         }
 
         return detTensor
     }
 
     private fun luMatrixInv(
-        lu: BufferedTensor2D<Double>,
-        pivots: BufferedTensor1D<Int>,
-        invMatrix : BufferedTensor2D<Double>
-    ): Unit {
+        lu: MutableStructure2D<Double>,
+        pivots: MutableStructure1D<Int>,
+        invMatrix: MutableStructure2D<Double>
+    ) {
         val m = lu.shape[0]
 
         for (j in 0 until m) {
             for (i in 0 until m) {
-                if (pivots[i] == j){
+                if (pivots[i] == j) {
                     invMatrix[i, j] = 1.0
                 }
 
-                for (k in 0 until i){
+                for (k in 0 until i) {
                     invMatrix[i, j] -= lu[i, k] * invMatrix[k, j]
                 }
             }
@@ -205,13 +242,12 @@ public class DoubleLinearOpsTensorAlgebra :
 
     public fun DoubleTensor.invLU(): DoubleTensor {
         val (luTensor, pivotsTensor) = lu()
-        val n = shape.size
         val invTensor = luTensor.zeroesLike()
 
         val seq = luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
         for ((luP, invMatrix) in seq) {
             val (lu, pivots) = luP
-            luMatrixInv(lu, pivots, invMatrix)
+            luMatrixInv(lu.as2D(), pivots.as1D(), invMatrix.as2D())
         }
 
         return invTensor
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 0a2e8c86c..b25f0164b 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -1,8 +1,7 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.linear.Matrix
 import space.kscience.kmath.nd.MutableStructure2D
-import space.kscience.kmath.nd.Structure2D
+import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
 import kotlin.math.abs
 
@@ -224,6 +223,23 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return this.view(other.shape)
     }
 
+    private inline fun dotHelper(
+        a: MutableStructure2D<Double>,
+        b: MutableStructure2D<Double>,
+        res: MutableStructure2D<Double>,
+        l: Int, m: Int, n: Int
+    ) {
+        for (i in 0 until l) {
+            for (j in 0 until n) {
+                var curr = 0.0
+                for (k in 0 until m) {
+                    curr += a[i, k] * b[k, j]
+                }
+                res[i, j] = curr
+            }
+        }
+    }
+
     override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
         if (this.shape.size == 1 && other.shape.size == 1) {
             return DoubleTensor(intArrayOf(1), doubleArrayOf(this.times(other).buffer.array().sum()))
@@ -240,7 +256,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         }
         if (other.shape.size == 1) {
             lastDim = true
-            newOther = other.view(other.shape + intArrayOf(1) )
+            newOther = other.view(other.shape + intArrayOf(1))
         }
 
         val broadcastTensors = broadcastOuterTensors(newThis, newOther)
@@ -248,7 +264,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         newOther = broadcastTensors[1]
 
         val l = newThis.shape[newThis.shape.size - 2]
-        val m1= newThis.shape[newThis.shape.size - 1]
+        val m1 = newThis.shape[newThis.shape.size - 1]
         val m2 = newOther.shape[newOther.shape.size - 2]
         val n = newOther.shape[newOther.shape.size - 1]
         if (m1 != m2) {
@@ -262,21 +278,14 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
         for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
             val (a, b) = ab
-
-            for (i in 0 until l) {
-                for (j in 0 until n) {
-                    var curr = 0.0
-                    for (k in 0 until m) {
-                        curr += a[i, k] * b[k, j]
-                    }
-                   res[i, j] = curr
-                }
-            }
+            dotHelper(a.as2D(), b.as2D(), res.as2D(), l, m, n)
         }
 
         if (penultimateDim) {
-            return resTensor.view(resTensor.shape.dropLast(2).toIntArray() +
-                    intArrayOf(resTensor.shape.last()))
+            return resTensor.view(
+                resTensor.shape.dropLast(2).toIntArray() +
+                        intArrayOf(resTensor.shape.last())
+            )
         }
         if (lastDim) {
             return resTensor.view(resTensor.shape.dropLast(1).toIntArray())
@@ -307,15 +316,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     public fun DoubleTensor.eq(other: DoubleTensor): Boolean = this.eq(other, 1e-5)
 
-    public fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
-        if (!(this.shape contentEquals other.shape)) {
-            return false
-        }
-        return this.eq(other, eqFunction)
-    }
+    public fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean =
+        this.eq(other, eqFunction)
 
-    public fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
-        // todo broadcasting checking
+    private fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
+        checkShapesCompatible(this, other)
         val n = this.linearStructure.size
         if (n != other.linearStructure.size) {
             return false
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index c644a295c..247ed3913 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -11,14 +11,14 @@ internal inline fun <T, TensorType : TensorStructure<T>,
         "Illegal empty shape provided"
     }
 
-internal inline fun < TensorType : TensorStructure<Double>,
+internal inline fun <TensorType : TensorStructure<Double>,
         TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
         TorchTensorAlgebraType.checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
     check(buffer.isNotEmpty()) {
         "Illegal empty buffer provided"
     }
 
-internal inline fun < TensorType : TensorStructure<Double>,
+internal inline fun <TensorType : TensorStructure<Double>,
         TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
         TorchTensorAlgebraType.checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
     check(buffer.size == shape.reduce(Int::times)) {
@@ -56,4 +56,4 @@ internal inline fun <T, TensorType : TensorStructure<T>,
     check(shape[n - 1] == shape[n - 2]) {
         "Tensor must be batches of square matrices, but they are ${shape[n - 1]} by ${shape[n - 1]} matrices"
     }
-}
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 40597f539..5e6fdcdc5 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -1,5 +1,7 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.nd.as1D
+import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.structures.toDoubleArray
 import kotlin.test.Test
 import kotlin.test.assertEquals

From 51eca003af9daeb72a53fc05867d955d1167154c Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 29 Mar 2021 22:11:34 +0100
Subject: [PATCH 071/178] Moved back value to main algebra context

---
 kmath-core/api/kmath-core.api                  | 17 +++--------------
 .../kmath/tensors/ReduceOpsTensorAlgebra.kt    |  7 -------
 .../kscience/kmath/tensors/TensorAlgebra.kt    |  2 ++
 .../core/DoubleReduceOpsTensorAlgebra.kt       | 18 ------------------
 .../kmath/tensors/core/DoubleTensorAlgebra.kt  |  7 +++++++
 .../tensors/core/TestDoubleLinearOpsAlgebra.kt |  9 +++------
 .../kmath/tensors/core/TestDoubleTensor.kt     | 10 +++++++++-
 7 files changed, 24 insertions(+), 46 deletions(-)
 delete mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ReduceOpsTensorAlgebra.kt
 delete mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebra.kt

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 786b81d19..373920204 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -2654,10 +2654,6 @@ public final class space/kscience/kmath/tensors/LinearOpsTensorAlgebra$DefaultIm
 	public static synthetic fun symEig$default (Lspace/kscience/kmath/tensors/LinearOpsTensorAlgebra;Lspace/kscience/kmath/nd/MutableStructureND;ZILjava/lang/Object;)Lkotlin/Pair;
 }
 
-public abstract interface class space/kscience/kmath/tensors/ReduceOpsTensorAlgebra : space/kscience/kmath/tensors/TensorAlgebra {
-	public abstract fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
-}
-
 public abstract interface class space/kscience/kmath/tensors/TensorAlgebra {
 	public abstract fun copy (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2686,6 +2682,7 @@ public abstract interface class space/kscience/kmath/tensors/TensorAlgebra {
 	public abstract fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
 	public abstract fun transpose (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun unaryMinus (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
 	public abstract fun view (Lspace/kscience/kmath/nd/MutableStructureND;[I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun viewAs (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun zeroesLike (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2809,16 +2806,6 @@ public final class space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebr
 	public static final fun DoubleLinearOpsTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
 }
 
-public final class space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/ReduceOpsTensorAlgebra {
-	public fun <init> ()V
-	public synthetic fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
-	public fun value (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Ljava/lang/Double;
-}
-
-public final class space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebraKt {
-	public static final fun DoubleReduceOpsTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
-}
-
 public final class space/kscience/kmath/tensors/core/DoubleTensor : space/kscience/kmath/tensors/core/BufferedTensor {
 }
 
@@ -2892,6 +2879,8 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public fun transpose (Lspace/kscience/kmath/tensors/core/DoubleTensor;II)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun unaryMinus (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun unaryMinus (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
+	public fun value (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Ljava/lang/Double;
 	public synthetic fun view (Lspace/kscience/kmath/nd/MutableStructureND;[I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun view (Lspace/kscience/kmath/tensors/core/DoubleTensor;[I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun viewAs (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ReduceOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ReduceOpsTensorAlgebra.kt
deleted file mode 100644
index 820b9b25b..000000000
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/ReduceOpsTensorAlgebra.kt
+++ /dev/null
@@ -1,7 +0,0 @@
-package space.kscience.kmath.tensors
-
-public interface ReduceOpsTensorAlgebra<T, TensorType : TensorStructure<T>> :
-    TensorAlgebra<T, TensorType> {
-    public fun TensorType.value(): T
-
-}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index b05105ed8..5b7515b20 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -5,6 +5,8 @@ import space.kscience.kmath.tensors.core.DoubleTensor
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
+    public fun TensorType.value(): T
+
     //https://pytorch.org/docs/stable/generated/torch.full.html
     public fun full(value: T, shape: IntArray): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebra.kt
deleted file mode 100644
index 9a8aa9ebf..000000000
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleReduceOpsTensorAlgebra.kt
+++ /dev/null
@@ -1,18 +0,0 @@
-package space.kscience.kmath.tensors.core
-
-import space.kscience.kmath.tensors.ReduceOpsTensorAlgebra
-
-public class DoubleReduceOpsTensorAlgebra:
-    DoubleTensorAlgebra(),
-    ReduceOpsTensorAlgebra<Double, DoubleTensor> {
-
-    override fun DoubleTensor.value(): Double {
-        check(this.shape contentEquals intArrayOf(1)) {
-            "Inconsistent value for tensor of shape ${shape.toList()}"
-        }
-        return this.buffer.array()[this.bufferStart]
-    }
-}
-
-public inline fun <R> DoubleReduceOpsTensorAlgebra(block: DoubleReduceOpsTensorAlgebra.() -> R): R =
-    DoubleReduceOpsTensorAlgebra().block()
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index b25f0164b..b418f3647 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -7,6 +7,13 @@ import kotlin.math.abs
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
 
+    override fun DoubleTensor.value(): Double {
+        check(this.shape contentEquals intArrayOf(1)) {
+            "Inconsistent value for tensor of shape ${shape.toList()}"
+        }
+        return this.buffer.array()[this.bufferStart]
+    }
+
     public fun fromArray(shape: IntArray, buffer: DoubleArray): DoubleTensor {
         checkEmptyShape(shape)
         checkEmptyDoubleBuffer(buffer)
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 3a129b03c..d2d4ffb67 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -72,11 +72,8 @@ class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testScalarProduct() = DoubleLinearOpsTensorAlgebra {
-        val a = fromArray(intArrayOf(3), doubleArrayOf(1.8,2.5, 6.8))
-        val b = fromArray(intArrayOf(3), doubleArrayOf(5.5,2.6, 6.4))
-        DoubleReduceOpsTensorAlgebra {
-            assertEquals(a.dot(b).value(), 59.92)
-        }
-
+        val a = fromArray(intArrayOf(3), doubleArrayOf(1.8, 2.5, 6.8))
+        val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4))
+        assertEquals(a.dot(b).value(), 59.92)
     }
 }
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 5e6fdcdc5..4f4d9bbdf 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -10,7 +10,7 @@ import kotlin.test.assertTrue
 class TestDoubleTensor {
 
     @Test
-    fun valueTest() = DoubleReduceOpsTensorAlgebra {
+    fun valueTest() = DoubleTensorAlgebra {
         val value = 12.5
         val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
@@ -37,5 +37,13 @@ class TestDoubleTensor {
 
         vector[0] = 109.56
         assertEquals(tensor[intArrayOf(0,1,0)], 109.56)
+
+        tensor.matrixSequence().forEach {
+            val a = it.asTensor()
+            val secondRow = a[1].as1D()
+            val secondColumn = a.transpose(0,1)[1].as1D()
+            assertEquals(secondColumn[0], 77.89)
+            assertEquals(secondRow[1], secondColumn[1])
+        }
     }
 }
\ No newline at end of file

From d281dfca3ab869fcfb16181f82cc91b57bb4d26d Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 30 Mar 2021 11:22:55 +0100
Subject: [PATCH 072/178] Separate linear algebra utils into dedicated module

---
 .../kmath/tensors/core/BufferedTensor.kt      |  38 ----
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 134 +------------
 .../kmath/tensors/core/DoubleTensorAlgebra.kt |  20 +-
 .../kscience/kmath/tensors/core/linutils.kt   | 188 ++++++++++++++++++
 4 files changed, 190 insertions(+), 190 deletions(-)
 create mode 100644 kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index d99c35569..9a4d13d2c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -1,7 +1,5 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.nd.MutableStructure1D
-import space.kscience.kmath.nd.MutableStructure2D
 import space.kscience.kmath.structures.*
 import space.kscience.kmath.tensors.TensorStructure
 
@@ -18,9 +16,6 @@ public open class BufferedTensor<T>(
     public val numel: Int
         get() = linearStructure.size
 
-    internal constructor(tensor: BufferedTensor<T>) :
-            this(tensor.shape, tensor.buffer, tensor.bufferStart)
-
     override fun get(index: IntArray): T = buffer[bufferStart + linearStructure.offset(index)]
 
     override fun set(index: IntArray, value: T) {
@@ -35,39 +30,6 @@ public open class BufferedTensor<T>(
 
     override fun hashCode(): Int = 0
 
-    internal fun vectorSequence(): Sequence<BufferedTensor<T>> = sequence {
-        val n = shape.size
-        val vectorOffset = shape[n - 1]
-        val vectorShape = intArrayOf(shape.last())
-        for (offset in 0 until numel step vectorOffset) {
-            val vector = BufferedTensor(vectorShape, buffer, offset)
-            yield(vector)
-        }
-    }
-
-    internal fun matrixSequence(): Sequence<BufferedTensor<T>> = sequence {
-        check(shape.size >= 2) { "todo" }
-        val n = shape.size
-        val matrixOffset = shape[n - 1] * shape[n - 2]
-        val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
-        for (offset in 0 until numel step matrixOffset) {
-            val matrix = BufferedTensor(matrixShape, buffer, offset)
-            yield(matrix)
-        }
-    }
-
-    internal inline fun forEachVector(vectorAction: (BufferedTensor<T>) -> Unit): Unit {
-        for (vector in vectorSequence()) {
-            vectorAction(vector)
-        }
-    }
-
-    internal inline fun forEachMatrix(matrixAction: (BufferedTensor<T>) -> Unit): Unit {
-        for (matrix in matrixSequence()) {
-            matrixAction(matrix)
-        }
-    }
-
 }
 
 public class IntTensor internal constructor(
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 0d19fd3f6..b81cf0364 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -1,11 +1,8 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.nd.MutableStructure1D
-import space.kscience.kmath.nd.MutableStructure2D
+import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
-import kotlin.math.sqrt
 
 public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
@@ -15,48 +12,6 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun DoubleTensor.det(): DoubleTensor = detLU()
 
-    private inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStructure1D<Int>, m: Int) {
-        for (row in 0 until m) pivots[row] = row
-
-        for (i in 0 until m) {
-            var maxVal = -1.0
-            var maxInd = i
-
-            for (k in i until m) {
-                val absA = kotlin.math.abs(lu[k, i])
-                if (absA > maxVal) {
-                    maxVal = absA
-                    maxInd = k
-                }
-            }
-
-            //todo check singularity
-
-            if (maxInd != i) {
-
-                val j = pivots[i]
-                pivots[i] = pivots[maxInd]
-                pivots[maxInd] = j
-
-                for (k in 0 until m) {
-                    val tmp = lu[i, k]
-                    lu[i, k] = lu[maxInd, k]
-                    lu[maxInd, k] = tmp
-                }
-
-                pivots[m] += 1
-
-            }
-
-            for (j in i + 1 until m) {
-                lu[j, i] /= lu[i, i]
-                for (k in i + 1 until m) {
-                    lu[j, k] -= lu[j, i] * lu[i, k]
-                }
-            }
-        }
-    }
-
     override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
 
         checkSquareMatrix(shape)
@@ -80,37 +35,6 @@ public class DoubleLinearOpsTensorAlgebra :
 
     }
 
-    private inline fun pivInit(
-        p: MutableStructure2D<Double>,
-        pivot: MutableStructure1D<Int>,
-        n: Int
-    ) {
-        for (i in 0 until n) {
-            p[i, pivot[i]] = 1.0
-        }
-    }
-
-    private inline fun luPivotHelper(
-        l: MutableStructure2D<Double>,
-        u: MutableStructure2D<Double>,
-        lu: MutableStructure2D<Double>,
-        n: Int
-    ) {
-        for (i in 0 until n) {
-            for (j in 0 until n) {
-                if (i == j) {
-                    l[i, j] = 1.0
-                }
-                if (j < i) {
-                    l[i, j] = lu[i, j]
-                }
-                if (j >= i) {
-                    u[i, j] = lu[i, j]
-                }
-            }
-        }
-    }
-
     override fun luPivot(
         luTensor: DoubleTensor,
         pivotsTensor: IntTensor
@@ -139,27 +63,6 @@ public class DoubleLinearOpsTensorAlgebra :
 
     }
 
-    private inline fun choleskyHelper(
-        a: MutableStructure2D<Double>,
-        l: MutableStructure2D<Double>,
-        n: Int
-    ) {
-        for (i in 0 until n) {
-            for (j in 0 until i) {
-                var h = a[i, j]
-                for (k in 0 until j) {
-                    h -= l[i, k] * l[j, k]
-                }
-                l[i, j] = h / l[j, j]
-            }
-            var h = a[i, i]
-            for (j in 0 until i) {
-                h -= l[i, j] * l[i, j]
-            }
-            l[i, i] = sqrt(h)
-        }
-    }
-
     override fun DoubleTensor.cholesky(): DoubleTensor {
         // todo checks
         checkSquareMatrix(shape)
@@ -185,14 +88,6 @@ public class DoubleLinearOpsTensorAlgebra :
         TODO("ANDREI")
     }
 
-    private fun luMatrixDet(luTensor: MutableStructure2D<Double>, pivotsTensor: MutableStructure1D<Int>): Double {
-        val lu = luTensor.as2D()
-        val pivots = pivotsTensor.as1D()
-        val m = lu.shape[0]
-        val sign = if ((pivots[m] - m) % 2 == 0) 1.0 else -1.0
-        return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
-    }
-
     public fun DoubleTensor.detLU(): DoubleTensor {
         val (luTensor, pivotsTensor) = lu()
         val n = shape.size
@@ -213,33 +108,6 @@ public class DoubleLinearOpsTensorAlgebra :
         return detTensor
     }
 
-    private fun luMatrixInv(
-        lu: MutableStructure2D<Double>,
-        pivots: MutableStructure1D<Int>,
-        invMatrix: MutableStructure2D<Double>
-    ) {
-        val m = lu.shape[0]
-
-        for (j in 0 until m) {
-            for (i in 0 until m) {
-                if (pivots[i] == j) {
-                    invMatrix[i, j] = 1.0
-                }
-
-                for (k in 0 until i) {
-                    invMatrix[i, j] -= lu[i, k] * invMatrix[k, j]
-                }
-            }
-
-            for (i in m - 1 downTo 0) {
-                for (k in i + 1 until m) {
-                    invMatrix[i, j] -= lu[i, k] * invMatrix[k, j]
-                }
-                invMatrix[i, j] /= lu[i, i]
-            }
-        }
-    }
-
     public fun DoubleTensor.invLU(): DoubleTensor {
         val (luTensor, pivotsTensor) = lu()
         val invTensor = luTensor.zeroesLike()
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index b418f3647..c878fc58c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -1,8 +1,7 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.nd.MutableStructure2D
-import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
+import space.kscience.kmath.nd.as2D
 import kotlin.math.abs
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
@@ -230,23 +229,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return this.view(other.shape)
     }
 
-    private inline fun dotHelper(
-        a: MutableStructure2D<Double>,
-        b: MutableStructure2D<Double>,
-        res: MutableStructure2D<Double>,
-        l: Int, m: Int, n: Int
-    ) {
-        for (i in 0 until l) {
-            for (j in 0 until n) {
-                var curr = 0.0
-                for (k in 0 until m) {
-                    curr += a[i, k] * b[k, j]
-                }
-                res[i, j] = curr
-            }
-        }
-    }
-
     override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
         if (this.shape.size == 1 && other.shape.size == 1) {
             return DoubleTensor(intArrayOf(1), doubleArrayOf(this.times(other).buffer.array().sum()))
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
new file mode 100644
index 000000000..e78cc33c7
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -0,0 +1,188 @@
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.nd.MutableStructure1D
+import space.kscience.kmath.nd.MutableStructure2D
+import space.kscience.kmath.nd.as1D
+import space.kscience.kmath.nd.as2D
+import kotlin.math.sqrt
+
+
+internal inline fun <T> BufferedTensor<T>.vectorSequence(): Sequence<BufferedTensor<T>> = sequence {
+    val n = shape.size
+    val vectorOffset = shape[n - 1]
+    val vectorShape = intArrayOf(shape.last())
+    for (offset in 0 until numel step vectorOffset) {
+        val vector = BufferedTensor(vectorShape, buffer, offset)
+        yield(vector)
+    }
+}
+
+internal inline fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTensor<T>> = sequence {
+    check(shape.size >= 2) { "todo" }
+    val n = shape.size
+    val matrixOffset = shape[n - 1] * shape[n - 2]
+    val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
+    for (offset in 0 until numel step matrixOffset) {
+        val matrix = BufferedTensor(matrixShape, buffer, offset)
+        yield(matrix)
+    }
+}
+
+internal inline fun <T> BufferedTensor<T>.forEachVector(vectorAction: (BufferedTensor<T>) -> Unit): Unit {
+    for (vector in vectorSequence()) {
+        vectorAction(vector)
+    }
+}
+
+internal inline fun <T> BufferedTensor<T>.forEachMatrix(matrixAction: (BufferedTensor<T>) -> Unit): Unit {
+    for (matrix in matrixSequence()) {
+        matrixAction(matrix)
+    }
+}
+
+
+internal inline fun dotHelper(
+    a: MutableStructure2D<Double>,
+    b: MutableStructure2D<Double>,
+    res: MutableStructure2D<Double>,
+    l: Int, m: Int, n: Int
+) {
+    for (i in 0 until l) {
+        for (j in 0 until n) {
+            var curr = 0.0
+            for (k in 0 until m) {
+                curr += a[i, k] * b[k, j]
+            }
+            res[i, j] = curr
+        }
+    }
+}
+
+internal inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStructure1D<Int>, m: Int) {
+    for (row in 0 until m) pivots[row] = row
+
+    for (i in 0 until m) {
+        var maxVal = -1.0
+        var maxInd = i
+
+        for (k in i until m) {
+            val absA = kotlin.math.abs(lu[k, i])
+            if (absA > maxVal) {
+                maxVal = absA
+                maxInd = k
+            }
+        }
+
+        //todo check singularity
+
+        if (maxInd != i) {
+
+            val j = pivots[i]
+            pivots[i] = pivots[maxInd]
+            pivots[maxInd] = j
+
+            for (k in 0 until m) {
+                val tmp = lu[i, k]
+                lu[i, k] = lu[maxInd, k]
+                lu[maxInd, k] = tmp
+            }
+
+            pivots[m] += 1
+
+        }
+
+        for (j in i + 1 until m) {
+            lu[j, i] /= lu[i, i]
+            for (k in i + 1 until m) {
+                lu[j, k] -= lu[j, i] * lu[i, k]
+            }
+        }
+    }
+}
+
+internal inline fun pivInit(
+    p: MutableStructure2D<Double>,
+    pivot: MutableStructure1D<Int>,
+    n: Int
+) {
+    for (i in 0 until n) {
+        p[i, pivot[i]] = 1.0
+    }
+}
+
+internal inline fun luPivotHelper(
+    l: MutableStructure2D<Double>,
+    u: MutableStructure2D<Double>,
+    lu: MutableStructure2D<Double>,
+    n: Int
+) {
+    for (i in 0 until n) {
+        for (j in 0 until n) {
+            if (i == j) {
+                l[i, j] = 1.0
+            }
+            if (j < i) {
+                l[i, j] = lu[i, j]
+            }
+            if (j >= i) {
+                u[i, j] = lu[i, j]
+            }
+        }
+    }
+}
+
+internal inline fun choleskyHelper(
+    a: MutableStructure2D<Double>,
+    l: MutableStructure2D<Double>,
+    n: Int
+) {
+    for (i in 0 until n) {
+        for (j in 0 until i) {
+            var h = a[i, j]
+            for (k in 0 until j) {
+                h -= l[i, k] * l[j, k]
+            }
+            l[i, j] = h / l[j, j]
+        }
+        var h = a[i, i]
+        for (j in 0 until i) {
+            h -= l[i, j] * l[i, j]
+        }
+        l[i, i] = sqrt(h)
+    }
+}
+
+internal inline fun luMatrixDet(luTensor: MutableStructure2D<Double>, pivotsTensor: MutableStructure1D<Int>): Double {
+    val lu = luTensor.as2D()
+    val pivots = pivotsTensor.as1D()
+    val m = lu.shape[0]
+    val sign = if ((pivots[m] - m) % 2 == 0) 1.0 else -1.0
+    return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
+}
+
+internal inline fun luMatrixInv(
+    lu: MutableStructure2D<Double>,
+    pivots: MutableStructure1D<Int>,
+    invMatrix: MutableStructure2D<Double>
+) {
+    val m = lu.shape[0]
+
+    for (j in 0 until m) {
+        for (i in 0 until m) {
+            if (pivots[i] == j) {
+                invMatrix[i, j] = 1.0
+            }
+
+            for (k in 0 until i) {
+                invMatrix[i, j] -= lu[i, k] * invMatrix[k, j]
+            }
+        }
+
+        for (i in m - 1 downTo 0) {
+            for (k in i + 1 until m) {
+                invMatrix[i, j] -= lu[i, k] * invMatrix[k, j]
+            }
+            invMatrix[i, j] /= lu[i, i]
+        }
+    }
+}

From 2503d35ba8629055aaffc03f6b2e12e2030d7dd3 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Tue, 30 Mar 2021 14:53:19 +0300
Subject: [PATCH 073/178] complete qr + test qr and lu

---
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 51 +++++++++++++++++--
 .../core/TestDoubleLinearOpsAlgebra.kt        | 46 +++++++++++++++++
 2 files changed, 92 insertions(+), 5 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 1558e6af9..59ec944f2 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -118,7 +118,8 @@ public class DoubleLinearOpsTensorAlgebra :
         //todo checks
         checkSquareMatrix(luTensor.shape)
         check(
-            luTensor.shape.dropLast(1).toIntArray() contentEquals pivotsTensor.shape
+            luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() ||
+                    luTensor.shape.last() == pivotsTensor.shape.last() - 1
         ) { "Bed shapes ((" } //todo rewrite
 
         val n = luTensor.shape.last()
@@ -173,16 +174,56 @@ public class DoubleLinearOpsTensorAlgebra :
         return lTensor
     }
 
-    private fun matrixQR(
-        matrix: Structure2D<Double>,
+    private fun MutableStructure1D<Double>.dot(other: MutableStructure1D<Double>): Double {
+        var res = 0.0
+        for (i in 0 until size) {
+            res += this[i] * other[i]
+        }
+        return res
+    }
+
+    private fun MutableStructure1D<Double>.l2Norm(): Double {
+        var squareSum = 0.0
+        for (i in 0 until size) {
+            squareSum += this[i] * this[i]
+        }
+        return sqrt(squareSum)
+    }
+
+    fun qrHelper(
+        matrix: MutableStructure2D<Double>,
         q: MutableStructure2D<Double>,
         r: MutableStructure2D<Double>
     ) {
-
+        //todo check square
+        val n = matrix.colNum
+        for (j in 0 until n) {
+            val v = matrix.columns[j]
+            if (j > 0) {
+                for (i in 0 until j) {
+                    r[i, j] = q.columns[i].dot(matrix.columns[j])
+                    for (k in 0 until n) {
+                        v[k] = v[k] - r[i, j] * q.columns[i][k]
+                    }
+                }
+            }
+            r[j, j] = v.l2Norm()
+            for (i in 0 until n) {
+                q[i, j] = v[i] / r[j, j]
+            }
+        }
     }
 
     override fun DoubleTensor.qr(): Pair<DoubleTensor, DoubleTensor> {
-        TODO("ANDREI")
+        checkSquareMatrix(shape)
+        val qTensor = zeroesLike()
+        val rTensor = zeroesLike()
+        val seq = matrixSequence().zip((qTensor.matrixSequence().zip(rTensor.matrixSequence())))
+        for ((matrix, qr) in seq) {
+            val (q, r) = qr
+            qrHelper(matrix.as2D(), q.as2D(), r.as2D())
+        }
+        return Pair(qTensor, rTensor)
     }
 
     override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index d2d4ffb67..b79c54dd1 100644
--- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -76,4 +76,50 @@ class TestDoubleLinearOpsTensorAlgebra {
         val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4))
         assertEquals(a.dot(b).value(), 59.92)
     }
+
+    @Test
+    fun testQR() = DoubleLinearOpsTensorAlgebra {
+        val shape = intArrayOf(2, 2, 2)
+        val buffer = doubleArrayOf(
+            1.0, 3.0,
+            1.0, 2.0,
+            1.5, 1.0,
+            10.0, 2.0
+        )
+
+        val tensor = fromArray(shape, buffer)
+
+        val (q, r) = tensor.qr()
+
+        assertTrue { q.shape contentEquals shape }
+        assertTrue { r.shape contentEquals shape }
+
+        assertTrue { q.dot(r).buffer.array().epsEqual(buffer) }
+
+        //todo check orthogonality/upper triang.
+    }
+
+    @Test
+    fun testLU() = DoubleLinearOpsTensorAlgebra {
+        val shape = intArrayOf(2, 2, 2)
+        val buffer = doubleArrayOf(
+            1.0, 3.0,
+            1.0, 2.0,
+            1.5, 1.0,
+            10.0, 2.0
+        )
+        val tensor = fromArray(shape, buffer)
+
+        val (lu, pivots) = tensor.lu()
+
+        // todo check lu
+
+        val (p, l, u) = luPivot(lu, pivots)
+
+        assertTrue { p.shape contentEquals shape }
+        assertTrue { l.shape contentEquals shape }
+        assertTrue { u.shape contentEquals shape }
+
+        assertTrue { p.dot(tensor).buffer.array().epsEqual(l.dot(u).buffer.array()) }
+    }
 }

From 139534fdb343b984c94f5bc089df34b9e1d5946b Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Tue, 30 Mar 2021 15:13:45 +0300
Subject: [PATCH 074/178] fix

---
 .../kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt    | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 59ec944f2..6be50e710 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -183,11 +183,7 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     private fun MutableStructure1D<Double>.l2Norm(): Double {
-        var squareSum = 0.0
-        for (i in 0 until size) {
-            squareSum += this[i] * this[i]
-        }
-        return sqrt(squareSum)
+        return sqrt((0 until size).sumOf { this[it] * this[it] })
     }
 
     fun qrHelper(
@@ -223,7 +219,7 @@ public class DoubleLinearOpsTensorAlgebra :
             val (q, r) = qr
             qrHelper(matrix.as2D(), q.as2D(), r.as2D())
         }
-        return Pair(qTensor, rTensor)
+        return qTensor to rTensor
     }
 
     override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {

From 370bab462cae6d883677d0ed5cda7e0b885caa96 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 30 Mar 2021 13:14:09 +0100
Subject: [PATCH 075/178] merged pull request

---
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 40 -------------------
 .../kscience/kmath/tensors/core/linutils.kt   | 40 +++++++++++++++++++
 2 files changed, 40 insertions(+), 40 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 46ab5f80f..13fb18c01 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -77,46 +77,6 @@ public class DoubleLinearOpsTensorAlgebra :
         return lTensor
     }
 
-    private fun MutableStructure1D<Double>.dot(other: MutableStructure1D<Double>): Double {
-        var res = 0.0
-        for (i in 0 until size) {
-            res += this[i] * other[i]
-        }
-        return res
-    }
-
-    private fun MutableStructure1D<Double>.l2Norm(): Double {
-        var squareSum = 0.0
-        for (i in 0 until size) {
-            squareSum += this[i] * this[i]
-        }
-        return sqrt(squareSum)
-    }
-
-    fun qrHelper(
-        matrix: MutableStructure2D<Double>,
-        q: MutableStructure2D<Double>,
-        r: MutableStructure2D<Double>
-    ) {
-        //todo check square
-        val n = matrix.colNum
-        for (j in 0 until n) {
-            val v = matrix.columns[j]
-            if (j > 0) {
-                for (i in 0 until j) {
-                    r[i, j] = q.columns[i].dot(matrix.columns[j])
-                    for (k in 0 until n) {
-                        v[k] = v[k] - r[i, j] * q.columns[i][k]
-                    }
-                }
-            }
-            r[j, j] = v.l2Norm()
-            for (i in 0 until n) {
-                q[i, j] = v[i] / r[j, j]
-            }
-        }
-    }
-
     override fun DoubleTensor.qr(): Pair<DoubleTensor, DoubleTensor> {
         checkSquareMatrix(shape)
         val qTensor = zeroesLike()
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index e78cc33c7..49a4384c2 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -186,3 +186,43 @@ internal inline fun luMatrixInv(
         }
     }
 }
+
+internal inline fun MutableStructure1D<Double>.dot(other: MutableStructure1D<Double>): Double {
+    var res = 0.0
+    for (i in 0 until size) {
+        res += this[i] * other[i]
+    }
+    return res
+}
+
+internal inline fun MutableStructure1D<Double>.l2Norm(): Double {
+    var squareSum = 0.0
+    for (i in 0 until size) {
+        squareSum += this[i] * this[i]
+    }
+    return sqrt(squareSum)
+}
+
+internal inline fun qrHelper(
+    matrix: MutableStructure2D<Double>,
+    q: MutableStructure2D<Double>,
+    r: MutableStructure2D<Double>
+) {
+    //todo check square
+    val n = matrix.colNum
+    for (j in 0 until n) {
+        val v = matrix.columns[j]
+        if (j > 0) {
+            for (i in 0 until j) {
+                r[i, j] = q.columns[i].dot(matrix.columns[j])
+                for (k in 0 until n) {
+                    v[k] = v[k] - r[i, j] * q.columns[i][k]
+                }
+            }
+        }
+        r[j, j] = v.l2Norm()
+        for (i in 0 until n) {
+            q[i, j] = v[i] / r[j, j]
+        }
+    }
+}

From 581c13c573179dd9b2b67e65a4a2c84047ed9171 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 30 Mar 2021 14:14:05 +0100
Subject: [PATCH 076/178] drop code duplication

---
 kmath-core/api/kmath-core.api                 |  6 +--
 .../core/DoubleLinearOpsTensorAlgebra.kt      |  2 +-
 .../kscience/kmath/tensors/core/linutils.kt   | 42 +++++++------------
 3 files changed, 20 insertions(+), 30 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 373920204..634f38bed 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -2645,7 +2645,7 @@ public abstract interface class space/kscience/kmath/tensors/LinearOpsTensorAlge
 	public abstract fun inv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun lu (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Pair;
 	public abstract fun luPivot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
-	public abstract fun qr (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
+	public abstract fun qr (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Pair;
 	public abstract fun svd (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
 	public abstract fun symEig (Lspace/kscience/kmath/nd/MutableStructureND;Z)Lkotlin/Pair;
 }
@@ -2794,8 +2794,8 @@ public final class space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebr
 	public fun lu (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lkotlin/Pair;
 	public synthetic fun luPivot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
 	public fun luPivot (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/IntTensor;)Lkotlin/Triple;
-	public synthetic fun qr (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun qr (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public synthetic fun qr (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Pair;
+	public fun qr (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lkotlin/Pair;
 	public synthetic fun svd (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
 	public fun svd (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lkotlin/Triple;
 	public synthetic fun symEig (Lspace/kscience/kmath/nd/MutableStructureND;Z)Lkotlin/Pair;
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 13fb18c01..3d2d21e0c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -84,7 +84,7 @@ public class DoubleLinearOpsTensorAlgebra :
         val seq = matrixSequence().zip((qTensor.matrixSequence().zip(rTensor.matrixSequence())))
         for ((matrix, qr) in seq) {
             val (q, r) = qr
-            qrHelper(matrix.as2D(), q.as2D(), r.as2D())
+            qrHelper(matrix.asTensor(), q.asTensor(), r.as2D())
         }
         return Pair(qTensor, rTensor)
     }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index 49a4384c2..4067de5f4 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -187,42 +187,32 @@ internal inline fun luMatrixInv(
     }
 }
 
-internal inline fun MutableStructure1D<Double>.dot(other: MutableStructure1D<Double>): Double {
-    var res = 0.0
-    for (i in 0 until size) {
-        res += this[i] * other[i]
-    }
-    return res
-}
-
-internal inline fun MutableStructure1D<Double>.l2Norm(): Double {
-    var squareSum = 0.0
-    for (i in 0 until size) {
-        squareSum += this[i] * this[i]
-    }
-    return sqrt(squareSum)
-}
-
-internal inline fun qrHelper(
-    matrix: MutableStructure2D<Double>,
-    q: MutableStructure2D<Double>,
+internal inline fun DoubleLinearOpsTensorAlgebra.qrHelper(
+    matrix: DoubleTensor,
+    q: DoubleTensor,
     r: MutableStructure2D<Double>
 ) {
-    //todo check square
-    val n = matrix.colNum
+    checkSquareMatrix(matrix.shape)
+    val n = matrix.shape[0]
+    val qM = q.as2D()
+    val matrixT = matrix.transpose(0,1)
+    val qT = q.transpose(0,1)
+
     for (j in 0 until n) {
-        val v = matrix.columns[j]
+        val v = matrixT[j]
+        val vv = v.as1D()
         if (j > 0) {
             for (i in 0 until j) {
-                r[i, j] = q.columns[i].dot(matrix.columns[j])
+                r[i, j] = qT[i].dot(matrixT[j]).value()
                 for (k in 0 until n) {
-                    v[k] = v[k] - r[i, j] * q.columns[i][k]
+                    val qTi = qT[i].as1D()
+                    vv[k] = vv[k] - r[i, j] * qTi[k]
                 }
             }
         }
-        r[j, j] = v.l2Norm()
+        r[j, j] = DoubleAnalyticTensorAlgebra { v.dot(v).sqrt().value() }
         for (i in 0 until n) {
-            q[i, j] = v[i] / r[j, j]
+            qM[i, j] = vv[i] / r[j, j]
         }
     }
 }

From 03455a3bebc617e09392e6da7ac361040babf67a Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 30 Mar 2021 14:36:59 +0100
Subject: [PATCH 077/178] infix dot forgotten

---
 .../space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt  | 2 +-
 .../kotlin/space/kscience/kmath/tensors/core/linutils.kt      | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index c878fc58c..965aef1ea 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -229,7 +229,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return this.view(other.shape)
     }
 
-    override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
+    override infix fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
         if (this.shape.size == 1 && other.shape.size == 1) {
             return DoubleTensor(intArrayOf(1), doubleArrayOf(this.times(other).buffer.array().sum()))
         }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index 4067de5f4..f6cf71b07 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -203,14 +203,14 @@ internal inline fun DoubleLinearOpsTensorAlgebra.qrHelper(
         val vv = v.as1D()
         if (j > 0) {
             for (i in 0 until j) {
-                r[i, j] = qT[i].dot(matrixT[j]).value()
+                r[i, j] = (qT[i] dot matrixT[j]).value()
                 for (k in 0 until n) {
                     val qTi = qT[i].as1D()
                     vv[k] = vv[k] - r[i, j] * qTi[k]
                 }
             }
         }
-        r[j, j] = DoubleAnalyticTensorAlgebra { v.dot(v).sqrt().value() }
+        r[j, j] = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
         for (i in 0 until n) {
             qM[i, j] = vv[i] / r[j, j]
         }

From b5d3ca76db28552aa534f04813234047b2b6a9ae Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 30 Mar 2021 19:20:20 +0100
Subject: [PATCH 078/178] Dropping creation methods from interface

---
 .../kscience/kmath/tensors/TensorAlgebra.kt   | 21 -------------------
 .../tensors/TensorPartialDivisionAlgebra.kt   |  2 +-
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 18 ++++++++--------
 .../kscience/kmath/tensors/core/utils.kt      |  8 ++++++-
 4 files changed, 17 insertions(+), 32 deletions(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 5b7515b20..d7c6eaefd 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -1,29 +1,10 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.tensors.core.DoubleTensor
-
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     public fun TensorType.value(): T
 
-    //https://pytorch.org/docs/stable/generated/torch.full.html
-    public fun full(value: T, shape: IntArray): TensorType
-
-    public fun ones(shape: IntArray): TensorType
-    public fun zeros(shape: IntArray): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
-    public fun TensorType.fullLike(value: T): TensorType
-
-    public fun TensorType.zeroesLike(): TensorType
-    public fun TensorType.onesLike(): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.eye.html
-    public fun eye(n: Int): TensorType
-
-    public fun TensorType.copy(): TensorType
-
     public operator fun T.plus(other: TensorType): TensorType
     public operator fun TensorType.plus(value: T): TensorType
     public operator fun TensorType.plus(other: TensorType): TensorType
@@ -53,8 +34,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public fun TensorType.view(shape: IntArray): TensorType
     public fun TensorType.viewAs(other: TensorType): TensorType
 
-    public fun TensorType.eq(other: TensorType, delta: T): Boolean
-
     //https://pytorch.org/docs/stable/generated/torch.matmul.html
     public infix fun TensorType.dot(other: TensorType): TensorType
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
index 67b9c9d73..0b9079967 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
@@ -2,7 +2,7 @@ package space.kscience.kmath.tensors
 
 // https://proofwiki.org/wiki/Definition:Division_Algebra
 public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
-    TensorAlgebra<T, TensorType> {
+        TensorAlgebra<T, TensorType> {
     public operator fun TensorType.div(value: T): TensorType
     public operator fun TensorType.div(other: TensorType): TensorType
     public operator fun TensorType.divAssign(value: T)
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 965aef1ea..323ade45d 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -27,27 +27,27 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return DoubleTensor(newShape, this.buffer.array(), newStart)
     }
 
-    override fun full(value: Double, shape: IntArray): DoubleTensor {
+    public fun full(value: Double, shape: IntArray): DoubleTensor {
         checkEmptyShape(shape)
         val buffer = DoubleArray(shape.reduce(Int::times)) { value }
         return DoubleTensor(shape, buffer)
     }
 
-    override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
+    public fun DoubleTensor.fullLike(value: Double): DoubleTensor {
         val shape = this.shape
         val buffer = DoubleArray(this.linearStructure.size) { value }
         return DoubleTensor(shape, buffer)
     }
 
-    override fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
+    public fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
 
-    override fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
+    public fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
 
-    override fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
+    public fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
 
-    override fun DoubleTensor.onesLike(): DoubleTensor = this.fullLike(1.0)
+    public fun DoubleTensor.onesLike(): DoubleTensor = this.fullLike(1.0)
 
-    override fun eye(n: Int): DoubleTensor {
+    public fun eye(n: Int): DoubleTensor {
         val shape = intArrayOf(n, n)
         val buffer = DoubleArray(n * n) { 0.0 }
         val res = DoubleTensor(shape, buffer)
@@ -57,7 +57,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return res
     }
 
-    override fun DoubleTensor.copy(): DoubleTensor {
+    public fun DoubleTensor.copy(): DoubleTensor {
         return DoubleTensor(this.shape, this.buffer.array().copyOf(), this.bufferStart)
     }
 
@@ -299,7 +299,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return this.contentEquals(other) { x, y -> abs(x - y) < delta }
     }
 
-    override fun DoubleTensor.eq(other: DoubleTensor, delta: Double): Boolean {
+    public fun DoubleTensor.eq(other: DoubleTensor, delta: Double): Boolean {
         return this.eq(other) { x, y -> abs(x - y) < delta }
     }
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index 591ebb89c..33e78db33 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -1,7 +1,8 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.structures.*
-
+import kotlin.random.Random
+import kotlin.math.*
 
 /**
  * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
@@ -34,3 +35,8 @@ internal fun Buffer<Double>.array(): DoubleArray = when (this) {
     is DoubleBuffer -> array
     else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
 }
+
+internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
+    val u = Random(seed)
+    return (0 until n).map { sqrt(-2.0 * u.nextDouble()) * cos(2.0 * PI * u.nextDouble()) }.toDoubleArray()
+}

From 07b6f988c296592cc28c93de12009fced255f43d Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 30 Mar 2021 19:31:42 +0100
Subject: [PATCH 079/178] forgot ln

---
 .../kotlin/space/kscience/kmath/tensors/core/utils.kt           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index 33e78db33..d8a073d64 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -38,5 +38,5 @@ internal fun Buffer<Double>.array(): DoubleArray = when (this) {
 
 internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
     val u = Random(seed)
-    return (0 until n).map { sqrt(-2.0 * u.nextDouble()) * cos(2.0 * PI * u.nextDouble()) }.toDoubleArray()
+    return (0 until n).map { sqrt(-2.0 * ln(u.nextDouble())) * cos(2.0 * PI * u.nextDouble()) }.toDoubleArray()
 }

From 6305acea68a8b34e9ddd01b7daf1711f17829151 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 31 Mar 2021 08:32:53 +0100
Subject: [PATCH 080/178] API dump

---
 kmath-core/api/kmath-core.api | 36 +++++++++--------------------------
 1 file changed, 9 insertions(+), 27 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 634f38bed..f0e749d2b 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -2655,21 +2655,14 @@ public final class space/kscience/kmath/tensors/LinearOpsTensorAlgebra$DefaultIm
 }
 
 public abstract interface class space/kscience/kmath/tensors/TensorAlgebra {
-	public abstract fun copy (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun dot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun eq (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Z
-	public abstract fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun full (Ljava/lang/Object;[I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun fullLike (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun get (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun minus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
 	public abstract fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public abstract fun ones ([I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun onesLike (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun plus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2685,8 +2678,6 @@ public abstract interface class space/kscience/kmath/tensors/TensorAlgebra {
 	public abstract fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
 	public abstract fun view (Lspace/kscience/kmath/nd/MutableStructureND;[I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public abstract fun viewAs (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun zeroesLike (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun zeros ([I)Lspace/kscience/kmath/nd/MutableStructureND;
 }
 
 public final class space/kscience/kmath/tensors/TensorAlgebra$DefaultImpls {
@@ -2814,8 +2805,7 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public final fun contentEquals (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
 	public final fun contentEquals (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function2;)Z
 	public static synthetic fun contentEquals$default (Lspace/kscience/kmath/tensors/core/DoubleTensorAlgebra;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;DILjava/lang/Object;)Z
-	public synthetic fun copy (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun copy (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun copy (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun diagonalEmbedding (Lspace/kscience/kmath/tensors/core/DoubleTensor;III)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun div (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2828,16 +2818,12 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public fun divAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
 	public synthetic fun dot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun dot (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun eq (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Z
 	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Z
-	public fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
-	public synthetic fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun eye (I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
+	public final fun eye (I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public final fun fromArray ([I[D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public fun full (D[I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun full (Ljava/lang/Object;[I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public synthetic fun fullLike (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun fullLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun full (D[I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun fullLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun get (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun get (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public final fun map (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function1;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
@@ -2851,10 +2837,8 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public synthetic fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
 	public fun minusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)V
 	public fun minusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public synthetic fun ones ([I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun ones ([I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun onesLike (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun onesLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun ones ([I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun onesLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public fun plus (DLspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun plus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public synthetic fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
@@ -2885,10 +2869,8 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public fun view (Lspace/kscience/kmath/tensors/core/DoubleTensor;[I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public synthetic fun viewAs (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun viewAs (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun zeroesLike (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun zeroesLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun zeros ([I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun zeros ([I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun zeroesLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
+	public final fun zeros ([I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 }
 
 public final class space/kscience/kmath/tensors/core/DoubleTensorAlgebraKt {

From 5abd63cde28fcf63e72809799b10211647adb73b Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 31 Mar 2021 09:07:45 +0100
Subject: [PATCH 081/178] moving to kmath-tensors module

---
 kmath-core/api/kmath-core.api                 | 278 ------------------
 kmath-tensors/build.gradle.kts                |  16 +
 .../kmath/tensors/AnalyticTensorAlgebra.kt    |   0
 .../kmath/tensors/LinearOpsTensorAlgebra.kt   |   0
 .../kscience/kmath/tensors/TensorAlgebra.kt   |   0
 .../tensors/TensorPartialDivisionAlgebra.kt   |   0
 .../kscience/kmath/tensors/TensorStructure.kt |   0
 .../core/BroadcastDoubleTensorAlgebra.kt      |   0
 .../kmath/tensors/core/BufferedTensor.kt      |   0
 .../core/DoubleAnalyticTensorAlgebra.kt       |   0
 .../core/DoubleLinearOpsTensorAlgebra.kt      |   0
 .../kmath/tensors/core/DoubleTensorAlgebra.kt |   0
 .../tensors/core/TensorLinearStructure.kt     |   0
 .../kscience/kmath/tensors/core/checks.kt     |   0
 .../kscience/kmath/tensors/core/linutils.kt   |   0
 .../kscience/kmath/tensors/core/utils.kt      |   0
 .../kmath/tensors/core/TestBroadcasting.kt    |   0
 .../core/TestDoubleAnalyticTensorAlgebra.kt   |   0
 .../core/TestDoubleLinearOpsAlgebra.kt        |   0
 .../kmath/tensors/core/TestDoubleTensor.kt    |   0
 .../tensors/core/TestDoubleTensorAlgebra.kt   |   0
 settings.gradle.kts                           |   1 +
 22 files changed, 17 insertions(+), 278 deletions(-)
 create mode 100644 kmath-tensors/build.gradle.kts
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt (100%)
 rename {kmath-core => kmath-tensors}/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt (100%)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index cc6c93c20..fee874b12 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -2602,281 +2602,3 @@ public final class space/kscience/kmath/structures/VirtualMutableBuffer : space/
 	public fun set (ILjava/lang/Object;)V
 }
 
-public abstract interface class space/kscience/kmath/tensors/AnalyticTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
-	public abstract fun acos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun acosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun asin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun asinh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun atan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun atanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun ceil (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun cos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun cosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun exp (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun floor (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun log (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun sin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun sinh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun sqrt (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun tan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun tanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-}
-
-public abstract interface class space/kscience/kmath/tensors/LinearOpsTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
-	public abstract fun cholesky (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun det (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun inv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun lu (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Pair;
-	public abstract fun luPivot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
-	public abstract fun qr (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Pair;
-	public abstract fun svd (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
-	public abstract fun symEig (Lspace/kscience/kmath/nd/MutableStructureND;Z)Lkotlin/Pair;
-}
-
-public final class space/kscience/kmath/tensors/LinearOpsTensorAlgebra$DefaultImpls {
-	public static synthetic fun symEig$default (Lspace/kscience/kmath/tensors/LinearOpsTensorAlgebra;Lspace/kscience/kmath/nd/MutableStructureND;ZILjava/lang/Object;)Lkotlin/Pair;
-}
-
-public abstract interface class space/kscience/kmath/tensors/TensorAlgebra {
-	public abstract fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun dot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun get (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun minus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
-	public abstract fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public abstract fun plus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
-	public abstract fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public abstract fun times (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun times (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun times (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
-	public abstract fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public abstract fun transpose (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun unaryMinus (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
-	public abstract fun view (Lspace/kscience/kmath/nd/MutableStructureND;[I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun viewAs (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-}
-
-public final class space/kscience/kmath/tensors/TensorAlgebra$DefaultImpls {
-	public static synthetic fun diagonalEmbedding$default (Lspace/kscience/kmath/tensors/TensorAlgebra;Lspace/kscience/kmath/nd/MutableStructureND;IIIILjava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-}
-
-public abstract interface class space/kscience/kmath/tensors/TensorPartialDivisionAlgebra : space/kscience/kmath/tensors/TensorAlgebra {
-	public abstract fun div (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun div (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public abstract fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
-	public abstract fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-}
-
-public final class space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra {
-	public fun <init> ()V
-	public synthetic fun div (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun div (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public fun divAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public synthetic fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun minus (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public fun minusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public synthetic fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun plus (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public fun plusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public synthetic fun times (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun times (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public fun timesAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-}
-
-public final class space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebraKt {
-	public static final fun BroadcastDoubleTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
-}
-
-public class space/kscience/kmath/tensors/core/BufferedTensor : space/kscience/kmath/nd/MutableStructureND {
-	public fun <init> ([ILspace/kscience/kmath/structures/MutableBuffer;I)V
-	public fun elements ()Lkotlin/sequences/Sequence;
-	public fun equals (Ljava/lang/Object;)Z
-	public fun get ([I)Ljava/lang/Object;
-	public final fun getBuffer ()Lspace/kscience/kmath/structures/MutableBuffer;
-	public fun getDimension ()I
-	public final fun getLinearStructure ()Lspace/kscience/kmath/tensors/core/TensorLinearStructure;
-	public final fun getNumel ()I
-	public fun getShape ()[I
-	public fun hashCode ()I
-	public fun set ([ILjava/lang/Object;)V
-}
-
-public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/AnalyticTensorAlgebra {
-	public fun <init> ()V
-	public synthetic fun acos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun acos (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun acosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun acosh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun asin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun asin (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun asinh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun asinh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun atan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun atan (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun atanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun atanh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun ceil (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun ceil (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun cos (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun cos (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun cosh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun cosh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun exp (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun exp (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun floor (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun floor (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun log (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun log (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun sin (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun sin (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun sinh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun sinh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun sqrt (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun sqrt (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun tan (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun tan (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun tanh (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun tanh (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-}
-
-public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebraKt {
-	public static final fun DoubleAnalyticTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
-}
-
-public final class space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/LinearOpsTensorAlgebra {
-	public fun <init> ()V
-	public synthetic fun cholesky (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun cholesky (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun det (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun det (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun detLU (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun inv (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun inv (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun invLU (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun lu (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Pair;
-	public fun lu (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lkotlin/Pair;
-	public synthetic fun luPivot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
-	public fun luPivot (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/IntTensor;)Lkotlin/Triple;
-	public synthetic fun qr (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Pair;
-	public fun qr (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lkotlin/Pair;
-	public synthetic fun svd (Lspace/kscience/kmath/nd/MutableStructureND;)Lkotlin/Triple;
-	public fun svd (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lkotlin/Triple;
-	public synthetic fun symEig (Lspace/kscience/kmath/nd/MutableStructureND;Z)Lkotlin/Pair;
-	public fun symEig (Lspace/kscience/kmath/tensors/core/DoubleTensor;Z)Lkotlin/Pair;
-}
-
-public final class space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebraKt {
-	public static final fun DoubleLinearOpsTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
-}
-
-public final class space/kscience/kmath/tensors/core/DoubleTensor : space/kscience/kmath/tensors/core/BufferedTensor {
-}
-
-public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscience/kmath/tensors/TensorPartialDivisionAlgebra {
-	public fun <init> ()V
-	public final fun contentEquals (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
-	public final fun contentEquals (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function2;)Z
-	public static synthetic fun contentEquals$default (Lspace/kscience/kmath/tensors/core/DoubleTensorAlgebra;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;DILjava/lang/Object;)Z
-	public final fun copy (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun diagonalEmbedding (Lspace/kscience/kmath/nd/MutableStructureND;III)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun diagonalEmbedding (Lspace/kscience/kmath/tensors/core/DoubleTensor;III)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun div (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public synthetic fun div (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun div (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public fun div (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
-	public synthetic fun divAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public fun divAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)V
-	public fun divAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public synthetic fun dot (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun dot (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Z
-	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
-	public final fun eye (I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun fromArray ([I[D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun full (D[I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun fullLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun get (Lspace/kscience/kmath/nd/MutableStructureND;I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun get (Lspace/kscience/kmath/tensors/core/DoubleTensor;I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun map (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function1;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public fun minus (DLspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun minus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public synthetic fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public synthetic fun minus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun minus (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public fun minus (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
-	public synthetic fun minusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public fun minusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)V
-	public fun minusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public final fun ones ([I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun onesLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public fun plus (DLspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun plus (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public synthetic fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public synthetic fun plus (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun plus (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public fun plus (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
-	public synthetic fun plusAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public fun plusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)V
-	public fun plusAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public fun times (DLspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun times (Ljava/lang/Object;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public synthetic fun times (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public synthetic fun times (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun times (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public fun times (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)V
-	public synthetic fun timesAssign (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)V
-	public fun timesAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;D)V
-	public fun timesAssign (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)V
-	public synthetic fun transpose (Lspace/kscience/kmath/nd/MutableStructureND;II)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun transpose (Lspace/kscience/kmath/tensors/core/DoubleTensor;II)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun unaryMinus (Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun unaryMinus (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun value (Lspace/kscience/kmath/nd/MutableStructureND;)Ljava/lang/Object;
-	public fun value (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Ljava/lang/Double;
-	public synthetic fun view (Lspace/kscience/kmath/nd/MutableStructureND;[I)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun view (Lspace/kscience/kmath/tensors/core/DoubleTensor;[I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public synthetic fun viewAs (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;)Lspace/kscience/kmath/nd/MutableStructureND;
-	public fun viewAs (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun zeroesLike (Lspace/kscience/kmath/tensors/core/DoubleTensor;)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-	public final fun zeros ([I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
-}
-
-public final class space/kscience/kmath/tensors/core/DoubleTensorAlgebraKt {
-	public static final fun DoubleTensorAlgebra (Lkotlin/jvm/functions/Function1;)Ljava/lang/Object;
-}
-
-public final class space/kscience/kmath/tensors/core/FloatTensor : space/kscience/kmath/tensors/core/BufferedTensor {
-}
-
-public final class space/kscience/kmath/tensors/core/IntTensor : space/kscience/kmath/tensors/core/BufferedTensor {
-}
-
-public final class space/kscience/kmath/tensors/core/LongTensor : space/kscience/kmath/tensors/core/BufferedTensor {
-}
-
-public final class space/kscience/kmath/tensors/core/TensorLinearStructure {
-	public fun <init> ([I)V
-	public final fun getShape ()[I
-	public final fun getSize ()I
-	public final fun getStrides ()[I
-	public final fun index (I)[I
-	public final fun indices ()Lkotlin/sequences/Sequence;
-	public final fun offset ([I)I
-	public final fun stepIndex ([I)[I
-}
-
diff --git a/kmath-tensors/build.gradle.kts b/kmath-tensors/build.gradle.kts
new file mode 100644
index 000000000..8e823416b
--- /dev/null
+++ b/kmath-tensors/build.gradle.kts
@@ -0,0 +1,16 @@
+plugins {
+    id("ru.mipt.npm.gradle.mpp")
+}
+
+kotlin.sourceSets {
+    commonMain {
+        dependencies {
+            api(project(":kmath-core"))
+            api(project(":kmath-stat"))
+        }
+    }
+}
+
+readme {
+    maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
similarity index 100%
rename from kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
similarity index 100%
rename from kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
rename to kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
rename to kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
rename to kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
similarity index 100%
rename from kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
rename to kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
similarity index 100%
rename from kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
rename to kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
diff --git a/settings.gradle.kts b/settings.gradle.kts
index 4467d5ed6..ffc93d576 100644
--- a/settings.gradle.kts
+++ b/settings.gradle.kts
@@ -40,5 +40,6 @@ include(
     ":kmath-ast",
     ":kmath-ejml",
     ":kmath-kotlingrad",
+    ":kmath-tensors",
     ":examples"
 )

From 706a44fd3300f050115e4b40042fe32046b181e0 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 31 Mar 2021 09:15:55 +0100
Subject: [PATCH 082/178] get normals TBD

---
 .../space/kscience/kmath/tensors/core/utils.kt       | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index d8a073d64..e46d39cf2 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -1,5 +1,7 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.samplers.BoxMullerNormalizedGaussianSampler
 import space.kscience.kmath.structures.*
 import kotlin.random.Random
 import kotlin.math.*
@@ -35,8 +37,10 @@ internal fun Buffer<Double>.array(): DoubleArray = when (this) {
     is DoubleBuffer -> array
     else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
 }
-
-internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
-    val u = Random(seed)
-    return (0 until n).map { sqrt(-2.0 * ln(u.nextDouble())) * cos(2.0 * PI * u.nextDouble()) }.toDoubleArray()
+/*
+internal inline fun getRandomNormals(n: Int,): DoubleArray {
+    val sampler = BoxMullerNormalizedGaussianSampler.of()
+    val chain = sampler.sample(RandomGenerator.default)
+    return (0 until n).map { chain.next() }.toDoubleArray()
 }
+*/
\ No newline at end of file

From b36281fa39d810ae1e60d2e7a9e042be754b8687 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 31 Mar 2021 09:23:41 +0100
Subject: [PATCH 083/178] roll back

---
 .../space/kscience/kmath/tensors/core/utils.kt       | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index e46d39cf2..fc0ebe7fa 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -37,10 +37,8 @@ internal fun Buffer<Double>.array(): DoubleArray = when (this) {
     is DoubleBuffer -> array
     else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
 }
-/*
-internal inline fun getRandomNormals(n: Int,): DoubleArray {
-    val sampler = BoxMullerNormalizedGaussianSampler.of()
-    val chain = sampler.sample(RandomGenerator.default)
-    return (0 until n).map { chain.next() }.toDoubleArray()
-}
-*/
\ No newline at end of file
+
+internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
+    val u = Random(seed)
+    return (0 until n).map { sqrt(-2.0 * ln(u.nextDouble())) * cos(2.0 * PI * u.nextDouble()) }.toDoubleArray()
+}
\ No newline at end of file

From 3e98240b94b99170ffd8525d9b307ac6c715ebd6 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Thu, 1 Apr 2021 20:21:14 +0300
Subject: [PATCH 084/178] add function diagonalEmbedding with tests

---
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 42 ++++++++++++++++++-
 .../tensors/core/TestDoubleTensorAlgebra.kt   | 33 +++++++++++++++
 2 files changed, 74 insertions(+), 1 deletion(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 323ade45d..ab692c0f9 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -283,7 +283,47 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
-        TODO("Alya")
+        val n = diagonalEntries.shape.size
+        if (dim1 == dim2) {
+            throw RuntimeException("Diagonal dimensions cannot be identical $dim1, $dim2")
+        }
+        if (dim1 > n || dim2 > n) {
+            throw RuntimeException("Dimension out of range")
+        }
+
+        var lessDim = dim1
+        var greaterDim = dim2
+        var realOffset = offset
+        if (lessDim > greaterDim) {
+            realOffset *= -1
+            lessDim = greaterDim.also {greaterDim = lessDim}
+        }
+
+        val resShape = diagonalEntries.shape.slice(0 until lessDim).toIntArray() +
+                intArrayOf(diagonalEntries.shape[n - 1] + abs(realOffset)) +
+                diagonalEntries.shape.slice(lessDim until greaterDim - 1).toIntArray() +
+                intArrayOf(diagonalEntries.shape[n - 1] + abs(realOffset)) +
+                diagonalEntries.shape.slice(greaterDim - 1 until n - 1).toIntArray()
+        val resTensor = zeros(resShape)
+
+        for (i in 0 until diagonalEntries.linearStructure.size) {
+            val multiIndex = diagonalEntries.linearStructure.index(i)
+
+            var offset1 = 0
+            var offset2 = abs(realOffset)
+            if (realOffset < 0) {
+                offset1 = offset2.also {offset2 = offset1}
+            }
+            val diagonalMultiIndex = multiIndex.slice(0 until lessDim).toIntArray() +
+                    intArrayOf(multiIndex[n - 1] + offset1) +
+                    multiIndex.slice(lessDim until greaterDim - 1).toIntArray() +
+                    intArrayOf(multiIndex[n - 1] + offset2) +
+                    multiIndex.slice(greaterDim - 1 until n - 1).toIntArray()
+
+            resTensor[diagonalMultiIndex] = diagonalEntries[multiIndex]
+        }
+
+        return resTensor
     }
 
 
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index 168d80a9d..692db69af 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -115,6 +115,39 @@ class TestDoubleTensorAlgebra {
         assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(5, 4, 2, 8, 3, 8, 5))
     }
 
+    @Test
+    fun diagonalEmbedding() = DoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor3 = zeros(intArrayOf(2, 3, 4, 5))
+
+        assertTrue(diagonalEmbedding(tensor3, 0, 3, 4).shape contentEquals
+                intArrayOf(2, 3, 4, 5, 5))
+        assertTrue(diagonalEmbedding(tensor3, 1, 3, 4).shape contentEquals
+                intArrayOf(2, 3, 4, 6, 6))
+        assertTrue(diagonalEmbedding(tensor3, 2, 0, 3).shape contentEquals
+                intArrayOf(7, 2, 3, 7, 4))
+
+        val diagonal1 = diagonalEmbedding(tensor1, 0, 1, 0)
+        assertTrue(diagonal1.shape contentEquals intArrayOf(3, 3))
+        assertTrue(diagonal1.buffer.array() contentEquals
+                doubleArrayOf(10.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 30.0))
+
+        val diagonal1_offset = diagonalEmbedding(tensor1, 1, 1, 0)
+        assertTrue(diagonal1_offset.shape contentEquals intArrayOf(4, 4))
+        assertTrue(diagonal1_offset.buffer.array() contentEquals
+                doubleArrayOf(0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 0.0, 30.0, 0.0))
+
+        val diagonal2 = diagonalEmbedding(tensor2, 1, 0, 2)
+        assertTrue(diagonal2.shape contentEquals intArrayOf(4, 2, 4))
+        assertTrue(diagonal2.buffer.array() contentEquals
+                doubleArrayOf(
+                    0.0, 1.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0,
+                    0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 5.0, 0.0,
+                    0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 6.0,
+                    0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0))
+    }
+
     @Test
     fun testContentEqual() = DoubleTensorAlgebra {
         //TODO()

From 814eab8cde1dff4b94b49efbb8d93c6b0bc06002 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Tue, 6 Apr 2021 00:06:14 +0300
Subject: [PATCH 085/178] implement svd function and tests for it

---
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 77 ++++++++++++++++++-
 .../core/TestDoubleLinearOpsAlgebra.kt        | 32 ++++++++
 2 files changed, 108 insertions(+), 1 deletion(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index c571fe446..fbd9da18e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -3,6 +3,8 @@ package space.kscience.kmath.tensors.core
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import kotlin.math.abs
+import kotlin.math.min
 
 public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
@@ -89,8 +91,81 @@ public class DoubleLinearOpsTensorAlgebra :
         return qTensor to rTensor
     }
 
+    internal fun svd1d(a: DoubleTensor, epsilon: Double = 1e-10): DoubleTensor {
+        val (n, m) = a.shape
+        var v: DoubleTensor
+        val b: DoubleTensor
+        if (n > m) {
+            b = a.transpose(0, 1).dot(a)
+            v = DoubleTensor(intArrayOf(m), getRandomNormals(m, 0))
+        } else {
+            b = a.dot(a.transpose(0, 1))
+            v = DoubleTensor(intArrayOf(n), getRandomNormals(n, 0))
+        }
+
+        var lastV: DoubleTensor
+        while (true) {
+            lastV = v
+            v = b.dot(lastV)
+            val norm = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
+            v = v.times(1.0 / norm)
+            if (abs(v.dot(lastV).value()) > 1 - epsilon) {
+                return v
+            }
+        }
+    }
+
     override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        TODO("ALYA")
+        val size = this.shape.size
+        val commonShape = this.shape.sliceArray(0 until size - 2)
+        val (n, m) = this.shape.sliceArray(size - 2 until size)
+        val resU = zeros(commonShape + intArrayOf(n, min(n, m)))
+        val resS = zeros(commonShape + intArrayOf(min(n, m)))
+        val resV = zeros(commonShape + intArrayOf(min(n, m), m))
+
+        for ((matrix, USV) in this.matrixSequence()
+            .zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
+            val res = ArrayList<Triple<Double, DoubleTensor, DoubleTensor>>(0)
+            val (matrixU, SV) = USV
+            val (matrixS, matrixV) = SV
+
+            for (k in 0 until min(n, m)) {
+                var a = matrix.asTensor().copy()
+                for ((singularValue, u, v) in res.slice(0 until k)) {
+                    val outerProduct = DoubleArray(u.shape[0] * v.shape[0])
+                    for (i in 0 until u.shape[0]) {
+                        for (j in 0 until v.shape[0]) {
+                            outerProduct[i * v.shape[0] + j] = u[i].value() * v[j].value()
+                        }
+                    }
+                    a = a - singularValue.times(DoubleTensor(intArrayOf(u.shape[0], v.shape[0]), outerProduct))
+                }
+                var v: DoubleTensor
+                var u: DoubleTensor
+                var norm: Double
+                if (n > m) {
+                    v = svd1d(a)
+                    u = matrix.asTensor().dot(v)
+                    norm = DoubleAnalyticTensorAlgebra { (u dot u).sqrt().value() }
+                    u = u.times(1.0 / norm)
+                } else {
+                    u = svd1d(a)
+                    v = matrix.asTensor().transpose(0, 1).dot(u)
+                    norm = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
+                    v = v.times(1.0 / norm)
+                }
+
+                res.add(Triple(norm, u, v))
+            }
+
+            val s = res.map { it.first }.toDoubleArray()
+            val uBuffer = res.map { it.second }.flatMap { it.buffer.array().toList() }.toDoubleArray()
+            val vBuffer = res.map { it.third }.flatMap { it.buffer.array().toList() }.toDoubleArray()
+            uBuffer.copyInto(matrixU.buffer.array())
+            s.copyInto(matrixS.buffer.array())
+            vBuffer.copyInto(matrixV.buffer.array())
+        }
+        return Triple(resU, resS, resV.transpose(size - 2, size - 1))
     }
 
     override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index b79c54dd1..843707153 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -122,4 +122,36 @@ class TestDoubleLinearOpsTensorAlgebra {
 
         assertTrue { p.dot(tensor).buffer.array().epsEqual(l.dot(u).buffer.array()) }
     }
+
+    @Test
+    fun svd1d() = DoubleLinearOpsTensorAlgebra {
+        val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+
+        val res = svd1d(tensor2)
+
+        assertTrue(res.shape contentEquals intArrayOf(2))
+        assertTrue { abs(abs(res.buffer.array()[res.bufferStart]) -  0.386) < 0.01}
+        assertTrue { abs(abs(res.buffer.array()[res.bufferStart + 1]) -  0.922) < 0.01}
+    }
+
+    @Test
+    fun svd() = DoubleLinearOpsTensorAlgebra {
+        val epsilon = 1e-10
+        fun test_tensor(tensor: DoubleTensor) {
+            val svd = tensor.svd()
+
+            val tensorSVD = svd.first
+                .dot(
+                    diagonalEmbedding(svd.second, 0, 0, 1)
+                        .dot(svd.third.transpose(0, 1))
+                )
+
+            for ((x1, x2) in tensor.buffer.array() zip tensorSVD.buffer.array()) {
+                assertTrue { abs(x1 - x2) < epsilon }
+            }
+        }
+        test_tensor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
+        test_tensor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
+
+    }
 }

From 4336788a6ba846af6a63be1352d1bec1c1f39b56 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 6 Apr 2021 09:00:13 +0100
Subject: [PATCH 086/178] Moving Alya's SVD implementation to linutils

---
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 66 +---------------
 .../kscience/kmath/tensors/core/linutils.kt   | 76 ++++++++++++++++++-
 .../core/TestDoubleLinearOpsAlgebra.kt        |  4 +-
 3 files changed, 78 insertions(+), 68 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index fbd9da18e..7db812d9d 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -91,29 +91,6 @@ public class DoubleLinearOpsTensorAlgebra :
         return qTensor to rTensor
     }
 
-    internal fun svd1d(a: DoubleTensor, epsilon: Double = 1e-10): DoubleTensor {
-        val (n, m) = a.shape
-        var v: DoubleTensor
-        val b: DoubleTensor
-        if (n > m) {
-            b = a.transpose(0, 1).dot(a)
-            v = DoubleTensor(intArrayOf(m), getRandomNormals(m, 0))
-        } else {
-            b = a.dot(a.transpose(0, 1))
-            v = DoubleTensor(intArrayOf(n), getRandomNormals(n, 0))
-        }
-
-        var lastV: DoubleTensor
-        while (true) {
-            lastV = v
-            v = b.dot(lastV)
-            val norm = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
-            v = v.times(1.0 / norm)
-            if (abs(v.dot(lastV).value()) > 1 - epsilon) {
-                return v
-            }
-        }
-    }
 
     override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         val size = this.shape.size
@@ -124,47 +101,8 @@ public class DoubleLinearOpsTensorAlgebra :
         val resV = zeros(commonShape + intArrayOf(min(n, m), m))
 
         for ((matrix, USV) in this.matrixSequence()
-            .zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
-            val res = ArrayList<Triple<Double, DoubleTensor, DoubleTensor>>(0)
-            val (matrixU, SV) = USV
-            val (matrixS, matrixV) = SV
-
-            for (k in 0 until min(n, m)) {
-                var a = matrix.asTensor().copy()
-                for ((singularValue, u, v) in res.slice(0 until k)) {
-                    val outerProduct = DoubleArray(u.shape[0] * v.shape[0])
-                    for (i in 0 until u.shape[0]) {
-                        for (j in 0 until v.shape[0]) {
-                            outerProduct[i * v.shape[0] + j] = u[i].value() * v[j].value()
-                        }
-                    }
-                    a = a - singularValue.times(DoubleTensor(intArrayOf(u.shape[0], v.shape[0]), outerProduct))
-                }
-                var v: DoubleTensor
-                var u: DoubleTensor
-                var norm: Double
-                if (n > m) {
-                    v = svd1d(a)
-                    u = matrix.asTensor().dot(v)
-                    norm = DoubleAnalyticTensorAlgebra { (u dot u).sqrt().value() }
-                    u = u.times(1.0 / norm)
-                } else {
-                    u = svd1d(a)
-                    v = matrix.asTensor().transpose(0, 1).dot(u)
-                    norm = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
-                    v = v.times(1.0 / norm)
-                }
-
-                res.add(Triple(norm, u, v))
-            }
-
-            val s = res.map { it.first }.toDoubleArray()
-            val uBuffer = res.map { it.second }.flatMap { it.buffer.array().toList() }.toDoubleArray()
-            val vBuffer = res.map { it.third }.flatMap { it.buffer.array().toList() }.toDoubleArray()
-            uBuffer.copyInto(matrixU.buffer.array())
-            s.copyInto(matrixS.buffer.array())
-            vBuffer.copyInto(matrixV.buffer.array())
-        }
+            .zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence()))))
+            svdHelper(matrix.asTensor(), USV, m, n)
         return Triple(resU, resS, resV.transpose(size - 2, size - 1))
     }
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index f6cf71b07..e1d5c1819 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -4,6 +4,8 @@ import space.kscience.kmath.nd.MutableStructure1D
 import space.kscience.kmath.nd.MutableStructure2D
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import kotlin.math.abs
+import kotlin.math.min
 import kotlin.math.sqrt
 
 
@@ -195,8 +197,8 @@ internal inline fun DoubleLinearOpsTensorAlgebra.qrHelper(
     checkSquareMatrix(matrix.shape)
     val n = matrix.shape[0]
     val qM = q.as2D()
-    val matrixT = matrix.transpose(0,1)
-    val qT = q.transpose(0,1)
+    val matrixT = matrix.transpose(0, 1)
+    val qT = q.transpose(0, 1)
 
     for (j in 0 until n) {
         val v = matrixT[j]
@@ -216,3 +218,73 @@ internal inline fun DoubleLinearOpsTensorAlgebra.qrHelper(
         }
     }
 }
+
+internal inline fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10): DoubleTensor {
+    val (n, m) = a.shape
+    var v: DoubleTensor
+    val b: DoubleTensor
+    if (n > m) {
+        b = a.transpose(0, 1).dot(a)
+        v = DoubleTensor(intArrayOf(m), getRandomNormals(m, 0))
+    } else {
+        b = a.dot(a.transpose(0, 1))
+        v = DoubleTensor(intArrayOf(n), getRandomNormals(n, 0))
+    }
+
+    var lastV: DoubleTensor
+    while (true) {
+        lastV = v
+        v = b.dot(lastV)
+        val norm = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
+        v = v.times(1.0 / norm)
+        if (abs(v.dot(lastV).value()) > 1 - epsilon) {
+            return v
+        }
+    }
+}
+
+internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
+    matrix: DoubleTensor,
+    USV: Pair<BufferedTensor<Double>, Pair<BufferedTensor<Double>, BufferedTensor<Double>>>,
+    m: Int, n: Int
+): Unit {
+    val res = ArrayList<Triple<Double, DoubleTensor, DoubleTensor>>(0)
+    val (matrixU, SV) = USV
+    val (matrixS, matrixV) = SV
+
+    for (k in 0 until min(n, m)) {
+        var a = matrix.copy()
+        for ((singularValue, u, v) in res.slice(0 until k)) {
+            val outerProduct = DoubleArray(u.shape[0] * v.shape[0])
+            for (i in 0 until u.shape[0]) {
+                for (j in 0 until v.shape[0]) {
+                    outerProduct[i * v.shape[0] + j] = u[i].value() * v[j].value()
+                }
+            }
+            a = a - singularValue.times(DoubleTensor(intArrayOf(u.shape[0], v.shape[0]), outerProduct))
+        }
+        var v: DoubleTensor
+        var u: DoubleTensor
+        var norm: Double
+        if (n > m) {
+            v = svd1d(a)
+            u = matrix.dot(v)
+            norm = DoubleAnalyticTensorAlgebra { (u dot u).sqrt().value() }
+            u = u.times(1.0 / norm)
+        } else {
+            u = svd1d(a)
+            v = matrix.transpose(0, 1).dot(u)
+            norm = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
+            v = v.times(1.0 / norm)
+        }
+
+        res.add(Triple(norm, u, v))
+    }
+
+    val s = res.map { it.first }.toDoubleArray()
+    val uBuffer = res.map { it.second }.flatMap { it.buffer.array().toList() }.toDoubleArray()
+    val vBuffer = res.map { it.third }.flatMap { it.buffer.array().toList() }.toDoubleArray()
+    uBuffer.copyInto(matrixU.buffer.array())
+    s.copyInto(matrixS.buffer.array())
+    vBuffer.copyInto(matrixV.buffer.array())
+}
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 843707153..60f7f4a97 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -124,7 +124,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun svd1d() = DoubleLinearOpsTensorAlgebra {
+    fun testSVD1D() = DoubleLinearOpsTensorAlgebra {
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
 
         val res = svd1d(tensor2)
@@ -135,7 +135,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun svd() = DoubleLinearOpsTensorAlgebra {
+    fun testSVD() = DoubleLinearOpsTensorAlgebra {
         val epsilon = 1e-10
         fun test_tensor(tensor: DoubleTensor) {
             val svd = tensor.svd()

From dcdc22dd9dddf8d3157fb51f1ee7b6bf226b3b8c Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 6 Apr 2021 11:04:00 +0100
Subject: [PATCH 087/178] SVD test to be fixed

---
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  2 +-
 .../kmath/tensors/core/DoubleTensorAlgebra.kt |  6 ++
 .../kscience/kmath/tensors/core/utils.kt      |  2 -
 .../core/TestDoubleLinearOpsAlgebra.kt        | 65 +++++++++++--------
 4 files changed, 44 insertions(+), 31 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index d7c6eaefd..966806de1 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -40,7 +40,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
     public fun diagonalEmbedding(
         diagonalEntries: TensorType,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+        offset: Int = 0, dim1: Int = 0, dim2: Int = 1
     ): TensorType
 
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index ab692c0f9..53063a066 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -362,6 +362,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return true
     }
 
+    public fun randNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
+        DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed))
+
+    public fun DoubleTensor.randNormalLike(seed: Long = 0): DoubleTensor =
+        DoubleTensor(this.shape, getRandomNormals(this.shape.reduce(Int::times), seed))
+
 }
 
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index fc0ebe7fa..5fd3cfd28 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -1,7 +1,5 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.stat.RandomGenerator
-import space.kscience.kmath.stat.samplers.BoxMullerNormalizedGaussianSampler
 import space.kscience.kmath.structures.*
 import kotlin.random.Random
 import kotlin.math.*
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 60f7f4a97..0bd0ff5bd 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -7,21 +7,6 @@ import kotlin.test.assertTrue
 
 class TestDoubleLinearOpsTensorAlgebra {
 
-    private val eps = 1e-5
-
-    private fun Double.epsEqual(other: Double): Boolean {
-        return abs(this - other) < eps
-    }
-
-    fun DoubleArray.epsEqual(other: DoubleArray, eps: Double = 1e-5): Boolean {
-        for ((elem1, elem2) in this.asSequence().zip(other.asSequence())) {
-            if (abs(elem1 - elem2) > eps) {
-                return false
-            }
-        }
-        return true
-    }
-
     @Test
     fun testDetLU() = DoubleLinearOpsTensorAlgebra {
         val tensor = fromArray(
@@ -136,22 +121,46 @@ class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testSVD() = DoubleLinearOpsTensorAlgebra {
-        val epsilon = 1e-10
-        fun test_tensor(tensor: DoubleTensor) {
-            val svd = tensor.svd()
+        testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
+        testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
+    }
 
-            val tensorSVD = svd.first
-                .dot(
-                    diagonalEmbedding(svd.second, 0, 0, 1)
-                        .dot(svd.third.transpose(0, 1))
-                )
+    @Test
+    fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
+        val tensor = randNormal(intArrayOf(7, 5, 3), 0)
+        val (tensorU, tensorS, tensorV) = tensor.svd()
+        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS,0,1,2) dot tensorV)
+        println(tensor.eq(tensorSVD))
+    }
 
-            for ((x1, x2) in tensor.buffer.array() zip tensorSVD.buffer.array()) {
-                assertTrue { abs(x1 - x2) < epsilon }
-            }
+
+}
+
+private inline fun Double.epsEqual(other: Double, eps: Double = 1e-5): Boolean {
+    return abs(this - other) < eps
+}
+
+private inline fun DoubleArray.epsEqual(other: DoubleArray, eps: Double = 1e-5): Boolean {
+    for ((elem1, elem2) in this.asSequence().zip(other.asSequence())) {
+        if (abs(elem1 - elem2) > eps) {
+            return false
         }
-        test_tensor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
-        test_tensor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
+    }
+    return true
+}
 
+private inline fun DoubleLinearOpsTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10): Unit {
+    val svd = tensor.svd()
+
+    val tensorSVD = svd.first
+        .dot(
+            diagonalEmbedding(svd.second, 0, 0, 1)
+                .dot(svd.third.transpose(0, 1))
+        )
+
+    for ((x1, x2) in tensor.buffer.array() zip tensorSVD.buffer.array()) {
+        assertTrue { abs(x1 - x2) < epsilon }
     }
 }
+
+

From 2bbe10e41c83e5ce44a920ad0a4c071b7d25f252 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 6 Apr 2021 11:41:41 +0100
Subject: [PATCH 088/178] SymEig test

---
 .../tensors/core/TestDoubleLinearOpsAlgebra.kt     | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 0bd0ff5bd..29632e771 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -1,6 +1,7 @@
 package space.kscience.kmath.tensors.core
 
 import kotlin.math.abs
+import kotlin.test.Ignore
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
@@ -125,12 +126,21 @@ class TestDoubleLinearOpsTensorAlgebra {
         testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
     }
 
-    @Test
+    @Test @Ignore
     fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
         val tensor = randNormal(intArrayOf(7, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
         val tensorSVD = tensorU dot (diagonalEmbedding(tensorS,0,1,2) dot tensorV)
-        println(tensor.eq(tensorSVD))
+        assertTrue(tensor.eq(tensorSVD))
+    }
+
+    @Test @Ignore
+    fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra {
+        val tensor = randNormal(shape = intArrayOf(5, 2, 2), 0)
+        val tensorSigma = tensor + tensor.transpose(1, 2)
+        val (tensorS, tensorV) = tensorSigma.symEig()
+        val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS, 0,1,2) dot tensorV.transpose(1, 2))
+        assertTrue(tensorSigma.eq(tensorSigmaCalc))
     }
 
 

From 174f6566e116b43e70622785c58291532b170369 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 6 Apr 2021 12:07:39 +0100
Subject: [PATCH 089/178] Negative indices

---
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  4 ++--
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 20 +++++++++++--------
 .../tensors/core/TensorLinearStructure.kt     |  3 +++
 .../kscience/kmath/tensors/core/utils.kt      | 12 ++++++++++-
 .../core/TestDoubleLinearOpsAlgebra.kt        |  4 ++--
 .../tensors/core/TestDoubleTensorAlgebra.kt   |  4 ++--
 6 files changed, 32 insertions(+), 15 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 966806de1..8fd1cf2ed 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -28,7 +28,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public operator fun TensorType.get(i: Int): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.transpose.html
-    public fun TensorType.transpose(i: Int, j: Int): TensorType
+    public fun TensorType.transpose(i: Int = -2, j: Int = -1): TensorType
 
     //https://pytorch.org/docs/stable/tensor_view.html
     public fun TensorType.view(shape: IntArray): TensorType
@@ -40,7 +40,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
     public fun diagonalEmbedding(
         diagonalEntries: TensorType,
-        offset: Int = 0, dim1: Int = 0, dim2: Int = 1
+        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
     ): TensorType
 
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 53063a066..870dbe8a7 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -198,19 +198,21 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.transpose(i: Int, j: Int): DoubleTensor {
-        checkTranspose(this.dimension, i, j)
+        val ii = minusIndex(i)
+        val jj = minusIndex(j)
+        checkTranspose(this.dimension, ii, jj)
         val n = this.linearStructure.size
         val resBuffer = DoubleArray(n)
 
         val resShape = this.shape.copyOf()
-        resShape[i] = resShape[j].also { resShape[j] = resShape[i] }
+        resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
 
         val resTensor = DoubleTensor(resShape, resBuffer)
 
         for (offset in 0 until n) {
             val oldMultiIndex = this.linearStructure.index(offset)
             val newMultiIndex = oldMultiIndex.copyOf()
-            newMultiIndex[i] = newMultiIndex[j].also { newMultiIndex[j] = newMultiIndex[i] }
+            newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
 
             val linearIndex = resTensor.linearStructure.offset(newMultiIndex)
             resTensor.buffer.array()[linearIndex] =
@@ -283,16 +285,18 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
+        val d1 = minusIndexFrom(diagonalEntries.linearStructure.dim + 1, dim1)
+        val d2 = minusIndexFrom(diagonalEntries.linearStructure.dim + 1, dim2)
         val n = diagonalEntries.shape.size
-        if (dim1 == dim2) {
-            throw RuntimeException("Diagonal dimensions cannot be identical $dim1, $dim2")
+        if (d1 == d2) {
+            throw RuntimeException("Diagonal dimensions cannot be identical $d1, $d2")
         }
-        if (dim1 > n || dim2 > n) {
+        if (d1 > n || d2 > n) {
             throw RuntimeException("Dimension out of range")
         }
 
-        var lessDim = dim1
-        var greaterDim = dim2
+        var lessDim = d1
+        var greaterDim = d2
         var realOffset = offset
         if (lessDim > greaterDim) {
             realOffset *= -1
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
index 97ce29657..47745c2be 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
@@ -73,6 +73,9 @@ public class TensorLinearStructure(public val shape: IntArray)
     public val size: Int
         get() = shape.reduce(Int::times)
 
+    public val dim: Int
+        get() = shape.size
+
     public fun indices(): Sequence<IntArray> = (0 until size).asSequence().map {
         index(it)
     }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index 5fd3cfd28..785b59ede 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -39,4 +39,14 @@ internal fun Buffer<Double>.array(): DoubleArray = when (this) {
 internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
     val u = Random(seed)
     return (0 until n).map { sqrt(-2.0 * ln(u.nextDouble())) * cos(2.0 * PI * u.nextDouble()) }.toDoubleArray()
-}
\ No newline at end of file
+}
+
+internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
+    val ii = n + i
+    check(ii >= 0) {
+        "Out of bound index $i for tensor of dim $n"
+    }
+    ii
+}
+
+internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int =  minusIndexFrom(this.linearStructure.dim, i)
\ No newline at end of file
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 29632e771..56f9332f6 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -130,7 +130,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
         val tensor = randNormal(intArrayOf(7, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
-        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS,0,1,2) dot tensorV)
+        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV)
         assertTrue(tensor.eq(tensorSVD))
     }
 
@@ -139,7 +139,7 @@ class TestDoubleLinearOpsTensorAlgebra {
         val tensor = randNormal(shape = intArrayOf(5, 2, 2), 0)
         val tensorSigma = tensor + tensor.transpose(1, 2)
         val (tensorS, tensorV) = tensorSigma.symEig()
-        val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS, 0,1,2) dot tensorV.transpose(1, 2))
+        val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose(1, 2))
         assertTrue(tensorSigma.eq(tensorSigmaCalc))
     }
 
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index 692db69af..fa7a8fd32 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -35,8 +35,8 @@ class TestDoubleTensorAlgebra {
     fun transpose1x2x3() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res01 = tensor.transpose(0, 1)
-        val res02 = tensor.transpose(0, 2)
-        val res12 = tensor.transpose(1, 2)
+        val res02 = tensor.transpose(-3, 2)
+        val res12 = tensor.transpose()
 
         assertTrue(res01.shape contentEquals intArrayOf(2, 1, 3))
         assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))

From 74dfca4e2e265a17bcba9b4d968a56ddd5d07ad9 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 7 Apr 2021 15:11:00 +0100
Subject: [PATCH 090/178] Using kmath-stat for random normals

---
 .../kotlin/space/kscience/kmath/tensors/core/utils.kt    | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index 785b59ede..c99f9d156 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -1,8 +1,8 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.samplers.GaussianSampler
+import space.kscience.kmath.stat.RandomGenerator
 import space.kscience.kmath.structures.*
-import kotlin.random.Random
-import kotlin.math.*
 
 /**
  * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
@@ -37,8 +37,9 @@ internal fun Buffer<Double>.array(): DoubleArray = when (this) {
 }
 
 internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
-    val u = Random(seed)
-    return (0 until n).map { sqrt(-2.0 * ln(u.nextDouble())) * cos(2.0 * PI * u.nextDouble()) }.toDoubleArray()
+    val distribution = GaussianSampler(0.0, 1.0)
+    val generator = RandomGenerator.default(seed)
+    return distribution.sample(generator).nextBufferBlocking(n).toDoubleArray()
 }
 
 internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {

From ea4d6618b4c2c7ecc22f82c4376b2b06904ff7c2 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Thu, 8 Apr 2021 23:58:44 +0300
Subject: [PATCH 091/178] fix bugs in svd

---
 .../tensors/core/DoubleLinearOpsTensorAlgebra.kt | 14 ++++++++++----
 .../kscience/kmath/tensors/core/linutils.kt      | 16 +++++++++++-----
 .../space/kscience/kmath/tensors/core/utils.kt   |  7 +++++++
 .../tensors/core/TestDoubleLinearOpsAlgebra.kt   |  4 ++--
 4 files changed, 30 insertions(+), 11 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 7db812d9d..83a41b80d 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -96,14 +96,20 @@ public class DoubleLinearOpsTensorAlgebra :
         val size = this.shape.size
         val commonShape = this.shape.sliceArray(0 until size - 2)
         val (n, m) = this.shape.sliceArray(size - 2 until size)
-        val resU = zeros(commonShape + intArrayOf(n, min(n, m)))
+        val resU = zeros(commonShape + intArrayOf(min(n, m), n))
         val resS = zeros(commonShape + intArrayOf(min(n, m)))
         val resV = zeros(commonShape + intArrayOf(min(n, m), m))
 
         for ((matrix, USV) in this.matrixSequence()
-            .zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence()))))
-            svdHelper(matrix.asTensor(), USV, m, n)
-        return Triple(resU, resS, resV.transpose(size - 2, size - 1))
+            .zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
+            val size = matrix.shape.reduce { acc, i -> acc * i }
+            val curMatrix = DoubleTensor(
+                matrix.shape,
+                matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + size).toDoubleArray()
+            )
+            svdHelper(curMatrix, USV, m, n)
+        }
+        return Triple(resU.transpose(size - 2, size - 1), resS, resV)
     }
 
     override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index e1d5c1819..1bdfee2d5 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -225,10 +225,10 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon:
     val b: DoubleTensor
     if (n > m) {
         b = a.transpose(0, 1).dot(a)
-        v = DoubleTensor(intArrayOf(m), getRandomNormals(m, 0))
+        v = DoubleTensor(intArrayOf(m), getRandomUnitVector(m, 0))
     } else {
         b = a.dot(a.transpose(0, 1))
-        v = DoubleTensor(intArrayOf(n), getRandomNormals(n, 0))
+        v = DoubleTensor(intArrayOf(n), getRandomUnitVector(n, 0))
     }
 
     var lastV: DoubleTensor
@@ -284,7 +284,13 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
     val s = res.map { it.first }.toDoubleArray()
     val uBuffer = res.map { it.second }.flatMap { it.buffer.array().toList() }.toDoubleArray()
     val vBuffer = res.map { it.third }.flatMap { it.buffer.array().toList() }.toDoubleArray()
-    uBuffer.copyInto(matrixU.buffer.array())
-    s.copyInto(matrixS.buffer.array())
-    vBuffer.copyInto(matrixV.buffer.array())
+    for (i in uBuffer.indices) {
+        matrixU.buffer.array()[matrixU.bufferStart + i] = uBuffer[i]
+    }
+    for (i in s.indices) {
+        matrixS.buffer.array()[matrixS.bufferStart + i] = s[i]
+    }
+    for (i in vBuffer.indices) {
+        matrixV.buffer.array()[matrixV.bufferStart + i] = vBuffer[i]
+    }
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index c99f9d156..392abd1c2 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -3,6 +3,7 @@ package space.kscience.kmath.tensors.core
 import space.kscience.kmath.samplers.GaussianSampler
 import space.kscience.kmath.stat.RandomGenerator
 import space.kscience.kmath.structures.*
+import kotlin.math.sqrt
 
 /**
  * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
@@ -42,6 +43,12 @@ internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
     return distribution.sample(generator).nextBufferBlocking(n).toDoubleArray()
 }
 
+internal inline fun getRandomUnitVector(n: Int, seed: Long): DoubleArray {
+    val unnorm = getRandomNormals(n, seed)
+    val norm = sqrt(unnorm.map { it * it }.sum())
+    return unnorm.map { it / norm }.toDoubleArray()
+}
+
 internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
     val ii = n + i
     check(ii >= 0) {
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 56f9332f6..35390968e 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -126,9 +126,9 @@ class TestDoubleLinearOpsTensorAlgebra {
         testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
     }
 
-    @Test @Ignore
+    @Test
     fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
-        val tensor = randNormal(intArrayOf(7, 5, 3), 0)
+        val tensor = randNormal(intArrayOf(1, 15, 4, 7, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
         val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV)
         assertTrue(tensor.eq(tensorSVD))

From a09a1c7adc73615d46b4fa5d1611c3a694b8d5a8 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 9 Apr 2021 07:33:25 +0100
Subject: [PATCH 092/178] Fixed tests

---
 .../core/DoubleLinearOpsTensorAlgebra.kt      |  6 +-
 .../kmath/tensors/core/DoubleTensorAlgebra.kt |  7 ---
 .../kscience/kmath/tensors/core/checks.kt     |  3 +-
 .../core/TestDoubleLinearOpsAlgebra.kt        | 63 ++++++++-----------
 4 files changed, 31 insertions(+), 48 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 83a41b80d..916361abe 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -93,7 +93,7 @@ public class DoubleLinearOpsTensorAlgebra :
 
 
     override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        val size = this.shape.size
+        val size = this.linearStructure.dim
         val commonShape = this.shape.sliceArray(0 until size - 2)
         val (n, m) = this.shape.sliceArray(size - 2 until size)
         val resU = zeros(commonShape + intArrayOf(min(n, m), n))
@@ -109,11 +109,11 @@ public class DoubleLinearOpsTensorAlgebra :
             )
             svdHelper(curMatrix, USV, m, n)
         }
-        return Triple(resU.transpose(size - 2, size - 1), resS, resV)
+        return Triple(resU.transpose(), resS, resV.transpose())
     }
 
     override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
-        TODO("ANDREI")
+        TODO()
     }
 
     public fun DoubleTensor.detLU(): DoubleTensor {
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 870dbe8a7..8f355dd3d 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -339,19 +339,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         )
     }
 
-    public fun DoubleTensor.contentEquals(other: DoubleTensor, delta: Double = 1e-5): Boolean {
-        return this.contentEquals(other) { x, y -> abs(x - y) < delta }
-    }
-
     public fun DoubleTensor.eq(other: DoubleTensor, delta: Double): Boolean {
         return this.eq(other) { x, y -> abs(x - y) < delta }
     }
 
     public fun DoubleTensor.eq(other: DoubleTensor): Boolean = this.eq(other, 1e-5)
 
-    public fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean =
-        this.eq(other, eqFunction)
-
     private fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
         checkShapesCompatible(this, other)
         val n = this.linearStructure.size
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index 247ed3913..f994324ff 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -56,4 +56,5 @@ internal inline fun <T, TensorType : TensorStructure<T>,
     check(shape[n - 1] == shape[n - 2]) {
         "Tensor must be batches of square matrices, but they are ${shape[n - 1]} by ${shape[n - 1]} matrices"
     }
-}
\ No newline at end of file
+}
+
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 35390968e..df896bace 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -20,15 +20,17 @@ class TestDoubleLinearOpsTensorAlgebra {
             )
         )
 
-        val expectedShape = intArrayOf(2, 1)
-        val expectedBuffer = doubleArrayOf(
-            -1.0,
-            -7.0
+        val expectedTensor = fromArray(
+            intArrayOf(2, 1),
+            doubleArrayOf(
+                -1.0,
+                -7.0
+            )
         )
         val detTensor = tensor.detLU()
 
-        assertTrue { detTensor.shape contentEquals expectedShape }
-        assertTrue { detTensor.buffer.array().epsEqual(expectedBuffer) }
+        assertTrue(detTensor.eq(expectedTensor))
+
     }
 
     @Test
@@ -43,17 +45,17 @@ class TestDoubleLinearOpsTensorAlgebra {
             )
         )
 
-        val expectedShape = intArrayOf(2, 2, 2)
-        val expectedBuffer = doubleArrayOf(
-            1.0, 0.0,
-            0.0, 0.5,
-            0.0, 1.0,
-            1.0, -1.0
+        val expectedTensor = fromArray(
+            intArrayOf(2, 2, 2), doubleArrayOf(
+                1.0, 0.0,
+                0.0, 0.5,
+                0.0, 1.0,
+                1.0, -1.0
+            )
         )
 
         val invTensor = tensor.invLU()
-        assertTrue { invTensor.shape contentEquals expectedShape }
-        assertTrue { invTensor.buffer.array().epsEqual(expectedBuffer) }
+        assertTrue(invTensor.eq(expectedTensor))
     }
 
     @Test
@@ -80,7 +82,7 @@ class TestDoubleLinearOpsTensorAlgebra {
         assertTrue { q.shape contentEquals shape }
         assertTrue { r.shape contentEquals shape }
 
-        assertTrue { q.dot(r).buffer.array().epsEqual(buffer) }
+        assertTrue((q dot r).eq(tensor))
 
         //todo check orthogonality/upper triang.
     }
@@ -106,7 +108,7 @@ class TestDoubleLinearOpsTensorAlgebra {
         assertTrue { l.shape contentEquals shape }
         assertTrue { u.shape contentEquals shape }
 
-        assertTrue { p.dot(tensor).buffer.array().epsEqual(l.dot(u).buffer.array()) }
+        assertTrue((p dot tensor).eq(l dot u))
     }
 
     @Test
@@ -116,8 +118,8 @@ class TestDoubleLinearOpsTensorAlgebra {
         val res = svd1d(tensor2)
 
         assertTrue(res.shape contentEquals intArrayOf(2))
-        assertTrue { abs(abs(res.buffer.array()[res.bufferStart]) -  0.386) < 0.01}
-        assertTrue { abs(abs(res.buffer.array()[res.bufferStart + 1]) -  0.922) < 0.01}
+        assertTrue { abs(abs(res.buffer.array()[res.bufferStart]) - 0.386) < 0.01 }
+        assertTrue { abs(abs(res.buffer.array()[res.bufferStart + 1]) - 0.922) < 0.01 }
     }
 
     @Test
@@ -130,11 +132,12 @@ class TestDoubleLinearOpsTensorAlgebra {
     fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
         val tensor = randNormal(intArrayOf(1, 15, 4, 7, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
-        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV)
+        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
         assertTrue(tensor.eq(tensorSVD))
     }
 
-    @Test @Ignore
+    @Test
+    @Ignore
     fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra {
         val tensor = randNormal(shape = intArrayOf(5, 2, 2), 0)
         val tensorSigma = tensor + tensor.transpose(1, 2)
@@ -146,31 +149,17 @@ class TestDoubleLinearOpsTensorAlgebra {
 
 }
 
-private inline fun Double.epsEqual(other: Double, eps: Double = 1e-5): Boolean {
-    return abs(this - other) < eps
-}
-
-private inline fun DoubleArray.epsEqual(other: DoubleArray, eps: Double = 1e-5): Boolean {
-    for ((elem1, elem2) in this.asSequence().zip(other.asSequence())) {
-        if (abs(elem1 - elem2) > eps) {
-            return false
-        }
-    }
-    return true
-}
 
 private inline fun DoubleLinearOpsTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10): Unit {
     val svd = tensor.svd()
 
     val tensorSVD = svd.first
         .dot(
-            diagonalEmbedding(svd.second, 0, 0, 1)
-                .dot(svd.third.transpose(0, 1))
+            diagonalEmbedding(svd.second)
+                .dot(svd.third.transpose())
         )
 
-    for ((x1, x2) in tensor.buffer.array() zip tensorSVD.buffer.array()) {
-        assertTrue { abs(x1 - x2) < epsilon }
-    }
+    assertTrue(tensor.eq(tensorSVD, epsilon))
 }
 
 

From 8c1131dd5818f75b1b0eb9e0b3ef23f12f6a7c07 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 9 Apr 2021 08:03:47 +0100
Subject: [PATCH 093/178] Approaching SymEig through SVD

---
 .../kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt      | 6 ++++--
 .../kotlin/space/kscience/kmath/tensors/core/checks.kt      | 4 ++++
 .../kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt        | 4 ++--
 3 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 916361abe..43502e79a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -67,7 +67,7 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     override fun DoubleTensor.cholesky(): DoubleTensor {
-        // todo checks
+        checkSymmetric(this)
         checkSquareMatrix(shape)
 
         val n = shape.last()
@@ -113,7 +113,9 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
-        TODO()
+        checkSymmetric(this)
+        val svd = this.svd()
+        TODO("U might have some columns negative to V")
     }
 
     public fun DoubleTensor.detLU(): DoubleTensor {
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index f994324ff..1f70eb300 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -58,3 +58,7 @@ internal inline fun <T, TensorType : TensorStructure<T>,
     }
 }
 
+internal inline fun DoubleTensorAlgebra.checkSymmetric(tensor: DoubleTensor): Unit =
+    check(tensor.eq(tensor.transpose())){
+        "Tensor is not symmetric about the last 2 dimensions"
+    }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index df896bace..cc127b5a7 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -140,9 +140,9 @@ class TestDoubleLinearOpsTensorAlgebra {
     @Ignore
     fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra {
         val tensor = randNormal(shape = intArrayOf(5, 2, 2), 0)
-        val tensorSigma = tensor + tensor.transpose(1, 2)
+        val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
-        val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose(1, 2))
+        val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
         assertTrue(tensorSigma.eq(tensorSigmaCalc))
     }
 

From 5d8b42da906fec8c5450a4babf461a4873a8aee9 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 9 Apr 2021 08:29:08 +0100
Subject: [PATCH 094/178] minor corrections to linear stuff

---
 .../kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt        | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 43502e79a..4e67e1d16 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -67,6 +67,7 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     override fun DoubleTensor.cholesky(): DoubleTensor {
+        //positive definite check
         checkSymmetric(this)
         checkSquareMatrix(shape)
 
@@ -114,8 +115,7 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
         checkSymmetric(this)
-        val svd = this.svd()
-        TODO("U might have some columns negative to V")
+        TODO("maybe use SVD")
     }
 
     public fun DoubleTensor.detLU(): DoubleTensor {

From e4dbabc30f5a740deae2055c33c982637463d85e Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 9 Apr 2021 08:32:03 +0100
Subject: [PATCH 095/178] more infor

---
 .../kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 4e67e1d16..7d99b7b51 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -115,6 +115,8 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
         checkSymmetric(this)
+        //http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
+        //see the last point
         TODO("maybe use SVD")
     }
 

From 3f0dff3ce9f830f6c04ac72346eb6053c61ae0bf Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 9 Apr 2021 09:18:00 +0100
Subject: [PATCH 096/178] Approximate spectral decomposition for symmetric
 matrices based on SVD

---
 .../tensors/core/DoubleLinearOpsTensorAlgebra.kt      | 11 ++++++++---
 .../kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt  |  6 +++---
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 7d99b7b51..268f2fe55 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -3,8 +3,10 @@ package space.kscience.kmath.tensors.core
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.structures.toList
 import kotlin.math.abs
 import kotlin.math.min
+import kotlin.math.sign
 
 public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
@@ -113,11 +115,14 @@ public class DoubleLinearOpsTensorAlgebra :
         return Triple(resU.transpose(), resS, resV.transpose())
     }
 
+    //http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
     override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
         checkSymmetric(this)
-        //http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
-        //see the last point
-        TODO("maybe use SVD")
+        val (u, s, v) = this.svd()
+        val shp = s.shape + intArrayOf(1)
+        val utv = (u.transpose() dot v).map { if (abs(it) < 0.99) 0.0 else sign(it) }
+        val eig = (utv dot s.view(shp)).view(s.shape)
+        return Pair(eig, v)
     }
 
     public fun DoubleTensor.detLU(): DoubleTensor {
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index cc127b5a7..c96fcb1c0 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -1,5 +1,6 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.structures.toList
 import kotlin.math.abs
 import kotlin.test.Ignore
 import kotlin.test.Test
@@ -137,13 +138,12 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    @Ignore
     fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra {
-        val tensor = randNormal(shape = intArrayOf(5, 2, 2), 0)
+        val tensor = randNormal(shape = intArrayOf(5, 3, 3), 0)
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
         val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
-        assertTrue(tensorSigma.eq(tensorSigmaCalc))
+        assertTrue(tensorSigma.eq(tensorSigmaCalc, 0.01))
     }
 
 

From fe8579180d8ebe92b8ba2a41a74d5932d90694a9 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 9 Apr 2021 09:56:37 +0100
Subject: [PATCH 097/178] Open epsilon to client to control numerical precision
 for power methods

---
 .../kmath/tensors/LinearOpsTensorAlgebra.kt     |  4 ++--
 .../core/DoubleLinearOpsTensorAlgebra.kt        | 17 +++++++++++------
 .../space/kscience/kmath/tensors/core/checks.kt |  6 +++---
 .../kscience/kmath/tensors/core/linutils.kt     |  6 +++---
 .../tensors/core/TestDoubleLinearOpsAlgebra.kt  |  2 +-
 5 files changed, 20 insertions(+), 15 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index d980c510f..87c459f35 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -1,7 +1,7 @@
 package space.kscience.kmath.tensors
 
 
-public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, IndexTensorType: TensorStructure<Int>> :
+public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, IndexTensorType : TensorStructure<Int>> :
     TensorPartialDivisionAlgebra<T, TensorType> {
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
@@ -26,6 +26,6 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, Inde
     public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.symeig.html
-    public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
+    public fun TensorType.symEig(): Pair<TensorType, TensorType>
 
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 268f2fe55..95b668917 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -94,8 +94,10 @@ public class DoubleLinearOpsTensorAlgebra :
         return qTensor to rTensor
     }
 
+    override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
+        svd(epsilon = 1e-10)
 
-    override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+    public fun DoubleTensor.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         val size = this.linearStructure.dim
         val commonShape = this.shape.sliceArray(0 until size - 2)
         val (n, m) = this.shape.sliceArray(size - 2 until size)
@@ -110,17 +112,20 @@ public class DoubleLinearOpsTensorAlgebra :
                 matrix.shape,
                 matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + size).toDoubleArray()
             )
-            svdHelper(curMatrix, USV, m, n)
+            svdHelper(curMatrix, USV, m, n, epsilon)
         }
         return Triple(resU.transpose(), resS, resV.transpose())
     }
 
+    override fun DoubleTensor.symEig(): Pair<DoubleTensor, DoubleTensor> =
+        symEig(epsilon = 1e-15)
+
     //http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
-    override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
-        checkSymmetric(this)
-        val (u, s, v) = this.svd()
+    public fun DoubleTensor.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
+        checkSymmetric(this, epsilon)
+        val (u, s, v) = this.svd(epsilon)
         val shp = s.shape + intArrayOf(1)
-        val utv = (u.transpose() dot v).map { if (abs(it) < 0.99) 0.0 else sign(it) }
+        val utv = (u.transpose() dot v).map { if (abs(it) < 0.9) 0.0 else sign(it) }
         val eig = (utv dot s.view(shp)).view(s.shape)
         return Pair(eig, v)
     }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index 1f70eb300..1dde2ea56 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -58,7 +58,7 @@ internal inline fun <T, TensorType : TensorStructure<T>,
     }
 }
 
-internal inline fun DoubleTensorAlgebra.checkSymmetric(tensor: DoubleTensor): Unit =
-    check(tensor.eq(tensor.transpose())){
-        "Tensor is not symmetric about the last 2 dimensions"
+internal inline fun DoubleTensorAlgebra.checkSymmetric(tensor: DoubleTensor, epsilon: Double = 1e-6): Unit =
+    check(tensor.eq(tensor.transpose(), epsilon)) {
+        "Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
     }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index 1bdfee2d5..685c16a1b 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -246,7 +246,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon:
 internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
     matrix: DoubleTensor,
     USV: Pair<BufferedTensor<Double>, Pair<BufferedTensor<Double>, BufferedTensor<Double>>>,
-    m: Int, n: Int
+    m: Int, n: Int, epsilon: Double
 ): Unit {
     val res = ArrayList<Triple<Double, DoubleTensor, DoubleTensor>>(0)
     val (matrixU, SV) = USV
@@ -267,12 +267,12 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
         var u: DoubleTensor
         var norm: Double
         if (n > m) {
-            v = svd1d(a)
+            v = svd1d(a, epsilon)
             u = matrix.dot(v)
             norm = DoubleAnalyticTensorAlgebra { (u dot u).sqrt().value() }
             u = u.times(1.0 / norm)
         } else {
-            u = svd1d(a)
+            u = svd1d(a, epsilon)
             v = matrix.transpose(0, 1).dot(u)
             norm = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
             v = v.times(1.0 / norm)
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index c96fcb1c0..0997a9b86 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -143,7 +143,7 @@ class TestDoubleLinearOpsTensorAlgebra {
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
         val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
-        assertTrue(tensorSigma.eq(tensorSigmaCalc, 0.01))
+        assertTrue(tensorSigma.eq(tensorSigmaCalc))
     }
 
 

From a692412cff00241031258e37e1f7c2ad151518f4 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 9 Apr 2021 10:08:55 +0100
Subject: [PATCH 098/178] Safer cleaner for symeig

---
 .../tensors/core/DoubleLinearOpsTensorAlgebra.kt     | 10 ++++++----
 .../space/kscience/kmath/tensors/core/linutils.kt    | 12 ++++++++++++
 2 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 95b668917..c26046e37 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -3,10 +3,8 @@ package space.kscience.kmath.tensors.core
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.structures.toList
-import kotlin.math.abs
 import kotlin.math.min
-import kotlin.math.sign
+
 
 public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
@@ -125,7 +123,11 @@ public class DoubleLinearOpsTensorAlgebra :
         checkSymmetric(this, epsilon)
         val (u, s, v) = this.svd(epsilon)
         val shp = s.shape + intArrayOf(1)
-        val utv = (u.transpose() dot v).map { if (abs(it) < 0.9) 0.0 else sign(it) }
+        val utv = u.transpose() dot v
+        val n = s.shape.last()
+        for( matrix in utv.matrixSequence())
+            cleanSymHelper(matrix.as2D(),n)
+
         val eig = (utv dot s.view(shp)).view(s.shape)
         return Pair(eig, v)
     }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index 685c16a1b..b3cfc1092 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -6,6 +6,7 @@ import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
 import kotlin.math.abs
 import kotlin.math.min
+import kotlin.math.sign
 import kotlin.math.sqrt
 
 
@@ -294,3 +295,14 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
         matrixV.buffer.array()[matrixV.bufferStart + i] = vBuffer[i]
     }
 }
+
+internal inline fun cleanSymHelper(matrix: MutableStructure2D<Double>, n: Int): Unit {
+    for (i in 0 until n)
+        for (j in 0 until n) {
+            if (i == j) {
+                matrix[i, j] = sign(matrix[i, j])
+            } else {
+                matrix[i, j] = 0.0
+            }
+        }
+}

From 1e8da7a87bcc00830338d66f8d68fb2dfda7698b Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 9 Apr 2021 10:53:36 +0100
Subject: [PATCH 099/178] Cholesky decomp tests and checks - det to be fixed

---
 .../tensors/core/DoubleLinearOpsTensorAlgebra.kt    |  3 ++-
 .../space/kscience/kmath/tensors/core/checks.kt     |  9 ++++++++-
 .../tensors/core/TestDoubleLinearOpsAlgebra.kt      | 13 +++++++++++++
 3 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index c26046e37..4d6d14764 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -67,9 +67,10 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     override fun DoubleTensor.cholesky(): DoubleTensor {
-        //positive definite check
         checkSymmetric(this)
         checkSquareMatrix(shape)
+        //TODO("Andrei the det routine has bugs")
+        //checkPositiveDefinite(this)
 
         val n = shape.last()
         val lTensor = zeroesLike()
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index 1dde2ea56..8dbf9eb81 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -61,4 +61,11 @@ internal inline fun <T, TensorType : TensorStructure<T>,
 internal inline fun DoubleTensorAlgebra.checkSymmetric(tensor: DoubleTensor, epsilon: Double = 1e-6): Unit =
     check(tensor.eq(tensor.transpose(), epsilon)) {
         "Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
-    }
\ No newline at end of file
+    }
+
+internal inline fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor): Unit {
+    for( mat in tensor.matrixSequence())
+        check(mat.asTensor().detLU().value() > 0.0){
+            "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"
+        }
+}
\ No newline at end of file
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 0997a9b86..2914b216b 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -112,6 +112,19 @@ class TestDoubleLinearOpsTensorAlgebra {
         assertTrue((p dot tensor).eq(l dot u))
     }
 
+    @Test
+    fun testCholesky() = DoubleLinearOpsTensorAlgebra {
+        val tensor = randNormal(intArrayOf(2, 5, 5), 0)
+        val sigma = (tensor dot tensor.transpose()) + diagonalEmbedding(
+            fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
+        )
+        //checkPositiveDefinite(sigma) sigma must be positive definite
+        val low = sigma.cholesky()
+        val sigmChol = low dot low.transpose()
+        assertTrue(sigma.eq(sigmChol))
+
+    }
+
     @Test
     fun testSVD1D() = DoubleLinearOpsTensorAlgebra {
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))

From b51427d2abb401d1402eebd7bf61e93bca47f3a2 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 9 Apr 2021 12:26:03 +0100
Subject: [PATCH 100/178] test to fix determinant

---
 .../tensors/core/TestDoubleLinearOpsAlgebra.kt      | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 2914b216b..37caf88fe 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -34,6 +34,19 @@ class TestDoubleLinearOpsTensorAlgebra {
 
     }
 
+    @Test
+    fun testDet() = DoubleLinearOpsTensorAlgebra {
+        val m = fromArray(
+            intArrayOf(3, 3), doubleArrayOf(
+                2.1843, 1.4391, -0.4845,
+                1.4391, 1.7772, 0.4055,
+                -0.4845, 0.4055, 0.7519
+            )
+        )
+        println(m.det().value())
+        println(0.0197) //expected value
+    }
+
     @Test
     fun testInvLU() = DoubleLinearOpsTensorAlgebra {
         val tensor = fromArray(

From 75783bcb038d1649f5587a15e906d9450fce5c8c Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 9 Apr 2021 14:06:44 +0100
Subject: [PATCH 101/178] some todos

---
 .../kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt  | 1 +
 1 file changed, 1 insertion(+)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 4d6d14764..82f63dd40 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -154,6 +154,7 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     public fun DoubleTensor.invLU(): DoubleTensor {
+        //TODO("Andrei the det is non-zero")
         val (luTensor, pivotsTensor) = lu()
         val invTensor = luTensor.zeroesLike()
 

From a2d41d5e73e8eef1413a4f1671d0717b8b97aa9b Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 14 Apr 2021 20:30:42 +0100
Subject: [PATCH 102/178] Cholesky with precision in client API

---
 .../kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt     | 7 ++++---
 .../kotlin/space/kscience/kmath/tensors/core/checks.kt     | 4 +++-
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 82f63dd40..e0ec49333 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -66,11 +66,10 @@ public class DoubleLinearOpsTensorAlgebra :
 
     }
 
-    override fun DoubleTensor.cholesky(): DoubleTensor {
-        checkSymmetric(this)
+    public fun DoubleTensor.cholesky(epsilon: Double): DoubleTensor {
         checkSquareMatrix(shape)
         //TODO("Andrei the det routine has bugs")
-        //checkPositiveDefinite(this)
+        //checkPositiveDefinite(this, epsilon)
 
         val n = shape.last()
         val lTensor = zeroesLike()
@@ -81,6 +80,8 @@ public class DoubleLinearOpsTensorAlgebra :
         return lTensor
     }
 
+    override fun DoubleTensor.cholesky(): DoubleTensor = cholesky(1e-6)
+
     override fun DoubleTensor.qr(): Pair<DoubleTensor, DoubleTensor> {
         checkSquareMatrix(shape)
         val qTensor = zeroesLike()
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index 8dbf9eb81..730b6ed9a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -63,7 +63,9 @@ internal inline fun DoubleTensorAlgebra.checkSymmetric(tensor: DoubleTensor, eps
         "Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
     }
 
-internal inline fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor): Unit {
+internal inline fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(
+    tensor: DoubleTensor, epsilon: Double = 1e-6): Unit {
+    checkSymmetric(tensor, epsilon)
     for( mat in tensor.matrixSequence())
         check(mat.asTensor().detLU().value() > 0.0){
             "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"

From c7669d4fba20ccd856918504d4836d2619961927 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Wed, 14 Apr 2021 23:05:54 +0300
Subject: [PATCH 103/178] fix det

---
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 22 +++++++++----
 .../kscience/kmath/tensors/core/checks.kt     | 10 ++++++
 .../kscience/kmath/tensors/core/linutils.kt   | 11 +++++--
 .../core/TestDoubleLinearOpsAlgebra.kt        | 17 ++++++++--
 kmath-tensors/src/jvmMain/kotlin/andMain.kt   | 32 +++++++++++++++++++
 5 files changed, 81 insertions(+), 11 deletions(-)
 create mode 100644 kmath-tensors/src/jvmMain/kotlin/andMain.kt

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 82f63dd40..59678947a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -14,8 +14,7 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun DoubleTensor.det(): DoubleTensor = detLU()
 
-    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
-
+    internal fun DoubleTensor.luForDet(forDet: Boolean = false): Pair<DoubleTensor, IntTensor> {
         checkSquareMatrix(shape)
 
         val luTensor = copy()
@@ -31,10 +30,22 @@ public class DoubleLinearOpsTensorAlgebra :
         )
 
         for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
-            luHelper(lu.as2D(), pivots.as1D(), m)
+            try {
+                luHelper(lu.as2D(), pivots.as1D(), m)
+            } catch (e: RuntimeException) {
+                if (forDet) {
+                    lu.as2D()[intArrayOf(0, 0)] = 0.0
+                } else {
+                    throw IllegalStateException("LUP decomposition can't be performed")
+                }
+            }
+
 
         return Pair(luTensor, pivotsTensor)
+    }
 
+    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
+        return luForDet(false)
     }
 
     override fun luPivot(
@@ -69,8 +80,7 @@ public class DoubleLinearOpsTensorAlgebra :
     override fun DoubleTensor.cholesky(): DoubleTensor {
         checkSymmetric(this)
         checkSquareMatrix(shape)
-        //TODO("Andrei the det routine has bugs")
-        //checkPositiveDefinite(this)
+        checkPositiveDefinite(this)
 
         val n = shape.last()
         val lTensor = zeroesLike()
@@ -134,7 +144,7 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     public fun DoubleTensor.detLU(): DoubleTensor {
-        val (luTensor, pivotsTensor) = lu()
+        val (luTensor, pivotsTensor) = luForDet(forDet = true)
         val n = shape.size
 
         val detTensorShape = IntArray(n - 1) { i -> shape[i] }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index 8dbf9eb81..c61759da2 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -2,6 +2,7 @@ package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.TensorAlgebra
 import space.kscience.kmath.tensors.TensorStructure
+import kotlin.math.abs
 
 
 internal inline fun <T, TensorType : TensorStructure<T>,
@@ -68,4 +69,13 @@ internal inline fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(tensor: D
         check(mat.asTensor().detLU().value() > 0.0){
             "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"
         }
+}
+
+internal inline fun DoubleLinearOpsTensorAlgebra.checkNonSingularMatrix(tensor: DoubleTensor): Unit {
+    for( mat in tensor.matrixSequence()) {
+        val detTensor = mat.asTensor().detLU()
+        check(!(detTensor.eq(detTensor.zeroesLike()))){
+            "Tensor contains matrices which are singular"
+        }
+    }
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index b3cfc1092..f2c9d8c76 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -62,10 +62,10 @@ internal inline fun dotHelper(
 }
 
 internal inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStructure1D<Int>, m: Int) {
-    for (row in 0 until m) pivots[row] = row
+    for (row in 0..m) pivots[row] = row
 
     for (i in 0 until m) {
-        var maxVal = -1.0
+        var maxVal = 0.0
         var maxInd = i
 
         for (k in i until m) {
@@ -76,7 +76,9 @@ internal inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStru
             }
         }
 
-        //todo check singularity
+        if (abs(maxVal) < 1e-9) {
+            throw RuntimeException()
+        }
 
         if (maxInd != i) {
 
@@ -158,6 +160,9 @@ internal inline fun choleskyHelper(
 internal inline fun luMatrixDet(luTensor: MutableStructure2D<Double>, pivotsTensor: MutableStructure1D<Int>): Double {
     val lu = luTensor.as2D()
     val pivots = pivotsTensor.as1D()
+    if (lu[0, 0] == 0.0) {
+        return 0.0
+    }
     val m = lu.shape[0]
     val sign = if ((pivots[m] - m) % 2 == 0) 1.0 else -1.0
     return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 37caf88fe..75ff12355 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -36,6 +36,7 @@ class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testDet() = DoubleLinearOpsTensorAlgebra {
+        val expectedValue = 0.019827417
         val m = fromArray(
             intArrayOf(3, 3), doubleArrayOf(
                 2.1843, 1.4391, -0.4845,
@@ -43,8 +44,20 @@ class TestDoubleLinearOpsTensorAlgebra {
                 -0.4845, 0.4055, 0.7519
             )
         )
-        println(m.det().value())
-        println(0.0197) //expected value
+
+        assertTrue { abs(m.det().value() - expectedValue) < 1e-5}
+    }
+
+    @Test
+    fun testDetSingle() = DoubleLinearOpsTensorAlgebra {
+        val expectedValue = 48.151623
+        val m = fromArray(
+            intArrayOf(1, 1), doubleArrayOf(
+                expectedValue
+            )
+        )
+
+        assertTrue { abs(m.det().value() - expectedValue) < 1e-5}
     }
 
     @Test
diff --git a/kmath-tensors/src/jvmMain/kotlin/andMain.kt b/kmath-tensors/src/jvmMain/kotlin/andMain.kt
new file mode 100644
index 000000000..68facf467
--- /dev/null
+++ b/kmath-tensors/src/jvmMain/kotlin/andMain.kt
@@ -0,0 +1,32 @@
+import space.kscience.kmath.nd.*
+import space.kscience.kmath.tensors.core.DoubleLinearOpsTensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.array
+import kotlin.math.abs
+import kotlin.math.sqrt
+
+
+fun main() {
+
+    DoubleTensorAlgebra {
+        val tensor = fromArray(
+            intArrayOf(2, 2, 2),
+            doubleArrayOf(
+                1.0, 3.0,
+                1.0, 2.0,
+                1.5, 1.0,
+                10.0, 2.0
+            )
+        )
+        val tensor2 = fromArray(
+            intArrayOf(2, 2),
+            doubleArrayOf(
+                0.0, 0.0,
+                0.0, 0.0
+            )
+        )
+        DoubleLinearOpsTensorAlgebra {
+            println(tensor2.det().value())
+        }
+    }
+}

From aeb71b5d270f7e40f8c5b8c2416c163e6baec206 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Wed, 14 Apr 2021 23:10:42 +0300
Subject: [PATCH 104/178] fix det

---
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 28 +++++++++++++------
 .../kscience/kmath/tensors/core/checks.kt     | 14 +++++++++-
 .../kscience/kmath/tensors/core/linutils.kt   | 11 ++++++--
 .../core/TestDoubleLinearOpsAlgebra.kt        | 17 +++++++++--
 4 files changed, 56 insertions(+), 14 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index 82f63dd40..027fcf5d0 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -14,8 +14,7 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun DoubleTensor.det(): DoubleTensor = detLU()
 
-    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
-
+    internal fun DoubleTensor.luForDet(forDet: Boolean = false): Pair<DoubleTensor, IntTensor> {
         checkSquareMatrix(shape)
 
         val luTensor = copy()
@@ -31,10 +30,22 @@ public class DoubleLinearOpsTensorAlgebra :
         )
 
         for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
-            luHelper(lu.as2D(), pivots.as1D(), m)
+            try {
+                luHelper(lu.as2D(), pivots.as1D(), m)
+            } catch (e: RuntimeException) {
+                if (forDet) {
+                    lu.as2D()[intArrayOf(0, 0)] = 0.0
+                } else {
+                    throw IllegalStateException("LUP decomposition can't be performed")
+                }
+            }
+
 
         return Pair(luTensor, pivotsTensor)
+    }
 
+    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
+        return luForDet(false)
     }
 
     override fun luPivot(
@@ -66,11 +77,10 @@ public class DoubleLinearOpsTensorAlgebra :
 
     }
 
-    override fun DoubleTensor.cholesky(): DoubleTensor {
-        checkSymmetric(this)
+    public fun DoubleTensor.cholesky(epsilon: Double): DoubleTensor {
         checkSquareMatrix(shape)
-        //TODO("Andrei the det routine has bugs")
-        //checkPositiveDefinite(this)
+        checkPositiveDefinite(this)
+        //checkPositiveDefinite(this, epsilon)
 
         val n = shape.last()
         val lTensor = zeroesLike()
@@ -81,6 +91,8 @@ public class DoubleLinearOpsTensorAlgebra :
         return lTensor
     }
 
+    override fun DoubleTensor.cholesky(): DoubleTensor = cholesky(1e-6)
+
     override fun DoubleTensor.qr(): Pair<DoubleTensor, DoubleTensor> {
         checkSquareMatrix(shape)
         val qTensor = zeroesLike()
@@ -134,7 +146,7 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     public fun DoubleTensor.detLU(): DoubleTensor {
-        val (luTensor, pivotsTensor) = lu()
+        val (luTensor, pivotsTensor) = luForDet(forDet = true)
         val n = shape.size
 
         val detTensorShape = IntArray(n - 1) { i -> shape[i] }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index 8dbf9eb81..45a71d1bc 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -2,6 +2,7 @@ package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.TensorAlgebra
 import space.kscience.kmath.tensors.TensorStructure
+import kotlin.math.abs
 
 
 internal inline fun <T, TensorType : TensorStructure<T>,
@@ -63,9 +64,20 @@ internal inline fun DoubleTensorAlgebra.checkSymmetric(tensor: DoubleTensor, eps
         "Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
     }
 
-internal inline fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor): Unit {
+internal inline fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(
+    tensor: DoubleTensor, epsilon: Double = 1e-6): Unit {
+    checkSymmetric(tensor, epsilon)
     for( mat in tensor.matrixSequence())
         check(mat.asTensor().detLU().value() > 0.0){
             "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"
         }
+}
+
+internal inline fun DoubleLinearOpsTensorAlgebra.checkNonSingularMatrix(tensor: DoubleTensor): Unit {
+    for( mat in tensor.matrixSequence()) {
+        val detTensor = mat.asTensor().detLU()
+        check(!(detTensor.eq(detTensor.zeroesLike()))){
+            "Tensor contains matrices which are singular"
+        }
+    }
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index b3cfc1092..f2c9d8c76 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -62,10 +62,10 @@ internal inline fun dotHelper(
 }
 
 internal inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStructure1D<Int>, m: Int) {
-    for (row in 0 until m) pivots[row] = row
+    for (row in 0..m) pivots[row] = row
 
     for (i in 0 until m) {
-        var maxVal = -1.0
+        var maxVal = 0.0
         var maxInd = i
 
         for (k in i until m) {
@@ -76,7 +76,9 @@ internal inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStru
             }
         }
 
-        //todo check singularity
+        if (abs(maxVal) < 1e-9) {
+            throw RuntimeException()
+        }
 
         if (maxInd != i) {
 
@@ -158,6 +160,9 @@ internal inline fun choleskyHelper(
 internal inline fun luMatrixDet(luTensor: MutableStructure2D<Double>, pivotsTensor: MutableStructure1D<Int>): Double {
     val lu = luTensor.as2D()
     val pivots = pivotsTensor.as1D()
+    if (lu[0, 0] == 0.0) {
+        return 0.0
+    }
     val m = lu.shape[0]
     val sign = if ((pivots[m] - m) % 2 == 0) 1.0 else -1.0
     return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 37caf88fe..75ff12355 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -36,6 +36,7 @@ class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testDet() = DoubleLinearOpsTensorAlgebra {
+        val expectedValue = 0.019827417
         val m = fromArray(
             intArrayOf(3, 3), doubleArrayOf(
                 2.1843, 1.4391, -0.4845,
@@ -43,8 +44,20 @@ class TestDoubleLinearOpsTensorAlgebra {
                 -0.4845, 0.4055, 0.7519
             )
         )
-        println(m.det().value())
-        println(0.0197) //expected value
+
+        assertTrue { abs(m.det().value() - expectedValue) < 1e-5}
+    }
+
+    @Test
+    fun testDetSingle() = DoubleLinearOpsTensorAlgebra {
+        val expectedValue = 48.151623
+        val m = fromArray(
+            intArrayOf(1, 1), doubleArrayOf(
+                expectedValue
+            )
+        )
+
+        assertTrue { abs(m.det().value() - expectedValue) < 1e-5}
     }
 
     @Test

From b46e8c5fe23bb64fa6868363848e2e13e0c1edfb Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 14 Apr 2021 22:13:54 +0100
Subject: [PATCH 105/178] LU and det refactored

---
 .../core/DoubleLinearOpsTensorAlgebra.kt      | 64 ++++++-------------
 .../kscience/kmath/tensors/core/linutils.kt   | 43 +++++++++++--
 .../core/TestDoubleLinearOpsAlgebra.kt        |  8 +--
 3 files changed, 59 insertions(+), 56 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index a6bd5d5f7..e72948c84 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -10,43 +10,15 @@ public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
     DoubleTensorAlgebra() {
 
-    override fun DoubleTensor.inv(): DoubleTensor = invLU()
+    override fun DoubleTensor.inv(): DoubleTensor = invLU(1e-9)
 
-    override fun DoubleTensor.det(): DoubleTensor = detLU()
+    override fun DoubleTensor.det(): DoubleTensor = detLU(1e-9)
 
-    internal fun DoubleTensor.luForDet(forDet: Boolean = false): Pair<DoubleTensor, IntTensor> {
-        checkSquareMatrix(shape)
+    public fun DoubleTensor.lu(epsilon: Double): Pair<DoubleTensor, IntTensor> =
+        computeLU(this, epsilon) ?:
+        throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
 
-        val luTensor = copy()
-
-        val n = shape.size
-        val m = shape.last()
-        val pivotsShape = IntArray(n - 1) { i -> shape[i] }
-        pivotsShape[n - 2] = m + 1
-
-        val pivotsTensor = IntTensor(
-            pivotsShape,
-            IntArray(pivotsShape.reduce(Int::times)) { 0 }
-        )
-
-        for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
-            try {
-                luHelper(lu.as2D(), pivots.as1D(), m)
-            } catch (e: RuntimeException) {
-                if (forDet) {
-                    lu.as2D()[intArrayOf(0, 0)] = 0.0
-                } else {
-                    throw IllegalStateException("LUP decomposition can't be performed")
-                }
-            }
-
-
-        return Pair(luTensor, pivotsTensor)
-    }
-
-    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
-        return luForDet(false)
-    }
+    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> = lu(1e-9)
 
     override fun luPivot(
         luTensor: DoubleTensor,
@@ -79,9 +51,7 @@ public class DoubleLinearOpsTensorAlgebra :
 
     public fun DoubleTensor.cholesky(epsilon: Double): DoubleTensor {
         checkSquareMatrix(shape)
-        checkPositiveDefinite(this)
-        //checkPositiveDefinite(this, epsilon)
-
+        checkPositiveDefinite(this, epsilon)
 
         val n = shape.last()
         val lTensor = zeroesLike()
@@ -139,15 +109,19 @@ public class DoubleLinearOpsTensorAlgebra :
         val shp = s.shape + intArrayOf(1)
         val utv = u.transpose() dot v
         val n = s.shape.last()
-        for( matrix in utv.matrixSequence())
-            cleanSymHelper(matrix.as2D(),n)
+        for (matrix in utv.matrixSequence())
+            cleanSymHelper(matrix.as2D(), n)
 
         val eig = (utv dot s.view(shp)).view(s.shape)
         return Pair(eig, v)
     }
 
-    public fun DoubleTensor.detLU(): DoubleTensor {
-        val (luTensor, pivotsTensor) = luForDet(forDet = true)
+    public fun DoubleTensor.detLU(epsilon: Double = 1e-9): DoubleTensor {
+
+        checkSquareMatrix(this.shape)
+        val luTensor = this.copy()
+        val pivotsTensor = this.setUpPivots()
+
         val n = shape.size
 
         val detTensorShape = IntArray(n - 1) { i -> shape[i] }
@@ -160,15 +134,15 @@ public class DoubleLinearOpsTensorAlgebra :
         )
 
         luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).forEachIndexed { index, (lu, pivots) ->
-            resBuffer[index] = luMatrixDet(lu.as2D(), pivots.as1D())
+            resBuffer[index] = if (luHelper(lu.as2D(), pivots.as1D(), epsilon))
+                0.0 else luMatrixDet(lu.as2D(), pivots.as1D())
         }
 
         return detTensor
     }
 
-    public fun DoubleTensor.invLU(): DoubleTensor {
-        //TODO("Andrei the det is non-zero")
-        val (luTensor, pivotsTensor) = lu()
+    public fun DoubleTensor.invLU(epsilon: Double = 1e-9): DoubleTensor {
+        val (luTensor, pivotsTensor) = lu(epsilon)
         val invTensor = luTensor.zeroesLike()
 
         val seq = luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index f2c9d8c76..ee159b614 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -61,7 +61,13 @@ internal inline fun dotHelper(
     }
 }
 
-internal inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStructure1D<Int>, m: Int) {
+internal inline fun luHelper(
+    lu: MutableStructure2D<Double>,
+    pivots: MutableStructure1D<Int>,
+    epsilon: Double): Boolean {
+
+    val m = lu.rowNum
+
     for (row in 0..m) pivots[row] = row
 
     for (i in 0 until m) {
@@ -69,16 +75,15 @@ internal inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStru
         var maxInd = i
 
         for (k in i until m) {
-            val absA = kotlin.math.abs(lu[k, i])
+            val absA = abs(lu[k, i])
             if (absA > maxVal) {
                 maxVal = absA
                 maxInd = k
             }
         }
 
-        if (abs(maxVal) < 1e-9) {
-            throw RuntimeException()
-        }
+        if (abs(maxVal) < epsilon)
+            return true // matrix is singular
 
         if (maxInd != i) {
 
@@ -103,6 +108,34 @@ internal inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStru
             }
         }
     }
+    return false
+}
+
+internal inline fun <T> BufferedTensor<T>.setUpPivots(): IntTensor {
+    val n = this.shape.size
+    val m = this.shape.last()
+    val pivotsShape = IntArray(n - 1) { i -> this.shape[i] }
+    pivotsShape[n - 2] = m + 1
+
+    return IntTensor(
+        pivotsShape,
+        IntArray(pivotsShape.reduce(Int::times)) { 0 }
+    )
+}
+
+internal inline fun DoubleLinearOpsTensorAlgebra.computeLU(
+    tensor: DoubleTensor,
+    epsilon: Double): Pair<DoubleTensor, IntTensor>? {
+
+    checkSquareMatrix(tensor.shape)
+    val luTensor = tensor.copy()
+    val pivotsTensor = tensor.setUpPivots()
+
+    for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
+        if(luHelper(lu.as2D(), pivots.as1D(), epsilon))
+            return null
+
+    return Pair(luTensor, pivotsTensor)
 }
 
 internal inline fun pivInit(
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 75ff12355..d19d0b6f6 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -1,8 +1,6 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.structures.toList
 import kotlin.math.abs
-import kotlin.test.Ignore
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
@@ -45,7 +43,7 @@ class TestDoubleLinearOpsTensorAlgebra {
             )
         )
 
-        assertTrue { abs(m.det().value() - expectedValue) < 1e-5}
+        assertTrue { abs(m.det().value() - expectedValue) < 1e-5 }
     }
 
     @Test
@@ -57,7 +55,7 @@ class TestDoubleLinearOpsTensorAlgebra {
             )
         )
 
-        assertTrue { abs(m.det().value() - expectedValue) < 1e-5}
+        assertTrue { abs(m.det().value() - expectedValue) < 1e-5 }
     }
 
     @Test
@@ -144,11 +142,9 @@ class TestDoubleLinearOpsTensorAlgebra {
         val sigma = (tensor dot tensor.transpose()) + diagonalEmbedding(
             fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
         )
-        //checkPositiveDefinite(sigma) sigma must be positive definite
         val low = sigma.cholesky()
         val sigmChol = low dot low.transpose()
         assertTrue(sigma.eq(sigmChol))
-
     }
 
     @Test

From 0fa73e1e9e4cca8cf4377d580bbfbcceb394d8c0 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 14 Apr 2021 22:21:18 +0100
Subject: [PATCH 106/178] Drop unused code

---
 .../kotlin/space/kscience/kmath/tensors/core/checks.kt | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index 45a71d1bc..730b6ed9a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -2,7 +2,6 @@ package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.TensorAlgebra
 import space.kscience.kmath.tensors.TensorStructure
-import kotlin.math.abs
 
 
 internal inline fun <T, TensorType : TensorStructure<T>,
@@ -71,13 +70,4 @@ internal inline fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(
         check(mat.asTensor().detLU().value() > 0.0){
             "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"
         }
-}
-
-internal inline fun DoubleLinearOpsTensorAlgebra.checkNonSingularMatrix(tensor: DoubleTensor): Unit {
-    for( mat in tensor.matrixSequence()) {
-        val detTensor = mat.asTensor().detLU()
-        check(!(detTensor.eq(detTensor.zeroesLike()))){
-            "Tensor contains matrices which are singular"
-        }
-    }
 }
\ No newline at end of file

From b7da52edb196769ac5afb42ae5dbdca6bedd58f5 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Thu, 15 Apr 2021 23:10:15 +0300
Subject: [PATCH 107/178] pretty printer

---
 .../kscience/kmath/tensors/core/utils.kt      | 48 +++++++++++++-
 .../src/jvmMain/kotlin/tensorPrettyPrinter.kt | 64 +++++++++++++++++++
 2 files changed, 111 insertions(+), 1 deletion(-)
 create mode 100644 kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index 392abd1c2..e67591c9e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -1,5 +1,7 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.nd.as1D
+import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.samplers.GaussianSampler
 import space.kscience.kmath.stat.RandomGenerator
 import space.kscience.kmath.structures.*
@@ -57,4 +59,48 @@ internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
     ii
 }
 
-internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int =  minusIndexFrom(this.linearStructure.dim, i)
\ No newline at end of file
+internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int =  minusIndexFrom(this.linearStructure.dim, i)
+
+public fun DoubleTensor.toPrettyString(): String = buildString {
+    var offset = 0
+    val shape = this@toPrettyString.shape
+    val linearStructure = this@toPrettyString.linearStructure
+    var vectorSize = shape.last()
+    val initString = "DoubleTensor(\n"
+    append(initString)
+    var charOffset = 3
+    for (vector in vectorSequence()) {
+        append(" ".repeat(charOffset))
+        val index = linearStructure.index(offset)
+        for (ind in index.reversed()) {
+            if (ind != 0) {
+                break
+            }
+            append("[")
+            charOffset += 1
+        }
+        // todo refactor
+        val values = mutableListOf<Double>()
+        for (i in 0 until vectorSize) {
+            values.add(vector[intArrayOf(i)])
+        }
+        // todo apply exp formatting
+        append(values.joinToString(", "))
+        append("]")
+        charOffset -= 1
+        for ((ind, maxInd) in index.reversed().zip(shape.reversed()).drop(1)){
+            if (ind != maxInd - 1) {
+                break
+            }
+            append("]")
+            charOffset -=1
+        }
+        offset += vectorSize
+        // todo refactor
+        if (this@toPrettyString.numel == offset) {
+            break
+        }
+        append(",\n")
+    }
+    append("\n)")
+}
diff --git a/kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt b/kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt
new file mode 100644
index 000000000..669a5494b
--- /dev/null
+++ b/kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt
@@ -0,0 +1,64 @@
+
+import space.kscience.kmath.tensors.core.DoubleTensor
+import space.kscience.kmath.tensors.core.vectorSequence
+import java.lang.StringBuilder
+
+internal fun format(value: Double, digits: Int = 4): String {
+    val res = "%.${digits}e".format(value).replace(',', '.')
+    if (value < 0.0) {
+        return res
+    }
+    return StringBuilder().append(" ").append(res).toString()
+}
+
+public fun DoubleTensor.toPrettyString(): String {
+    val builder = StringBuilder()
+    with(builder) {
+        var offset = 0
+        val shape = this@toPrettyString.shape
+        val linearStructure = this@toPrettyString.linearStructure
+        var vectorSize = shape.last()
+        val initString = "DoubleTensor(\n"
+        append(initString)
+        var charOffset = 3
+        for (vector in vectorSequence()) {
+            append(" ".repeat(charOffset))
+            val index = linearStructure.index(offset)
+            for (ind in index.reversed()) {
+                if (ind != 0) {
+                    break
+                }
+                append("[")
+                charOffset += 1
+            }
+            // todo refactor
+            val values = mutableListOf<Double>()
+            for (i in 0 until vectorSize) {
+                values.add(vector[intArrayOf(i)])
+            }
+            append(values.map { format(it) }.joinToString(", "))
+            append("]")
+            charOffset -= 1
+            for (i in shape.size - 2 downTo 0){
+                val ind = index[i]
+                val maxInd = shape[i]
+                if (ind != maxInd - 1) {
+                    break
+                }
+                append("]")
+                charOffset -=1
+            }
+            offset += vectorSize
+            // todo refactor
+            if (this@toPrettyString.numel == offset) {
+                break
+            }
+            append(",\n")
+        }
+        append("\n)")
+    }
+    return builder.toString()
+}
+
+
+

From 41ac72b4fbf5527a9a37296da90ff412e66cf542 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 16 Apr 2021 07:45:31 +0100
Subject: [PATCH 108/178] MPP toString for DoubleTensor

---
 .../kmath/tensors/core/BufferedTensor.kt      |  4 ++-
 .../kscience/kmath/tensors/core/linutils.kt   |  4 +--
 .../kscience/kmath/tensors/core/utils.kt      | 33 ++++++++++++++-----
 .../src/jvmMain/kotlin/tensorPrettyPrinter.kt |  4 +--
 4 files changed, 30 insertions(+), 15 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 9a4d13d2c..9e393f1a8 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -4,7 +4,6 @@ import space.kscience.kmath.structures.*
 import space.kscience.kmath.tensors.TensorStructure
 
 
-
 public open class BufferedTensor<T>(
     override val shape: IntArray,
     public val buffer: MutableBuffer<T>,
@@ -70,6 +69,9 @@ public class DoubleTensor internal constructor(
 {
     internal constructor(bufferedTensor: BufferedTensor<Double>):
             this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
+
+    override fun toString(): String = toPrettyString()
+
 }
 
 internal fun BufferedTensor<Int>.asTensor(): IntTensor = IntTensor(this)
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index ee159b614..ba5e0caaf 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -190,9 +190,7 @@ internal inline fun choleskyHelper(
     }
 }
 
-internal inline fun luMatrixDet(luTensor: MutableStructure2D<Double>, pivotsTensor: MutableStructure1D<Int>): Double {
-    val lu = luTensor.as2D()
-    val pivots = pivotsTensor.as1D()
+internal inline fun luMatrixDet(lu: MutableStructure2D<Double>, pivots: MutableStructure1D<Int>): Double {
     if (lu[0, 0] == 0.0) {
         return 0.0
     }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index e67591c9e..daab79016 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -1,11 +1,11 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.as1D
-import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.samplers.GaussianSampler
 import space.kscience.kmath.stat.RandomGenerator
 import space.kscience.kmath.structures.*
-import kotlin.math.sqrt
+import kotlin.math.*
 
 /**
  * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
@@ -61,7 +61,25 @@ internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
 
 internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int =  minusIndexFrom(this.linearStructure.dim, i)
 
-public fun DoubleTensor.toPrettyString(): String = buildString {
+internal inline fun format(value: Double, digits: Int = 4): String {
+    val ten = 10.0
+    val approxOrder = ceil(log10(abs(value))).toInt()
+    val order = if(
+        ((value % ten) == 0.0) or
+        (value == 1.0) or
+        ((1/value) % ten == 0.0)) approxOrder else approxOrder - 1
+
+    val lead = value / ten.pow(order)
+    val leadDisplay = round(lead*ten.pow(digits)) / ten.pow(digits)
+    val orderDisplay = if(order >= 0) "+$order" else "$order"
+    val valueDisplay = "${leadDisplay}E$orderDisplay"
+    val res = if(value < 0.0) valueDisplay else " $valueDisplay"
+    val fLength = digits + 6
+    val endSpace = " ".repeat(fLength - res.length)
+    return "$res$endSpace"
+}
+
+internal inline fun DoubleTensor.toPrettyString(): String = buildString {
     var offset = 0
     val shape = this@toPrettyString.shape
     val linearStructure = this@toPrettyString.linearStructure
@@ -79,12 +97,9 @@ public fun DoubleTensor.toPrettyString(): String = buildString {
             append("[")
             charOffset += 1
         }
-        // todo refactor
-        val values = mutableListOf<Double>()
-        for (i in 0 until vectorSize) {
-            values.add(vector[intArrayOf(i)])
-        }
-        // todo apply exp formatting
+
+        val values = vector.as1D().toMutableList().map(::format)
+
         append(values.joinToString(", "))
         append("]")
         charOffset -= 1
diff --git a/kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt b/kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt
index 669a5494b..d3ce5933e 100644
--- a/kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt
+++ b/kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt
@@ -2,7 +2,7 @@
 import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.vectorSequence
 import java.lang.StringBuilder
-
+/*
 internal fun format(value: Double, digits: Int = 4): String {
     val res = "%.${digits}e".format(value).replace(',', '.')
     if (value < 0.0) {
@@ -59,6 +59,6 @@ public fun DoubleTensor.toPrettyString(): String {
     }
     return builder.toString()
 }
-
+*/
 
 

From baa303171eb9a6a99b3f5e83cae87a1e0a1abb5f Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 16 Apr 2021 07:47:06 +0100
Subject: [PATCH 109/178] No jvm specifics please

---
 .../src/jvmMain/kotlin/tensorPrettyPrinter.kt | 64 -------------------
 1 file changed, 64 deletions(-)
 delete mode 100644 kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt

diff --git a/kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt b/kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt
deleted file mode 100644
index d3ce5933e..000000000
--- a/kmath-tensors/src/jvmMain/kotlin/tensorPrettyPrinter.kt
+++ /dev/null
@@ -1,64 +0,0 @@
-
-import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.vectorSequence
-import java.lang.StringBuilder
-/*
-internal fun format(value: Double, digits: Int = 4): String {
-    val res = "%.${digits}e".format(value).replace(',', '.')
-    if (value < 0.0) {
-        return res
-    }
-    return StringBuilder().append(" ").append(res).toString()
-}
-
-public fun DoubleTensor.toPrettyString(): String {
-    val builder = StringBuilder()
-    with(builder) {
-        var offset = 0
-        val shape = this@toPrettyString.shape
-        val linearStructure = this@toPrettyString.linearStructure
-        var vectorSize = shape.last()
-        val initString = "DoubleTensor(\n"
-        append(initString)
-        var charOffset = 3
-        for (vector in vectorSequence()) {
-            append(" ".repeat(charOffset))
-            val index = linearStructure.index(offset)
-            for (ind in index.reversed()) {
-                if (ind != 0) {
-                    break
-                }
-                append("[")
-                charOffset += 1
-            }
-            // todo refactor
-            val values = mutableListOf<Double>()
-            for (i in 0 until vectorSize) {
-                values.add(vector[intArrayOf(i)])
-            }
-            append(values.map { format(it) }.joinToString(", "))
-            append("]")
-            charOffset -= 1
-            for (i in shape.size - 2 downTo 0){
-                val ind = index[i]
-                val maxInd = shape[i]
-                if (ind != maxInd - 1) {
-                    break
-                }
-                append("]")
-                charOffset -=1
-            }
-            offset += vectorSize
-            // todo refactor
-            if (this@toPrettyString.numel == offset) {
-                break
-            }
-            append(",\n")
-        }
-        append("\n)")
-    }
-    return builder.toString()
-}
-*/
-
-

From 1e7cf39150be8393acd0f24572d0a1a93ef87e15 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 16 Apr 2021 11:58:42 +0100
Subject: [PATCH 110/178] Don't print 0 order

---
 .../kotlin/space/kscience/kmath/tensors/core/utils.kt       | 6 +++---
 .../space/kscience/kmath/tensors/core/TestDoubleTensor.kt   | 6 ++++++
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index daab79016..40d40b593 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -71,8 +71,8 @@ internal inline fun format(value: Double, digits: Int = 4): String {
 
     val lead = value / ten.pow(order)
     val leadDisplay = round(lead*ten.pow(digits)) / ten.pow(digits)
-    val orderDisplay = if(order >= 0) "+$order" else "$order"
-    val valueDisplay = "${leadDisplay}E$orderDisplay"
+    val orderDisplay = if(order == 0) "" else if(order > 0) "E+$order" else "E$order"
+    val valueDisplay = "${leadDisplay}$orderDisplay"
     val res = if(value < 0.0) valueDisplay else " $valueDisplay"
     val fLength = digits + 6
     val endSpace = " ".repeat(fLength - res.length)
@@ -83,7 +83,7 @@ internal inline fun DoubleTensor.toPrettyString(): String = buildString {
     var offset = 0
     val shape = this@toPrettyString.shape
     val linearStructure = this@toPrettyString.linearStructure
-    var vectorSize = shape.last()
+    val vectorSize = shape.last()
     val initString = "DoubleTensor(\n"
     append(initString)
     var charOffset = 3
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 4f4d9bbdf..ddcf0369c 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -46,4 +46,10 @@ class TestDoubleTensor {
             assertEquals(secondRow[1], secondColumn[1])
         }
     }
+
+    @Test
+    fun toStringTest() = DoubleTensorAlgebra {
+        val tensor = randNormal(intArrayOf(2,3))
+        println(tensor)
+    }
 }
\ No newline at end of file

From 4f8ab4dd78ea1a9a8925fce8898c33313e6be9ec Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 16 Apr 2021 12:03:27 +0100
Subject: [PATCH 111/178] Ignore print test

---
 .../kotlin/space/kscience/kmath/tensors/core/utils.kt         | 3 +--
 .../space/kscience/kmath/tensors/core/TestDoubleTensor.kt     | 4 +++-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index 40d40b593..0b2b5c0df 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -1,6 +1,5 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.samplers.GaussianSampler
 import space.kscience.kmath.stat.RandomGenerator
@@ -72,7 +71,7 @@ internal inline fun format(value: Double, digits: Int = 4): String {
     val lead = value / ten.pow(order)
     val leadDisplay = round(lead*ten.pow(digits)) / ten.pow(digits)
     val orderDisplay = if(order == 0) "" else if(order > 0) "E+$order" else "E$order"
-    val valueDisplay = "${leadDisplay}$orderDisplay"
+    val valueDisplay = "$leadDisplay$orderDisplay"
     val res = if(value < 0.0) valueDisplay else " $valueDisplay"
     val fLength = digits + 6
     val endSpace = " ".repeat(fLength - res.length)
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index ddcf0369c..f950dfce3 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -1,8 +1,10 @@
 package space.kscience.kmath.tensors.core
 
+import kotlinx.coroutines.InternalCoroutinesApi
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.structures.toDoubleArray
+import kotlin.test.Ignore
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
@@ -47,7 +49,7 @@ class TestDoubleTensor {
         }
     }
 
-    @Test
+    @Test @Ignore
     fun toStringTest() = DoubleTensorAlgebra {
         val tensor = randNormal(intArrayOf(2,3))
         println(tensor)

From 82d8394a9fd19461e90a331157e5ac4eee705aab Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 16 Apr 2021 12:05:18 +0100
Subject: [PATCH 112/178] remove kolinx import

---
 .../kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index f950dfce3..fd75f77fe 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -1,6 +1,5 @@
 package space.kscience.kmath.tensors.core
 
-import kotlinx.coroutines.InternalCoroutinesApi
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.structures.toDoubleArray

From cc11df617489c6d3d8105b72cf5a472af1c90c01 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 21 Apr 2021 19:51:34 +0100
Subject: [PATCH 113/178] Casting receiver

---
 .../kmath/tensors/AnalyticTensorAlgebra.kt    |  38 +--
 .../kmath/tensors/LinearOpsTensorAlgebra.kt   |  21 +-
 .../kscience/kmath/tensors/TensorAlgebra.kt   |  50 ++--
 .../tensors/TensorPartialDivisionAlgebra.kt   |  12 +-
 .../core/BroadcastDoubleTensorAlgebra.kt      |  57 ++--
 .../kmath/tensors/core/BufferedTensor.kt      |  48 ++-
 .../core/DoubleAnalyticTensorAlgebra.kt       |  37 +--
 .../core/DoubleLinearOpsTensorAlgebra.kt      |  65 ++--
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 279 +++++++++---------
 .../kscience/kmath/tensors/core/checks.kt     |  41 +--
 .../kscience/kmath/tensors/core/linutils.kt   |   4 +-
 .../kscience/kmath/tensors/core/utils.kt      |   4 +-
 12 files changed, 326 insertions(+), 330 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
index fa1de81fa..2a8b228f4 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
@@ -1,58 +1,58 @@
 package space.kscience.kmath.tensors
 
 
-public interface AnalyticTensorAlgebra<T, TensorType : TensorStructure<T>> :
-    TensorPartialDivisionAlgebra<T, TensorType> {
+public interface AnalyticTensorAlgebra<T> :
+    TensorPartialDivisionAlgebra<T> {
 
     //https://pytorch.org/docs/stable/generated/torch.exp.html
-    public fun TensorType.exp(): TensorType
+    public fun TensorStructure<T>.exp(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.log.html
-    public fun TensorType.log(): TensorType
+    public fun TensorStructure<T>.log(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.sqrt.html
-    public fun TensorType.sqrt(): TensorType
+    public fun TensorStructure<T>.sqrt(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.acos.html#torch.cos
-    public fun TensorType.cos(): TensorType
+    public fun TensorStructure<T>.cos(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.acos.html#torch.acos
-    public fun TensorType.acos(): TensorType
+    public fun TensorStructure<T>.acos(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.cosh
-    public fun TensorType.cosh(): TensorType
+    public fun TensorStructure<T>.cosh(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.acosh
-    public fun TensorType.acosh(): TensorType
+    public fun TensorStructure<T>.acosh(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sin
-    public fun TensorType.sin(): TensorType
+    public fun TensorStructure<T>.sin(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asin
-    public fun TensorType.asin(): TensorType
+    public fun TensorStructure<T>.asin(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sinh
-    public fun TensorType.sinh(): TensorType
+    public fun TensorStructure<T>.sinh(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asinh
-    public fun TensorType.asinh(): TensorType
+    public fun TensorStructure<T>.asinh(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.atan.html#torch.tan
-    public fun TensorType.tan(): TensorType
+    public fun TensorStructure<T>.tan(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.atan.html#torch.atan
-    public fun TensorType.atan(): TensorType
+    public fun TensorStructure<T>.atan(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.tanh
-    public fun TensorType.tanh(): TensorType
+    public fun TensorStructure<T>.tanh(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.atanh
-    public fun TensorType.atanh(): TensorType
+    public fun TensorStructure<T>.atanh(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.ceil.html#torch.ceil
-    public fun TensorType.ceil(): TensorType
+    public fun TensorStructure<T>.ceil(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.floor.html#torch.floor
-    public fun TensorType.floor(): TensorType
+    public fun TensorStructure<T>.floor(): TensorStructure<T>
 
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
index 87c459f35..8ca8945a5 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
@@ -1,31 +1,32 @@
 package space.kscience.kmath.tensors
 
 
-public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, IndexTensorType : TensorStructure<Int>> :
-    TensorPartialDivisionAlgebra<T, TensorType> {
+public interface LinearOpsTensorAlgebra<T> :
+    TensorPartialDivisionAlgebra<T> {
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
-    public fun TensorType.det(): TensorType
+    public fun TensorStructure<T>.det(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
-    public fun TensorType.inv(): TensorType
+    public fun TensorStructure<T>.inv(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.cholesky
-    public fun TensorType.cholesky(): TensorType
+    public fun TensorStructure<T>.cholesky(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
-    public fun TensorType.qr(): Pair<TensorType, TensorType>
+    public fun TensorStructure<T>.qr(): Pair<TensorStructure<T>, TensorStructure<T>>
 
     //https://pytorch.org/docs/stable/generated/torch.lu.html
-    public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
+    public fun TensorStructure<T>.lu(): Pair<TensorStructure<T>, TensorStructure<Int>>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
-    public fun luPivot(luTensor: TensorType, pivotsTensor: IndexTensorType): Triple<TensorType, TensorType, TensorType>
+    public fun luPivot(luTensor: TensorStructure<T>, pivotsTensor: TensorStructure<Int>):
+            Triple<TensorStructure<T>, TensorStructure<T>, TensorStructure<T>>
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
-    public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
+    public fun TensorStructure<T>.svd(): Triple<TensorStructure<T>, TensorStructure<T>, TensorStructure<T>>
 
     //https://pytorch.org/docs/stable/generated/torch.symeig.html
-    public fun TensorType.symEig(): Pair<TensorType, TensorType>
+    public fun TensorStructure<T>.symEig(): Pair<TensorStructure<T>, TensorStructure<T>>
 
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
index 8fd1cf2ed..f64d49dd8 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
@@ -1,46 +1,46 @@
 package space.kscience.kmath.tensors
 
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
-public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
+public interface TensorAlgebra<T> {
 
-    public fun TensorType.value(): T
+    public fun TensorStructure<T>.value(): T
 
-    public operator fun T.plus(other: TensorType): TensorType
-    public operator fun TensorType.plus(value: T): TensorType
-    public operator fun TensorType.plus(other: TensorType): TensorType
-    public operator fun TensorType.plusAssign(value: T): Unit
-    public operator fun TensorType.plusAssign(other: TensorType): Unit
+    public operator fun T.plus(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun TensorStructure<T>.plus(value: T): TensorStructure<T>
+    public operator fun TensorStructure<T>.plus(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun TensorStructure<T>.plusAssign(value: T): Unit
+    public operator fun TensorStructure<T>.plusAssign(other: TensorStructure<T>): Unit
 
-    public operator fun T.minus(other: TensorType): TensorType
-    public operator fun TensorType.minus(value: T): TensorType
-    public operator fun TensorType.minus(other: TensorType): TensorType
-    public operator fun TensorType.minusAssign(value: T): Unit
-    public operator fun TensorType.minusAssign(other: TensorType): Unit
+    public operator fun T.minus(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun TensorStructure<T>.minus(value: T): TensorStructure<T>
+    public operator fun TensorStructure<T>.minus(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun TensorStructure<T>.minusAssign(value: T): Unit
+    public operator fun TensorStructure<T>.minusAssign(other: TensorStructure<T>): Unit
 
-    public operator fun T.times(other: TensorType): TensorType
-    public operator fun TensorType.times(value: T): TensorType
-    public operator fun TensorType.times(other: TensorType): TensorType
-    public operator fun TensorType.timesAssign(value: T): Unit
-    public operator fun TensorType.timesAssign(other: TensorType): Unit
-    public operator fun TensorType.unaryMinus(): TensorType
+    public operator fun T.times(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun TensorStructure<T>.times(value: T): TensorStructure<T>
+    public operator fun TensorStructure<T>.times(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun TensorStructure<T>.timesAssign(value: T): Unit
+    public operator fun TensorStructure<T>.timesAssign(other: TensorStructure<T>): Unit
+    public operator fun TensorStructure<T>.unaryMinus(): TensorStructure<T>
 
     //https://pytorch.org/cppdocs/notes/tensor_indexing.html
-    public operator fun TensorType.get(i: Int): TensorType
+    public operator fun TensorStructure<T>.get(i: Int): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.transpose.html
-    public fun TensorType.transpose(i: Int = -2, j: Int = -1): TensorType
+    public fun TensorStructure<T>.transpose(i: Int = -2, j: Int = -1): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/tensor_view.html
-    public fun TensorType.view(shape: IntArray): TensorType
-    public fun TensorType.viewAs(other: TensorType): TensorType
+    public fun TensorStructure<T>.view(shape: IntArray): TensorStructure<T>
+    public fun TensorStructure<T>.viewAs(other: TensorStructure<T>): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.matmul.html
-    public infix fun TensorType.dot(other: TensorType): TensorType
+    public infix fun TensorStructure<T>.dot(other: TensorStructure<T>): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
     public fun diagonalEmbedding(
-        diagonalEntries: TensorType,
+        diagonalEntries: TensorStructure<T>,
         offset: Int = 0, dim1: Int = -2, dim2: Int = -1
-    ): TensorType
+    ): TensorStructure<T>
 
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
index 0b9079967..b37ac6b6e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
@@ -1,10 +1,10 @@
 package space.kscience.kmath.tensors
 
 // https://proofwiki.org/wiki/Definition:Division_Algebra
-public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
-        TensorAlgebra<T, TensorType> {
-    public operator fun TensorType.div(value: T): TensorType
-    public operator fun TensorType.div(other: TensorType): TensorType
-    public operator fun TensorType.divAssign(value: T)
-    public operator fun TensorType.divAssign(other: TensorType)
+public interface TensorPartialDivisionAlgebra<T> :
+        TensorAlgebra<T> {
+    public operator fun TensorStructure<T>.div(value: T): TensorStructure<T>
+    public operator fun TensorStructure<T>.div(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun TensorStructure<T>.divAssign(value: T)
+    public operator fun TensorStructure<T>.divAssign(other: TensorStructure<T>)
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
index f315f6b51..53c385197 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
@@ -1,11 +1,12 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.tensors.TensorStructure
 import kotlin.math.max
 
 public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
-    override fun DoubleTensor.plus(other: DoubleTensor): DoubleTensor {
-        val broadcast = broadcastTensors(this, other)
+    override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
@@ -14,16 +15,16 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
-    override fun DoubleTensor.plusAssign(other: DoubleTensor) {
-        val newOther = broadcastTo(other, this.shape)
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] +=
-                newOther.buffer.array()[this.bufferStart + i]
+    override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] +=
+                newOther.buffer.array()[tensor.bufferStart + i]
         }
     }
 
-    override fun DoubleTensor.minus(other: DoubleTensor): DoubleTensor {
-        val broadcast = broadcastTensors(this, other)
+    override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
@@ -32,16 +33,16 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
-    override fun DoubleTensor.minusAssign(other: DoubleTensor) {
-        val newOther = broadcastTo(other, this.shape)
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] -=
-                newOther.buffer.array()[this.bufferStart + i]
+    override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] -=
+                newOther.buffer.array()[tensor.bufferStart + i]
         }
     }
 
-    override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
-        val broadcast = broadcastTensors(this, other)
+    override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
@@ -51,16 +52,16 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
-    override fun DoubleTensor.timesAssign(other: DoubleTensor) {
-        val newOther = broadcastTo(other, this.shape)
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] *=
-                newOther.buffer.array()[this.bufferStart + i]
+    override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] *=
+                newOther.buffer.array()[tensor.bufferStart + i]
         }
     }
 
-    override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
-        val broadcast = broadcastTensors(this, other)
+    override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
@@ -70,11 +71,11 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
-    override fun DoubleTensor.divAssign(other: DoubleTensor) {
-        val newOther = broadcastTo(other, this.shape)
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] /=
-                newOther.buffer.array()[this.bufferStart + i]
+    override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] /=
+                newOther.buffer.array()[tensor.bufferStart + i]
         }
     }
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 9e393f1a8..2b61e10b2 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -6,13 +6,13 @@ import space.kscience.kmath.tensors.TensorStructure
 
 public open class BufferedTensor<T>(
     override val shape: IntArray,
-    public val buffer: MutableBuffer<T>,
+    internal val buffer: MutableBuffer<T>,
     internal val bufferStart: Int
 ) : TensorStructure<T> {
     public val linearStructure: TensorLinearStructure
         get() = TensorLinearStructure(shape)
 
-    public val numel: Int
+    public val numElements: Int
         get() = linearStructure.size
 
     override fun get(index: IntArray): T = buffer[bufferStart + linearStructure.offset(index)]
@@ -41,26 +41,6 @@ public class IntTensor internal constructor(
             this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
 }
 
-public class LongTensor internal constructor(
-    shape: IntArray,
-    buffer: LongArray,
-    offset: Int = 0
-) : BufferedTensor<Long>(shape, LongBuffer(buffer), offset)
-{
-    internal constructor(bufferedTensor: BufferedTensor<Long>):
-            this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
-}
-
-public class FloatTensor internal constructor(
-    shape: IntArray,
-    buffer: FloatArray,
-    offset: Int = 0
-) : BufferedTensor<Float>(shape, FloatBuffer(buffer), offset)
-{
-    internal constructor(bufferedTensor: BufferedTensor<Float>):
-            this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
-}
-
 public class DoubleTensor internal constructor(
     shape: IntArray,
     buffer: DoubleArray,
@@ -74,7 +54,23 @@ public class DoubleTensor internal constructor(
 
 }
 
-internal fun BufferedTensor<Int>.asTensor(): IntTensor = IntTensor(this)
-internal fun BufferedTensor<Long>.asTensor(): LongTensor = LongTensor(this)
-internal fun BufferedTensor<Float>.asTensor(): FloatTensor = FloatTensor(this)
-internal fun BufferedTensor<Double>.asTensor(): DoubleTensor = DoubleTensor(this)
+internal inline fun BufferedTensor<Int>.asTensor(): IntTensor = IntTensor(this)
+internal inline fun BufferedTensor<Double>.asTensor(): DoubleTensor = DoubleTensor(this)
+
+internal inline fun <T> TensorStructure<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
+    is BufferedTensor<T> -> this
+    else -> BufferedTensor(this.shape, this.elements().map{ it.second }.toMutableList().asMutableBuffer(), 0)
+}
+
+internal val TensorStructure<Double>.tensor: DoubleTensor
+    get() = when (this) {
+        is DoubleTensor -> this
+        else -> this.toBufferedTensor().asTensor()
+    }
+
+internal val TensorStructure<Int>.tensor: IntTensor
+    get() = when (this) {
+        is IntTensor -> this
+        else -> this.toBufferedTensor().asTensor()
+    }
+
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
index b46c7fa9c..233217f2f 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
@@ -1,45 +1,46 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.AnalyticTensorAlgebra
+import space.kscience.kmath.tensors.TensorStructure
 import kotlin.math.*
 
 public class DoubleAnalyticTensorAlgebra:
-    AnalyticTensorAlgebra<Double, DoubleTensor>,
+    AnalyticTensorAlgebra<Double>,
         DoubleTensorAlgebra()
 {
-    override fun DoubleTensor.exp(): DoubleTensor = this.map(::exp)
+    override fun TensorStructure<Double>.exp(): DoubleTensor = tensor.map(::exp)
 
-    override fun DoubleTensor.log(): DoubleTensor = this.map(::ln)
+    override fun TensorStructure<Double>.log(): DoubleTensor = tensor.map(::ln)
 
-    override fun DoubleTensor.sqrt(): DoubleTensor = this.map(::sqrt)
+    override fun TensorStructure<Double>.sqrt(): DoubleTensor = tensor.map(::sqrt)
 
-    override fun DoubleTensor.cos(): DoubleTensor = this.map(::cos)
+    override fun TensorStructure<Double>.cos(): DoubleTensor = tensor.map(::cos)
 
-    override fun DoubleTensor.acos(): DoubleTensor = this.map(::acos)
+    override fun TensorStructure<Double>.acos(): DoubleTensor = tensor.map(::acos)
 
-    override fun DoubleTensor.cosh(): DoubleTensor = this.map(::cosh)
+    override fun TensorStructure<Double>.cosh(): DoubleTensor = tensor.map(::cosh)
 
-    override fun DoubleTensor.acosh(): DoubleTensor = this.map(::acosh)
+    override fun TensorStructure<Double>.acosh(): DoubleTensor = tensor.map(::acosh)
 
-    override fun DoubleTensor.sin(): DoubleTensor = this.map(::sin)
+    override fun TensorStructure<Double>.sin(): DoubleTensor = tensor.map(::sin)
 
-    override fun DoubleTensor.asin(): DoubleTensor = this.map(::asin)
+    override fun TensorStructure<Double>.asin(): DoubleTensor = tensor.map(::asin)
 
-    override fun DoubleTensor.sinh(): DoubleTensor = this.map(::sinh)
+    override fun TensorStructure<Double>.sinh(): DoubleTensor = tensor.map(::sinh)
 
-    override fun DoubleTensor.asinh(): DoubleTensor = this.map(::asinh)
+    override fun TensorStructure<Double>.asinh(): DoubleTensor = tensor.map(::asinh)
 
-    override fun DoubleTensor.tan(): DoubleTensor = this.map(::tan)
+    override fun TensorStructure<Double>.tan(): DoubleTensor = tensor.map(::tan)
 
-    override fun DoubleTensor.atan(): DoubleTensor = this.map(::atan)
+    override fun TensorStructure<Double>.atan(): DoubleTensor = tensor.map(::atan)
 
-    override fun DoubleTensor.tanh(): DoubleTensor = this.map(::tanh)
+    override fun TensorStructure<Double>.tanh(): DoubleTensor = tensor.map(::tanh)
 
-    override fun DoubleTensor.atanh(): DoubleTensor = this.map(::atanh)
+    override fun TensorStructure<Double>.atanh(): DoubleTensor = tensor.map(::atanh)
 
-    override fun DoubleTensor.ceil(): DoubleTensor = this.map(::ceil)
+    override fun TensorStructure<Double>.ceil(): DoubleTensor = tensor.map(::ceil)
 
-    override fun DoubleTensor.floor(): DoubleTensor = this.map(::floor)
+    override fun TensorStructure<Double>.floor(): DoubleTensor = tensor.map(::floor)
 
 }
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
index e72948c84..386fc2a1b 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
@@ -3,44 +3,45 @@ package space.kscience.kmath.tensors.core
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.tensors.TensorStructure
 import kotlin.math.min
 
 
 public class DoubleLinearOpsTensorAlgebra :
-    LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
+    LinearOpsTensorAlgebra<Double>,
     DoubleTensorAlgebra() {
 
-    override fun DoubleTensor.inv(): DoubleTensor = invLU(1e-9)
+    override fun TensorStructure<Double>.inv(): DoubleTensor = invLU(1e-9)
 
-    override fun DoubleTensor.det(): DoubleTensor = detLU(1e-9)
+    override fun TensorStructure<Double>.det(): DoubleTensor = detLU(1e-9)
 
-    public fun DoubleTensor.lu(epsilon: Double): Pair<DoubleTensor, IntTensor> =
-        computeLU(this, epsilon) ?:
+    public fun TensorStructure<Double>.lu(epsilon: Double): Pair<DoubleTensor, IntTensor> =
+        computeLU(tensor, epsilon) ?:
         throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
 
-    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> = lu(1e-9)
+    override fun TensorStructure<Double>.lu(): Pair<DoubleTensor, IntTensor> = lu(1e-9)
 
     override fun luPivot(
-        luTensor: DoubleTensor,
-        pivotsTensor: IntTensor
+        luTensor: TensorStructure<Double>,
+        pivotsTensor: TensorStructure<Int>
     ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         //todo checks
         checkSquareMatrix(luTensor.shape)
         check(
             luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() ||
                     luTensor.shape.last() == pivotsTensor.shape.last() - 1
-        ) { "Bed shapes ((" } //todo rewrite
+        ) { "Bad shapes ((" } //todo rewrite
 
         val n = luTensor.shape.last()
         val pTensor = luTensor.zeroesLike()
-        for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
+        for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.tensor.vectorSequence()))
             pivInit(p.as2D(), pivot.as1D(), n)
 
         val lTensor = luTensor.zeroesLike()
         val uTensor = luTensor.zeroesLike()
 
         for ((pairLU, lu) in lTensor.matrixSequence().zip(uTensor.matrixSequence())
-            .zip(luTensor.matrixSequence())) {
+            .zip(luTensor.tensor.matrixSequence())) {
             val (l, u) = pairLU
             luPivotHelper(l.as2D(), u.as2D(), lu.as2D(), n)
         }
@@ -49,26 +50,26 @@ public class DoubleLinearOpsTensorAlgebra :
 
     }
 
-    public fun DoubleTensor.cholesky(epsilon: Double): DoubleTensor {
+    public fun TensorStructure<Double>.cholesky(epsilon: Double): DoubleTensor {
         checkSquareMatrix(shape)
-        checkPositiveDefinite(this, epsilon)
+        checkPositiveDefinite(tensor, epsilon)
 
         val n = shape.last()
         val lTensor = zeroesLike()
 
-        for ((a, l) in this.matrixSequence().zip(lTensor.matrixSequence()))
+        for ((a, l) in tensor.matrixSequence().zip(lTensor.matrixSequence()))
             for (i in 0 until n) choleskyHelper(a.as2D(), l.as2D(), n)
 
         return lTensor
     }
 
-    override fun DoubleTensor.cholesky(): DoubleTensor = cholesky(1e-6)
+    override fun TensorStructure<Double>.cholesky(): DoubleTensor = cholesky(1e-6)
 
-    override fun DoubleTensor.qr(): Pair<DoubleTensor, DoubleTensor> {
+    override fun TensorStructure<Double>.qr(): Pair<DoubleTensor, DoubleTensor> {
         checkSquareMatrix(shape)
         val qTensor = zeroesLike()
         val rTensor = zeroesLike()
-        val seq = matrixSequence().zip((qTensor.matrixSequence().zip(rTensor.matrixSequence())))
+        val seq = tensor.matrixSequence().zip((qTensor.matrixSequence().zip(rTensor.matrixSequence())))
         for ((matrix, qr) in seq) {
             val (q, r) = qr
             qrHelper(matrix.asTensor(), q.asTensor(), r.as2D())
@@ -76,18 +77,18 @@ public class DoubleLinearOpsTensorAlgebra :
         return qTensor to rTensor
     }
 
-    override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
+    override fun TensorStructure<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
         svd(epsilon = 1e-10)
 
-    public fun DoubleTensor.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        val size = this.linearStructure.dim
-        val commonShape = this.shape.sliceArray(0 until size - 2)
-        val (n, m) = this.shape.sliceArray(size - 2 until size)
+    public fun TensorStructure<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+        val size = tensor.linearStructure.dim
+        val commonShape = tensor.shape.sliceArray(0 until size - 2)
+        val (n, m) = tensor.shape.sliceArray(size - 2 until size)
         val resU = zeros(commonShape + intArrayOf(min(n, m), n))
         val resS = zeros(commonShape + intArrayOf(min(n, m)))
         val resV = zeros(commonShape + intArrayOf(min(n, m), m))
 
-        for ((matrix, USV) in this.matrixSequence()
+        for ((matrix, USV) in tensor.matrixSequence()
             .zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
             val size = matrix.shape.reduce { acc, i -> acc * i }
             val curMatrix = DoubleTensor(
@@ -99,13 +100,13 @@ public class DoubleLinearOpsTensorAlgebra :
         return Triple(resU.transpose(), resS, resV.transpose())
     }
 
-    override fun DoubleTensor.symEig(): Pair<DoubleTensor, DoubleTensor> =
+    override fun TensorStructure<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> =
         symEig(epsilon = 1e-15)
 
     //http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
-    public fun DoubleTensor.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
-        checkSymmetric(this, epsilon)
-        val (u, s, v) = this.svd(epsilon)
+    public fun TensorStructure<Double>.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
+        checkSymmetric(tensor, epsilon)
+        val (u, s, v) = tensor.svd(epsilon)
         val shp = s.shape + intArrayOf(1)
         val utv = u.transpose() dot v
         val n = s.shape.last()
@@ -116,11 +117,11 @@ public class DoubleLinearOpsTensorAlgebra :
         return Pair(eig, v)
     }
 
-    public fun DoubleTensor.detLU(epsilon: Double = 1e-9): DoubleTensor {
+    public fun TensorStructure<Double>.detLU(epsilon: Double = 1e-9): DoubleTensor {
 
-        checkSquareMatrix(this.shape)
-        val luTensor = this.copy()
-        val pivotsTensor = this.setUpPivots()
+        checkSquareMatrix(tensor.shape)
+        val luTensor = tensor.copy()
+        val pivotsTensor = tensor.setUpPivots()
 
         val n = shape.size
 
@@ -141,7 +142,7 @@ public class DoubleLinearOpsTensorAlgebra :
         return detTensor
     }
 
-    public fun DoubleTensor.invLU(epsilon: Double = 1e-9): DoubleTensor {
+    public fun TensorStructure<Double>.invLU(epsilon: Double = 1e-9): DoubleTensor {
         val (luTensor, pivotsTensor) = lu(epsilon)
         val invTensor = luTensor.zeroesLike()
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 8f355dd3d..7544bf68e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -1,16 +1,18 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
+import space.kscience.kmath.tensors.TensorStructure
 import kotlin.math.abs
 
-public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
+public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
-    override fun DoubleTensor.value(): Double {
-        check(this.shape contentEquals intArrayOf(1)) {
+
+    override fun TensorStructure<Double>.value(): Double {
+        check(tensor.shape contentEquals intArrayOf(1)) {
             "Inconsistent value for tensor of shape ${shape.toList()}"
         }
-        return this.buffer.array()[this.bufferStart]
+        return tensor.buffer.array()[tensor.bufferStart]
     }
 
     public fun fromArray(shape: IntArray, buffer: DoubleArray): DoubleTensor {
@@ -20,11 +22,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return DoubleTensor(shape, buffer, 0)
     }
 
-    override operator fun DoubleTensor.get(i: Int): DoubleTensor {
-        val lastShape = this.shape.drop(1).toIntArray()
+    override operator fun TensorStructure<Double>.get(i: Int): DoubleTensor {
+        val lastShape = tensor.shape.drop(1).toIntArray()
         val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
-        val newStart = newShape.reduce(Int::times) * i + this.bufferStart
-        return DoubleTensor(newShape, this.buffer.array(), newStart)
+        val newStart = newShape.reduce(Int::times) * i + tensor.bufferStart
+        return DoubleTensor(newShape, tensor.buffer.array(), newStart)
     }
 
     public fun full(value: Double, shape: IntArray): DoubleTensor {
@@ -33,19 +35,19 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return DoubleTensor(shape, buffer)
     }
 
-    public fun DoubleTensor.fullLike(value: Double): DoubleTensor {
-        val shape = this.shape
-        val buffer = DoubleArray(this.linearStructure.size) { value }
+    public fun TensorStructure<Double>.fullLike(value: Double): DoubleTensor {
+        val shape = tensor.shape
+        val buffer = DoubleArray(tensor.numElements) { value }
         return DoubleTensor(shape, buffer)
     }
 
     public fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
 
-    public fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
+    public fun TensorStructure<Double>.zeroesLike(): DoubleTensor = tensor.fullLike(0.0)
 
     public fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
 
-    public fun DoubleTensor.onesLike(): DoubleTensor = this.fullLike(1.0)
+    public fun TensorStructure<Double>.onesLike(): DoubleTensor = tensor.fullLike(1.0)
 
     public fun eye(n: Int): DoubleTensor {
         val shape = intArrayOf(n, n)
@@ -57,200 +59,200 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return res
     }
 
-    public fun DoubleTensor.copy(): DoubleTensor {
-        return DoubleTensor(this.shape, this.buffer.array().copyOf(), this.bufferStart)
+    public fun TensorStructure<Double>.copy(): DoubleTensor {
+        return DoubleTensor(tensor.shape, tensor.buffer.array().copyOf(), tensor.bufferStart)
     }
 
-    override fun Double.plus(other: DoubleTensor): DoubleTensor {
-        val resBuffer = DoubleArray(other.linearStructure.size) { i ->
-            other.buffer.array()[other.bufferStart + i] + this
+    override fun Double.plus(other: TensorStructure<Double>): DoubleTensor {
+        val resBuffer = DoubleArray(other.tensor.numElements) { i ->
+            other.tensor.buffer.array()[other.tensor.bufferStart + i] + this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
-    override fun DoubleTensor.plus(value: Double): DoubleTensor = value + this
+    override fun TensorStructure<Double>.plus(value: Double): DoubleTensor = value + tensor
 
-    override fun DoubleTensor.plus(other: DoubleTensor): DoubleTensor {
-        checkShapesCompatible(this, other)
-        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
-            this.buffer.array()[i] + other.buffer.array()[i]
+    override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
+        checkShapesCompatible(tensor, other.tensor)
+        val resBuffer = DoubleArray(tensor.numElements) { i ->
+            tensor.buffer.array()[i] + other.tensor.buffer.array()[i]
         }
-        return DoubleTensor(this.shape, resBuffer)
+        return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun DoubleTensor.plusAssign(value: Double) {
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] += value
+    override fun TensorStructure<Double>.plusAssign(value: Double) {
+        for (i in 0 until tensor.numElements) {
+            tensor.buffer.array()[tensor.bufferStart + i] += value
         }
     }
 
-    override fun DoubleTensor.plusAssign(other: DoubleTensor) {
-        checkShapesCompatible(this, other)
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] +=
-                other.buffer.array()[this.bufferStart + i]
+    override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
+        checkShapesCompatible(tensor, other.tensor)
+        for (i in 0 until tensor.numElements) {
+            tensor.buffer.array()[tensor.bufferStart + i] +=
+                other.tensor.buffer.array()[tensor.bufferStart + i]
         }
     }
 
-    override fun Double.minus(other: DoubleTensor): DoubleTensor {
-        val resBuffer = DoubleArray(other.linearStructure.size) { i ->
-            this - other.buffer.array()[other.bufferStart + i]
+    override fun Double.minus(other: TensorStructure<Double>): DoubleTensor {
+        val resBuffer = DoubleArray(other.tensor.numElements) { i ->
+            this - other.tensor.buffer.array()[other.tensor.bufferStart + i]
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
-    override fun DoubleTensor.minus(value: Double): DoubleTensor {
-        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
-            this.buffer.array()[this.bufferStart + i] - value
+    override fun TensorStructure<Double>.minus(value: Double): DoubleTensor {
+        val resBuffer = DoubleArray(tensor.numElements) { i ->
+            tensor.buffer.array()[tensor.bufferStart + i] - value
         }
-        return DoubleTensor(this.shape, resBuffer)
+        return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun DoubleTensor.minus(other: DoubleTensor): DoubleTensor {
-        checkShapesCompatible(this, other)
-        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
-            this.buffer.array()[i] - other.buffer.array()[i]
+    override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
+        checkShapesCompatible(tensor, other)
+        val resBuffer = DoubleArray(tensor.numElements) { i ->
+            tensor.buffer.array()[i] - other.tensor.buffer.array()[i]
         }
-        return DoubleTensor(this.shape, resBuffer)
+        return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun DoubleTensor.minusAssign(value: Double) {
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] -= value
+    override fun TensorStructure<Double>.minusAssign(value: Double) {
+        for (i in 0 until tensor.numElements) {
+            tensor.buffer.array()[tensor.bufferStart + i] -= value
         }
     }
 
-    override fun DoubleTensor.minusAssign(other: DoubleTensor) {
-        checkShapesCompatible(this, other)
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] -=
-                other.buffer.array()[this.bufferStart + i]
+    override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
+        checkShapesCompatible(tensor, other)
+        for (i in 0 until tensor.numElements) {
+            tensor.buffer.array()[tensor.bufferStart + i] -=
+                other.tensor.buffer.array()[tensor.bufferStart + i]
         }
     }
 
-    override fun Double.times(other: DoubleTensor): DoubleTensor {
-        val resBuffer = DoubleArray(other.linearStructure.size) { i ->
-            other.buffer.array()[other.bufferStart + i] * this
+    override fun Double.times(other: TensorStructure<Double>): DoubleTensor {
+        val resBuffer = DoubleArray(other.tensor.numElements) { i ->
+            other.tensor.buffer.array()[other.tensor.bufferStart + i] * this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
-    override fun DoubleTensor.times(value: Double): DoubleTensor = value * this
+    override fun TensorStructure<Double>.times(value: Double): DoubleTensor = value * tensor
 
-    override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
-        checkShapesCompatible(this, other)
-        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
-            this.buffer.array()[this.bufferStart + i] *
-                    other.buffer.array()[other.bufferStart + i]
+    override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
+        checkShapesCompatible(tensor, other)
+        val resBuffer = DoubleArray(tensor.numElements) { i ->
+            tensor.buffer.array()[tensor.bufferStart + i] *
+                    other.tensor.buffer.array()[other.tensor.bufferStart + i]
         }
-        return DoubleTensor(this.shape, resBuffer)
+        return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun DoubleTensor.timesAssign(value: Double) {
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] *= value
+    override fun TensorStructure<Double>.timesAssign(value: Double) {
+        for (i in 0 until tensor.numElements) {
+            tensor.buffer.array()[tensor.bufferStart + i] *= value
         }
     }
 
-    override fun DoubleTensor.timesAssign(other: DoubleTensor) {
-        checkShapesCompatible(this, other)
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] *=
-                other.buffer.array()[this.bufferStart + i]
+    override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
+        checkShapesCompatible(tensor, other)
+        for (i in 0 until tensor.numElements) {
+            tensor.buffer.array()[tensor.bufferStart + i] *=
+                other.tensor.buffer.array()[tensor.bufferStart + i]
         }
     }
 
-    override fun DoubleTensor.div(value: Double): DoubleTensor {
-        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
-            this.buffer.array()[this.bufferStart + i] / value
+    override fun TensorStructure<Double>.div(value: Double): DoubleTensor {
+        val resBuffer = DoubleArray(tensor.numElements) { i ->
+            tensor.buffer.array()[tensor.bufferStart + i] / value
         }
-        return DoubleTensor(this.shape, resBuffer)
+        return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
-        checkShapesCompatible(this, other)
-        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
-            this.buffer.array()[other.bufferStart + i] /
-                    other.buffer.array()[other.bufferStart + i]
+    override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
+        checkShapesCompatible(tensor, other)
+        val resBuffer = DoubleArray(tensor.numElements) { i ->
+            tensor.buffer.array()[other.tensor.bufferStart + i] /
+                    other.tensor.buffer.array()[other.tensor.bufferStart + i]
         }
-        return DoubleTensor(this.shape, resBuffer)
+        return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun DoubleTensor.divAssign(value: Double) {
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] /= value
+    override fun TensorStructure<Double>.divAssign(value: Double) {
+        for (i in 0 until tensor.numElements) {
+            tensor.buffer.array()[tensor.bufferStart + i] /= value
         }
     }
 
-    override fun DoubleTensor.divAssign(other: DoubleTensor) {
-        checkShapesCompatible(this, other)
-        for (i in 0 until this.linearStructure.size) {
-            this.buffer.array()[this.bufferStart + i] /=
-                other.buffer.array()[this.bufferStart + i]
+    override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
+        checkShapesCompatible(tensor, other)
+        for (i in 0 until tensor.numElements) {
+            tensor.buffer.array()[tensor.bufferStart + i] /=
+                other.tensor.buffer.array()[tensor.bufferStart + i]
         }
     }
 
-    override fun DoubleTensor.unaryMinus(): DoubleTensor {
-        val resBuffer = DoubleArray(this.linearStructure.size) { i ->
-            this.buffer.array()[this.bufferStart + i].unaryMinus()
+    override fun TensorStructure<Double>.unaryMinus(): DoubleTensor {
+        val resBuffer = DoubleArray(tensor.numElements) { i ->
+            tensor.buffer.array()[tensor.bufferStart + i].unaryMinus()
         }
-        return DoubleTensor(this.shape, resBuffer)
+        return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun DoubleTensor.transpose(i: Int, j: Int): DoubleTensor {
-        val ii = minusIndex(i)
-        val jj = minusIndex(j)
-        checkTranspose(this.dimension, ii, jj)
-        val n = this.linearStructure.size
+    override fun TensorStructure<Double>.transpose(i: Int, j: Int): DoubleTensor {
+        val ii = tensor.minusIndex(i)
+        val jj = tensor.minusIndex(j)
+        checkTranspose(tensor.dimension, ii, jj)
+        val n = tensor.numElements
         val resBuffer = DoubleArray(n)
 
-        val resShape = this.shape.copyOf()
+        val resShape = tensor.shape.copyOf()
         resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
 
         val resTensor = DoubleTensor(resShape, resBuffer)
 
         for (offset in 0 until n) {
-            val oldMultiIndex = this.linearStructure.index(offset)
+            val oldMultiIndex = tensor.linearStructure.index(offset)
             val newMultiIndex = oldMultiIndex.copyOf()
             newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
 
             val linearIndex = resTensor.linearStructure.offset(newMultiIndex)
             resTensor.buffer.array()[linearIndex] =
-                this.buffer.array()[this.bufferStart + offset]
+                tensor.buffer.array()[tensor.bufferStart + offset]
         }
         return resTensor
     }
 
 
-    override fun DoubleTensor.view(shape: IntArray): DoubleTensor {
-        checkView(this, shape)
-        return DoubleTensor(shape, this.buffer.array(), this.bufferStart)
+    override fun TensorStructure<Double>.view(shape: IntArray): DoubleTensor {
+        checkView(tensor, shape)
+        return DoubleTensor(shape, tensor.buffer.array(), tensor.bufferStart)
     }
 
-    override fun DoubleTensor.viewAs(other: DoubleTensor): DoubleTensor {
-        return this.view(other.shape)
+    override fun TensorStructure<Double>.viewAs(other: TensorStructure<Double>): DoubleTensor {
+        return tensor.view(other.shape)
     }
 
-    override infix fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
-        if (this.shape.size == 1 && other.shape.size == 1) {
-            return DoubleTensor(intArrayOf(1), doubleArrayOf(this.times(other).buffer.array().sum()))
+    override infix fun TensorStructure<Double>.dot(other: TensorStructure<Double>): DoubleTensor {
+        if (tensor.shape.size == 1 && other.shape.size == 1) {
+            return DoubleTensor(intArrayOf(1), doubleArrayOf(tensor.times(other).tensor.buffer.array().sum()))
         }
 
-        var newThis = this.copy()
+        var newThis = tensor.copy()
         var newOther = other.copy()
 
         var penultimateDim = false
         var lastDim = false
-        if (this.shape.size == 1) {
+        if (tensor.shape.size == 1) {
             penultimateDim = true
-            newThis = this.view(intArrayOf(1) + this.shape)
+            newThis = tensor.view(intArrayOf(1) + tensor.shape)
         }
         if (other.shape.size == 1) {
             lastDim = true
-            newOther = other.view(other.shape + intArrayOf(1))
+            newOther = other.tensor.view(other.shape + intArrayOf(1))
         }
 
-        val broadcastTensors = broadcastOuterTensors(newThis, newOther)
+        val broadcastTensors = broadcastOuterTensors(newThis.tensor, newOther.tensor)
         newThis = broadcastTensors[0]
         newOther = broadcastTensors[1]
 
@@ -284,10 +286,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return resTensor
     }
 
-    override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
-        val d1 = minusIndexFrom(diagonalEntries.linearStructure.dim + 1, dim1)
-        val d2 = minusIndexFrom(diagonalEntries.linearStructure.dim + 1, dim2)
+    override fun diagonalEmbedding(diagonalEntries: TensorStructure<Double>, offset: Int, dim1: Int, dim2: Int):
+            DoubleTensor {
         val n = diagonalEntries.shape.size
+        val d1 = minusIndexFrom(n + 1, dim1)
+        val d2 = minusIndexFrom(n + 1, dim2)
+
         if (d1 == d2) {
             throw RuntimeException("Diagonal dimensions cannot be identical $d1, $d2")
         }
@@ -300,7 +304,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         var realOffset = offset
         if (lessDim > greaterDim) {
             realOffset *= -1
-            lessDim = greaterDim.also {greaterDim = lessDim}
+            lessDim = greaterDim.also { greaterDim = lessDim }
         }
 
         val resShape = diagonalEntries.shape.slice(0 until lessDim).toIntArray() +
@@ -310,13 +314,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
                 diagonalEntries.shape.slice(greaterDim - 1 until n - 1).toIntArray()
         val resTensor = zeros(resShape)
 
-        for (i in 0 until diagonalEntries.linearStructure.size) {
-            val multiIndex = diagonalEntries.linearStructure.index(i)
+        for (i in 0 until diagonalEntries.tensor.numElements) {
+            val multiIndex = diagonalEntries.tensor.linearStructure.index(i)
 
             var offset1 = 0
             var offset2 = abs(realOffset)
             if (realOffset < 0) {
-                offset1 = offset2.also {offset2 = offset1}
+                offset1 = offset2.also { offset2 = offset1 }
             }
             val diagonalMultiIndex = multiIndex.slice(0 until lessDim).toIntArray() +
                     intArrayOf(multiIndex[n - 1] + offset1) +
@@ -327,32 +331,35 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
             resTensor[diagonalMultiIndex] = diagonalEntries[multiIndex]
         }
 
-        return resTensor
+        return resTensor.tensor
     }
 
 
-    public fun DoubleTensor.map(transform: (Double) -> Double): DoubleTensor {
+    public fun TensorStructure<Double>.map(transform: (Double) -> Double): DoubleTensor {
         return DoubleTensor(
-            this.shape,
-            this.buffer.array().map { transform(it) }.toDoubleArray(),
-            this.bufferStart
+            tensor.shape,
+            tensor.buffer.array().map { transform(it) }.toDoubleArray(),
+            tensor.bufferStart
         )
     }
 
-    public fun DoubleTensor.eq(other: DoubleTensor, delta: Double): Boolean {
-        return this.eq(other) { x, y -> abs(x - y) < delta }
+    public fun TensorStructure<Double>.eq(other: TensorStructure<Double>, delta: Double): Boolean {
+        return tensor.eq(other) { x, y -> abs(x - y) < delta }
     }
 
-    public fun DoubleTensor.eq(other: DoubleTensor): Boolean = this.eq(other, 1e-5)
+    public fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
 
-    private fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
-        checkShapesCompatible(this, other)
-        val n = this.linearStructure.size
-        if (n != other.linearStructure.size) {
+    private fun TensorStructure<Double>.eq(
+        other: TensorStructure<Double>,
+        eqFunction: (Double, Double) -> Boolean
+    ): Boolean {
+        checkShapesCompatible(tensor, other)
+        val n = tensor.numElements
+        if (n != other.tensor.numElements) {
             return false
         }
         for (i in 0 until n) {
-            if (!eqFunction(this.buffer[this.bufferStart + i], other.buffer[other.bufferStart + i])) {
+            if (!eqFunction(tensor.buffer[tensor.bufferStart + i], other.tensor.buffer[other.tensor.bufferStart + i])) {
                 return false
             }
         }
@@ -362,8 +369,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     public fun randNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
         DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed))
 
-    public fun DoubleTensor.randNormalLike(seed: Long = 0): DoubleTensor =
-        DoubleTensor(this.shape, getRandomNormals(this.shape.reduce(Int::times), seed))
+    public fun TensorStructure<Double>.randNormalLike(seed: Long = 0): DoubleTensor =
+        DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
 }
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index 730b6ed9a..28828eb09 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -1,54 +1,40 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.tensors.TensorAlgebra
 import space.kscience.kmath.tensors.TensorStructure
 
 
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkEmptyShape(shape: IntArray): Unit =
+internal inline fun checkEmptyShape(shape: IntArray): Unit =
     check(shape.isNotEmpty()) {
         "Illegal empty shape provided"
     }
 
-internal inline fun <TensorType : TensorStructure<Double>,
-        TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
-        TorchTensorAlgebraType.checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
+internal inline fun checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
     check(buffer.isNotEmpty()) {
         "Illegal empty buffer provided"
     }
 
-internal inline fun <TensorType : TensorStructure<Double>,
-        TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
-        TorchTensorAlgebraType.checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
+internal inline fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
     check(buffer.size == shape.reduce(Int::times)) {
         "Inconsistent shape ${shape.toList()} for buffer of size ${buffer.size} provided"
     }
 
 
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkShapesCompatible(a: TensorType, b: TensorType): Unit =
+internal inline fun <T> checkShapesCompatible(a: TensorStructure<T>, b: TensorStructure<T>): Unit =
     check(a.shape contentEquals b.shape) {
         "Incompatible shapes ${a.shape.toList()} and ${b.shape.toList()} "
     }
 
 
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkTranspose(dim: Int, i: Int, j: Int): Unit =
+internal inline fun checkTranspose(dim: Int, i: Int, j: Int): Unit =
     check((i < dim) and (j < dim)) {
         "Cannot transpose $i to $j for a tensor of dim $dim"
     }
 
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkView(a: TensorType, shape: IntArray): Unit =
+internal inline fun <T> checkView(a: TensorStructure<T>, shape: IntArray): Unit =
     check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
 
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkSquareMatrix(shape: IntArray): Unit {
+
+internal inline fun checkSquareMatrix(shape: IntArray): Unit {
     val n = shape.size
     check(n >= 2) {
         "Expected tensor with 2 or more dimensions, got size $n instead"
@@ -58,16 +44,19 @@ internal inline fun <T, TensorType : TensorStructure<T>,
     }
 }
 
-internal inline fun DoubleTensorAlgebra.checkSymmetric(tensor: DoubleTensor, epsilon: Double = 1e-6): Unit =
+internal inline fun DoubleTensorAlgebra.checkSymmetric(
+    tensor: TensorStructure<Double>, epsilon: Double = 1e-6
+): Unit =
     check(tensor.eq(tensor.transpose(), epsilon)) {
         "Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
     }
 
 internal inline fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(
-    tensor: DoubleTensor, epsilon: Double = 1e-6): Unit {
+    tensor: DoubleTensor, epsilon: Double = 1e-6
+): Unit {
     checkSymmetric(tensor, epsilon)
-    for( mat in tensor.matrixSequence())
-        check(mat.asTensor().detLU().value() > 0.0){
+    for (mat in tensor.matrixSequence())
+        check(mat.asTensor().detLU().value() > 0.0) {
             "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"
         }
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
index ba5e0caaf..166f1d6c4 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
@@ -14,7 +14,7 @@ internal inline fun <T> BufferedTensor<T>.vectorSequence(): Sequence<BufferedTen
     val n = shape.size
     val vectorOffset = shape[n - 1]
     val vectorShape = intArrayOf(shape.last())
-    for (offset in 0 until numel step vectorOffset) {
+    for (offset in 0 until numElements step vectorOffset) {
         val vector = BufferedTensor(vectorShape, buffer, offset)
         yield(vector)
     }
@@ -25,7 +25,7 @@ internal inline fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTen
     val n = shape.size
     val matrixOffset = shape[n - 1] * shape[n - 2]
     val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
-    for (offset in 0 until numel step matrixOffset) {
+    for (offset in 0 until numElements step matrixOffset) {
         val matrix = BufferedTensor(matrixShape, buffer, offset)
         yield(matrix)
     }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index 0b2b5c0df..ac823185b 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -58,7 +58,7 @@ internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
     ii
 }
 
-internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int =  minusIndexFrom(this.linearStructure.dim, i)
+internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int =  minusIndexFrom(this.dimension, i)
 
 internal inline fun format(value: Double, digits: Int = 4): String {
     val ten = 10.0
@@ -111,7 +111,7 @@ internal inline fun DoubleTensor.toPrettyString(): String = buildString {
         }
         offset += vectorSize
         // todo refactor
-        if (this@toPrettyString.numel == offset) {
+        if (this@toPrettyString.numElements == offset) {
             break
         }
         append(",\n")

From 559e8b24ab6189b523e3707b605b7f2c75c8b92c Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Wed, 21 Apr 2021 23:44:39 +0300
Subject: [PATCH 114/178] rework structure + fixes

---
 .../{ => api}/AnalyticTensorAlgebra.kt        |   7 +-
 .../{ => api}/LinearOpsTensorAlgebra.kt       |   7 +-
 .../kmath/tensors/{ => api}/TensorAlgebra.kt  |  11 +-
 .../{ => api}/TensorPartialDivisionAlgebra.kt |   9 +-
 .../tensors/{ => api}/TensorStructure.kt      |   2 +-
 .../kmath/tensors/core/BufferedTensor.kt      |   3 +-
 .../algebras/BroadcastDoubleTensorAlgebra.kt  |  89 ++++++++++
 .../DoubleAnalyticTensorAlgebra.kt            |  13 +-
 .../DoubleLinearOpsTensorAlgebra.kt           |  27 +++-
 .../{ => algebras}/DoubleTensorAlgebra.kt     |  27 +++-
 .../{ => algebras}/TensorLinearStructure.kt   |   7 +-
 ...ubleTensorAlgebra.kt => broadcastUtils.kt} | 153 ++++--------------
 .../kscience/kmath/tensors/core/checks.kt     |   4 +-
 .../tensors/core/{linutils.kt => linUtils.kt} |  10 +-
 .../kscience/kmath/tensors/core/utils.kt      |   1 -
 .../kmath/tensors/core/TestBroadcasting.kt    |   1 +
 .../core/TestDoubleAnalyticTensorAlgebra.kt   |   1 +
 .../core/TestDoubleLinearOpsAlgebra.kt        |   3 +-
 .../kmath/tensors/core/TestDoubleTensor.kt    |   2 +-
 .../tensors/core/TestDoubleTensorAlgebra.kt   |  20 ++-
 20 files changed, 237 insertions(+), 160 deletions(-)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => api}/AnalyticTensorAlgebra.kt (91%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => api}/LinearOpsTensorAlgebra.kt (86%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => api}/TensorAlgebra.kt (89%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => api}/TensorPartialDivisionAlgebra.kt (64%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/{ => api}/TensorStructure.kt (73%)
 create mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{ => algebras}/DoubleAnalyticTensorAlgebra.kt (77%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{ => algebras}/DoubleLinearOpsTensorAlgebra.kt (84%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{ => algebras}/DoubleTensorAlgebra.kt (92%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{ => algebras}/TensorLinearStructure.kt (90%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{BroadcastDoubleTensorAlgebra.kt => broadcastUtils.kt} (52%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{linutils.kt => linUtils.kt} (97%)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
similarity index 91%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
index 2a8b228f4..315f39027 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
@@ -1,4 +1,9 @@
-package space.kscience.kmath.tensors
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.api
 
 
 public interface AnalyticTensorAlgebra<T> :
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
similarity index 86%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
index 8ca8945a5..5cd48ca78 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
@@ -1,4 +1,9 @@
-package space.kscience.kmath.tensors
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.api
 
 
 public interface LinearOpsTensorAlgebra<T> :
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
similarity index 89%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index f64d49dd8..c1657d916 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -1,4 +1,9 @@
-package space.kscience.kmath.tensors
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.api
 
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T> {
@@ -40,7 +45,9 @@ public interface TensorAlgebra<T> {
     //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
     public fun diagonalEmbedding(
         diagonalEntries: TensorStructure<T>,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+        offset: Int = 0,
+        dim1: Int = -2,
+        dim2: Int = -1
     ): TensorStructure<T>
 
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
similarity index 64%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
index b37ac6b6e..075e86e61 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
@@ -1,8 +1,13 @@
-package space.kscience.kmath.tensors
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.api
 
 // https://proofwiki.org/wiki/Definition:Division_Algebra
 public interface TensorPartialDivisionAlgebra<T> :
-        TensorAlgebra<T> {
+    TensorAlgebra<T> {
     public operator fun TensorStructure<T>.div(value: T): TensorStructure<T>
     public operator fun TensorStructure<T>.div(other: TensorStructure<T>): TensorStructure<T>
     public operator fun TensorStructure<T>.divAssign(value: T)
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorStructure.kt
similarity index 73%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorStructure.kt
index 64c4d98b8..edecd6383 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorStructure.kt
@@ -1,4 +1,4 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.api
 
 import space.kscience.kmath.nd.MutableStructureND
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 2b61e10b2..5c0d9c4ea 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -1,7 +1,8 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.structures.*
-import space.kscience.kmath.tensors.TensorStructure
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
 
 
 public open class BufferedTensor<T>(
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
new file mode 100644
index 000000000..1b00197ff
--- /dev/null
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
@@ -0,0 +1,89 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core.algebras
+
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.*
+import space.kscience.kmath.tensors.core.broadcastTensors
+import space.kscience.kmath.tensors.core.broadcastTo
+
+public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
+
+    override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+            newThis.buffer.array()[i] + newOther.buffer.array()[i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] +=
+                newOther.buffer.array()[tensor.bufferStart + i]
+        }
+    }
+
+    override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+            newThis.buffer.array()[i] - newOther.buffer.array()[i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] -=
+                newOther.buffer.array()[tensor.bufferStart + i]
+        }
+    }
+
+    override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+            newThis.buffer.array()[newThis.bufferStart + i] *
+                    newOther.buffer.array()[newOther.bufferStart + i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] *=
+                newOther.buffer.array()[tensor.bufferStart + i]
+        }
+    }
+
+    override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+            newThis.buffer.array()[newOther.bufferStart + i] /
+                    newOther.buffer.array()[newOther.bufferStart + i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] /=
+                newOther.buffer.array()[tensor.bufferStart + i]
+        }
+    }
+
+}
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
similarity index 77%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
index 233217f2f..7d1cceb15 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
@@ -1,7 +1,14 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
 
-import space.kscience.kmath.tensors.AnalyticTensorAlgebra
-import space.kscience.kmath.tensors.TensorStructure
+package space.kscience.kmath.tensors.core.algebras
+
+import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.DoubleTensor
+import space.kscience.kmath.tensors.core.tensor
 import kotlin.math.*
 
 public class DoubleAnalyticTensorAlgebra:
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
similarity index 84%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
index 386fc2a1b..62629f3db 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
@@ -1,9 +1,23 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
 
-import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
+package space.kscience.kmath.tensors.core.algebras
+
+import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.tensors.TensorStructure
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.*
+import space.kscience.kmath.tensors.core.checkSquareMatrix
+import space.kscience.kmath.tensors.core.choleskyHelper
+import space.kscience.kmath.tensors.core.cleanSymHelper
+import space.kscience.kmath.tensors.core.luHelper
+import space.kscience.kmath.tensors.core.luMatrixDet
+import space.kscience.kmath.tensors.core.luMatrixInv
+import space.kscience.kmath.tensors.core.luPivotHelper
+import space.kscience.kmath.tensors.core.pivInit
 import kotlin.math.min
 
 
@@ -25,12 +39,11 @@ public class DoubleLinearOpsTensorAlgebra :
         luTensor: TensorStructure<Double>,
         pivotsTensor: TensorStructure<Int>
     ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        //todo checks
         checkSquareMatrix(luTensor.shape)
         check(
             luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() ||
                     luTensor.shape.last() == pivotsTensor.shape.last() - 1
-        ) { "Bad shapes ((" } //todo rewrite
+        ) { "Inappropriate shapes of input tensors" }
 
         val n = luTensor.shape.last()
         val pTensor = luTensor.zeroesLike()
@@ -90,10 +103,10 @@ public class DoubleLinearOpsTensorAlgebra :
 
         for ((matrix, USV) in tensor.matrixSequence()
             .zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
-            val size = matrix.shape.reduce { acc, i -> acc * i }
+            val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
             val curMatrix = DoubleTensor(
                 matrix.shape,
-                matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + size).toDoubleArray()
+                matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + matrixSize).toDoubleArray()
             )
             svdHelper(curMatrix, USV, m, n, epsilon)
         }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
similarity index 92%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index 7544bf68e..651db2c21 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -1,8 +1,24 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core.algebras
 
 import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
-import space.kscience.kmath.tensors.TensorStructure
+import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.*
+import space.kscience.kmath.tensors.core.broadcastOuterTensors
+import space.kscience.kmath.tensors.core.checkBufferShapeConsistency
+import space.kscience.kmath.tensors.core.checkEmptyDoubleBuffer
+import space.kscience.kmath.tensors.core.checkEmptyShape
+import space.kscience.kmath.tensors.core.checkShapesCompatible
+import space.kscience.kmath.tensors.core.checkTranspose
+import space.kscience.kmath.tensors.core.checkView
+import space.kscience.kmath.tensors.core.dotHelper
+import space.kscience.kmath.tensors.core.getRandomNormals
+import space.kscience.kmath.tensors.core.minusIndexFrom
 import kotlin.math.abs
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
@@ -263,7 +279,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         if (m1 != m2) {
             throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
         }
-        val m = m1
 
         val resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
         val resSize = resShape.reduce { acc, i -> acc * i }
@@ -271,7 +286,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
         for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
             val (a, b) = ab
-            dotHelper(a.as2D(), b.as2D(), res.as2D(), l, m, n)
+            dotHelper(a.as2D(), b.as2D(), res.as2D(), l, m1, n)
         }
 
         if (penultimateDim) {
@@ -347,7 +362,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return tensor.eq(other) { x, y -> abs(x - y) < delta }
     }
 
-    public fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
+    public infix fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
 
     private fun TensorStructure<Double>.eq(
         other: TensorStructure<Double>,
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
similarity index 90%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
index 47745c2be..b16739892 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
@@ -1,4 +1,9 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core.algebras
 
 import kotlin.math.max
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
similarity index 52%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
index 53c385197..4378e9ac9 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
@@ -1,89 +1,31 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.tensors.TensorStructure
+import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
 import kotlin.math.max
 
-public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
-
-    override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
-        val broadcast = broadcastTensors(tensor, other.tensor)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[i] + newOther.buffer.array()[i]
-        }
-        return DoubleTensor(newThis.shape, resBuffer)
-    }
-
-    override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
-        val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] +=
-                newOther.buffer.array()[tensor.bufferStart + i]
-        }
-    }
-
-    override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
-        val broadcast = broadcastTensors(tensor, other.tensor)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[i] - newOther.buffer.array()[i]
-        }
-        return DoubleTensor(newThis.shape, resBuffer)
-    }
-
-    override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
-        val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] -=
-                newOther.buffer.array()[tensor.bufferStart + i]
-        }
-    }
-
-    override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
-        val broadcast = broadcastTensors(tensor, other.tensor)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[newThis.bufferStart + i] *
-                    newOther.buffer.array()[newOther.bufferStart + i]
-        }
-        return DoubleTensor(newThis.shape, resBuffer)
-    }
-
-    override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
-        val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] *=
-                newOther.buffer.array()[tensor.bufferStart + i]
-        }
-    }
-
-    override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
-        val broadcast = broadcastTensors(tensor, other.tensor)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[newOther.bufferStart + i] /
-                    newOther.buffer.array()[newOther.bufferStart + i]
-        }
-        return DoubleTensor(newThis.shape, resBuffer)
-    }
-
-    override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
-        val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] /=
-                newOther.buffer.array()[tensor.bufferStart + i]
-        }
-    }
-
-}
-
 public inline fun <R> BroadcastDoubleTensorAlgebra(block: BroadcastDoubleTensorAlgebra.() -> R): R =
     BroadcastDoubleTensorAlgebra().block()
 
+internal inline fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: DoubleTensor, linearSize: Int) {
+    for (linearIndex in 0 until linearSize) {
+        val totalMultiIndex = resTensor.linearStructure.index(linearIndex)
+        val curMultiIndex = tensor.shape.copyOf()
+
+        val offset = totalMultiIndex.size - curMultiIndex.size
+
+        for (i in curMultiIndex.indices) {
+            if (curMultiIndex[i] != 1) {
+                curMultiIndex[i] = totalMultiIndex[i + offset]
+            } else {
+                curMultiIndex[i] = 0
+            }
+        }
+
+        val curLinearIndex = tensor.linearStructure.offset(curMultiIndex)
+        resTensor.buffer.array()[linearIndex] =
+            tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
+    }
+}
 
 internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
     var totalDim = 0
@@ -129,24 +71,7 @@ internal inline fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): Doubl
         }
     }
 
-    for (linearIndex in 0 until n) {
-        val totalMultiIndex = resTensor.linearStructure.index(linearIndex)
-        val curMultiIndex = tensor.shape.copyOf()
-
-        val offset = totalMultiIndex.size - curMultiIndex.size
-
-        for (i in curMultiIndex.indices) {
-            if (curMultiIndex[i] != 1) {
-                curMultiIndex[i] = totalMultiIndex[i + offset]
-            } else {
-                curMultiIndex[i] = 0
-            }
-        }
-
-        val curLinearIndex = tensor.linearStructure.offset(curMultiIndex)
-        resTensor.buffer.array()[linearIndex] =
-            tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
-    }
+    multiIndexBroadCasting(tensor, resTensor, n)
     return resTensor
 }
 
@@ -157,25 +82,7 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
     val res = ArrayList<DoubleTensor>(0)
     for (tensor in tensors) {
         val resTensor = DoubleTensor(totalShape, DoubleArray(n))
-
-        for (linearIndex in 0 until n) {
-            val totalMultiIndex = resTensor.linearStructure.index(linearIndex)
-            val curMultiIndex = tensor.shape.copyOf()
-
-            val offset = totalMultiIndex.size - curMultiIndex.size
-
-            for (i in curMultiIndex.indices) {
-                if (curMultiIndex[i] != 1) {
-                    curMultiIndex[i] = totalMultiIndex[i + offset]
-                } else {
-                    curMultiIndex[i] = 0
-                }
-            }
-
-            val curLinearIndex = tensor.linearStructure.offset(curMultiIndex)
-            resTensor.buffer.array()[linearIndex] =
-                tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
-        }
+        multiIndexBroadCasting(tensor, resTensor, n)
         res.add(resTensor)
     }
 
@@ -208,7 +115,7 @@ internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<Do
         for (linearIndex in 0 until n) {
             val totalMultiIndex = outerTensor.linearStructure.index(linearIndex)
             var curMultiIndex = tensor.shape.sliceArray(0..tensor.shape.size - 3).copyOf()
-            curMultiIndex = IntArray(totalMultiIndex.size - curMultiIndex.size) {1} + curMultiIndex
+            curMultiIndex = IntArray(totalMultiIndex.size - curMultiIndex.size) { 1 } + curMultiIndex
 
             val newTensor = DoubleTensor(curMultiIndex + matrixShape, tensor.buffer.array())
 
@@ -221,10 +128,14 @@ internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<Do
             }
 
             for (i in 0 until matrixSize) {
-                val curLinearIndex = newTensor.linearStructure.offset(curMultiIndex +
-                        matrix.linearStructure.index(i))
-                val newLinearIndex = resTensor.linearStructure.offset(totalMultiIndex +
-                        matrix.linearStructure.index(i))
+                val curLinearIndex = newTensor.linearStructure.offset(
+                    curMultiIndex +
+                            matrix.linearStructure.index(i)
+                )
+                val newLinearIndex = resTensor.linearStructure.offset(
+                    totalMultiIndex +
+                            matrix.linearStructure.index(i)
+                )
 
                 resTensor.buffer.array()[resTensor.bufferStart + newLinearIndex] =
                     newTensor.buffer.array()[newTensor.bufferStart + curLinearIndex]
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index 28828eb09..fd5c8413d 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -1,6 +1,8 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.tensors.TensorStructure
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 
 
 internal inline fun checkEmptyShape(shape: IntArray): Unit =
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
similarity index 97%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
index 166f1d6c4..3a130c71e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
@@ -4,6 +4,8 @@ import space.kscience.kmath.nd.MutableStructure1D
 import space.kscience.kmath.nd.MutableStructure2D
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import kotlin.math.abs
 import kotlin.math.min
 import kotlin.math.sign
@@ -31,13 +33,13 @@ internal inline fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTen
     }
 }
 
-internal inline fun <T> BufferedTensor<T>.forEachVector(vectorAction: (BufferedTensor<T>) -> Unit): Unit {
+internal inline fun <T> BufferedTensor<T>.forEachVector(vectorAction: (BufferedTensor<T>) -> Unit) {
     for (vector in vectorSequence()) {
         vectorAction(vector)
     }
 }
 
-internal inline fun <T> BufferedTensor<T>.forEachMatrix(matrixAction: (BufferedTensor<T>) -> Unit): Unit {
+internal inline fun <T> BufferedTensor<T>.forEachMatrix(matrixAction: (BufferedTensor<T>) -> Unit) {
     for (matrix in matrixSequence()) {
         matrixAction(matrix)
     }
@@ -284,7 +286,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
     matrix: DoubleTensor,
     USV: Pair<BufferedTensor<Double>, Pair<BufferedTensor<Double>, BufferedTensor<Double>>>,
     m: Int, n: Int, epsilon: Double
-): Unit {
+) {
     val res = ArrayList<Triple<Double, DoubleTensor, DoubleTensor>>(0)
     val (matrixU, SV) = USV
     val (matrixS, matrixV) = SV
@@ -332,7 +334,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
     }
 }
 
-internal inline fun cleanSymHelper(matrix: MutableStructure2D<Double>, n: Int): Unit {
+internal inline fun cleanSymHelper(matrix: MutableStructure2D<Double>, n: Int) {
     for (i in 0 until n)
         for (j in 0 until n) {
             if (i == j) {
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index ac823185b..d7d006e42 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -110,7 +110,6 @@ internal inline fun DoubleTensor.toPrettyString(): String = buildString {
             charOffset -=1
         }
         offset += vectorSize
-        // todo refactor
         if (this@toPrettyString.numElements == offset) {
             break
         }
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
index 41c9b72f7..d677f6966 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -1,5 +1,6 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
index 4dcdb7848..060bc1607 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -1,5 +1,6 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import kotlin.math.abs
 import kotlin.math.exp
 import kotlin.test.Test
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index d19d0b6f6..6120f0e4a 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -1,5 +1,6 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import kotlin.math.abs
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -125,8 +126,6 @@ class TestDoubleLinearOpsTensorAlgebra {
 
         val (lu, pivots) = tensor.lu()
 
-        // todo check lu
-
         val (p, l, u) = luPivot(lu, pivots)
 
         assertTrue { p.shape contentEquals shape }
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 4de69e1ad..5c068ce83 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -3,7 +3,7 @@ package space.kscience.kmath.tensors.core
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.structures.toDoubleArray
-import kotlin.test.Ignore
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index fa7a8fd32..10a9176dc 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -1,7 +1,9 @@
 package space.kscience.kmath.tensors.core
 
 
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
+import kotlin.test.assertFalse
 import kotlin.test.assertTrue
 
 class TestDoubleTensorAlgebra {
@@ -133,9 +135,9 @@ class TestDoubleTensorAlgebra {
         assertTrue(diagonal1.buffer.array() contentEquals
                 doubleArrayOf(10.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 30.0))
 
-        val diagonal1_offset = diagonalEmbedding(tensor1, 1, 1, 0)
-        assertTrue(diagonal1_offset.shape contentEquals intArrayOf(4, 4))
-        assertTrue(diagonal1_offset.buffer.array() contentEquals
+        val diagonal1Offset = diagonalEmbedding(tensor1, 1, 1, 0)
+        assertTrue(diagonal1Offset.shape contentEquals intArrayOf(4, 4))
+        assertTrue(diagonal1Offset.buffer.array() contentEquals
                 doubleArrayOf(0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 0.0, 30.0, 0.0))
 
         val diagonal2 = diagonalEmbedding(tensor2, 1, 0, 2)
@@ -149,7 +151,15 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun testContentEqual() = DoubleTensorAlgebra {
-        //TODO()
+    fun testEq() = DoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor3 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0))
+        val tensor4 = fromArray(intArrayOf(6, 1), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+
+        assertTrue(tensor1 eq tensor1)
+        assertTrue(tensor1 eq tensor2)
+        assertFalse(tensor1.eq(tensor3))
+
     }
 }
\ No newline at end of file

From 287e2aeba2ba274508f42f1ab0a839ed69861bc1 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 23 Apr 2021 14:22:52 +0100
Subject: [PATCH 115/178] div operator

---
 .../kmath/tensors/api/TensorPartialDivisionAlgebra.kt  |  1 +
 .../kmath/tensors/core/algebras/DoubleTensorAlgebra.kt | 10 ++++++----
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
index 075e86e61..4dab3dd07 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
@@ -8,6 +8,7 @@ package space.kscience.kmath.tensors.api
 // https://proofwiki.org/wiki/Definition:Division_Algebra
 public interface TensorPartialDivisionAlgebra<T> :
     TensorAlgebra<T> {
+    public operator fun T.div(other: TensorStructure<T>): TensorStructure<T>
     public operator fun TensorStructure<T>.div(value: T): TensorStructure<T>
     public operator fun TensorStructure<T>.div(other: TensorStructure<T>): TensorStructure<T>
     public operator fun TensorStructure<T>.divAssign(value: T)
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index 651db2c21..b61a484d0 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -178,13 +178,15 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         }
     }
 
-    override fun TensorStructure<Double>.div(value: Double): DoubleTensor {
-        val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.buffer.array()[tensor.bufferStart + i] / value
+    override fun Double.div(other: TensorStructure<Double>): DoubleTensor {
+        val resBuffer = DoubleArray(other.tensor.numElements) { i ->
+            other.tensor.buffer.array()[other.tensor.bufferStart + i] / this
         }
-        return DoubleTensor(tensor.shape, resBuffer)
+        return DoubleTensor(other.shape, resBuffer)
     }
 
+    override fun TensorStructure<Double>.div(value: Double): DoubleTensor = value / tensor
+
     override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
         checkShapesCompatible(tensor, other)
         val resBuffer = DoubleArray(tensor.numElements) { i ->

From 4f593aec63cd931ebe2d254baf6cee0ef3ca342c Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sat, 24 Apr 2021 18:53:21 +0100
Subject: [PATCH 116/178] Buffer mini-protocol

---
 kmath-core/api/kmath-core.api                 |  8 ++++-
 .../space/kscience/kmath/nd/BufferND.kt       | 35 ++++++++++++++++++-
 .../kmath/tensors/core/BufferedTensor.kt      |  2 ++
 .../kscience/kmath/tensors/core/utils.kt      | 23 +++---------
 .../kmath/tensors/core/TestDoubleTensor.kt    | 34 ++++++++++++++++++
 5 files changed, 81 insertions(+), 21 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 38cfbd58d..41b85b3bb 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -748,7 +748,7 @@ public final class space/kscience/kmath/nd/BufferAlgebraNDKt {
 	public static final fun ring (Lspace/kscience/kmath/nd/AlgebraND$Companion;Lspace/kscience/kmath/operations/Ring;Lkotlin/jvm/functions/Function2;[I)Lspace/kscience/kmath/nd/BufferedRingND;
 }
 
-public final class space/kscience/kmath/nd/BufferND : space/kscience/kmath/nd/StructureND {
+public class space/kscience/kmath/nd/BufferND : space/kscience/kmath/nd/StructureND {
 	public fun <init> (Lspace/kscience/kmath/nd/Strides;Lspace/kscience/kmath/structures/Buffer;)V
 	public fun elements ()Lkotlin/sequences/Sequence;
 	public fun get ([I)Ljava/lang/Object;
@@ -876,6 +876,12 @@ public abstract interface class space/kscience/kmath/nd/GroupND : space/kscience
 public final class space/kscience/kmath/nd/GroupND$Companion {
 }
 
+public final class space/kscience/kmath/nd/MutableBufferND : space/kscience/kmath/nd/BufferND, space/kscience/kmath/nd/MutableStructureND {
+	public fun <init> (Lspace/kscience/kmath/nd/Strides;Lspace/kscience/kmath/structures/MutableBuffer;)V
+	public final fun getMutableBuffer ()Lspace/kscience/kmath/structures/MutableBuffer;
+	public fun set ([ILjava/lang/Object;)V
+}
+
 public abstract interface class space/kscience/kmath/nd/MutableStructure1D : space/kscience/kmath/nd/MutableStructureND, space/kscience/kmath/nd/Structure1D, space/kscience/kmath/structures/MutableBuffer {
 	public fun set ([ILjava/lang/Object;)V
 }
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt
index 23d961a7e..1f608f478 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt
@@ -7,6 +7,8 @@ package space.kscience.kmath.nd
 
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.BufferFactory
+import space.kscience.kmath.structures.MutableBuffer
+import space.kscience.kmath.structures.MutableBufferFactory
 
 /**
  * Represents [StructureND] over [Buffer].
@@ -15,7 +17,7 @@ import space.kscience.kmath.structures.BufferFactory
  * @param strides The strides to access elements of [Buffer] by linear indices.
  * @param buffer The underlying buffer.
  */
-public class BufferND<T>(
+public open class BufferND<T>(
     public val strides: Strides,
     public val buffer: Buffer<T>,
 ) : StructureND<T> {
@@ -50,4 +52,35 @@ public inline fun <T, reified R : Any> StructureND<T>.mapToBuffer(
         val strides = DefaultStrides(shape)
         BufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
     }
+}
+
+/**
+ * Represents [MutableStructureND] over [MutableBuffer].
+ *
+ * @param T the type of items.
+ * @param strides The strides to access elements of [MutableBuffer] by linear indices.
+ * @param mutableBuffer The underlying buffer.
+ */
+public class MutableBufferND<T>(
+    strides: Strides,
+    public val mutableBuffer: MutableBuffer<T>,
+) : MutableStructureND<T>, BufferND<T>(strides, mutableBuffer) {
+    override fun set(index: IntArray, value: T) {
+        mutableBuffer[strides.offset(index)] = value
+    }
+}
+
+/**
+ * Transform structure to a new structure using provided [MutableBufferFactory] and optimizing if argument is [MutableBufferND]
+ */
+public inline fun <T, reified R : Any> MutableStructureND<T>.mapToMutableBuffer(
+    factory: MutableBufferFactory<R> = MutableBuffer.Companion::auto,
+    crossinline transform: (T) -> R,
+): MutableBufferND<R> {
+    return if (this is MutableBufferND<T>)
+        MutableBufferND(this.strides, factory.invoke(strides.linearSize) { transform(mutableBuffer[it]) })
+    else {
+        val strides = DefaultStrides(shape)
+        MutableBufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
+    }
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 5c0d9c4ea..1e9b458b1 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -1,5 +1,6 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.nd.MutableBufferND
 import space.kscience.kmath.structures.*
 import space.kscience.kmath.tensors.api.TensorStructure
 import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
@@ -60,6 +61,7 @@ internal inline fun BufferedTensor<Double>.asTensor(): DoubleTensor = DoubleTens
 
 internal inline fun <T> TensorStructure<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
     is BufferedTensor<T> -> this
+    is MutableBufferND<T> -> BufferedTensor(this.shape, this.mutableBuffer, 0)
     else -> BufferedTensor(this.shape, this.elements().map{ it.second }.toMutableList().asMutableBuffer(), 0)
 }
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index d7d006e42..89e9d0d96 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -7,35 +7,20 @@ import space.kscience.kmath.structures.*
 import kotlin.math.*
 
 /**
- * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
+ * Returns a reference to [IntArray] containing all of the elements of this [Buffer] or copy the data.
  */
 internal fun Buffer<Int>.array(): IntArray = when (this) {
     is IntBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to IntArray")
+    else -> this.toIntArray()
 }
 
-/**
- * Returns a reference to [LongArray] containing all of the elements of this [Buffer].
- */
-internal fun Buffer<Long>.array(): LongArray = when (this) {
-    is LongBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to LongArray")
-}
 
 /**
- * Returns a reference to [FloatArray] containing all of the elements of this [Buffer].
- */
-internal fun Buffer<Float>.array(): FloatArray = when (this) {
-    is FloatBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to FloatArray")
-}
-
-/**
- * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer].
+ * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer] or copy the data.
  */
 internal fun Buffer<Double>.array(): DoubleArray = when (this) {
     is DoubleBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
+    else -> this.toDoubleArray()
 }
 
 internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 5c068ce83..f9d15c216 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -1,7 +1,11 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.nd.DefaultStrides
+import space.kscience.kmath.nd.MutableBufferND
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.structures.DoubleBuffer
+import space.kscience.kmath.structures.asMutableBuffer
 import space.kscience.kmath.structures.toDoubleArray
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
@@ -47,4 +51,34 @@ class TestDoubleTensor {
             assertEquals(secondRow[1], secondColumn[1])
         }
     }
+
+    @Test
+    fun bufferProtocol() {
+
+        // create buffers
+        val doubleBuffer = DoubleBuffer(doubleArrayOf(1.0,2.0,3.0))
+        val doubleList = MutableList(3, doubleBuffer::get)
+
+        // create ND buffers
+        val ndBuffer = MutableBufferND(DefaultStrides(intArrayOf(3)), doubleBuffer)
+        val ndList = MutableBufferND(DefaultStrides(intArrayOf(3)), doubleList.asMutableBuffer())
+
+        // map to tensors
+        val bufferedTensorBuffer = ndBuffer.toBufferedTensor() // strides are flipped
+        val tensorBuffer = bufferedTensorBuffer.asTensor() // no data copied
+
+        val bufferedTensorList = ndList.toBufferedTensor() // strides are flipped
+        val tensorList = bufferedTensorList.asTensor() // data copied
+
+        tensorBuffer[intArrayOf(0)] = 55.9
+        assertEquals(ndBuffer[intArrayOf(0)], 55.9)
+        assertEquals(doubleBuffer[0], 55.9)
+
+        tensorList[intArrayOf(0)] = 55.9
+        assertEquals(ndList[intArrayOf(0)], 1.0)
+        assertEquals(doubleList[0], 1.0)
+
+        ndList[intArrayOf(0)] = 55.9
+        assertEquals(doubleList[0], 55.9)
+    }
 }
\ No newline at end of file

From 2c001cb1b32f25dc4c245bc32b4046bf78e85cca Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Mon, 26 Apr 2021 17:07:49 +0300
Subject: [PATCH 117/178] fix div + simple tests

---
 .../tensors/core/algebras/DoubleTensorAlgebra.kt   |  9 +++++++--
 .../kmath/tensors/core/TestDoubleTensorAlgebra.kt  | 14 ++++++++++++++
 2 files changed, 21 insertions(+), 2 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index b61a484d0..7414a4469 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -180,12 +180,17 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
     override fun Double.div(other: TensorStructure<Double>): DoubleTensor {
         val resBuffer = DoubleArray(other.tensor.numElements) { i ->
-            other.tensor.buffer.array()[other.tensor.bufferStart + i] / this
+            this / other.tensor.buffer.array()[other.tensor.bufferStart + i]
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.div(value: Double): DoubleTensor = value / tensor
+    override fun TensorStructure<Double>.div(value: Double): DoubleTensor {
+        val resBuffer = DoubleArray(tensor.numElements) { i ->
+            tensor.buffer.array()[tensor.bufferStart + i] / value
+        }
+        return DoubleTensor(shape, resBuffer)
+    }
 
     override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
         checkShapesCompatible(tensor, other)
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index 10a9176dc..1333a7a1f 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -15,6 +15,20 @@ class TestDoubleTensorAlgebra {
         assertTrue(res.buffer.array() contentEquals doubleArrayOf(11.0, 12.0))
     }
 
+    @Test
+    fun doubleDiv() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(2), doubleArrayOf(2.0, 4.0))
+        val res = 2.0/tensor
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(1.0, 0.5))
+    }
+
+    @Test
+    fun divDouble() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(2), doubleArrayOf(10.0, 5.0))
+        val res = tensor / 2.5
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(4.0, 2.0))
+    }
+
     @Test
     fun transpose1x1() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(1), doubleArrayOf(0.0))

From 30ca333c040b09ca65d528b052fa27492161654a Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Mon, 26 Apr 2021 17:27:50 +0300
Subject: [PATCH 118/178] OLS/SVD example

---
 examples/build.gradle.kts                     |  1 +
 .../kscience/kmath/tensors/OLSWithSVD.kt      | 68 +++++++++++++++++++
 2 files changed, 69 insertions(+)
 create mode 100644 examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt

diff --git a/examples/build.gradle.kts b/examples/build.gradle.kts
index 56feee9dc..a4eabc2b8 100644
--- a/examples/build.gradle.kts
+++ b/examples/build.gradle.kts
@@ -28,6 +28,7 @@ dependencies {
     implementation(project(":kmath-dimensions"))
     implementation(project(":kmath-ejml"))
     implementation(project(":kmath-nd4j"))
+    implementation(project(":kmath-tensors"))
 
     implementation(project(":kmath-for-real"))
 
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
new file mode 100644
index 000000000..063a1d1c4
--- /dev/null
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
@@ -0,0 +1,68 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.tensors.core.DoubleTensor
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+
+// OLS estimator using SVD
+
+fun main() {
+    //seed for random
+    val randSeed = 100500L
+
+    // work in context with linear operations
+    DoubleLinearOpsTensorAlgebra {
+        // take coefficient vector from normal distribution
+        val alpha = randNormal(
+            intArrayOf(5),
+            randSeed
+        ) + fromArray(
+            intArrayOf(5),
+            doubleArrayOf(1.0, 2.5, 3.4, 5.0, 10.1)
+        )
+
+        println("Real alpha:\n" +
+                "$alpha")
+
+        // also take sample of size 20 from normal distribution for x TODO rename
+        val x = randNormal(
+            intArrayOf(20, 5),
+            randSeed
+        )
+
+        // calculate y and add gaussian noise (N(0, 0.05)) TODO rename
+        val y = x dot alpha
+        y += y.randNormalLike(randSeed) * 0.05
+
+        // now restore the coefficient vector with OSL estimator with SVD
+        val (u, singValues, v) = x.svd()
+
+        // we have to make sure the singular values of the matrix are not close to zero
+        println("Singular values:\n" +
+                "$singValues")
+        // TODO something with Boolean tensors
+
+        // inverse Sigma matrix can be restored from singular values with diagonalEmbedding function
+        val sigma = diagonalEmbedding(1.0/singValues)
+
+        val alphaOLS = v dot sigma dot u.transpose() dot y
+        println("Estimated alpha:\n" +
+                "$alphaOLS")
+
+        // figure out MSE of approximation
+        fun mse(yTrue: DoubleTensor, yPred: DoubleTensor): Double = DoubleAnalyticTensorAlgebra{
+            require(yTrue.shape.size == 1)
+            require(yTrue.shape contentEquals yPred.shape)
+
+            val diff = yTrue - yPred
+            diff.dot(diff).sqrt().value()
+        }
+
+        println("MSE: ${mse(alpha, alphaOLS)}")
+    }
+}
\ No newline at end of file

From c2db3a23e1e6a14463d3dadac6d279ccabb49272 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 26 Apr 2021 16:24:26 +0100
Subject: [PATCH 119/178] Feedback for SVD

---
 .../space/kscience/kmath/tensors/OLSWithSVD.kt  | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
index 063a1d1c4..a9b154017 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
@@ -9,6 +9,8 @@ import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 
+import kotlin.math.abs
+
 // OLS estimator using SVD
 
 fun main() {
@@ -26,8 +28,7 @@ fun main() {
             doubleArrayOf(1.0, 2.5, 3.4, 5.0, 10.1)
         )
 
-        println("Real alpha:\n" +
-                "$alpha")
+        println("Real alpha:\n$alpha")
 
         // also take sample of size 20 from normal distribution for x TODO rename
         val x = randNormal(
@@ -35,20 +36,22 @@ fun main() {
             randSeed
         )
 
-        // calculate y and add gaussian noise (N(0, 0.05)) TODO rename
+        // calculate y and add gaussian noise (N(0, 0.05))
+        // TODO: please add an intercept: Y = beta * X + alpha + N(0,0.5)
         val y = x dot alpha
         y += y.randNormalLike(randSeed) * 0.05
 
         // now restore the coefficient vector with OSL estimator with SVD
+        // TODO: you need to change accordingly [X 1] [alpha beta] = Y
+        // TODO: inverting [X 1] via SVD
         val (u, singValues, v) = x.svd()
 
         // we have to make sure the singular values of the matrix are not close to zero
-        println("Singular values:\n" +
-                "$singValues")
-        // TODO something with Boolean tensors
+        println("Singular values:\n$singValues")
+
 
         // inverse Sigma matrix can be restored from singular values with diagonalEmbedding function
-        val sigma = diagonalEmbedding(1.0/singValues)
+        val sigma = diagonalEmbedding(singValues.map{ x -> if (abs(x) < 1e-3) 0.0 else 1.0/x })
 
         val alphaOLS = v dot sigma dot u.transpose() dot y
         println("Estimated alpha:\n" +

From 23ea4a95a151e8ce1a70566a4ede3c7d343e5caa Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Tue, 27 Apr 2021 19:01:54 +0100
Subject: [PATCH 120/178] Fixed strides flipping

---
 .../kmath/tensors/core/BufferedTensor.kt      | 23 +++++---
 .../kmath/tensors/core/TestDoubleTensor.kt    | 58 ++++++++++---------
 2 files changed, 45 insertions(+), 36 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 1e9b458b1..6e1cab11a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -37,9 +37,8 @@ public class IntTensor internal constructor(
     shape: IntArray,
     buffer: IntArray,
     offset: Int = 0
-) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset)
-{
-    internal constructor(bufferedTensor: BufferedTensor<Int>):
+) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset) {
+    internal constructor(bufferedTensor: BufferedTensor<Int>) :
             this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
 }
 
@@ -47,9 +46,8 @@ public class DoubleTensor internal constructor(
     shape: IntArray,
     buffer: DoubleArray,
     offset: Int = 0
-) : BufferedTensor<Double>(shape, DoubleBuffer(buffer), offset)
-{
-    internal constructor(bufferedTensor: BufferedTensor<Double>):
+) : BufferedTensor<Double>(shape, DoubleBuffer(buffer), offset) {
+    internal constructor(bufferedTensor: BufferedTensor<Double>) :
             this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
 
     override fun toString(): String = toPrettyString()
@@ -59,10 +57,17 @@ public class DoubleTensor internal constructor(
 internal inline fun BufferedTensor<Int>.asTensor(): IntTensor = IntTensor(this)
 internal inline fun BufferedTensor<Double>.asTensor(): DoubleTensor = DoubleTensor(this)
 
+internal inline fun <T> TensorStructure<T>.copyToBufferedTensor(): BufferedTensor<T> =
+    BufferedTensor(
+        this.shape,
+        TensorLinearStructure(this.shape).indices().map(this::get).toMutableList().asMutableBuffer(), 0
+    )
+
 internal inline fun <T> TensorStructure<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
     is BufferedTensor<T> -> this
-    is MutableBufferND<T> -> BufferedTensor(this.shape, this.mutableBuffer, 0)
-    else -> BufferedTensor(this.shape, this.elements().map{ it.second }.toMutableList().asMutableBuffer(), 0)
+    is MutableBufferND<T> -> if (this.strides.strides.toIntArray() contentEquals TensorLinearStructure(this.shape).strides)
+        BufferedTensor(this.shape, this.mutableBuffer, 0) else this.copyToBufferedTensor()
+    else -> this.copyToBufferedTensor()
 }
 
 internal val TensorStructure<Double>.tensor: DoubleTensor
@@ -77,3 +82,5 @@ internal val TensorStructure<Int>.tensor: IntTensor
         else -> this.toBufferedTensor().asTensor()
     }
 
+public fun TensorStructure<Double>.toTypedTensor(): DoubleTensor = this.tensor
+public fun TensorStructure<Int>.toTypedTensor(): IntTensor = this.tensor
\ No newline at end of file
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index f9d15c216..7103eafaa 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -23,62 +23,64 @@ class TestDoubleTensor {
 
     @Test
     fun stridesTest() = DoubleTensorAlgebra {
-        val tensor = fromArray(intArrayOf(2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
-        assertEquals(tensor[intArrayOf(0,1)], 5.8)
-        assertTrue(tensor.elements().map{ it.second }.toList().toDoubleArray() contentEquals tensor.buffer.toDoubleArray())
+        val tensor = fromArray(intArrayOf(2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
+        assertEquals(tensor[intArrayOf(0, 1)], 5.8)
+        assertTrue(
+            tensor.elements().map { it.second }.toList().toDoubleArray() contentEquals tensor.buffer.toDoubleArray()
+        )
     }
 
     @Test
     fun getTest() = DoubleTensorAlgebra {
-        val tensor = fromArray(intArrayOf(1,2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
+        val tensor = fromArray(intArrayOf(1, 2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
         val matrix = tensor[0].as2D()
-        assertEquals(matrix[0,1], 5.8)
+        assertEquals(matrix[0, 1], 5.8)
 
         val vector = tensor[0][1].as1D()
         assertEquals(vector[0], 58.4)
 
-        matrix[0,1] = 77.89
-        assertEquals(tensor[intArrayOf(0,0,1)], 77.89)
+        matrix[0, 1] = 77.89
+        assertEquals(tensor[intArrayOf(0, 0, 1)], 77.89)
 
         vector[0] = 109.56
-        assertEquals(tensor[intArrayOf(0,1,0)], 109.56)
+        assertEquals(tensor[intArrayOf(0, 1, 0)], 109.56)
 
         tensor.matrixSequence().forEach {
             val a = it.asTensor()
             val secondRow = a[1].as1D()
-            val secondColumn = a.transpose(0,1)[1].as1D()
+            val secondColumn = a.transpose(0, 1)[1].as1D()
             assertEquals(secondColumn[0], 77.89)
             assertEquals(secondRow[1], secondColumn[1])
         }
     }
 
     @Test
-    fun bufferProtocol() {
+    fun noBufferProtocol() {
 
-        // create buffers
-        val doubleBuffer = DoubleBuffer(doubleArrayOf(1.0,2.0,3.0))
-        val doubleList = MutableList(3, doubleBuffer::get)
+        // create buffer
+        val doubleArray = DoubleBuffer(doubleArrayOf(1.0, 2.0, 3.0))
 
-        // create ND buffers
-        val ndBuffer = MutableBufferND(DefaultStrides(intArrayOf(3)), doubleBuffer)
-        val ndList = MutableBufferND(DefaultStrides(intArrayOf(3)), doubleList.asMutableBuffer())
+        // create ND buffers, no data is copied
+        val ndArray = MutableBufferND(DefaultStrides(intArrayOf(3)), doubleArray)
 
         // map to tensors
-        val bufferedTensorBuffer = ndBuffer.toBufferedTensor() // strides are flipped
-        val tensorBuffer = bufferedTensorBuffer.asTensor() // no data copied
+        val bufferedTensorArray = ndArray.toBufferedTensor() // strides are flipped so data copied
+        val tensorArray = bufferedTensorArray.asTensor() // data not contiguous so copied again
 
-        val bufferedTensorList = ndList.toBufferedTensor() // strides are flipped
-        val tensorList = bufferedTensorList.asTensor() // data copied
+        val tensorArrayPublic = ndArray.toTypedTensor() // public API, data copied twice
+        val sharedTensorArray = tensorArrayPublic.toTypedTensor() // no data copied by matching type
 
-        tensorBuffer[intArrayOf(0)] = 55.9
-        assertEquals(ndBuffer[intArrayOf(0)], 55.9)
-        assertEquals(doubleBuffer[0], 55.9)
+        assertTrue(tensorArray.buffer.array() contentEquals sharedTensorArray.buffer.array())
 
-        tensorList[intArrayOf(0)] = 55.9
-        assertEquals(ndList[intArrayOf(0)], 1.0)
-        assertEquals(doubleList[0], 1.0)
+        tensorArray[intArrayOf(0)] = 55.9
+        assertEquals(tensorArrayPublic[intArrayOf(0)], 1.0)
+
+        tensorArrayPublic[intArrayOf(0)] = 55.9
+        assertEquals(sharedTensorArray[intArrayOf(0)], 55.9)
+        assertEquals(bufferedTensorArray[intArrayOf(0)], 1.0)
+
+        bufferedTensorArray[intArrayOf(0)] = 55.9
+        assertEquals(ndArray[intArrayOf(0)], 1.0)
 
-        ndList[intArrayOf(0)] = 55.9
-        assertEquals(doubleList[0], 55.9)
     }
 }
\ No newline at end of file

From b65a673173e6b1cd209fa355349bf9b90f718887 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 28 Apr 2021 14:16:57 +0100
Subject: [PATCH 121/178] Fix format for 0.0

---
 .../kotlin/space/kscience/kmath/tensors/core/utils.kt         | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index 89e9d0d96..cb23dbdd6 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -47,17 +47,17 @@ internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int =  minusIndexF
 
 internal inline fun format(value: Double, digits: Int = 4): String {
     val ten = 10.0
-    val approxOrder = ceil(log10(abs(value))).toInt()
+    val approxOrder = if(value == 0.0) 0 else ceil(log10(abs(value))).toInt()
     val order = if(
         ((value % ten) == 0.0) or
         (value == 1.0) or
         ((1/value) % ten == 0.0)) approxOrder else approxOrder - 1
-
     val lead = value / ten.pow(order)
     val leadDisplay = round(lead*ten.pow(digits)) / ten.pow(digits)
     val orderDisplay = if(order == 0) "" else if(order > 0) "E+$order" else "E$order"
     val valueDisplay = "$leadDisplay$orderDisplay"
     val res = if(value < 0.0) valueDisplay else " $valueDisplay"
+
     val fLength = digits + 6
     val endSpace = " ".repeat(fLength - res.length)
     return "$res$endSpace"

From a0e9180db6da06c2cdac36a25c4b44d951945850 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Wed, 28 Apr 2021 18:18:57 +0300
Subject: [PATCH 122/178] example with LUP

---
 .../tensors/LinearSystemSolvingWithLUP.kt     | 97 +++++++++++++++++++
 1 file changed, 97 insertions(+)
 create mode 100644 examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
new file mode 100644
index 000000000..526b6781f
--- /dev/null
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
@@ -0,0 +1,97 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.tensors.core.DoubleTensor
+import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+
+// solving linear system with LUP decomposition
+
+fun main () {
+
+    // work in context with linear operations
+    DoubleLinearOpsTensorAlgebra {
+
+        // set true value of x
+        val trueX = fromArray(
+            intArrayOf(4),
+            doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
+        )
+
+        // and A matrix
+        val a = fromArray(
+            intArrayOf(4, 4),
+            doubleArrayOf(
+                0.5, 10.5, 4.5, 1.0,
+                8.5, 0.9, 12.8, 0.1,
+                5.56, 9.19, 7.62, 5.45,
+                1.0, 2.0, -3.0, -2.5
+            )
+        )
+
+        // calculate y value
+        val b = a dot trueX
+
+        // check out A and b
+        println("A:\n$a")
+        println("b:\n$b")
+
+        // solve `Ax = b` system using LUP decomposition
+
+        // get P, L, U such that PA = LU
+        val (lu, pivots) = a.lu()
+        val (p, l, u) = luPivot(lu, pivots)
+
+        // check that P is permutation matrix
+        println("P:\n$p")
+        // L is lower triangular matrix and U is upper triangular matrix
+        println("L:\n$l")
+        println("U:\n$u")
+        // and PA = LU
+        println("PA:\n${p dot a}")
+        println("LU:\n${l dot u}")
+
+     /* Ax = b;
+        PAx = Pb;
+        LUx = Pb;
+        let y = Ux, then
+        Ly = Pb -- this system can be easily solved, since the matrix L is lower triangular;
+        Ux = y can be solved the same way, since the matrix L is upper triangular
+         */
+
+
+
+        // this function returns solution x of a system lx = b, l should be lower triangular
+        fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
+            val n = l.shape[0]
+            val x = zeros(intArrayOf(n))
+            for (i in 0 until n){
+                x[intArrayOf(i)] = (b[intArrayOf(i)] -  l[i].dot(x).value()) / l[intArrayOf(i, i)]
+            }
+            return x
+        }
+
+        val y = solveLT(l, p dot b)
+
+        // solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
+        // create it by placing ones on side diagonal
+        val revMat = u.zeroesLike()
+        val n = revMat.shape[0]
+        for (i in 0 until n) {
+            revMat[intArrayOf(i, n - 1 - i)] = 1.0
+        }
+
+        // solution of system ux = b, u should be upper triangular
+        fun solveUT(u: DoubleTensor, b: DoubleTensor): DoubleTensor = revMat dot solveLT(
+            revMat dot u dot revMat, revMat dot b
+        )
+
+        val x = solveUT(u, y)
+
+        println("True x:\n$trueX")
+        println("x founded with LU method:\n$x")
+    }
+}
\ No newline at end of file

From 51f084d28be21118c403ce5ae29775ab894971ae Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 28 Apr 2021 17:07:10 +0100
Subject: [PATCH 123/178] merge PR

---
 .../main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt   | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
index a9b154017..9318fe928 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
@@ -30,20 +30,17 @@ fun main() {
 
         println("Real alpha:\n$alpha")
 
-        // also take sample of size 20 from normal distribution for x TODO rename
+        // also take sample of size 20 from normal distribution for x
         val x = randNormal(
             intArrayOf(20, 5),
             randSeed
         )
 
         // calculate y and add gaussian noise (N(0, 0.05))
-        // TODO: please add an intercept: Y = beta * X + alpha + N(0,0.5)
         val y = x dot alpha
         y += y.randNormalLike(randSeed) * 0.05
 
         // now restore the coefficient vector with OSL estimator with SVD
-        // TODO: you need to change accordingly [X 1] [alpha beta] = Y
-        // TODO: inverting [X 1] via SVD
         val (u, singValues, v) = x.svd()
 
         // we have to make sure the singular values of the matrix are not close to zero

From 6f5b0f0a03e941b33d4adc22e341bce2f7d8bd00 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Thu, 29 Apr 2021 17:09:50 +0300
Subject: [PATCH 124/178] add README and documentation for the main functions
 of tensor algebra

---
 README.md                                     |  12 +
 kmath-tensors/README.md                       |  37 +++
 kmath-tensors/build.gradle.kts                |  26 ++-
 kmath-tensors/docs/README-TEMPLATE.md         |   7 +
 .../tensors/api/LinearOpsTensorAlgebra.kt     |  84 ++++++-
 .../kmath/tensors/api/TensorAlgebra.kt        | 211 +++++++++++++++++-
 .../algebras/BroadcastDoubleTensorAlgebra.kt  |   4 +
 7 files changed, 365 insertions(+), 16 deletions(-)
 create mode 100644 kmath-tensors/README.md
 create mode 100644 kmath-tensors/docs/README-TEMPLATE.md

diff --git a/README.md b/README.md
index 773eb6398..19c59e79e 100644
--- a/README.md
+++ b/README.md
@@ -230,6 +230,18 @@ One can still use generic algebras though.
 > **Maturity**: EXPERIMENTAL
 <hr/>
 
+* ### [kmath-tensors](kmath-tensors)
+> 
+>
+> **Maturity**: PROTOTYPE
+>
+> **Features:**
+> - [tensor algebra](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic linear algebra operations on tensors (plus, dot, etc.)
+> - [tensor algebra with broadcasting](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt) : Basic linear algebra operations implemented with broadcasting.
+> - [linear algebra operations](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Advanced linear algebra operations like LU decomposition, SVD, etc.
+
+<hr/>
+
 * ### [kmath-viktor](kmath-viktor)
 > 
 >
diff --git a/kmath-tensors/README.md b/kmath-tensors/README.md
new file mode 100644
index 000000000..3b82829f0
--- /dev/null
+++ b/kmath-tensors/README.md
@@ -0,0 +1,37 @@
+# Module kmath-tensors
+
+Common linear algebra operations on tensors.
+
+ - [tensor algebra](src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic linear algebra operations on tensors (plus, dot, etc.)
+ - [tensor algebra with broadcasting](src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt) : Basic linear algebra operations implemented with broadcasting.
+ - [linear algebra operations](src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Advanced linear algebra operations like LU decomposition, SVD, etc.
+
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-tensors:0.3.0-dev-7`.
+
+**Gradle:**
+```gradle
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    maven { url 'https://dl.bintray.com/hotkeytlt/maven' }
+    maven { url "https://dl.bintray.com/kotlin/kotlin-eap" } // include for builds based on kotlin-eap
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-tensors:0.3.0-dev-7'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    maven("https://dl.bintray.com/kotlin/kotlin-eap") // include for builds based on kotlin-eap
+    maven("https://dl.bintray.com/hotkeytlt/maven") // required for a
+}
+
+dependencies {
+    implementation("space.kscience:kmath-tensors:0.3.0-dev-7")
+}
+```
diff --git a/kmath-tensors/build.gradle.kts b/kmath-tensors/build.gradle.kts
index 8e823416b..af5116022 100644
--- a/kmath-tensors/build.gradle.kts
+++ b/kmath-tensors/build.gradle.kts
@@ -11,6 +11,30 @@ kotlin.sourceSets {
     }
 }
 
+tasks.dokkaHtml {
+    dependsOn(tasks.build)
+}
+
 readme {
-    maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
+    maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE
+    propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md"))
+
+    feature(
+        id = "tensor algebra",
+        description = "Basic linear algebra operations on tensors (plus, dot, etc.)",
+        ref = "src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt"
+    )
+
+    feature(
+        id = "tensor algebra with broadcasting",
+        description = "Basic linear algebra operations implemented with broadcasting.",
+        ref = "src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt"
+    )
+
+    feature(
+        id = "linear algebra operations",
+        description = "Advanced linear algebra operations like LU decomposition, SVD, etc.",
+        ref = "src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt"
+    )
+
 }
\ No newline at end of file
diff --git a/kmath-tensors/docs/README-TEMPLATE.md b/kmath-tensors/docs/README-TEMPLATE.md
new file mode 100644
index 000000000..5fd968afd
--- /dev/null
+++ b/kmath-tensors/docs/README-TEMPLATE.md
@@ -0,0 +1,7 @@
+# Module kmath-tensors
+
+Common linear algebra operations on tensors.
+
+${features}
+
+${artifact}
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
index 5cd48ca78..6735efacb 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
@@ -5,33 +5,99 @@
 
 package space.kscience.kmath.tensors.api
 
-
+/**
+ * Common linear algebra operations. Operates on [TensorStructure].
+ *
+ * @param T the type of items in the tensors.
+ */
 public interface LinearOpsTensorAlgebra<T> :
     TensorPartialDivisionAlgebra<T> {
 
-    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
+    /**
+     * Computes the determinant of a square matrix input, or of each square matrix in a batched input.
+     * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
+     *
+     * @return the determinant.
+     */
     public fun TensorStructure<T>.det(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
+    /**
+     * Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input.
+     * Given a square matrix `a`, return the matrix `aInv` satisfying
+     * ``a.dot(aInv) = aInv.dot(a) = eye(a.shape[0])``.
+     * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
+     *
+     * @return the multiplicative inverse of a matrix.
+     */
     public fun TensorStructure<T>.inv(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.cholesky
+    /**
+     * Cholesky decomposition.
+     *
+     * Computes the Cholesky decomposition of a Hermitian (or symmetric for real-valued matrices)
+     * positive-definite matrix or the Cholesky decompositions for a batch of such matrices.
+     * Each decomposition has the form:
+     * Given a tensor `input`, return the tensor `L` satisfying ``input = L * L.H``,
+     * where L is a lower-triangular matrix and L.H is the conjugate transpose of L,
+     * which is just a transpose for the case of real-valued input matrices.
+     * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.cholesky
+     *
+     * @return the batch of L matrices.
+     */
     public fun TensorStructure<T>.cholesky(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
+    /**
+     * QR decomposition.
+     *
+     * Computes the QR decomposition of a matrix or a batch of matrices, and returns a namedtuple `(Q, R)` of tensors.
+     * Given a tensor `input`, return tensors (Q, R) satisfying ``input = Q * R``,
+     * with `Q` being an orthogonal matrix or batch of orthogonal matrices
+     * and `R` being an upper triangular matrix or batch of upper triangular matrices.
+     * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
+     *
+     * @return tuple of Q and R tensors.
+     */
     public fun TensorStructure<T>.qr(): Pair<TensorStructure<T>, TensorStructure<T>>
 
-    //https://pytorch.org/docs/stable/generated/torch.lu.html
+    /**
+     * TODO('Andrew')
+     * For more information: https://pytorch.org/docs/stable/generated/torch.lu.html
+     *
+     * @return ...
+     */
     public fun TensorStructure<T>.lu(): Pair<TensorStructure<T>, TensorStructure<Int>>
 
-    //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
+    /**
+     * TODO('Andrew')
+     * For more information: https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
+     *
+     * @param luTensor ...
+     * @param pivotsTensor ...
+     * @return ...
+     */
     public fun luPivot(luTensor: TensorStructure<T>, pivotsTensor: TensorStructure<Int>):
             Triple<TensorStructure<T>, TensorStructure<T>, TensorStructure<T>>
 
-    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
+    /**
+     * Singular Value Decomposition.
+     *
+     * Computes the singular value decomposition of either a matrix or batch of matrices `input`.
+     * The singular value decomposition is represented as a namedtuple `(U, S, V)`,
+     * such that ``input = U.dot(diagonalEmbedding(S).dot(V.T))``.
+     * If input is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as input.
+     * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
+     *
+     * @return the determinant.
+     */
     public fun TensorStructure<T>.svd(): Triple<TensorStructure<T>, TensorStructure<T>, TensorStructure<T>>
 
-    //https://pytorch.org/docs/stable/generated/torch.symeig.html
+    /**
+     * Returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices,
+     * represented by a namedtuple (eigenvalues, eigenvectors).
+     * For more information: https://pytorch.org/docs/stable/generated/torch.symeig.html
+     *
+     * @return a namedtuple (eigenvalues, eigenvectors)
+     */
     public fun TensorStructure<T>.symEig(): Pair<TensorStructure<T>, TensorStructure<T>>
 
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index c1657d916..92aafb8ea 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -5,44 +5,243 @@
 
 package space.kscience.kmath.tensors.api
 
-// https://proofwiki.org/wiki/Definition:Algebra_over_Ring
+/**
+ * Basic linear algebra operations on [TensorStructure].
+ * For more information: https://proofwiki.org/wiki/Definition:Algebra_over_Ring
+ *
+ * @param T the type of items in the tensors.
+ */
 public interface TensorAlgebra<T> {
 
+    /**
+     * Returns a single tensor value of unit dimension. The tensor shape must be equal to [1].
+     *
+     * @return the value of a scalar tensor.
+     */
     public fun TensorStructure<T>.value(): T
 
+    /**
+     * Each element of the tensor [other] is added to this value.
+     * The resulting tensor is returned.
+     *
+     * @param other tensor to be added.
+     * @return the sum of this value and tensor [other].
+     */
     public operator fun T.plus(other: TensorStructure<T>): TensorStructure<T>
+
+    /**
+     * Adds the scalar [value] to each element of this tensor and returns a new resulting tensor.
+     *
+     * @param value the number to be added to each element of this tensor.
+     * @return the sum of this tensor and [value].
+     */
     public operator fun TensorStructure<T>.plus(value: T): TensorStructure<T>
+
+    /**
+     * Each element of the tensor [other] is added to each element of this tensor.
+     * The resulting tensor is returned.
+     *
+     * @param other tensor to be added.
+     * @return the sum of this tensor and [other].
+     */
     public operator fun TensorStructure<T>.plus(other: TensorStructure<T>): TensorStructure<T>
+
+    /**
+     * Adds the scalar [value] to each element of this tensor.
+     *
+     * @param value the number to be added to each element of this tensor.
+     */
     public operator fun TensorStructure<T>.plusAssign(value: T): Unit
+
+    /**
+     * Each element of the tensor [other] is added to each element of this tensor.
+     *
+     * @param other tensor to be added.
+     */
     public operator fun TensorStructure<T>.plusAssign(other: TensorStructure<T>): Unit
 
+
+    /**
+     * Each element of the tensor [other] is subtracted from this value.
+     * The resulting tensor is returned.
+     *
+     * @param other tensor to be subtracted.
+     * @return the difference between this value and tensor [other].
+     */
     public operator fun T.minus(other: TensorStructure<T>): TensorStructure<T>
+
+    /**
+     * Subtracts the scalar [value] from each element of this tensor and returns a new resulting tensor.
+     *
+     * @param value the number to be subtracted from each element of this tensor.
+     * @return the difference between this tensor and [value].
+     */
     public operator fun TensorStructure<T>.minus(value: T): TensorStructure<T>
+
+    /**
+     * Each element of the tensor [other] is subtracted from each element of this tensor.
+     * The resulting tensor is returned.
+     *
+     * @param other tensor to be subtracted.
+     * @return the difference between this tensor and [other].
+     */
     public operator fun TensorStructure<T>.minus(other: TensorStructure<T>): TensorStructure<T>
+
+    /**
+     * Subtracts the scalar [value] from each element of this tensor.
+     *
+     * @param value the number to be subtracted from each element of this tensor.
+     */
     public operator fun TensorStructure<T>.minusAssign(value: T): Unit
+
+    /**
+     * Each element of the tensor [other] is subtracted from each element of this tensor.
+     *
+     * @param other tensor to be subtracted.
+     */
     public operator fun TensorStructure<T>.minusAssign(other: TensorStructure<T>): Unit
 
+
+    /**
+     * Each element of the tensor [other] is multiplied by this value.
+     * The resulting tensor is returned.
+     *
+     * @param other tensor to be multiplied.
+     * @return the product of this value and tensor [other].
+     */
     public operator fun T.times(other: TensorStructure<T>): TensorStructure<T>
+
+    /**
+     * Multiplies the scalar [value] by each element of this tensor and returns a new resulting tensor.
+     *
+     * @param value the number to be multiplied by each element of this tensor.
+     * @return the product of this tensor and [value].
+     */
     public operator fun TensorStructure<T>.times(value: T): TensorStructure<T>
+
+    /**
+     * Each element of the tensor [other] is multiplied by each element of this tensor.
+     * The resulting tensor is returned.
+     *
+     * @param other tensor to be multiplied.
+     * @return the product of this tensor and [other].
+     */
     public operator fun TensorStructure<T>.times(other: TensorStructure<T>): TensorStructure<T>
+
+    /**
+     * Multiplies the scalar [value] by each element of this tensor.
+     *
+     * @param value the number to be multiplied by each element of this tensor.
+     */
     public operator fun TensorStructure<T>.timesAssign(value: T): Unit
+
+    /**
+     * Each element of the tensor [other] is multiplied by each element of this tensor.
+     *
+     * @param other tensor to be multiplied.
+     */
     public operator fun TensorStructure<T>.timesAssign(other: TensorStructure<T>): Unit
+
+    /**
+     * Numerical negative, element-wise.
+     *
+     * @return tensor - negation of the original tensor.
+     */
     public operator fun TensorStructure<T>.unaryMinus(): TensorStructure<T>
 
-    //https://pytorch.org/cppdocs/notes/tensor_indexing.html
+    /**
+     * Returns the tensor at index i
+     * For more information: https://pytorch.org/cppdocs/notes/tensor_indexing.html
+     *
+     * @param i index of the extractable tensor
+     * @return subtensor of the original tensor with index [i]
+     */
     public operator fun TensorStructure<T>.get(i: Int): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.transpose.html
+    /**
+     * Returns a tensor that is a transposed version of this tensor. The given dimensions [i] and [j] are swapped.
+     * For more information: https://pytorch.org/docs/stable/generated/torch.transpose.html
+     *
+     * @param i the first dimension to be transposed
+     * @param j the second dimension to be transposed
+     * @return transposed tensor
+     */
     public fun TensorStructure<T>.transpose(i: Int = -2, j: Int = -1): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/tensor_view.html
+    /**
+     * Returns a new tensor with the same data as the self tensor but of a different shape.
+     * The returned tensor shares the same data and must have the same number of elements, but may have a different size
+     * For more information: https://pytorch.org/docs/stable/tensor_view.html
+     *
+     * @param shape the desired size
+     * @return tensor with new shape
+     */
     public fun TensorStructure<T>.view(shape: IntArray): TensorStructure<T>
+
+    /**
+     * View this tensor as the same size as [other].
+     * ``this.viewAs(other) is equivalent to this.view(other.shape)``.
+     * For more information: https://pytorch.org/cppdocs/notes/tensor_indexing.html
+     *
+     * @param other the result tensor has the same size as other.
+     * @return the result tensor with the same size as other.
+     */
     public fun TensorStructure<T>.viewAs(other: TensorStructure<T>): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.matmul.html
+    /**
+     * Matrix product of two tensors.
+     *
+     * The behavior depends on the dimensionality of the tensors as follows:
+     * 1. If both tensors are 1-dimensional, the dot product (scalar) is returned.
+     *
+     * 2. If both arguments are 2-dimensional, the matrix-matrix product is returned.
+     *
+     * 3. If the first argument is 1-dimensional and the second argument is 2-dimensional,
+     * a 1 is prepended to its dimension for the purpose of the matrix multiply.
+     * After the matrix multiply, the prepended dimension is removed.
+     *
+     * 4. If the first argument is 2-dimensional and the second argument is 1-dimensional,
+     * the matrix-vector product is returned.
+     *
+     * 5. If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2),
+     * then a batched matrix multiply is returned. If the first argument is 1-dimensional,
+     * a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after.
+     * If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix
+     * multiple and removed after.
+     * The non-matrix (i.e. batch) dimensions are broadcasted (and thus must be broadcastable).
+     * For example, if `input` is a (j \times 1 \times n \times n) tensor and `other` is a
+     * (k \times n \times n) tensor, out will be a (j \times k \times n \times n) tensor.
+     *
+     * For more information: https://pytorch.org/docs/stable/generated/torch.matmul.html
+     *
+     * @param other tensor to be multiplied
+     * @return mathematical product of two tensors
+     */
     public infix fun TensorStructure<T>.dot(other: TensorStructure<T>): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
+    /**
+     * Creates a tensor whose diagonals of certain 2D planes (specified by [dim1] and [dim2])
+     * are filled by [diagonalEntries].
+     * To facilitate creating batched diagonal matrices,
+     * the 2D planes formed by the last two dimensions of the returned tensor are chosen by default.
+     *
+     * The argument [offset]  controls which diagonal to consider:
+     * 1. If [offset] = 0, it is the main diagonal.
+     * 2. If [offset] > 0, it is above the main diagonal.
+     * 3. If [offset] < 0, it is below the main diagonal.
+     *
+     * The size of the new matrix will be calculated
+     * to make the specified diagonal of the size of the last input dimension.
+     * For more information: https://pytorch.org/docs/stable/generated/torch.diag_embed.html
+     *
+     * @param diagonalEntries - the input tensor. Must be at least 1-dimensional.
+     * @param offset - which diagonal to consider. Default: 0 (main diagonal).
+     * @param dim1 - first dimension with respect to which to take diagonal. Default: -2.
+     * @param dim2 - second dimension with respect to which to take diagonal. Default: -1.
+     *
+     * @return tensor whose diagonals of certain 2D planes (specified by [dim1] and [dim2])
+     * are filled by [diagonalEntries]
+     */
     public fun diagonalEmbedding(
         diagonalEntries: TensorStructure<T>,
         offset: Int = 0,
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
index 1b00197ff..a49d9ab29 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
@@ -10,6 +10,10 @@ import space.kscience.kmath.tensors.core.*
 import space.kscience.kmath.tensors.core.broadcastTensors
 import space.kscience.kmath.tensors.core.broadcastTo
 
+/**
+ * Basic linear algebra operations implemented with broadcasting.
+ * For more information: https://pytorch.org/docs/stable/notes/broadcasting.html
+ */
 public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {

From 64c6cbf8603a267f7a85af65f16d567c448eeb0a Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Thu, 29 Apr 2021 17:21:29 +0300
Subject: [PATCH 125/178] change tuple to pair/triple

---
 .../kmath/tensors/api/LinearOpsTensorAlgebra.kt        | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
index 6735efacb..aa10ae49b 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
@@ -49,13 +49,13 @@ public interface LinearOpsTensorAlgebra<T> :
     /**
      * QR decomposition.
      *
-     * Computes the QR decomposition of a matrix or a batch of matrices, and returns a namedtuple `(Q, R)` of tensors.
+     * Computes the QR decomposition of a matrix or a batch of matrices, and returns a pair `(Q, R)` of tensors.
      * Given a tensor `input`, return tensors (Q, R) satisfying ``input = Q * R``,
      * with `Q` being an orthogonal matrix or batch of orthogonal matrices
      * and `R` being an upper triangular matrix or batch of upper triangular matrices.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
      *
-     * @return tuple of Q and R tensors.
+     * @return pair of Q and R tensors.
      */
     public fun TensorStructure<T>.qr(): Pair<TensorStructure<T>, TensorStructure<T>>
 
@@ -82,7 +82,7 @@ public interface LinearOpsTensorAlgebra<T> :
      * Singular Value Decomposition.
      *
      * Computes the singular value decomposition of either a matrix or batch of matrices `input`.
-     * The singular value decomposition is represented as a namedtuple `(U, S, V)`,
+     * The singular value decomposition is represented as a triple `(U, S, V)`,
      * such that ``input = U.dot(diagonalEmbedding(S).dot(V.T))``.
      * If input is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as input.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
@@ -93,10 +93,10 @@ public interface LinearOpsTensorAlgebra<T> :
 
     /**
      * Returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices,
-     * represented by a namedtuple (eigenvalues, eigenvectors).
+     * represented by a pair (eigenvalues, eigenvectors).
      * For more information: https://pytorch.org/docs/stable/generated/torch.symeig.html
      *
-     * @return a namedtuple (eigenvalues, eigenvectors)
+     * @return a pair (eigenvalues, eigenvectors)
      */
     public fun TensorStructure<T>.symEig(): Pair<TensorStructure<T>, TensorStructure<T>>
 

From b9f1f0e5259b771727a4cdbdfbefc5d5047cf08e Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Thu, 29 Apr 2021 20:00:05 +0300
Subject: [PATCH 126/178] fixes

---
 .../kmath/tensors/api/TensorAlgebra.kt         | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index 92aafb8ea..62d4a1b89 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -145,7 +145,7 @@ public interface TensorAlgebra<T> {
     /**
      * Numerical negative, element-wise.
      *
-     * @return tensor - negation of the original tensor.
+     * @return tensor negation of the original tensor.
      */
     public operator fun TensorStructure<T>.unaryMinus(): TensorStructure<T>
 
@@ -209,8 +209,8 @@ public interface TensorAlgebra<T> {
      * If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix
      * multiple and removed after.
      * The non-matrix (i.e. batch) dimensions are broadcasted (and thus must be broadcastable).
-     * For example, if `input` is a (j \times 1 \times n \times n) tensor and `other` is a
-     * (k \times n \times n) tensor, out will be a (j \times k \times n \times n) tensor.
+     * For example, if `input` is a (j &times; 1 &times; n &times; n) tensor and `other` is a
+     * (k &times; n &times; n) tensor, out will be a (j &times; k &times; n &times; n) tensor.
      *
      * For more information: https://pytorch.org/docs/stable/generated/torch.matmul.html
      *
@@ -227,17 +227,17 @@ public interface TensorAlgebra<T> {
      *
      * The argument [offset]  controls which diagonal to consider:
      * 1. If [offset] = 0, it is the main diagonal.
-     * 2. If [offset] > 0, it is above the main diagonal.
-     * 3. If [offset] < 0, it is below the main diagonal.
+     * 1. If [offset] > 0, it is above the main diagonal.
+     * 1. If [offset] < 0, it is below the main diagonal.
      *
      * The size of the new matrix will be calculated
      * to make the specified diagonal of the size of the last input dimension.
      * For more information: https://pytorch.org/docs/stable/generated/torch.diag_embed.html
      *
-     * @param diagonalEntries - the input tensor. Must be at least 1-dimensional.
-     * @param offset - which diagonal to consider. Default: 0 (main diagonal).
-     * @param dim1 - first dimension with respect to which to take diagonal. Default: -2.
-     * @param dim2 - second dimension with respect to which to take diagonal. Default: -1.
+     * @param diagonalEntries the input tensor. Must be at least 1-dimensional.
+     * @param offset which diagonal to consider. Default: 0 (main diagonal).
+     * @param dim1 first dimension with respect to which to take diagonal. Default: -2.
+     * @param dim2 second dimension with respect to which to take diagonal. Default: -1.
      *
      * @return tensor whose diagonals of certain 2D planes (specified by [dim1] and [dim2])
      * are filled by [diagonalEntries]

From 432f404d7c00c4d090f5496ae8ffb5e69d9330d8 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Thu, 29 Apr 2021 18:45:46 +0100
Subject: [PATCH 127/178] More descriptions

---
 kmath-tensors/README.md                       | 13 +++---
 .../tensors/api/AnalyticTensorAlgebra.kt      | 38 +++++++++--------
 .../tensors/api/LinearOpsTensorAlgebra.kt     |  2 +-
 .../kmath/tensors/api/TensorAlgebra.kt        |  2 +-
 .../api/TensorPartialDivisionAlgebra.kt       | 42 ++++++++++++++++++-
 5 files changed, 72 insertions(+), 25 deletions(-)

diff --git a/kmath-tensors/README.md b/kmath-tensors/README.md
index 3b82829f0..24d46cd16 100644
--- a/kmath-tensors/README.md
+++ b/kmath-tensors/README.md
@@ -1,12 +1,15 @@
 # Module kmath-tensors
 
-Common linear algebra operations on tensors.
-
- - [tensor algebra](src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic linear algebra operations on tensors (plus, dot, etc.)
- - [tensor algebra with broadcasting](src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt) : Basic linear algebra operations implemented with broadcasting.
- - [linear algebra operations](src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Advanced linear algebra operations like LU decomposition, SVD, etc.
+Common operations on tensors, the API consists of:
 
+ - [TensorAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic algebra operations on tensors (plus, dot, etc.)
+ - [TensorPartialDivisionAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt) : Emulates an algebra over a field
+ - [LinearOpsTensorAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Linear algebra operations including LU, QR, Cholesky LL and SVD decompositions
+ - [AnalyticTensorAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt) : Element-wise analytic operations
 
+The library offers a multiplatform implementation for this interface over the `Double`'s. As the highlight, the user can find:
+ - [BroadcastDoubleTensorAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt) : Basic algebra operations implemented with broadcasting.
+ - [DoubleLinearOpsTensorAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt) : Includes the power method for SVD and the spectrum of symmetric matrices.
 ## Artifact:
 
 The Maven coordinates of this project are `space.kscience:kmath-tensors:0.3.0-dev-7`.
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
index 315f39027..5c92c56c4 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
@@ -5,59 +5,63 @@
 
 package space.kscience.kmath.tensors.api
 
-
+/**
+ * Element-wise analytic operations on [TensorStructure].
+ *
+ * @param T the type of items closed under analytic functions in the tensors.
+ */
 public interface AnalyticTensorAlgebra<T> :
     TensorPartialDivisionAlgebra<T> {
 
-    //https://pytorch.org/docs/stable/generated/torch.exp.html
+    //For information: https://pytorch.org/docs/stable/generated/torch.exp.html
     public fun TensorStructure<T>.exp(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.log.html
+    //For information: https://pytorch.org/docs/stable/generated/torch.log.html
     public fun TensorStructure<T>.log(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.sqrt.html
+    //For information: https://pytorch.org/docs/stable/generated/torch.sqrt.html
     public fun TensorStructure<T>.sqrt(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.acos.html#torch.cos
+    //For information: https://pytorch.org/docs/stable/generated/torch.acos.html#torch.cos
     public fun TensorStructure<T>.cos(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.acos.html#torch.acos
+    //For information: https://pytorch.org/docs/stable/generated/torch.acos.html#torch.acos
     public fun TensorStructure<T>.acos(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.cosh
+    //For information: https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.cosh
     public fun TensorStructure<T>.cosh(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.acosh
+    //For information: https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.acosh
     public fun TensorStructure<T>.acosh(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sin
+    //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sin
     public fun TensorStructure<T>.sin(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asin
+    //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asin
     public fun TensorStructure<T>.asin(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sinh
+    //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sinh
     public fun TensorStructure<T>.sinh(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asinh
+    //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asinh
     public fun TensorStructure<T>.asinh(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.atan.html#torch.tan
+    //For information: https://pytorch.org/docs/stable/generated/torch.atan.html#torch.tan
     public fun TensorStructure<T>.tan(): TensorStructure<T>
 
     //https://pytorch.org/docs/stable/generated/torch.atan.html#torch.atan
     public fun TensorStructure<T>.atan(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.tanh
+    //For information: https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.tanh
     public fun TensorStructure<T>.tanh(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.atanh
+    //For information: https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.atanh
     public fun TensorStructure<T>.atanh(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.ceil.html#torch.ceil
+    //For information: https://pytorch.org/docs/stable/generated/torch.ceil.html#torch.ceil
     public fun TensorStructure<T>.ceil(): TensorStructure<T>
 
-    //https://pytorch.org/docs/stable/generated/torch.floor.html#torch.floor
+    //For information: https://pytorch.org/docs/stable/generated/torch.floor.html#torch.floor
     public fun TensorStructure<T>.floor(): TensorStructure<T>
 
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
index aa10ae49b..7a19c5d5a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
@@ -8,7 +8,7 @@ package space.kscience.kmath.tensors.api
 /**
  * Common linear algebra operations. Operates on [TensorStructure].
  *
- * @param T the type of items in the tensors.
+ * @param T the type of items closed under division in the tensors.
  */
 public interface LinearOpsTensorAlgebra<T> :
     TensorPartialDivisionAlgebra<T> {
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index 62d4a1b89..338064231 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -6,7 +6,7 @@
 package space.kscience.kmath.tensors.api
 
 /**
- * Basic linear algebra operations on [TensorStructure].
+ * Algebra over a ring on [TensorStructure].
  * For more information: https://proofwiki.org/wiki/Definition:Algebra_over_Ring
  *
  * @param T the type of items in the tensors.
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
index 4dab3dd07..eccc6fa0d 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
@@ -5,12 +5,52 @@
 
 package space.kscience.kmath.tensors.api
 
-// https://proofwiki.org/wiki/Definition:Division_Algebra
+/**
+ * Algebra over a field with partial division on [TensorStructure].
+ * For more information: https://proofwiki.org/wiki/Definition:Division_Algebra
+ *
+ * @param T the type of items closed under division in the tensors.
+ */
 public interface TensorPartialDivisionAlgebra<T> :
     TensorAlgebra<T> {
+
+    /**
+     * Each element of the tensor [other] is divided by this value.
+     * The resulting tensor is returned.
+     *
+     * @param other tensor to divide by.
+     * @return the division of this value by the tensor [other].
+     */
     public operator fun T.div(other: TensorStructure<T>): TensorStructure<T>
+
+    /**
+     * Divide by the scalar [value] each element of this tensor returns a new resulting tensor.
+     *
+     * @param value the number to divide by each element of this tensor.
+     * @return the division of this tensor by the [value].
+     */
     public operator fun TensorStructure<T>.div(value: T): TensorStructure<T>
+
+    /**
+     * Each element of the tensor [other] is divided by each element of this tensor.
+     * The resulting tensor is returned.
+     *
+     * @param other tensor to be divided by.
+     * @return the division of this tensor by [other].
+     */
     public operator fun TensorStructure<T>.div(other: TensorStructure<T>): TensorStructure<T>
+
+    /**
+     * Divides by the scalar [value] each element of this tensor.
+     *
+     * @param value the number to divide by each element of this tensor.
+     */
     public operator fun TensorStructure<T>.divAssign(value: T)
+
+    /**
+     * Each element of this tensor is divided by each element of the [other] tensor.
+     *
+     * @param other tensor to be divide by.
+     */
     public operator fun TensorStructure<T>.divAssign(other: TensorStructure<T>)
 }

From cba62a9468c84bb476dbf905bd44c4df90d9b735 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Thu, 29 Apr 2021 18:48:41 +0100
Subject: [PATCH 128/178] English typo

---
 kmath-tensors/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/kmath-tensors/README.md b/kmath-tensors/README.md
index 24d46cd16..a81b7277c 100644
--- a/kmath-tensors/README.md
+++ b/kmath-tensors/README.md
@@ -7,9 +7,9 @@ Common operations on tensors, the API consists of:
  - [LinearOpsTensorAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Linear algebra operations including LU, QR, Cholesky LL and SVD decompositions
  - [AnalyticTensorAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt) : Element-wise analytic operations
 
-The library offers a multiplatform implementation for this interface over the `Double`'s. As the highlight, the user can find:
+The library offers a multiplatform implementation for this interface over the `Double`'s. As a highlight, the user can find:
  - [BroadcastDoubleTensorAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt) : Basic algebra operations implemented with broadcasting.
- - [DoubleLinearOpsTensorAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt) : Includes the power method for SVD and the spectrum of symmetric matrices.
+ - [DoubleLinearOpsTensorAlgebra](src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt) : Contains the power method for SVD and the spectrum of symmetric matrices.
 ## Artifact:
 
 The Maven coordinates of this project are `space.kscience:kmath-tensors:0.3.0-dev-7`.

From 09f0a2879e50c364ee969930576af8e453e32748 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 30 Apr 2021 11:08:22 +0300
Subject: [PATCH 129/178] refactor lu + docs

---
 .../tensors/LinearSystemSolvingWithLUP.kt     |  3 +--
 .../tensors/api/LinearOpsTensorAlgebra.kt     | 22 +++++++------------
 .../algebras/DoubleLinearOpsTensorAlgebra.kt  | 17 ++++++++++----
 .../core/TestDoubleLinearOpsAlgebra.kt        |  4 +---
 4 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
index 526b6781f..38d8c1437 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
@@ -42,8 +42,7 @@ fun main () {
         // solve `Ax = b` system using LUP decomposition
 
         // get P, L, U such that PA = LU
-        val (lu, pivots) = a.lu()
-        val (p, l, u) = luPivot(lu, pivots)
+        val (p, l, u) = a.lu()
 
         // check that P is permutation matrix
         println("P:\n$p")
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
index 7a19c5d5a..bcbb52a1b 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
@@ -60,23 +60,17 @@ public interface LinearOpsTensorAlgebra<T> :
     public fun TensorStructure<T>.qr(): Pair<TensorStructure<T>, TensorStructure<T>>
 
     /**
-     * TODO('Andrew')
-     * For more information: https://pytorch.org/docs/stable/generated/torch.lu.html
+     * LUP decomposition
      *
-     * @return ...
-     */
-    public fun TensorStructure<T>.lu(): Pair<TensorStructure<T>, TensorStructure<Int>>
-
-    /**
-     * TODO('Andrew')
-     * For more information: https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
+     * Computes the LUP decomposition of a matrix or a batch of matrices.
+     * Given a tensor `input`, return tensors (P, L, U) satisfying ``P * input = L * U``,
+     * with `P` being a permutation matrix or batch of matrices,
+     * `L` being a lower triangular matrix or batch of matrices,
+     * `U` being an upper triangular matrix or batch of matrices.
      *
-     * @param luTensor ...
-     * @param pivotsTensor ...
-     * @return ...
+     * * @return triple of P, L and U tensors
      */
-    public fun luPivot(luTensor: TensorStructure<T>, pivotsTensor: TensorStructure<Int>):
-            Triple<TensorStructure<T>, TensorStructure<T>, TensorStructure<T>>
+    public fun TensorStructure<T>.lu(): Triple<TensorStructure<T>, TensorStructure<T>, TensorStructure<T>>
 
     /**
      * Singular Value Decomposition.
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
index 62629f3db..97eed289a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
@@ -29,13 +29,13 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun TensorStructure<Double>.det(): DoubleTensor = detLU(1e-9)
 
-    public fun TensorStructure<Double>.lu(epsilon: Double): Pair<DoubleTensor, IntTensor> =
+    public fun TensorStructure<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
         computeLU(tensor, epsilon) ?:
         throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
 
-    override fun TensorStructure<Double>.lu(): Pair<DoubleTensor, IntTensor> = lu(1e-9)
+    public fun TensorStructure<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
 
-    override fun luPivot(
+    public fun luPivot(
         luTensor: TensorStructure<Double>,
         pivotsTensor: TensorStructure<Int>
     ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
@@ -156,7 +156,7 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     public fun TensorStructure<Double>.invLU(epsilon: Double = 1e-9): DoubleTensor {
-        val (luTensor, pivotsTensor) = lu(epsilon)
+        val (luTensor, pivotsTensor) = luFactor(epsilon)
         val invTensor = luTensor.zeroesLike()
 
         val seq = luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
@@ -167,6 +167,15 @@ public class DoubleLinearOpsTensorAlgebra :
 
         return invTensor
     }
+
+    public fun TensorStructure<Double>.lu(epsilon: Double = 1e-9): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+        val (lu, pivots) = this.luFactor(epsilon)
+        return luPivot(lu, pivots)
+    }
+
+    override fun TensorStructure<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
+
+
 }
 
 public inline fun <R> DoubleLinearOpsTensorAlgebra(block: DoubleLinearOpsTensorAlgebra.() -> R): R =
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 6120f0e4a..1f7e955d4 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -124,9 +124,7 @@ class TestDoubleLinearOpsTensorAlgebra {
         )
         val tensor = fromArray(shape, buffer)
 
-        val (lu, pivots) = tensor.lu()
-
-        val (p, l, u) = luPivot(lu, pivots)
+        val (p, l, u) = tensor.lu()
 
         assertTrue { p.shape contentEquals shape }
         assertTrue { l.shape contentEquals shape }

From b546f3f78bf019c65b6bf4e1dda7cf521da46884 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 30 Apr 2021 09:19:30 +0100
Subject: [PATCH 130/178] Merge PR

---
 .../kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
index 97eed289a..03f383728 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
@@ -116,7 +116,7 @@ public class DoubleLinearOpsTensorAlgebra :
     override fun TensorStructure<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> =
         symEig(epsilon = 1e-15)
 
-    //http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
+    //For information: http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
     public fun TensorStructure<Double>.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
         checkSymmetric(tensor, epsilon)
         val (u, s, v) = tensor.svd(epsilon)

From bd068b2c1438e912a1a0b96bab72ffe55478374e Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 30 Apr 2021 09:54:25 +0100
Subject: [PATCH 131/178] Smaller SVD test

---
 .../kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 1f7e955d4..b2ef6b4c4 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -163,7 +163,7 @@ class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
-        val tensor = randNormal(intArrayOf(1, 15, 4, 7, 5, 3), 0)
+        val tensor = randNormal(intArrayOf(2, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
         val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
         assertTrue(tensor.eq(tensorSVD))
@@ -171,7 +171,7 @@ class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra {
-        val tensor = randNormal(shape = intArrayOf(5, 3, 3), 0)
+        val tensor = randNormal(shape = intArrayOf(2, 3, 3), 0)
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
         val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose())

From e5e62bc544143ef4f341257c7d143a5b8a813656 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 30 Apr 2021 14:38:03 +0100
Subject: [PATCH 132/178] Extending from Algebra

---
 .../kmath/tensors/api/TensorAlgebra.kt        |  4 ++-
 .../algebras/BroadcastDoubleTensorAlgebra.kt  |  3 +-
 .../algebras/DoubleAnalyticTensorAlgebra.kt   | 10 ++-----
 .../algebras/DoubleLinearOpsTensorAlgebra.kt  | 12 +++-----
 .../core/algebras/DoubleTensorAlgebra.kt      |  5 +---
 .../kmath/tensors/core/broadcastUtils.kt      |  4 ---
 .../kscience/kmath/tensors/core/linUtils.kt   |  3 +-
 .../kmath/tensors/core/TestBroadcasting.kt    | 16 ++++++-----
 .../core/TestDoubleAnalyticTensorAlgebra.kt   |  5 ++--
 .../core/TestDoubleLinearOpsAlgebra.kt        | 28 +++++++++----------
 .../kmath/tensors/core/TestDoubleTensor.kt    |  9 +++---
 .../tensors/core/TestDoubleTensorAlgebra.kt   | 21 +++++++-------
 12 files changed, 56 insertions(+), 64 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index 338064231..96d6985d8 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -5,13 +5,15 @@
 
 package space.kscience.kmath.tensors.api
 
+import space.kscience.kmath.operations.Algebra
+
 /**
  * Algebra over a ring on [TensorStructure].
  * For more information: https://proofwiki.org/wiki/Definition:Algebra_over_Ring
  *
  * @param T the type of items in the tensors.
  */
-public interface TensorAlgebra<T> {
+public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
 
     /**
      * Returns a single tensor value of unit dimension. The tensor shape must be equal to [1].
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
index a49d9ab29..fa64092d6 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
@@ -14,7 +14,7 @@ import space.kscience.kmath.tensors.core.broadcastTo
  * Basic linear algebra operations implemented with broadcasting.
  * For more information: https://pytorch.org/docs/stable/notes/broadcasting.html
  */
-public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
+public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
         val broadcast = broadcastTensors(tensor, other.tensor)
@@ -89,5 +89,4 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
                 newOther.buffer.array()[tensor.bufferStart + i]
         }
     }
-
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
index 7d1cceb15..4a942df84 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
@@ -11,10 +11,9 @@ import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.tensor
 import kotlin.math.*
 
-public class DoubleAnalyticTensorAlgebra:
+public object DoubleAnalyticTensorAlgebra :
     AnalyticTensorAlgebra<Double>,
-        DoubleTensorAlgebra()
-{
+    DoubleTensorAlgebra() {
     override fun TensorStructure<Double>.exp(): DoubleTensor = tensor.map(::exp)
 
     override fun TensorStructure<Double>.log(): DoubleTensor = tensor.map(::ln)
@@ -49,7 +48,4 @@ public class DoubleAnalyticTensorAlgebra:
 
     override fun TensorStructure<Double>.floor(): DoubleTensor = tensor.map(::floor)
 
-}
-
-public inline fun <R> DoubleAnalyticTensorAlgebra(block: DoubleAnalyticTensorAlgebra.() -> R): R =
-    DoubleAnalyticTensorAlgebra().block()
\ No newline at end of file
+}
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
index 03f383728..975755656 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
@@ -21,7 +21,7 @@ import space.kscience.kmath.tensors.core.pivInit
 import kotlin.math.min
 
 
-public class DoubleLinearOpsTensorAlgebra :
+public object DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double>,
     DoubleTensorAlgebra() {
 
@@ -30,8 +30,8 @@ public class DoubleLinearOpsTensorAlgebra :
     override fun TensorStructure<Double>.det(): DoubleTensor = detLU(1e-9)
 
     public fun TensorStructure<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
-        computeLU(tensor, epsilon) ?:
-        throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
+        computeLU(tensor, epsilon)
+            ?: throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
 
     public fun TensorStructure<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
 
@@ -175,8 +175,4 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun TensorStructure<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
 
-
-}
-
-public inline fun <R> DoubleLinearOpsTensorAlgebra(block: DoubleLinearOpsTensorAlgebra.() -> R): R =
-    DoubleLinearOpsTensorAlgebra().block()
\ No newline at end of file
+}
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index 7414a4469..8729358cc 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -23,6 +23,7 @@ import kotlin.math.abs
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
+    public companion object : DoubleTensorAlgebra()
 
     override fun TensorStructure<Double>.value(): Double {
         check(tensor.shape contentEquals intArrayOf(1)) {
@@ -395,7 +396,3 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
 }
-
-
-public inline fun <R> DoubleTensorAlgebra(block: DoubleTensorAlgebra.() -> R): R =
-    DoubleTensorAlgebra().block()
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
index 4378e9ac9..71ccbf97b 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
@@ -1,11 +1,7 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
 import kotlin.math.max
 
-public inline fun <R> BroadcastDoubleTensorAlgebra(block: BroadcastDoubleTensorAlgebra.() -> R): R =
-    BroadcastDoubleTensorAlgebra().block()
-
 internal inline fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: DoubleTensor, linearSize: Int) {
     for (linearIndex in 0 until linearSize) {
         val totalMultiIndex = resTensor.linearStructure.index(linearIndex)
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
index 3a130c71e..c7a730018 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
@@ -4,6 +4,7 @@ import space.kscience.kmath.nd.MutableStructure1D
 import space.kscience.kmath.nd.MutableStructure2D
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import kotlin.math.abs
@@ -251,7 +252,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.qrHelper(
                 }
             }
         }
-        r[j, j] = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
+        r[j, j] = DoubleAnalyticTensorAlgebra.invoke { (v dot v).sqrt().value() }
         for (i in 0 until n) {
             qM[i, j] = vv[i] / r[j, j]
         }
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
index d677f6966..207dd1c98 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -1,13 +1,15 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
-class TestBroadcasting {
+internal class TestBroadcasting {
 
     @Test
-    fun broadcastShapes() = DoubleTensorAlgebra {
+    fun broadcastShapes() = DoubleTensorAlgebra.invoke {
         assertTrue(
             broadcastShapes(
                 intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
@@ -22,7 +24,7 @@ class TestBroadcasting {
     }
 
     @Test
-    fun broadcastTo() = DoubleTensorAlgebra {
+    fun broadcastTo() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
 
@@ -32,7 +34,7 @@ class TestBroadcasting {
     }
 
     @Test
-    fun broadcastTensors() = DoubleTensorAlgebra {
+    fun broadcastTensors() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
@@ -49,7 +51,7 @@ class TestBroadcasting {
     }
 
     @Test
-    fun broadcastOuterTensors() = DoubleTensorAlgebra {
+    fun broadcastOuterTensors() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
@@ -66,7 +68,7 @@ class TestBroadcasting {
     }
 
     @Test
-    fun broadcastOuterTensorsShapes() = DoubleTensorAlgebra {
+    fun broadcastOuterTensorsShapes() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 1, 3, 2, 3), DoubleArray(2 * 1 * 3 * 2 * 3) {0.0})
         val tensor2 = fromArray(intArrayOf(4, 2, 5, 1, 3, 3), DoubleArray(4 * 2 * 5 * 1 * 3 * 3) {0.0})
         val tensor3 = fromArray(intArrayOf(1, 1), doubleArrayOf(500.0))
@@ -79,7 +81,7 @@ class TestBroadcasting {
     }
 
     @Test
-    fun minusTensor() = BroadcastDoubleTensorAlgebra {
+    fun minusTensor() = BroadcastDoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
index 060bc1607..3b3e22acc 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -1,12 +1,13 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import kotlin.math.abs
 import kotlin.math.exp
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
-class TestDoubleAnalyticTensorAlgebra {
+internal class TestDoubleAnalyticTensorAlgebra {
 
     val shape = intArrayOf(2, 1, 3, 2)
     val buffer = doubleArrayOf(27.1, 20.0, 19.84, 23.123, 0.0, 1.0, 3.23, 133.7, 25.3, 100.3, 11.0, 12.012)
@@ -26,7 +27,7 @@ class TestDoubleAnalyticTensorAlgebra {
     }
 
     @Test
-    fun testExp() = DoubleAnalyticTensorAlgebra {
+    fun testExp() = DoubleAnalyticTensorAlgebra.invoke {
         tensor.exp().let {
             assertTrue { shape contentEquals it.shape }
             assertTrue { buffer.fmap(::exp).epsEqual(it.buffer.array())}
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index b2ef6b4c4..aa68340d4 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -1,15 +1,16 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import kotlin.math.abs
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
 
-class TestDoubleLinearOpsTensorAlgebra {
+internal class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
-    fun testDetLU() = DoubleLinearOpsTensorAlgebra {
+    fun testDetLU() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor = fromArray(
             intArrayOf(2, 2, 2),
             doubleArrayOf(
@@ -34,7 +35,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testDet() = DoubleLinearOpsTensorAlgebra {
+    fun testDet() = DoubleLinearOpsTensorAlgebra.invoke {
         val expectedValue = 0.019827417
         val m = fromArray(
             intArrayOf(3, 3), doubleArrayOf(
@@ -48,7 +49,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testDetSingle() = DoubleLinearOpsTensorAlgebra {
+    fun testDetSingle() = DoubleLinearOpsTensorAlgebra.invoke {
         val expectedValue = 48.151623
         val m = fromArray(
             intArrayOf(1, 1), doubleArrayOf(
@@ -60,7 +61,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testInvLU() = DoubleLinearOpsTensorAlgebra {
+    fun testInvLU() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor = fromArray(
             intArrayOf(2, 2, 2),
             doubleArrayOf(
@@ -85,14 +86,14 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testScalarProduct() = DoubleLinearOpsTensorAlgebra {
+    fun testScalarProduct() = DoubleLinearOpsTensorAlgebra.invoke {
         val a = fromArray(intArrayOf(3), doubleArrayOf(1.8, 2.5, 6.8))
         val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4))
         assertEquals(a.dot(b).value(), 59.92)
     }
 
     @Test
-    fun testQR() = DoubleLinearOpsTensorAlgebra {
+    fun testQR() = DoubleLinearOpsTensorAlgebra.invoke {
         val shape = intArrayOf(2, 2, 2)
         val buffer = doubleArrayOf(
             1.0, 3.0,
@@ -110,11 +111,10 @@ class TestDoubleLinearOpsTensorAlgebra {
 
         assertTrue((q dot r).eq(tensor))
 
-        //todo check orthogonality/upper triang.
     }
 
     @Test
-    fun testLU() = DoubleLinearOpsTensorAlgebra {
+    fun testLU() = DoubleLinearOpsTensorAlgebra.invoke {
         val shape = intArrayOf(2, 2, 2)
         val buffer = doubleArrayOf(
             1.0, 3.0,
@@ -134,7 +134,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testCholesky() = DoubleLinearOpsTensorAlgebra {
+    fun testCholesky() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor = randNormal(intArrayOf(2, 5, 5), 0)
         val sigma = (tensor dot tensor.transpose()) + diagonalEmbedding(
             fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
@@ -145,7 +145,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testSVD1D() = DoubleLinearOpsTensorAlgebra {
+    fun testSVD1D() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
 
         val res = svd1d(tensor2)
@@ -156,13 +156,13 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testSVD() = DoubleLinearOpsTensorAlgebra {
+    fun testSVD() = DoubleLinearOpsTensorAlgebra.invoke{
         testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
         testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
     }
 
     @Test
-    fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
+    fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor = randNormal(intArrayOf(2, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
         val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
@@ -170,7 +170,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra {
+    fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor = randNormal(shape = intArrayOf(2, 3, 3), 0)
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 7103eafaa..20acc0901 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -4,6 +4,7 @@ import space.kscience.kmath.nd.DefaultStrides
 import space.kscience.kmath.nd.MutableBufferND
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.structures.DoubleBuffer
 import space.kscience.kmath.structures.asMutableBuffer
 import space.kscience.kmath.structures.toDoubleArray
@@ -12,17 +13,17 @@ import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
 
-class TestDoubleTensor {
+internal class TestDoubleTensor {
 
     @Test
-    fun valueTest() = DoubleTensorAlgebra {
+    fun valueTest() = DoubleTensorAlgebra.invoke {
         val value = 12.5
         val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
     }
 
     @Test
-    fun stridesTest() = DoubleTensorAlgebra {
+    fun stridesTest() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
         assertEquals(tensor[intArrayOf(0, 1)], 5.8)
         assertTrue(
@@ -31,7 +32,7 @@ class TestDoubleTensor {
     }
 
     @Test
-    fun getTest() = DoubleTensorAlgebra {
+    fun getTest() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(1, 2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
         val matrix = tensor[0].as2D()
         assertEquals(matrix[0, 1], 5.8)
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index 1333a7a1f..115016974 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -1,29 +1,30 @@
 package space.kscience.kmath.tensors.core
 
 
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
 import kotlin.test.assertFalse
 import kotlin.test.assertTrue
 
-class TestDoubleTensorAlgebra {
+internal class TestDoubleTensorAlgebra {
 
     @Test
-    fun doublePlus() = DoubleTensorAlgebra {
+    fun doublePlus() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
         assertTrue(res.buffer.array() contentEquals doubleArrayOf(11.0, 12.0))
     }
 
     @Test
-    fun doubleDiv() = DoubleTensorAlgebra {
+    fun doubleDiv() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(2.0, 4.0))
         val res = 2.0/tensor
         assertTrue(res.buffer.array() contentEquals doubleArrayOf(1.0, 0.5))
     }
 
     @Test
-    fun divDouble() = DoubleTensorAlgebra {
+    fun divDouble() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(10.0, 5.0))
         val res = tensor / 2.5
         assertTrue(res.buffer.array() contentEquals doubleArrayOf(4.0, 2.0))
@@ -39,7 +40,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun transpose3x2() = DoubleTensorAlgebra {
+    fun transpose3x2() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res = tensor.transpose(1, 0)
 
@@ -48,7 +49,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun transpose1x2x3() = DoubleTensorAlgebra {
+    fun transpose1x2x3() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res01 = tensor.transpose(0, 1)
         val res02 = tensor.transpose(-3, 2)
@@ -64,7 +65,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun linearStructure() = DoubleTensorAlgebra {
+    fun linearStructure() = DoubleTensorAlgebra.invoke {
         val shape = intArrayOf(3)
         val tensorA = full(value = -4.5, shape = shape)
         val tensorB = full(value = 10.9, shape = shape)
@@ -96,7 +97,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun dot() = DoubleTensorAlgebra {
+    fun dot() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor11 = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
@@ -132,7 +133,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun diagonalEmbedding() = DoubleTensorAlgebra {
+    fun diagonalEmbedding() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor3 = zeros(intArrayOf(2, 3, 4, 5))
@@ -165,7 +166,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun testEq() = DoubleTensorAlgebra {
+    fun testEq() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor3 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0))

From 6be5caa93f4086e222677a55d15edaca707b16f0 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 30 Apr 2021 14:44:42 +0100
Subject: [PATCH 133/178] No secondary constructors

---
 .../kmath/tensors/core/BufferedTensor.kt      | 25 +++----
 .../algebras/BroadcastDoubleTensorAlgebra.kt  | 28 ++++----
 .../algebras/DoubleLinearOpsTensorAlgebra.kt  |  2 +-
 .../core/algebras/DoubleTensorAlgebra.kt      | 68 +++++++++----------
 .../kmath/tensors/core/broadcastUtils.kt      | 10 +--
 .../kscience/kmath/tensors/core/linUtils.kt   | 14 ++--
 .../kmath/tensors/core/TestBroadcasting.kt    | 20 +++---
 .../core/TestDoubleAnalyticTensorAlgebra.kt   |  2 +-
 .../core/TestDoubleLinearOpsAlgebra.kt        |  4 +-
 .../kmath/tensors/core/TestDoubleTensor.kt    |  5 +-
 .../tensors/core/TestDoubleTensorAlgebra.kt   | 34 +++++-----
 11 files changed, 103 insertions(+), 109 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 6e1cab11a..fae59804d 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -8,7 +8,7 @@ import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
 
 public open class BufferedTensor<T>(
     override val shape: IntArray,
-    internal val buffer: MutableBuffer<T>,
+    internal val mutableBuffer: MutableBuffer<T>,
     internal val bufferStart: Int
 ) : TensorStructure<T> {
     public val linearStructure: TensorLinearStructure
@@ -17,10 +17,10 @@ public open class BufferedTensor<T>(
     public val numElements: Int
         get() = linearStructure.size
 
-    override fun get(index: IntArray): T = buffer[bufferStart + linearStructure.offset(index)]
+    override fun get(index: IntArray): T = mutableBuffer[bufferStart + linearStructure.offset(index)]
 
     override fun set(index: IntArray, value: T) {
-        buffer[bufferStart + linearStructure.offset(index)] = value
+        mutableBuffer[bufferStart + linearStructure.offset(index)] = value
     }
 
     override fun elements(): Sequence<Pair<IntArray, T>> = linearStructure.indices().map {
@@ -37,33 +37,28 @@ public class IntTensor internal constructor(
     shape: IntArray,
     buffer: IntArray,
     offset: Int = 0
-) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset) {
-    internal constructor(bufferedTensor: BufferedTensor<Int>) :
-            this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
-}
+) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset)
 
 public class DoubleTensor internal constructor(
     shape: IntArray,
     buffer: DoubleArray,
     offset: Int = 0
 ) : BufferedTensor<Double>(shape, DoubleBuffer(buffer), offset) {
-    internal constructor(bufferedTensor: BufferedTensor<Double>) :
-            this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
-
     override fun toString(): String = toPrettyString()
-
 }
 
-internal inline fun BufferedTensor<Int>.asTensor(): IntTensor = IntTensor(this)
-internal inline fun BufferedTensor<Double>.asTensor(): DoubleTensor = DoubleTensor(this)
+internal fun BufferedTensor<Int>.asTensor(): IntTensor =
+    IntTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
+internal fun BufferedTensor<Double>.asTensor(): DoubleTensor =
+    DoubleTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
 
-internal inline fun <T> TensorStructure<T>.copyToBufferedTensor(): BufferedTensor<T> =
+internal fun <T> TensorStructure<T>.copyToBufferedTensor(): BufferedTensor<T> =
     BufferedTensor(
         this.shape,
         TensorLinearStructure(this.shape).indices().map(this::get).toMutableList().asMutableBuffer(), 0
     )
 
-internal inline fun <T> TensorStructure<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
+internal fun <T> TensorStructure<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
     is BufferedTensor<T> -> this
     is MutableBufferND<T> -> if (this.strides.strides.toIntArray() contentEquals TensorLinearStructure(this.shape).strides)
         BufferedTensor(this.shape, this.mutableBuffer, 0) else this.copyToBufferedTensor()
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
index fa64092d6..350d939af 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
@@ -21,7 +21,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[i] + newOther.buffer.array()[i]
+            newThis.mutableBuffer.array()[i] + newOther.mutableBuffer.array()[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
@@ -29,8 +29,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
     override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
         for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] +=
-                newOther.buffer.array()[tensor.bufferStart + i]
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] +=
+                newOther.mutableBuffer.array()[tensor.bufferStart + i]
         }
     }
 
@@ -39,7 +39,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[i] - newOther.buffer.array()[i]
+            newThis.mutableBuffer.array()[i] - newOther.mutableBuffer.array()[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
@@ -47,8 +47,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
     override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
         for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] -=
-                newOther.buffer.array()[tensor.bufferStart + i]
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] -=
+                newOther.mutableBuffer.array()[tensor.bufferStart + i]
         }
     }
 
@@ -57,8 +57,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[newThis.bufferStart + i] *
-                    newOther.buffer.array()[newOther.bufferStart + i]
+            newThis.mutableBuffer.array()[newThis.bufferStart + i] *
+                    newOther.mutableBuffer.array()[newOther.bufferStart + i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
@@ -66,8 +66,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
     override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
         for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] *=
-                newOther.buffer.array()[tensor.bufferStart + i]
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] *=
+                newOther.mutableBuffer.array()[tensor.bufferStart + i]
         }
     }
 
@@ -76,8 +76,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[newOther.bufferStart + i] /
-                    newOther.buffer.array()[newOther.bufferStart + i]
+            newThis.mutableBuffer.array()[newOther.bufferStart + i] /
+                    newOther.mutableBuffer.array()[newOther.bufferStart + i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
@@ -85,8 +85,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
     override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
         for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] /=
-                newOther.buffer.array()[tensor.bufferStart + i]
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] /=
+                newOther.mutableBuffer.array()[tensor.bufferStart + i]
         }
     }
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
index 975755656..ab73b25b1 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
@@ -106,7 +106,7 @@ public object DoubleLinearOpsTensorAlgebra :
             val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
             val curMatrix = DoubleTensor(
                 matrix.shape,
-                matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + matrixSize).toDoubleArray()
+                matrix.mutableBuffer.array().slice(matrix.bufferStart until matrix.bufferStart + matrixSize).toDoubleArray()
             )
             svdHelper(curMatrix, USV, m, n, epsilon)
         }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index 8729358cc..f428b9d2e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -29,7 +29,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         check(tensor.shape contentEquals intArrayOf(1)) {
             "Inconsistent value for tensor of shape ${shape.toList()}"
         }
-        return tensor.buffer.array()[tensor.bufferStart]
+        return tensor.mutableBuffer.array()[tensor.bufferStart]
     }
 
     public fun fromArray(shape: IntArray, buffer: DoubleArray): DoubleTensor {
@@ -43,7 +43,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         val lastShape = tensor.shape.drop(1).toIntArray()
         val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
         val newStart = newShape.reduce(Int::times) * i + tensor.bufferStart
-        return DoubleTensor(newShape, tensor.buffer.array(), newStart)
+        return DoubleTensor(newShape, tensor.mutableBuffer.array(), newStart)
     }
 
     public fun full(value: Double, shape: IntArray): DoubleTensor {
@@ -77,12 +77,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     }
 
     public fun TensorStructure<Double>.copy(): DoubleTensor {
-        return DoubleTensor(tensor.shape, tensor.buffer.array().copyOf(), tensor.bufferStart)
+        return DoubleTensor(tensor.shape, tensor.mutableBuffer.array().copyOf(), tensor.bufferStart)
     }
 
     override fun Double.plus(other: TensorStructure<Double>): DoubleTensor {
         val resBuffer = DoubleArray(other.tensor.numElements) { i ->
-            other.tensor.buffer.array()[other.tensor.bufferStart + i] + this
+            other.tensor.mutableBuffer.array()[other.tensor.bufferStart + i] + this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
@@ -92,35 +92,35 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
         checkShapesCompatible(tensor, other.tensor)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.buffer.array()[i] + other.tensor.buffer.array()[i]
+            tensor.mutableBuffer.array()[i] + other.tensor.mutableBuffer.array()[i]
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
     override fun TensorStructure<Double>.plusAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
-            tensor.buffer.array()[tensor.bufferStart + i] += value
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] += value
         }
     }
 
     override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
         checkShapesCompatible(tensor, other.tensor)
         for (i in 0 until tensor.numElements) {
-            tensor.buffer.array()[tensor.bufferStart + i] +=
-                other.tensor.buffer.array()[tensor.bufferStart + i]
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] +=
+                other.tensor.mutableBuffer.array()[tensor.bufferStart + i]
         }
     }
 
     override fun Double.minus(other: TensorStructure<Double>): DoubleTensor {
         val resBuffer = DoubleArray(other.tensor.numElements) { i ->
-            this - other.tensor.buffer.array()[other.tensor.bufferStart + i]
+            this - other.tensor.mutableBuffer.array()[other.tensor.bufferStart + i]
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
     override fun TensorStructure<Double>.minus(value: Double): DoubleTensor {
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.buffer.array()[tensor.bufferStart + i] - value
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] - value
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
@@ -128,28 +128,28 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
         checkShapesCompatible(tensor, other)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.buffer.array()[i] - other.tensor.buffer.array()[i]
+            tensor.mutableBuffer.array()[i] - other.tensor.mutableBuffer.array()[i]
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
     override fun TensorStructure<Double>.minusAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
-            tensor.buffer.array()[tensor.bufferStart + i] -= value
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] -= value
         }
     }
 
     override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
         checkShapesCompatible(tensor, other)
         for (i in 0 until tensor.numElements) {
-            tensor.buffer.array()[tensor.bufferStart + i] -=
-                other.tensor.buffer.array()[tensor.bufferStart + i]
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] -=
+                other.tensor.mutableBuffer.array()[tensor.bufferStart + i]
         }
     }
 
     override fun Double.times(other: TensorStructure<Double>): DoubleTensor {
         val resBuffer = DoubleArray(other.tensor.numElements) { i ->
-            other.tensor.buffer.array()[other.tensor.bufferStart + i] * this
+            other.tensor.mutableBuffer.array()[other.tensor.bufferStart + i] * this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
@@ -159,36 +159,36 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
         checkShapesCompatible(tensor, other)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.buffer.array()[tensor.bufferStart + i] *
-                    other.tensor.buffer.array()[other.tensor.bufferStart + i]
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] *
+                    other.tensor.mutableBuffer.array()[other.tensor.bufferStart + i]
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
     override fun TensorStructure<Double>.timesAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
-            tensor.buffer.array()[tensor.bufferStart + i] *= value
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] *= value
         }
     }
 
     override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
         checkShapesCompatible(tensor, other)
         for (i in 0 until tensor.numElements) {
-            tensor.buffer.array()[tensor.bufferStart + i] *=
-                other.tensor.buffer.array()[tensor.bufferStart + i]
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] *=
+                other.tensor.mutableBuffer.array()[tensor.bufferStart + i]
         }
     }
 
     override fun Double.div(other: TensorStructure<Double>): DoubleTensor {
         val resBuffer = DoubleArray(other.tensor.numElements) { i ->
-            this / other.tensor.buffer.array()[other.tensor.bufferStart + i]
+            this / other.tensor.mutableBuffer.array()[other.tensor.bufferStart + i]
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
     override fun TensorStructure<Double>.div(value: Double): DoubleTensor {
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.buffer.array()[tensor.bufferStart + i] / value
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] / value
         }
         return DoubleTensor(shape, resBuffer)
     }
@@ -196,29 +196,29 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
         checkShapesCompatible(tensor, other)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.buffer.array()[other.tensor.bufferStart + i] /
-                    other.tensor.buffer.array()[other.tensor.bufferStart + i]
+            tensor.mutableBuffer.array()[other.tensor.bufferStart + i] /
+                    other.tensor.mutableBuffer.array()[other.tensor.bufferStart + i]
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
     override fun TensorStructure<Double>.divAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
-            tensor.buffer.array()[tensor.bufferStart + i] /= value
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] /= value
         }
     }
 
     override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
         checkShapesCompatible(tensor, other)
         for (i in 0 until tensor.numElements) {
-            tensor.buffer.array()[tensor.bufferStart + i] /=
-                other.tensor.buffer.array()[tensor.bufferStart + i]
+            tensor.mutableBuffer.array()[tensor.bufferStart + i] /=
+                other.tensor.mutableBuffer.array()[tensor.bufferStart + i]
         }
     }
 
     override fun TensorStructure<Double>.unaryMinus(): DoubleTensor {
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.buffer.array()[tensor.bufferStart + i].unaryMinus()
+            tensor.mutableBuffer.array()[tensor.bufferStart + i].unaryMinus()
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
@@ -241,8 +241,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
             newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
 
             val linearIndex = resTensor.linearStructure.offset(newMultiIndex)
-            resTensor.buffer.array()[linearIndex] =
-                tensor.buffer.array()[tensor.bufferStart + offset]
+            resTensor.mutableBuffer.array()[linearIndex] =
+                tensor.mutableBuffer.array()[tensor.bufferStart + offset]
         }
         return resTensor
     }
@@ -250,7 +250,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
     override fun TensorStructure<Double>.view(shape: IntArray): DoubleTensor {
         checkView(tensor, shape)
-        return DoubleTensor(shape, tensor.buffer.array(), tensor.bufferStart)
+        return DoubleTensor(shape, tensor.mutableBuffer.array(), tensor.bufferStart)
     }
 
     override fun TensorStructure<Double>.viewAs(other: TensorStructure<Double>): DoubleTensor {
@@ -259,7 +259,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
     override infix fun TensorStructure<Double>.dot(other: TensorStructure<Double>): DoubleTensor {
         if (tensor.shape.size == 1 && other.shape.size == 1) {
-            return DoubleTensor(intArrayOf(1), doubleArrayOf(tensor.times(other).tensor.buffer.array().sum()))
+            return DoubleTensor(intArrayOf(1), doubleArrayOf(tensor.times(other).tensor.mutableBuffer.array().sum()))
         }
 
         var newThis = tensor.copy()
@@ -361,7 +361,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     public fun TensorStructure<Double>.map(transform: (Double) -> Double): DoubleTensor {
         return DoubleTensor(
             tensor.shape,
-            tensor.buffer.array().map { transform(it) }.toDoubleArray(),
+            tensor.mutableBuffer.array().map { transform(it) }.toDoubleArray(),
             tensor.bufferStart
         )
     }
@@ -382,7 +382,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
             return false
         }
         for (i in 0 until n) {
-            if (!eqFunction(tensor.buffer[tensor.bufferStart + i], other.tensor.buffer[other.tensor.bufferStart + i])) {
+            if (!eqFunction(tensor.mutableBuffer[tensor.bufferStart + i], other.tensor.mutableBuffer[other.tensor.bufferStart + i])) {
                 return false
             }
         }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
index 71ccbf97b..2d4d6f0f7 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
@@ -18,8 +18,8 @@ internal inline fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: Doub
         }
 
         val curLinearIndex = tensor.linearStructure.offset(curMultiIndex)
-        resTensor.buffer.array()[linearIndex] =
-            tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
+        resTensor.mutableBuffer.array()[linearIndex] =
+            tensor.mutableBuffer.array()[tensor.bufferStart + curLinearIndex]
     }
 }
 
@@ -113,7 +113,7 @@ internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<Do
             var curMultiIndex = tensor.shape.sliceArray(0..tensor.shape.size - 3).copyOf()
             curMultiIndex = IntArray(totalMultiIndex.size - curMultiIndex.size) { 1 } + curMultiIndex
 
-            val newTensor = DoubleTensor(curMultiIndex + matrixShape, tensor.buffer.array())
+            val newTensor = DoubleTensor(curMultiIndex + matrixShape, tensor.mutableBuffer.array())
 
             for (i in curMultiIndex.indices) {
                 if (curMultiIndex[i] != 1) {
@@ -133,8 +133,8 @@ internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<Do
                             matrix.linearStructure.index(i)
                 )
 
-                resTensor.buffer.array()[resTensor.bufferStart + newLinearIndex] =
-                    newTensor.buffer.array()[newTensor.bufferStart + curLinearIndex]
+                resTensor.mutableBuffer.array()[resTensor.bufferStart + newLinearIndex] =
+                    newTensor.mutableBuffer.array()[newTensor.bufferStart + curLinearIndex]
             }
         }
         res += resTensor
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
index c7a730018..776a5e4d5 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
@@ -18,7 +18,7 @@ internal inline fun <T> BufferedTensor<T>.vectorSequence(): Sequence<BufferedTen
     val vectorOffset = shape[n - 1]
     val vectorShape = intArrayOf(shape.last())
     for (offset in 0 until numElements step vectorOffset) {
-        val vector = BufferedTensor(vectorShape, buffer, offset)
+        val vector = BufferedTensor(vectorShape, mutableBuffer, offset)
         yield(vector)
     }
 }
@@ -29,7 +29,7 @@ internal inline fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTen
     val matrixOffset = shape[n - 1] * shape[n - 2]
     val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
     for (offset in 0 until numElements step matrixOffset) {
-        val matrix = BufferedTensor(matrixShape, buffer, offset)
+        val matrix = BufferedTensor(matrixShape, mutableBuffer, offset)
         yield(matrix)
     }
 }
@@ -322,16 +322,16 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
     }
 
     val s = res.map { it.first }.toDoubleArray()
-    val uBuffer = res.map { it.second }.flatMap { it.buffer.array().toList() }.toDoubleArray()
-    val vBuffer = res.map { it.third }.flatMap { it.buffer.array().toList() }.toDoubleArray()
+    val uBuffer = res.map { it.second }.flatMap { it.mutableBuffer.array().toList() }.toDoubleArray()
+    val vBuffer = res.map { it.third }.flatMap { it.mutableBuffer.array().toList() }.toDoubleArray()
     for (i in uBuffer.indices) {
-        matrixU.buffer.array()[matrixU.bufferStart + i] = uBuffer[i]
+        matrixU.mutableBuffer.array()[matrixU.bufferStart + i] = uBuffer[i]
     }
     for (i in s.indices) {
-        matrixS.buffer.array()[matrixS.bufferStart + i] = s[i]
+        matrixS.mutableBuffer.array()[matrixS.bufferStart + i] = s[i]
     }
     for (i in vBuffer.indices) {
-        matrixV.buffer.array()[matrixV.bufferStart + i] = vBuffer[i]
+        matrixV.mutableBuffer.array()[matrixV.bufferStart + i] = vBuffer[i]
     }
 }
 
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
index 207dd1c98..1070a1115 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -30,7 +30,7 @@ internal class TestBroadcasting {
 
         val res = broadcastTo(tensor2, tensor1.shape)
         assertTrue(res.shape contentEquals intArrayOf(2, 3))
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+        assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
     }
 
     @Test
@@ -45,9 +45,9 @@ internal class TestBroadcasting {
         assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
         assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
 
-        assertTrue(res[0].buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res[1].buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
-        assertTrue(res[2].buffer.array() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
+        assertTrue(res[0].mutableBuffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res[1].mutableBuffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+        assertTrue(res[2].mutableBuffer.array() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
     }
 
     @Test
@@ -62,9 +62,9 @@ internal class TestBroadcasting {
         assertTrue(res[1].shape contentEquals intArrayOf(1, 1, 3))
         assertTrue(res[2].shape contentEquals intArrayOf(1, 1, 1))
 
-        assertTrue(res[0].buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res[1].buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0))
-        assertTrue(res[2].buffer.array() contentEquals doubleArrayOf(500.0))
+        assertTrue(res[0].mutableBuffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res[1].mutableBuffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0))
+        assertTrue(res[2].mutableBuffer.array() contentEquals doubleArrayOf(500.0))
     }
 
     @Test
@@ -91,16 +91,16 @@ internal class TestBroadcasting {
         val tensor32 = tensor3 - tensor2
 
         assertTrue(tensor21.shape contentEquals intArrayOf(2, 3))
-        assertTrue(tensor21.buffer.array() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
+        assertTrue(tensor21.mutableBuffer.array() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
 
         assertTrue(tensor31.shape contentEquals intArrayOf(1, 2, 3))
         assertTrue(
-            tensor31.buffer.array()
+            tensor31.mutableBuffer.array()
                     contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0)
         )
 
         assertTrue(tensor32.shape contentEquals intArrayOf(1, 1, 3))
-        assertTrue(tensor32.buffer.array() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
+        assertTrue(tensor32.mutableBuffer.array() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
     }
 
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
index 3b3e22acc..66959a0de 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -30,7 +30,7 @@ internal class TestDoubleAnalyticTensorAlgebra {
     fun testExp() = DoubleAnalyticTensorAlgebra.invoke {
         tensor.exp().let {
             assertTrue { shape contentEquals it.shape }
-            assertTrue { buffer.fmap(::exp).epsEqual(it.buffer.array())}
+            assertTrue { buffer.fmap(::exp).epsEqual(it.mutableBuffer.array())}
         }
     }
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index aa68340d4..1148c0aad 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -151,8 +151,8 @@ internal class TestDoubleLinearOpsTensorAlgebra {
         val res = svd1d(tensor2)
 
         assertTrue(res.shape contentEquals intArrayOf(2))
-        assertTrue { abs(abs(res.buffer.array()[res.bufferStart]) - 0.386) < 0.01 }
-        assertTrue { abs(abs(res.buffer.array()[res.bufferStart + 1]) - 0.922) < 0.01 }
+        assertTrue { abs(abs(res.mutableBuffer.array()[res.bufferStart]) - 0.386) < 0.01 }
+        assertTrue { abs(abs(res.mutableBuffer.array()[res.bufferStart + 1]) - 0.922) < 0.01 }
     }
 
     @Test
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 20acc0901..eca54f68b 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -6,7 +6,6 @@ import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.structures.DoubleBuffer
-import space.kscience.kmath.structures.asMutableBuffer
 import space.kscience.kmath.structures.toDoubleArray
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
@@ -27,7 +26,7 @@ internal class TestDoubleTensor {
         val tensor = fromArray(intArrayOf(2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
         assertEquals(tensor[intArrayOf(0, 1)], 5.8)
         assertTrue(
-            tensor.elements().map { it.second }.toList().toDoubleArray() contentEquals tensor.buffer.toDoubleArray()
+            tensor.elements().map { it.second }.toList().toDoubleArray() contentEquals tensor.mutableBuffer.toDoubleArray()
         )
     }
 
@@ -71,7 +70,7 @@ internal class TestDoubleTensor {
         val tensorArrayPublic = ndArray.toTypedTensor() // public API, data copied twice
         val sharedTensorArray = tensorArrayPublic.toTypedTensor() // no data copied by matching type
 
-        assertTrue(tensorArray.buffer.array() contentEquals sharedTensorArray.buffer.array())
+        assertTrue(tensorArray.mutableBuffer.array() contentEquals sharedTensorArray.mutableBuffer.array())
 
         tensorArray[intArrayOf(0)] = 55.9
         assertEquals(tensorArrayPublic[intArrayOf(0)], 1.0)
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index 115016974..34fe5d5f1 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -13,21 +13,21 @@ internal class TestDoubleTensorAlgebra {
     fun doublePlus() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(11.0, 12.0))
+        assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(11.0, 12.0))
     }
 
     @Test
     fun doubleDiv() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(2.0, 4.0))
         val res = 2.0/tensor
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(1.0, 0.5))
+        assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 0.5))
     }
 
     @Test
     fun divDouble() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(10.0, 5.0))
         val res = tensor / 2.5
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(4.0, 2.0))
+        assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(4.0, 2.0))
     }
 
     @Test
@@ -35,7 +35,7 @@ internal class TestDoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(1), doubleArrayOf(0.0))
         val res = tensor.transpose(0, 0)
 
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(0.0))
+        assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(0.0))
         assertTrue(res.shape contentEquals intArrayOf(1))
     }
 
@@ -44,7 +44,7 @@ internal class TestDoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res = tensor.transpose(1, 0)
 
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
+        assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
         assertTrue(res.shape contentEquals intArrayOf(2, 3))
     }
 
@@ -59,9 +59,9 @@ internal class TestDoubleTensorAlgebra {
         assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
         assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2))
 
-        assertTrue(res01.buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res02.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
-        assertTrue(res12.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res01.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res02.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res12.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
     }
 
     @Test
@@ -92,8 +92,8 @@ internal class TestDoubleTensorAlgebra {
         assignResult += tensorC
         assignResult += -39.4
 
-        assertTrue(expected.buffer.array() contentEquals result.buffer.array())
-        assertTrue(expected.buffer.array() contentEquals assignResult.buffer.array())
+        assertTrue(expected.mutableBuffer.array() contentEquals result.mutableBuffer.array())
+        assertTrue(expected.mutableBuffer.array() contentEquals assignResult.mutableBuffer.array())
     }
 
     @Test
@@ -104,19 +104,19 @@ internal class TestDoubleTensorAlgebra {
         val tensor3 = fromArray(intArrayOf(1, 1, 3), doubleArrayOf(-1.0, -2.0, -3.0))
 
         val res12 = tensor1.dot(tensor2)
-        assertTrue(res12.buffer.array() contentEquals doubleArrayOf(140.0, 320.0))
+        assertTrue(res12.mutableBuffer.array() contentEquals doubleArrayOf(140.0, 320.0))
         assertTrue(res12.shape contentEquals intArrayOf(2))
 
         val res32 = tensor3.dot(tensor2)
-        assertTrue(res32.buffer.array() contentEquals doubleArrayOf(-140.0))
+        assertTrue(res32.mutableBuffer.array() contentEquals doubleArrayOf(-140.0))
         assertTrue(res32.shape contentEquals intArrayOf(1, 1))
 
         val res22 = tensor2.dot(tensor2)
-        assertTrue(res22.buffer.array() contentEquals doubleArrayOf(1400.0))
+        assertTrue(res22.mutableBuffer.array() contentEquals doubleArrayOf(1400.0))
         assertTrue(res22.shape contentEquals intArrayOf(1))
 
         val res11 = tensor1.dot(tensor11)
-        assertTrue(res11.buffer.array() contentEquals doubleArrayOf(22.0, 28.0, 49.0, 64.0))
+        assertTrue(res11.mutableBuffer.array() contentEquals doubleArrayOf(22.0, 28.0, 49.0, 64.0))
         assertTrue(res11.shape contentEquals intArrayOf(2, 2))
 
         var tensor4 = fromArray(intArrayOf(10, 3, 4), DoubleArray(10 * 3 * 4) {0.0})
@@ -147,17 +147,17 @@ internal class TestDoubleTensorAlgebra {
 
         val diagonal1 = diagonalEmbedding(tensor1, 0, 1, 0)
         assertTrue(diagonal1.shape contentEquals intArrayOf(3, 3))
-        assertTrue(diagonal1.buffer.array() contentEquals
+        assertTrue(diagonal1.mutableBuffer.array() contentEquals
                 doubleArrayOf(10.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 30.0))
 
         val diagonal1Offset = diagonalEmbedding(tensor1, 1, 1, 0)
         assertTrue(diagonal1Offset.shape contentEquals intArrayOf(4, 4))
-        assertTrue(diagonal1Offset.buffer.array() contentEquals
+        assertTrue(diagonal1Offset.mutableBuffer.array() contentEquals
                 doubleArrayOf(0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 0.0, 30.0, 0.0))
 
         val diagonal2 = diagonalEmbedding(tensor2, 1, 0, 2)
         assertTrue(diagonal2.shape contentEquals intArrayOf(4, 2, 4))
-        assertTrue(diagonal2.buffer.array() contentEquals
+        assertTrue(diagonal2.mutableBuffer.array() contentEquals
                 doubleArrayOf(
                     0.0, 1.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0,
                     0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 5.0, 0.0,

From 86c2816cfde632cdb734d18d75e7245e3ae54f0c Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 30 Apr 2021 14:53:02 +0100
Subject: [PATCH 134/178] Fixed strides code duplication

---
 kmath-core/api/kmath-core.api                 |  4 ++--
 .../space/kscience/kmath/nd/StructureND.kt    |  6 +++---
 .../kmath/tensors/core/BufferedTensor.kt      |  4 ++--
 .../algebras/BroadcastDoubleTensorAlgebra.kt  | 16 +++++++-------
 .../core/algebras/TensorLinearStructure.kt    | 21 ++++++++++---------
 5 files changed, 26 insertions(+), 25 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 41b85b3bb..1b98f547c 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -789,7 +789,7 @@ public final class space/kscience/kmath/nd/DefaultStrides : space/kscience/kmath
 	public fun equals (Ljava/lang/Object;)Z
 	public fun getLinearSize ()I
 	public fun getShape ()[I
-	public fun getStrides ()Ljava/util/List;
+	public fun getStrides ()[I
 	public fun hashCode ()I
 	public fun index (I)[I
 	public fun offset ([I)I
@@ -931,7 +931,7 @@ public final class space/kscience/kmath/nd/ShortRingNDKt {
 public abstract interface class space/kscience/kmath/nd/Strides {
 	public abstract fun getLinearSize ()I
 	public abstract fun getShape ()[I
-	public abstract fun getStrides ()Ljava/util/List;
+	public abstract fun getStrides ()[I
 	public abstract fun index (I)[I
 	public fun indices ()Lkotlin/sequences/Sequence;
 	public abstract fun offset ([I)I
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
index 0656b1f7f..65c233012 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
@@ -184,7 +184,7 @@ public interface Strides {
     /**
      * Array strides
      */
-    public val strides: List<Int>
+    public val strides: IntArray
 
     /**
      * Get linear index from multidimensional index
@@ -221,7 +221,7 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
     /**
      * Strides for memory access
      */
-    override val strides: List<Int> by lazy {
+    override val strides: IntArray by lazy {
         sequence {
             var current = 1
             yield(1)
@@ -230,7 +230,7 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
                 current *= it
                 yield(current)
             }
-        }.toList()
+        }.toList().toIntArray()
     }
 
     override fun offset(index: IntArray): Int = index.mapIndexed { i, value ->
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index fae59804d..21030bbc7 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -15,7 +15,7 @@ public open class BufferedTensor<T>(
         get() = TensorLinearStructure(shape)
 
     public val numElements: Int
-        get() = linearStructure.size
+        get() = linearStructure.linearSize
 
     override fun get(index: IntArray): T = mutableBuffer[bufferStart + linearStructure.offset(index)]
 
@@ -60,7 +60,7 @@ internal fun <T> TensorStructure<T>.copyToBufferedTensor(): BufferedTensor<T> =
 
 internal fun <T> TensorStructure<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
     is BufferedTensor<T> -> this
-    is MutableBufferND<T> -> if (this.strides.strides.toIntArray() contentEquals TensorLinearStructure(this.shape).strides)
+    is MutableBufferND<T> -> if (this.strides.strides contentEquals TensorLinearStructure(this.shape).strides)
         BufferedTensor(this.shape, this.mutableBuffer, 0) else this.copyToBufferedTensor()
     else -> this.copyToBufferedTensor()
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
index 350d939af..9b97d5ef2 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
@@ -20,7 +20,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+        val resBuffer = DoubleArray(newThis.linearStructure.linearSize) { i ->
             newThis.mutableBuffer.array()[i] + newOther.mutableBuffer.array()[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
@@ -28,7 +28,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
+        for (i in 0 until tensor.linearStructure.linearSize) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] +=
                 newOther.mutableBuffer.array()[tensor.bufferStart + i]
         }
@@ -38,7 +38,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+        val resBuffer = DoubleArray(newThis.linearStructure.linearSize) { i ->
             newThis.mutableBuffer.array()[i] - newOther.mutableBuffer.array()[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
@@ -46,7 +46,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
+        for (i in 0 until tensor.linearStructure.linearSize) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] -=
                 newOther.mutableBuffer.array()[tensor.bufferStart + i]
         }
@@ -56,7 +56,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+        val resBuffer = DoubleArray(newThis.linearStructure.linearSize) { i ->
             newThis.mutableBuffer.array()[newThis.bufferStart + i] *
                     newOther.mutableBuffer.array()[newOther.bufferStart + i]
         }
@@ -65,7 +65,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
+        for (i in 0 until tensor.linearStructure.linearSize) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] *=
                 newOther.mutableBuffer.array()[tensor.bufferStart + i]
         }
@@ -75,7 +75,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+        val resBuffer = DoubleArray(newThis.linearStructure.linearSize) { i ->
             newThis.mutableBuffer.array()[newOther.bufferStart + i] /
                     newOther.mutableBuffer.array()[newOther.bufferStart + i]
         }
@@ -84,7 +84,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
+        for (i in 0 until tensor.linearStructure.linearSize) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] /=
                 newOther.mutableBuffer.array()[tensor.bufferStart + i]
         }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
index b16739892..8e83dafd6 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
@@ -5,16 +5,17 @@
 
 package space.kscience.kmath.tensors.core.algebras
 
+import space.kscience.kmath.nd.Strides
 import kotlin.math.max
 
 
-internal inline fun offsetFromIndex(index: IntArray, shape: IntArray, strides: IntArray): Int =
+internal fun offsetFromIndex(index: IntArray, shape: IntArray, strides: IntArray): Int =
     index.mapIndexed { i, value ->
         if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${shape[i]})")
         value * strides[i]
     }.sum()
 
-internal inline fun stridesFromShape(shape: IntArray): IntArray {
+internal fun stridesFromShape(shape: IntArray): IntArray {
     val nDim = shape.size
     val res = IntArray(nDim)
     if (nDim == 0)
@@ -31,7 +32,7 @@ internal inline fun stridesFromShape(shape: IntArray): IntArray {
 
 }
 
-internal inline fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {
+internal fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {
     val res = IntArray(nDim)
     var current = offset
     var strideIndex = 0
@@ -44,7 +45,7 @@ internal inline fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int):
     return res
 }
 
-internal inline fun stepIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
+internal fun stepIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
     val res = index.copyOf()
     var current = nDim - 1
     var carry = 0
@@ -62,26 +63,26 @@ internal inline fun stepIndex(index: IntArray, shape: IntArray, nDim: Int): IntA
 }
 
 
-public class TensorLinearStructure(public val shape: IntArray)
+public class TensorLinearStructure(override val shape: IntArray) : Strides
 {
-    public val strides: IntArray
+    override val strides: IntArray
         get() = stridesFromShape(shape)
 
-    public fun offset(index: IntArray): Int = offsetFromIndex(index, shape, strides)
+    override fun offset(index: IntArray): Int = offsetFromIndex(index, shape, strides)
 
-    public fun index(offset: Int): IntArray =
+    override fun index(offset: Int): IntArray =
         indexFromOffset(offset, strides, shape.size)
 
     public fun stepIndex(index: IntArray): IntArray =
         stepIndex(index, shape, shape.size)
 
-    public val size: Int
+    override val linearSize: Int
         get() = shape.reduce(Int::times)
 
     public val dim: Int
         get() = shape.size
 
-    public fun indices(): Sequence<IntArray> = (0 until size).asSequence().map {
+    override fun indices(): Sequence<IntArray> = (0 until linearSize).asSequence().map {
         index(it)
     }
 }
\ No newline at end of file

From 42ddd2e5692156ac4263f28fa9848e62180f2b38 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 30 Apr 2021 15:08:32 +0100
Subject: [PATCH 135/178] MutableBufferFromGenerator

---
 kmath-core/api/kmath-core.api                  | 18 +++++++++---------
 .../space/kscience/kmath/nd/Structure1D.kt     |  4 ++--
 .../space/kscience/kmath/nd/Structure2D.kt     |  6 +++---
 .../space/kscience/kmath/structures/Buffer.kt  |  2 +-
 4 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 1b98f547c..687de9cc8 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -1882,6 +1882,15 @@ public final class space/kscience/kmath/structures/MutableBuffer$Companion {
 	public final fun short-1yRgbGw (ILkotlin/jvm/functions/Function1;)[S
 }
 
+public final class space/kscience/kmath/structures/MutableBufferFromGenerator : space/kscience/kmath/structures/MutableBuffer {
+	public fun <init> (ILkotlin/jvm/functions/Function1;)V
+	public fun copy ()Lspace/kscience/kmath/structures/MutableBuffer;
+	public fun get (I)Ljava/lang/Object;
+	public fun getSize ()I
+	public fun iterator ()Ljava/util/Iterator;
+	public fun set (ILjava/lang/Object;)V
+}
+
 public final class space/kscience/kmath/structures/MutableListBuffer : space/kscience/kmath/structures/MutableBuffer {
 	public static final synthetic fun box-impl (Ljava/util/List;)Lspace/kscience/kmath/structures/MutableListBuffer;
 	public static fun constructor-impl (ILkotlin/jvm/functions/Function1;)Ljava/util/List;
@@ -1990,12 +1999,3 @@ public final class space/kscience/kmath/structures/VirtualBuffer : space/kscienc
 	public fun iterator ()Ljava/util/Iterator;
 }
 
-public final class space/kscience/kmath/structures/VirtualMutableBuffer : space/kscience/kmath/structures/MutableBuffer {
-	public fun <init> (ILkotlin/jvm/functions/Function1;)V
-	public fun copy ()Lspace/kscience/kmath/structures/MutableBuffer;
-	public fun get (I)Ljava/lang/Object;
-	public fun getSize ()I
-	public fun iterator ()Ljava/util/Iterator;
-	public fun set (ILjava/lang/Object;)V
-}
-
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
index 93fc48713..8ea6d0f02 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
@@ -52,7 +52,7 @@ private value class Structure1DWrapper<T>(val structure: StructureND<T>) : Struc
 /**
  * A 1D wrapper for a mutable nd-structure
  */
-private inline class MutableStructure1DWrapper<T>(val structure: MutableStructureND<T>) : MutableStructure1D<T> {
+private class MutableStructure1DWrapper<T>(val structure: MutableStructureND<T>) : MutableStructure1D<T> {
     override val shape: IntArray get() = structure.shape
     override val size: Int get() = structure.shape[0]
     override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
@@ -81,7 +81,7 @@ private value class Buffer1DWrapper<T>(val buffer: Buffer<T>) : Structure1D<T> {
     override operator fun get(index: Int): T = buffer[index]
 }
 
-internal inline class MutableBuffer1DWrapper<T>(val buffer: MutableBuffer<T>) : MutableStructure1D<T> {
+internal class MutableBuffer1DWrapper<T>(val buffer: MutableBuffer<T>) : MutableStructure1D<T> {
     override val shape: IntArray get() = intArrayOf(buffer.size)
     override val size: Int get() = buffer.size
 
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
index 4c17d25d0..d987d8cf4 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
@@ -8,7 +8,7 @@ package space.kscience.kmath.nd
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.VirtualBuffer
-import space.kscience.kmath.structures.VirtualMutableBuffer
+import space.kscience.kmath.structures.MutableBufferFromGenerator
 import kotlin.jvm.JvmInline
 import kotlin.reflect.KClass
 
@@ -81,13 +81,13 @@ public interface MutableStructure2D<T> : Structure2D<T>, MutableStructureND<T> {
      * The buffer of rows of this structure. It gets elements from the structure dynamically.
      */
     override val rows: List<MutableStructure1D<T>>
-        get() = List(rowNum) { i -> MutableBuffer1DWrapper(VirtualMutableBuffer(colNum) { j -> get(i, j) })}
+        get() = List(rowNum) { i -> MutableBuffer1DWrapper(MutableBufferFromGenerator(colNum) { j -> get(i, j) })}
 
     /**
      * The buffer of columns of this structure. It gets elements from the structure dynamically.
      */
     override val columns: List<MutableStructure1D<T>>
-        get() = List(colNum) { j -> MutableBuffer1DWrapper(VirtualMutableBuffer(rowNum) { i -> get(i, j) }) }
+        get() = List(colNum) { j -> MutableBuffer1DWrapper(MutableBufferFromGenerator(rowNum) { i -> get(i, j) }) }
 }
 
 /**
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
index 980fe698e..19018ffb5 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
@@ -292,7 +292,7 @@ public class VirtualBuffer<T>(override val size: Int, private val generator: (In
     override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map(generator).iterator()
 }
 
-public class VirtualMutableBuffer<T>(override val size: Int, private val generator: (Int) -> T) : MutableBuffer<T> {
+public class MutableBufferFromGenerator<T>(override val size: Int, private val generator: (Int) -> T) : MutableBuffer<T> {
 
     private val bufferHolder: MutableListBuffer<T> = (0 until size).map(generator).toMutableList().asMutableBuffer()
 

From 1695fc5075712c1c89121c0860c7c1e985737dbf Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 30 Apr 2021 15:25:45 +0100
Subject: [PATCH 136/178] Fix examples

---
 .../space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt | 3 ++-
 .../src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
index 38d8c1437..a84224bcf 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
@@ -5,6 +5,7 @@
 
 package space.kscience.kmath.tensors
 
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 
@@ -13,7 +14,7 @@ import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 fun main () {
 
     // work in context with linear operations
-    DoubleLinearOpsTensorAlgebra {
+    DoubleLinearOpsTensorAlgebra.invoke {
 
         // set true value of x
         val trueX = fromArray(
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
index 9318fe928..0408bba63 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
@@ -5,6 +5,7 @@
 
 package space.kscience.kmath.tensors
 
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
@@ -18,7 +19,7 @@ fun main() {
     val randSeed = 100500L
 
     // work in context with linear operations
-    DoubleLinearOpsTensorAlgebra {
+    DoubleLinearOpsTensorAlgebra.invoke {
         // take coefficient vector from normal distribution
         val alpha = randNormal(
             intArrayOf(5),

From e2c7751c7eb146641cec0bba6e10e51e849e8371 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 30 Apr 2021 19:45:31 +0300
Subject: [PATCH 137/178] refactor linops

---
 .../algebras/DoubleLinearOpsTensorAlgebra.kt  | 51 +++++++++++--------
 1 file changed, 29 insertions(+), 22 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
index ab73b25b1..700fafbeb 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
@@ -53,14 +53,15 @@ public object DoubleLinearOpsTensorAlgebra :
         val lTensor = luTensor.zeroesLike()
         val uTensor = luTensor.zeroesLike()
 
-        for ((pairLU, lu) in lTensor.matrixSequence().zip(uTensor.matrixSequence())
-            .zip(luTensor.tensor.matrixSequence())) {
-            val (l, u) = pairLU
-            luPivotHelper(l.as2D(), u.as2D(), lu.as2D(), n)
-        }
+        lTensor.matrixSequence()
+            .zip(uTensor.matrixSequence())
+            .zip(luTensor.tensor.matrixSequence())
+            .forEach { (pairLU, lu) ->
+                val (l, u) = pairLU
+                luPivotHelper(l.as2D(), u.as2D(), lu.as2D(), n)
+            }
 
         return Triple(pTensor, lTensor, uTensor)
-
     }
 
     public fun TensorStructure<Double>.cholesky(epsilon: Double): DoubleTensor {
@@ -82,11 +83,13 @@ public object DoubleLinearOpsTensorAlgebra :
         checkSquareMatrix(shape)
         val qTensor = zeroesLike()
         val rTensor = zeroesLike()
-        val seq = tensor.matrixSequence().zip((qTensor.matrixSequence().zip(rTensor.matrixSequence())))
-        for ((matrix, qr) in seq) {
+        tensor.matrixSequence()
+            .zip((qTensor.matrixSequence()
+                .zip(rTensor.matrixSequence()))).forEach { (matrix, qr) ->
             val (q, r) = qr
             qrHelper(matrix.asTensor(), q.asTensor(), r.as2D())
         }
+
         return qTensor to rTensor
     }
 
@@ -97,20 +100,24 @@ public object DoubleLinearOpsTensorAlgebra :
         val size = tensor.linearStructure.dim
         val commonShape = tensor.shape.sliceArray(0 until size - 2)
         val (n, m) = tensor.shape.sliceArray(size - 2 until size)
-        val resU = zeros(commonShape + intArrayOf(min(n, m), n))
-        val resS = zeros(commonShape + intArrayOf(min(n, m)))
-        val resV = zeros(commonShape + intArrayOf(min(n, m), m))
+        val uTensor = zeros(commonShape + intArrayOf(min(n, m), n))
+        val sTensor = zeros(commonShape + intArrayOf(min(n, m)))
+        val vTensor = zeros(commonShape + intArrayOf(min(n, m), m))
 
-        for ((matrix, USV) in tensor.matrixSequence()
-            .zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
-            val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
-            val curMatrix = DoubleTensor(
-                matrix.shape,
-                matrix.mutableBuffer.array().slice(matrix.bufferStart until matrix.bufferStart + matrixSize).toDoubleArray()
-            )
-            svdHelper(curMatrix, USV, m, n, epsilon)
-        }
-        return Triple(resU.transpose(), resS, resV.transpose())
+        tensor.matrixSequence()
+            .zip(uTensor.matrixSequence()
+                .zip(sTensor.vectorSequence()
+                    .zip(vTensor.matrixSequence()))).forEach { (matrix, USV) ->
+                val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
+                val curMatrix = DoubleTensor(
+                    matrix.shape,
+                    matrix.mutableBuffer.array().slice(matrix.bufferStart until matrix.bufferStart + matrixSize)
+                        .toDoubleArray()
+                )
+                svdHelper(curMatrix, USV, m, n, epsilon)
+            }
+
+        return Triple(uTensor.transpose(), sTensor, vTensor.transpose())
     }
 
     override fun TensorStructure<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> =
@@ -127,7 +134,7 @@ public object DoubleLinearOpsTensorAlgebra :
             cleanSymHelper(matrix.as2D(), n)
 
         val eig = (utv dot s.view(shp)).view(s.shape)
-        return Pair(eig, v)
+        return eig to v
     }
 
     public fun TensorStructure<Double>.detLU(epsilon: Double = 1e-9): DoubleTensor {

From f0cdb9b6571774bd861288f059abaf0571c232bf Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 30 Apr 2021 20:07:59 +0300
Subject: [PATCH 138/178] refactor utils and remove inline

---
 .../kmath/tensors/core/broadcastUtils.kt      | 77 +++++++++----------
 .../kscience/kmath/tensors/core/checks.kt     | 18 ++---
 .../kscience/kmath/tensors/core/linUtils.kt   | 41 +++++-----
 .../kscience/kmath/tensors/core/utils.kt      | 45 ++++++-----
 .../core/TestDoubleLinearOpsAlgebra.kt        |  2 +-
 5 files changed, 94 insertions(+), 89 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
index 2d4d6f0f7..58d8654af 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
@@ -2,7 +2,7 @@ package space.kscience.kmath.tensors.core
 
 import kotlin.math.max
 
-internal inline fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: DoubleTensor, linearSize: Int) {
+internal fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: DoubleTensor, linearSize: Int) {
     for (linearIndex in 0 until linearSize) {
         val totalMultiIndex = resTensor.linearStructure.index(linearIndex)
         val curMultiIndex = tensor.shape.copyOf()
@@ -23,7 +23,7 @@ internal inline fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: Doub
     }
 }
 
-internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
+internal fun broadcastShapes(vararg shapes: IntArray): IntArray {
     var totalDim = 0
     for (shape in shapes) {
         totalDim = max(totalDim, shape.size)
@@ -51,7 +51,7 @@ internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
     return totalShape
 }
 
-internal inline fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor {
+internal fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor {
     if (tensor.shape.size > newShape.size) {
         throw RuntimeException("Tensor is not compatible with the new shape")
     }
@@ -71,7 +71,7 @@ internal inline fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): Doubl
     return resTensor
 }
 
-internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
+internal fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
     val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
     val n = totalShape.reduce { acc, i -> acc * i }
 
@@ -85,7 +85,7 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
     return res
 }
 
-internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
+internal fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
     val onlyTwoDims = tensors.asSequence().onEach {
         require(it.shape.size >= 2) {
             throw RuntimeException("Tensors must have at least 2 dimensions")
@@ -99,46 +99,45 @@ internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<Do
     val totalShape = broadcastShapes(*(tensors.map { it.shape.sliceArray(0..it.shape.size - 3) }).toTypedArray())
     val n = totalShape.reduce { acc, i -> acc * i }
 
-    val res = ArrayList<DoubleTensor>(0)
-    for (tensor in tensors) {
-        val matrixShape = tensor.shape.sliceArray(tensor.shape.size - 2 until tensor.shape.size).copyOf()
-        val matrixSize = matrixShape[0] * matrixShape[1]
-        val matrix = DoubleTensor(matrixShape, DoubleArray(matrixSize))
+    return buildList {
+        for (tensor in tensors) {
+            val matrixShape = tensor.shape.sliceArray(tensor.shape.size - 2 until tensor.shape.size).copyOf()
+            val matrixSize = matrixShape[0] * matrixShape[1]
+            val matrix = DoubleTensor(matrixShape, DoubleArray(matrixSize))
 
-        val outerTensor = DoubleTensor(totalShape, DoubleArray(n))
-        val resTensor = DoubleTensor(totalShape + matrixShape, DoubleArray(n * matrixSize))
+            val outerTensor = DoubleTensor(totalShape, DoubleArray(n))
+            val resTensor = DoubleTensor(totalShape + matrixShape, DoubleArray(n * matrixSize))
 
-        for (linearIndex in 0 until n) {
-            val totalMultiIndex = outerTensor.linearStructure.index(linearIndex)
-            var curMultiIndex = tensor.shape.sliceArray(0..tensor.shape.size - 3).copyOf()
-            curMultiIndex = IntArray(totalMultiIndex.size - curMultiIndex.size) { 1 } + curMultiIndex
+            for (linearIndex in 0 until n) {
+                val totalMultiIndex = outerTensor.linearStructure.index(linearIndex)
+                var curMultiIndex = tensor.shape.sliceArray(0..tensor.shape.size - 3).copyOf()
+                curMultiIndex = IntArray(totalMultiIndex.size - curMultiIndex.size) { 1 } + curMultiIndex
 
-            val newTensor = DoubleTensor(curMultiIndex + matrixShape, tensor.mutableBuffer.array())
+                val newTensor = DoubleTensor(curMultiIndex + matrixShape, tensor.mutableBuffer.array())
 
-            for (i in curMultiIndex.indices) {
-                if (curMultiIndex[i] != 1) {
-                    curMultiIndex[i] = totalMultiIndex[i]
-                } else {
-                    curMultiIndex[i] = 0
+                for (i in curMultiIndex.indices) {
+                    if (curMultiIndex[i] != 1) {
+                        curMultiIndex[i] = totalMultiIndex[i]
+                    } else {
+                        curMultiIndex[i] = 0
+                    }
+                }
+
+                for (i in 0 until matrixSize) {
+                    val curLinearIndex = newTensor.linearStructure.offset(
+                        curMultiIndex +
+                                matrix.linearStructure.index(i)
+                    )
+                    val newLinearIndex = resTensor.linearStructure.offset(
+                        totalMultiIndex +
+                                matrix.linearStructure.index(i)
+                    )
+
+                    resTensor.mutableBuffer.array()[resTensor.bufferStart + newLinearIndex] =
+                        newTensor.mutableBuffer.array()[newTensor.bufferStart + curLinearIndex]
                 }
             }
-
-            for (i in 0 until matrixSize) {
-                val curLinearIndex = newTensor.linearStructure.offset(
-                    curMultiIndex +
-                            matrix.linearStructure.index(i)
-                )
-                val newLinearIndex = resTensor.linearStructure.offset(
-                    totalMultiIndex +
-                            matrix.linearStructure.index(i)
-                )
-
-                resTensor.mutableBuffer.array()[resTensor.bufferStart + newLinearIndex] =
-                    newTensor.mutableBuffer.array()[newTensor.bufferStart + curLinearIndex]
-            }
+            add(resTensor)
         }
-        res += resTensor
     }
-
-    return res
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index fd5c8413d..fd98be8b2 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -5,38 +5,38 @@ import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 
 
-internal inline fun checkEmptyShape(shape: IntArray): Unit =
+internal fun checkEmptyShape(shape: IntArray): Unit =
     check(shape.isNotEmpty()) {
         "Illegal empty shape provided"
     }
 
-internal inline fun checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
+internal fun checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
     check(buffer.isNotEmpty()) {
         "Illegal empty buffer provided"
     }
 
-internal inline fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
+internal fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
     check(buffer.size == shape.reduce(Int::times)) {
         "Inconsistent shape ${shape.toList()} for buffer of size ${buffer.size} provided"
     }
 
 
-internal inline fun <T> checkShapesCompatible(a: TensorStructure<T>, b: TensorStructure<T>): Unit =
+internal fun <T> checkShapesCompatible(a: TensorStructure<T>, b: TensorStructure<T>): Unit =
     check(a.shape contentEquals b.shape) {
         "Incompatible shapes ${a.shape.toList()} and ${b.shape.toList()} "
     }
 
 
-internal inline fun checkTranspose(dim: Int, i: Int, j: Int): Unit =
+internal fun checkTranspose(dim: Int, i: Int, j: Int): Unit =
     check((i < dim) and (j < dim)) {
         "Cannot transpose $i to $j for a tensor of dim $dim"
     }
 
-internal inline fun <T> checkView(a: TensorStructure<T>, shape: IntArray): Unit =
+internal fun <T> checkView(a: TensorStructure<T>, shape: IntArray): Unit =
     check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
 
 
-internal inline fun checkSquareMatrix(shape: IntArray): Unit {
+internal fun checkSquareMatrix(shape: IntArray): Unit {
     val n = shape.size
     check(n >= 2) {
         "Expected tensor with 2 or more dimensions, got size $n instead"
@@ -46,14 +46,14 @@ internal inline fun checkSquareMatrix(shape: IntArray): Unit {
     }
 }
 
-internal inline fun DoubleTensorAlgebra.checkSymmetric(
+internal fun DoubleTensorAlgebra.checkSymmetric(
     tensor: TensorStructure<Double>, epsilon: Double = 1e-6
 ): Unit =
     check(tensor.eq(tensor.transpose(), epsilon)) {
         "Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
     }
 
-internal inline fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(
+internal fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(
     tensor: DoubleTensor, epsilon: Double = 1e-6
 ): Unit {
     checkSymmetric(tensor, epsilon)
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
index 776a5e4d5..a152b3a17 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
@@ -13,7 +13,7 @@ import kotlin.math.sign
 import kotlin.math.sqrt
 
 
-internal inline fun <T> BufferedTensor<T>.vectorSequence(): Sequence<BufferedTensor<T>> = sequence {
+internal fun <T> BufferedTensor<T>.vectorSequence(): Sequence<BufferedTensor<T>> = sequence {
     val n = shape.size
     val vectorOffset = shape[n - 1]
     val vectorShape = intArrayOf(shape.last())
@@ -23,9 +23,9 @@ internal inline fun <T> BufferedTensor<T>.vectorSequence(): Sequence<BufferedTen
     }
 }
 
-internal inline fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTensor<T>> = sequence {
-    check(shape.size >= 2) { "todo" }
+internal fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTensor<T>> = sequence {
     val n = shape.size
+    check(n >= 2) { "Expected tensor with 2 or more dimensions, got size $n" }
     val matrixOffset = shape[n - 1] * shape[n - 2]
     val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
     for (offset in 0 until numElements step matrixOffset) {
@@ -46,8 +46,7 @@ internal inline fun <T> BufferedTensor<T>.forEachMatrix(matrixAction: (BufferedT
     }
 }
 
-
-internal inline fun dotHelper(
+internal fun dotHelper(
     a: MutableStructure2D<Double>,
     b: MutableStructure2D<Double>,
     res: MutableStructure2D<Double>,
@@ -64,10 +63,11 @@ internal inline fun dotHelper(
     }
 }
 
-internal inline fun luHelper(
+internal fun luHelper(
     lu: MutableStructure2D<Double>,
     pivots: MutableStructure1D<Int>,
-    epsilon: Double): Boolean {
+    epsilon: Double
+): Boolean {
 
     val m = lu.rowNum
 
@@ -114,7 +114,7 @@ internal inline fun luHelper(
     return false
 }
 
-internal inline fun <T> BufferedTensor<T>.setUpPivots(): IntTensor {
+internal fun <T> BufferedTensor<T>.setUpPivots(): IntTensor {
     val n = this.shape.size
     val m = this.shape.last()
     val pivotsShape = IntArray(n - 1) { i -> this.shape[i] }
@@ -126,22 +126,23 @@ internal inline fun <T> BufferedTensor<T>.setUpPivots(): IntTensor {
     )
 }
 
-internal inline fun DoubleLinearOpsTensorAlgebra.computeLU(
+internal fun DoubleLinearOpsTensorAlgebra.computeLU(
     tensor: DoubleTensor,
-    epsilon: Double): Pair<DoubleTensor, IntTensor>? {
+    epsilon: Double
+): Pair<DoubleTensor, IntTensor>? {
 
     checkSquareMatrix(tensor.shape)
     val luTensor = tensor.copy()
     val pivotsTensor = tensor.setUpPivots()
 
     for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
-        if(luHelper(lu.as2D(), pivots.as1D(), epsilon))
+        if (luHelper(lu.as2D(), pivots.as1D(), epsilon))
             return null
 
     return Pair(luTensor, pivotsTensor)
 }
 
-internal inline fun pivInit(
+internal fun pivInit(
     p: MutableStructure2D<Double>,
     pivot: MutableStructure1D<Int>,
     n: Int
@@ -151,7 +152,7 @@ internal inline fun pivInit(
     }
 }
 
-internal inline fun luPivotHelper(
+internal fun luPivotHelper(
     l: MutableStructure2D<Double>,
     u: MutableStructure2D<Double>,
     lu: MutableStructure2D<Double>,
@@ -172,7 +173,7 @@ internal inline fun luPivotHelper(
     }
 }
 
-internal inline fun choleskyHelper(
+internal fun choleskyHelper(
     a: MutableStructure2D<Double>,
     l: MutableStructure2D<Double>,
     n: Int
@@ -193,7 +194,7 @@ internal inline fun choleskyHelper(
     }
 }
 
-internal inline fun luMatrixDet(lu: MutableStructure2D<Double>, pivots: MutableStructure1D<Int>): Double {
+internal fun luMatrixDet(lu: MutableStructure2D<Double>, pivots: MutableStructure1D<Int>): Double {
     if (lu[0, 0] == 0.0) {
         return 0.0
     }
@@ -202,7 +203,7 @@ internal inline fun luMatrixDet(lu: MutableStructure2D<Double>, pivots: MutableS
     return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
 }
 
-internal inline fun luMatrixInv(
+internal fun luMatrixInv(
     lu: MutableStructure2D<Double>,
     pivots: MutableStructure1D<Int>,
     invMatrix: MutableStructure2D<Double>
@@ -229,7 +230,7 @@ internal inline fun luMatrixInv(
     }
 }
 
-internal inline fun DoubleLinearOpsTensorAlgebra.qrHelper(
+internal fun DoubleLinearOpsTensorAlgebra.qrHelper(
     matrix: DoubleTensor,
     q: DoubleTensor,
     r: MutableStructure2D<Double>
@@ -259,7 +260,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.qrHelper(
     }
 }
 
-internal inline fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10): DoubleTensor {
+internal fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10): DoubleTensor {
     val (n, m) = a.shape
     var v: DoubleTensor
     val b: DoubleTensor
@@ -283,7 +284,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon:
     }
 }
 
-internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
+internal fun DoubleLinearOpsTensorAlgebra.svdHelper(
     matrix: DoubleTensor,
     USV: Pair<BufferedTensor<Double>, Pair<BufferedTensor<Double>, BufferedTensor<Double>>>,
     m: Int, n: Int, epsilon: Double
@@ -335,7 +336,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
     }
 }
 
-internal inline fun cleanSymHelper(matrix: MutableStructure2D<Double>, n: Int) {
+internal fun cleanSymHelper(matrix: MutableStructure2D<Double>, n: Int) {
     for (i in 0 until n)
         for (j in 0 until n) {
             if (i == j) {
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index cb23dbdd6..58d280307 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -23,19 +23,19 @@ internal fun Buffer<Double>.array(): DoubleArray = when (this) {
     else -> this.toDoubleArray()
 }
 
-internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
+internal fun getRandomNormals(n: Int, seed: Long): DoubleArray {
     val distribution = GaussianSampler(0.0, 1.0)
     val generator = RandomGenerator.default(seed)
     return distribution.sample(generator).nextBufferBlocking(n).toDoubleArray()
 }
 
-internal inline fun getRandomUnitVector(n: Int, seed: Long): DoubleArray {
+internal fun getRandomUnitVector(n: Int, seed: Long): DoubleArray {
     val unnorm = getRandomNormals(n, seed)
     val norm = sqrt(unnorm.map { it * it }.sum())
     return unnorm.map { it / norm }.toDoubleArray()
 }
 
-internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
+internal fun minusIndexFrom(n: Int, i: Int): Int = if (i >= 0) i else {
     val ii = n + i
     check(ii >= 0) {
         "Out of bound index $i for tensor of dim $n"
@@ -43,27 +43,28 @@ internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
     ii
 }
 
-internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int =  minusIndexFrom(this.dimension, i)
+internal fun <T> BufferedTensor<T>.minusIndex(i: Int): Int = minusIndexFrom(this.dimension, i)
 
-internal inline fun format(value: Double, digits: Int = 4): String {
+internal fun format(value: Double, digits: Int = 4): String {
     val ten = 10.0
-    val approxOrder = if(value == 0.0) 0 else ceil(log10(abs(value))).toInt()
-    val order = if(
+    val approxOrder = if (value == 0.0) 0 else ceil(log10(abs(value))).toInt()
+    val order = if (
         ((value % ten) == 0.0) or
         (value == 1.0) or
-        ((1/value) % ten == 0.0)) approxOrder else approxOrder - 1
+        ((1 / value) % ten == 0.0)
+    ) approxOrder else approxOrder - 1
     val lead = value / ten.pow(order)
-    val leadDisplay = round(lead*ten.pow(digits)) / ten.pow(digits)
-    val orderDisplay = if(order == 0) "" else if(order > 0) "E+$order" else "E$order"
+    val leadDisplay = round(lead * ten.pow(digits)) / ten.pow(digits)
+    val orderDisplay = if (order == 0) "" else if (order > 0) "E+$order" else "E$order"
     val valueDisplay = "$leadDisplay$orderDisplay"
-    val res = if(value < 0.0) valueDisplay else " $valueDisplay"
+    val res = if (value < 0.0) valueDisplay else " $valueDisplay"
 
     val fLength = digits + 6
     val endSpace = " ".repeat(fLength - res.length)
     return "$res$endSpace"
 }
 
-internal inline fun DoubleTensor.toPrettyString(): String = buildString {
+internal fun DoubleTensor.toPrettyString(): String = buildString {
     var offset = 0
     val shape = this@toPrettyString.shape
     val linearStructure = this@toPrettyString.linearStructure
@@ -72,32 +73,36 @@ internal inline fun DoubleTensor.toPrettyString(): String = buildString {
     append(initString)
     var charOffset = 3
     for (vector in vectorSequence()) {
-        append(" ".repeat(charOffset))
+        repeat(charOffset) { append(' ') }
         val index = linearStructure.index(offset)
         for (ind in index.reversed()) {
             if (ind != 0) {
                 break
             }
-            append("[")
+            append('[')
             charOffset += 1
         }
 
         val values = vector.as1D().toMutableList().map(::format)
 
-        append(values.joinToString(", "))
-        append("]")
+        values.joinTo(this, separator = ", ")
+
+        append(']')
         charOffset -= 1
-        for ((ind, maxInd) in index.reversed().zip(shape.reversed()).drop(1)){
+
+        index.reversed().zip(shape.reversed()).drop(1).forEach { (ind, maxInd) ->
             if (ind != maxInd - 1) {
-                break
+                return@forEach
             }
-            append("]")
-            charOffset -=1
+            append(']')
+            charOffset -= 1
         }
+
         offset += vectorSize
         if (this@toPrettyString.numElements == offset) {
             break
         }
+
         append(",\n")
     }
     append("\n)")
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 1148c0aad..2282d7fcb 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -182,7 +182,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 }
 
 
-private inline fun DoubleLinearOpsTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10): Unit {
+private fun DoubleLinearOpsTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10): Unit {
     val svd = tensor.svd()
 
     val tensorSVD = svd.first

From 8a039326d40fe4e840bed09170607c457a89e5a3 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 30 Apr 2021 19:47:05 +0100
Subject: [PATCH 139/178] Minor corrections

---
 kmath-core/api/kmath-core.api                       |  3 +--
 .../kotlin/space/kscience/kmath/nd/StructureND.kt   | 10 ++++------
 .../tensors/core/algebras/TensorLinearStructure.kt  |  5 -----
 .../kscience/kmath/tensors/core/broadcastUtils.kt   | 13 ++++++-------
 .../kmath/tensors/core/TestDoubleTensorAlgebra.kt   |  1 -
 5 files changed, 11 insertions(+), 21 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 687de9cc8..261aa1e24 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -792,7 +792,6 @@ public final class space/kscience/kmath/nd/DefaultStrides : space/kscience/kmath
 	public fun getStrides ()[I
 	public fun hashCode ()I
 	public fun index (I)[I
-	public fun offset ([I)I
 }
 
 public final class space/kscience/kmath/nd/DefaultStrides$Companion {
@@ -934,7 +933,7 @@ public abstract interface class space/kscience/kmath/nd/Strides {
 	public abstract fun getStrides ()[I
 	public abstract fun index (I)[I
 	public fun indices ()Lkotlin/sequences/Sequence;
-	public abstract fun offset ([I)I
+	public fun offset ([I)I
 }
 
 public abstract interface class space/kscience/kmath/nd/Structure1D : space/kscience/kmath/nd/StructureND, space/kscience/kmath/structures/Buffer {
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
index 65c233012..a3331d71a 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
@@ -189,7 +189,10 @@ public interface Strides {
     /**
      * Get linear index from multidimensional index
      */
-    public fun offset(index: IntArray): Int
+    public fun offset(index: IntArray): Int = index.mapIndexed { i, value ->
+        if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${this.shape[i]})")
+        value * strides[i]
+    }.sum()
 
     /**
      * Get multidimensional from linear
@@ -233,11 +236,6 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
         }.toList().toIntArray()
     }
 
-    override fun offset(index: IntArray): Int = index.mapIndexed { i, value ->
-        if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${this.shape[i]})")
-        value * strides[i]
-    }.sum()
-
     override fun index(offset: Int): IntArray {
         val res = IntArray(shape.size)
         var current = offset
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
index 8e83dafd6..08aab5175 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
@@ -68,8 +68,6 @@ public class TensorLinearStructure(override val shape: IntArray) : Strides
     override val strides: IntArray
         get() = stridesFromShape(shape)
 
-    override fun offset(index: IntArray): Int = offsetFromIndex(index, shape, strides)
-
     override fun index(offset: Int): IntArray =
         indexFromOffset(offset, strides, shape.size)
 
@@ -82,7 +80,4 @@ public class TensorLinearStructure(override val shape: IntArray) : Strides
     public val dim: Int
         get() = shape.size
 
-    override fun indices(): Sequence<IntArray> = (0 until linearSize).asSequence().map {
-        index(it)
-    }
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
index 58d8654af..e883b7861 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
@@ -75,14 +75,13 @@ internal fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleTensor>
     val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
     val n = totalShape.reduce { acc, i -> acc * i }
 
-    val res = ArrayList<DoubleTensor>(0)
-    for (tensor in tensors) {
-        val resTensor = DoubleTensor(totalShape, DoubleArray(n))
-        multiIndexBroadCasting(tensor, resTensor, n)
-        res.add(resTensor)
+    return  buildList {
+        for (tensor in tensors) {
+            val resTensor = DoubleTensor(totalShape, DoubleArray(n))
+            multiIndexBroadCasting(tensor, resTensor, n)
+            add(resTensor)
+        }
     }
-
-    return res
 }
 
 internal fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index 34fe5d5f1..a0efa4573 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -170,7 +170,6 @@ internal class TestDoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor3 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0))
-        val tensor4 = fromArray(intArrayOf(6, 1), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
 
         assertTrue(tensor1 eq tensor1)
         assertTrue(tensor1 eq tensor2)

From 74773686b432eac9e7d7804ffabf38eb1dbf1dca Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 30 Apr 2021 19:49:43 +0100
Subject: [PATCH 140/178] toDoubleTensor and toIntTensor renaming

---
 .../space/kscience/kmath/tensors/core/BufferedTensor.kt       | 4 ++--
 .../space/kscience/kmath/tensors/core/TestDoubleTensor.kt     | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 21030bbc7..4e286b489 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -77,5 +77,5 @@ internal val TensorStructure<Int>.tensor: IntTensor
         else -> this.toBufferedTensor().asTensor()
     }
 
-public fun TensorStructure<Double>.toTypedTensor(): DoubleTensor = this.tensor
-public fun TensorStructure<Int>.toTypedTensor(): IntTensor = this.tensor
\ No newline at end of file
+public fun TensorStructure<Double>.toDoubleTensor(): DoubleTensor = this.tensor
+public fun TensorStructure<Int>.toIntTensor(): IntTensor = this.tensor
\ No newline at end of file
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index eca54f68b..333a7bdec 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -67,8 +67,8 @@ internal class TestDoubleTensor {
         val bufferedTensorArray = ndArray.toBufferedTensor() // strides are flipped so data copied
         val tensorArray = bufferedTensorArray.asTensor() // data not contiguous so copied again
 
-        val tensorArrayPublic = ndArray.toTypedTensor() // public API, data copied twice
-        val sharedTensorArray = tensorArrayPublic.toTypedTensor() // no data copied by matching type
+        val tensorArrayPublic = ndArray.toDoubleTensor() // public API, data copied twice
+        val sharedTensorArray = tensorArrayPublic.toDoubleTensor() // no data copied by matching type
 
         assertTrue(tensorArray.mutableBuffer.array() contentEquals sharedTensorArray.mutableBuffer.array())
 

From 1b6bd67b903f761bf009a0677e422f637dd775d4 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 30 Apr 2021 21:11:01 +0100
Subject: [PATCH 141/178] No virtual mutable buffers

---
 kmath-core/api/kmath-core.api                  |  9 ---------
 .../space/kscience/kmath/nd/Structure2D.kt     |  6 +++---
 .../space/kscience/kmath/structures/Buffer.kt  | 18 ------------------
 3 files changed, 3 insertions(+), 30 deletions(-)

diff --git a/kmath-core/api/kmath-core.api b/kmath-core/api/kmath-core.api
index 261aa1e24..865ad2f6d 100644
--- a/kmath-core/api/kmath-core.api
+++ b/kmath-core/api/kmath-core.api
@@ -1881,15 +1881,6 @@ public final class space/kscience/kmath/structures/MutableBuffer$Companion {
 	public final fun short-1yRgbGw (ILkotlin/jvm/functions/Function1;)[S
 }
 
-public final class space/kscience/kmath/structures/MutableBufferFromGenerator : space/kscience/kmath/structures/MutableBuffer {
-	public fun <init> (ILkotlin/jvm/functions/Function1;)V
-	public fun copy ()Lspace/kscience/kmath/structures/MutableBuffer;
-	public fun get (I)Ljava/lang/Object;
-	public fun getSize ()I
-	public fun iterator ()Ljava/util/Iterator;
-	public fun set (ILjava/lang/Object;)V
-}
-
 public final class space/kscience/kmath/structures/MutableListBuffer : space/kscience/kmath/structures/MutableBuffer {
 	public static final synthetic fun box-impl (Ljava/util/List;)Lspace/kscience/kmath/structures/MutableListBuffer;
 	public static fun constructor-impl (ILkotlin/jvm/functions/Function1;)Ljava/util/List;
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
index d987d8cf4..28ae07a3c 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
@@ -8,7 +8,7 @@ package space.kscience.kmath.nd
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.VirtualBuffer
-import space.kscience.kmath.structures.MutableBufferFromGenerator
+import space.kscience.kmath.structures.MutableListBuffer
 import kotlin.jvm.JvmInline
 import kotlin.reflect.KClass
 
@@ -81,13 +81,13 @@ public interface MutableStructure2D<T> : Structure2D<T>, MutableStructureND<T> {
      * The buffer of rows of this structure. It gets elements from the structure dynamically.
      */
     override val rows: List<MutableStructure1D<T>>
-        get() = List(rowNum) { i -> MutableBuffer1DWrapper(MutableBufferFromGenerator(colNum) { j -> get(i, j) })}
+        get() = List(rowNum) { i -> MutableBuffer1DWrapper(MutableListBuffer(colNum) { j -> get(i, j) })}
 
     /**
      * The buffer of columns of this structure. It gets elements from the structure dynamically.
      */
     override val columns: List<MutableStructure1D<T>>
-        get() = List(colNum) { j -> MutableBuffer1DWrapper(MutableBufferFromGenerator(rowNum) { i -> get(i, j) }) }
+        get() = List(colNum) { j -> MutableBuffer1DWrapper(MutableListBuffer(rowNum) { i -> get(i, j) }) }
 }
 
 /**
diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
index 19018ffb5..be5dfb359 100644
--- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
+++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
@@ -292,24 +292,6 @@ public class VirtualBuffer<T>(override val size: Int, private val generator: (In
     override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map(generator).iterator()
 }
 
-public class MutableBufferFromGenerator<T>(override val size: Int, private val generator: (Int) -> T) : MutableBuffer<T> {
-
-    private val bufferHolder: MutableListBuffer<T> = (0 until size).map(generator).toMutableList().asMutableBuffer()
-
-    override operator fun get(index: Int): T {
-        if (index < 0 || index >= size) throw IndexOutOfBoundsException("Expected index from 0 to ${size - 1}, but found $index")
-        return bufferHolder[index]
-    }
-
-    override operator fun iterator(): Iterator<T> = bufferHolder.iterator()
-
-    override fun set(index: Int, value: T) {
-        bufferHolder[index] = value
-    }
-
-    override fun copy(): MutableBuffer<T> = bufferHolder.copy()
-}
-
 /**
  * Convert this buffer to read-only buffer.
  */

From b7cac3a015d235d81d02d3bb4b8834dda2b48b95 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Sat, 1 May 2021 13:32:50 +0300
Subject: [PATCH 142/178] fix sequences + array casting

---
 .../kmath/tensors/core/BufferedTensor.kt      | 38 ++++++++++++++++++-
 .../kscience/kmath/tensors/core/linUtils.kt   |  4 +-
 2 files changed, 39 insertions(+), 3 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 4e286b489..867b4fb7a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -49,6 +49,7 @@ public class DoubleTensor internal constructor(
 
 internal fun BufferedTensor<Int>.asTensor(): IntTensor =
     IntTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
+
 internal fun BufferedTensor<Double>.asTensor(): DoubleTensor =
     DoubleTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
 
@@ -78,4 +79,39 @@ internal val TensorStructure<Int>.tensor: IntTensor
     }
 
 public fun TensorStructure<Double>.toDoubleTensor(): DoubleTensor = this.tensor
-public fun TensorStructure<Int>.toIntTensor(): IntTensor = this.tensor
\ No newline at end of file
+public fun TensorStructure<Int>.toIntTensor(): IntTensor = this.tensor
+
+public fun Array<DoubleArray>.toDoubleTensor(): DoubleTensor {
+    val n = size
+    check(n > 0) { "An empty array cannot be casted to tensor" }
+    val m = first().size
+    check(m > 0) { "Inner arrays must have at least 1 argument" }
+    check(all { size == m }) { "Inner arrays must be the same size" }
+
+    val shape = intArrayOf(n, m)
+    val buffer = this.flatMap { arr -> arr.map { it } }.toDoubleArray()
+
+    return DoubleTensor(shape, buffer, 0)
+}
+
+
+public fun Array<IntArray>.toIntTensor(): IntTensor {
+    val n = size
+    check(n > 0) { "An empty array cannot be casted to tensor" }
+    val m = first().size
+    check(m > 0) { "Inner arrays must have at least 1 argument" }
+    check(all { size == m }) { "Inner arrays must be the same size" }
+
+    val shape = intArrayOf(n, m)
+    val buffer = this.flatMap { arr -> arr.map { it } }.toIntArray()
+
+    return IntTensor(shape, buffer, 0)
+}
+
+public fun DoubleTensor.toDoubleArray(): DoubleArray {
+    return tensor.mutableBuffer.array().drop(bufferStart).take(numElements).toDoubleArray()
+}
+
+public fun IntTensor.toIntArray(): IntArray {
+    return tensor.mutableBuffer.array().drop(bufferStart).take(numElements).toIntArray()
+}
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
index a152b3a17..e54cc4d26 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
@@ -18,7 +18,7 @@ internal fun <T> BufferedTensor<T>.vectorSequence(): Sequence<BufferedTensor<T>>
     val vectorOffset = shape[n - 1]
     val vectorShape = intArrayOf(shape.last())
     for (offset in 0 until numElements step vectorOffset) {
-        val vector = BufferedTensor(vectorShape, mutableBuffer, offset)
+        val vector = BufferedTensor(vectorShape, mutableBuffer, bufferStart + offset)
         yield(vector)
     }
 }
@@ -29,7 +29,7 @@ internal fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTensor<T>>
     val matrixOffset = shape[n - 1] * shape[n - 2]
     val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
     for (offset in 0 until numElements step matrixOffset) {
-        val matrix = BufferedTensor(matrixShape, mutableBuffer, offset)
+        val matrix = BufferedTensor(matrixShape, mutableBuffer, bufferStart + offset)
         yield(matrix)
     }
 }

From fe81dea243860ad063d5490d13225182ddbfaedc Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Sat, 1 May 2021 14:22:05 +0300
Subject: [PATCH 143/178] stack

---
 .../core/algebras/DoubleTensorAlgebra.kt      | 22 ++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index f428b9d2e..c6fb301b5 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -382,7 +382,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
             return false
         }
         for (i in 0 until n) {
-            if (!eqFunction(tensor.mutableBuffer[tensor.bufferStart + i], other.tensor.mutableBuffer[other.tensor.bufferStart + i])) {
+            if (!eqFunction(
+                    tensor.mutableBuffer[tensor.bufferStart + i],
+                    other.tensor.mutableBuffer[other.tensor.bufferStart + i]
+                )
+            ) {
                 return false
             }
         }
@@ -395,4 +399,20 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     public fun TensorStructure<Double>.randNormalLike(seed: Long = 0): DoubleTensor =
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
+    // stack tensors by axis 0
+    public fun stack(tensors: List<DoubleTensor>): DoubleTensor {
+        val shape = tensors.firstOrNull()?.shape
+        check(shape != null) { "Collection must have at least 1 element" }
+        check(tensors.all { it.shape contentEquals shape }) {"Stacking tensors must have same shapes"}
+        val resShape = intArrayOf(tensors.size) + shape
+        val resBuffer = tensors.flatMap {
+            it.tensor.mutableBuffer.array().drop(it.bufferStart).take(it.numElements)
+        }.toDoubleArray()
+        return DoubleTensor(resShape, resBuffer, 0)
+    }
+
+    // build tensor from this rows by given indices
+    public fun TensorStructure<Double>.rowsByIndices(indices: IntArray): DoubleTensor {
+        return stack(indices.map { this[it] })
+    }
 }

From bfba653904793b74be89ea9f46461ad8c20a2b8e Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Sat, 1 May 2021 17:47:12 +0300
Subject: [PATCH 144/178] refactor

---
 .../kscience/kmath/tensors/OLSWithSVD.kt      |  6 ++---
 .../algebras/DoubleLinearOpsTensorAlgebra.kt  |  8 ++++---
 .../core/algebras/DoubleTensorAlgebra.kt      | 18 +++++++-------
 .../kmath/tensors/core/broadcastUtils.kt      | 24 +++++++++----------
 .../kscience/kmath/tensors/core/utils.kt      |  3 +--
 .../core/TestDoubleLinearOpsAlgebra.kt        |  6 ++---
 6 files changed, 32 insertions(+), 33 deletions(-)

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
index 0408bba63..095905f05 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
@@ -21,7 +21,7 @@ fun main() {
     // work in context with linear operations
     DoubleLinearOpsTensorAlgebra.invoke {
         // take coefficient vector from normal distribution
-        val alpha = randNormal(
+        val alpha = randomNormal(
             intArrayOf(5),
             randSeed
         ) + fromArray(
@@ -32,14 +32,14 @@ fun main() {
         println("Real alpha:\n$alpha")
 
         // also take sample of size 20 from normal distribution for x
-        val x = randNormal(
+        val x = randomNormal(
             intArrayOf(20, 5),
             randSeed
         )
 
         // calculate y and add gaussian noise (N(0, 0.05))
         val y = x dot alpha
-        y += y.randNormalLike(randSeed) * 0.05
+        y += y.randomNormalLike(randSeed) * 0.05
 
         // now restore the coefficient vector with OSL estimator with SVD
         val (u, singValues, v) = x.svd()
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
index 700fafbeb..dd5ad5a61 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
@@ -31,7 +31,7 @@ public object DoubleLinearOpsTensorAlgebra :
 
     public fun TensorStructure<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
         computeLU(tensor, epsilon)
-            ?: throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
+            ?: throw IllegalArgumentException("Tensor contains matrices which are singular at precision $epsilon")
 
     public fun TensorStructure<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
 
@@ -47,8 +47,10 @@ public object DoubleLinearOpsTensorAlgebra :
 
         val n = luTensor.shape.last()
         val pTensor = luTensor.zeroesLike()
-        for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.tensor.vectorSequence()))
-            pivInit(p.as2D(), pivot.as1D(), n)
+        pTensor
+            .matrixSequence()
+            .zip(pivotsTensor.tensor.vectorSequence())
+            .forEach { (p, pivot) -> pivInit(p.as2D(), pivot.as1D(), n) }
 
         val lTensor = luTensor.zeroesLike()
         val uTensor = luTensor.zeroesLike()
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index c6fb301b5..d3e8bf175 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -284,7 +284,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         val m1 = newThis.shape[newThis.shape.size - 1]
         val m2 = newOther.shape[newOther.shape.size - 2]
         val n = newOther.shape[newOther.shape.size - 1]
-        if (m1 != m2) {
+        check (m1 == m2) {
             throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
         }
 
@@ -315,11 +315,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         val d1 = minusIndexFrom(n + 1, dim1)
         val d2 = minusIndexFrom(n + 1, dim2)
 
-        if (d1 == d2) {
-            throw RuntimeException("Diagonal dimensions cannot be identical $d1, $d2")
+        check(d1 != d2) {
+            "Diagonal dimensions cannot be identical $d1, $d2"
         }
-        if (d1 > n || d2 > n) {
-            throw RuntimeException("Dimension out of range")
+        check(d1 <= n && d2 <= n) {
+            "Dimension out of range"
         }
 
         var lessDim = d1
@@ -366,8 +366,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         )
     }
 
-    public fun TensorStructure<Double>.eq(other: TensorStructure<Double>, delta: Double): Boolean {
-        return tensor.eq(other) { x, y -> abs(x - y) < delta }
+    public fun TensorStructure<Double>.eq(other: TensorStructure<Double>, epsilon: Double): Boolean {
+        return tensor.eq(other) { x, y -> abs(x - y) < epsilon }
     }
 
     public infix fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
@@ -393,10 +393,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return true
     }
 
-    public fun randNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
+    public fun randomNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
         DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed))
 
-    public fun TensorStructure<Double>.randNormalLike(seed: Long = 0): DoubleTensor =
+    public fun TensorStructure<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
     // stack tensors by axis 0
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
index e883b7861..dfac054b5 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
@@ -42,8 +42,8 @@ internal fun broadcastShapes(vararg shapes: IntArray): IntArray {
         for (i in shape.indices) {
             val curDim = shape[i]
             val offset = totalDim - shape.size
-            if (curDim != 1 && totalShape[i + offset] != curDim) {
-                throw RuntimeException("Shapes are not compatible and cannot be broadcast")
+            check(curDim == 1 || totalShape[i + offset] == curDim) {
+                "Shapes are not compatible and cannot be broadcast"
             }
         }
     }
@@ -52,8 +52,8 @@ internal fun broadcastShapes(vararg shapes: IntArray): IntArray {
 }
 
 internal fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor {
-    if (tensor.shape.size > newShape.size) {
-        throw RuntimeException("Tensor is not compatible with the new shape")
+    require(tensor.shape.size <= newShape.size) {
+        "Tensor is not compatible with the new shape"
     }
 
     val n = newShape.reduce { acc, i -> acc * i }
@@ -62,8 +62,8 @@ internal fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor
     for (i in tensor.shape.indices) {
         val curDim = tensor.shape[i]
         val offset = newShape.size - tensor.shape.size
-        if (curDim != 1 && newShape[i + offset] != curDim) {
-            throw RuntimeException("Tensor is not compatible with the new shape and cannot be broadcast")
+        check(curDim == 1 || newShape[i + offset] == curDim) {
+            "Tensor is not compatible with the new shape and cannot be broadcast"
         }
     }
 
@@ -75,19 +75,17 @@ internal fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleTensor>
     val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
     val n = totalShape.reduce { acc, i -> acc * i }
 
-    return  buildList {
-        for (tensor in tensors) {
-            val resTensor = DoubleTensor(totalShape, DoubleArray(n))
-            multiIndexBroadCasting(tensor, resTensor, n)
-            add(resTensor)
-        }
+    return tensors.map { tensor ->
+        val resTensor = DoubleTensor(totalShape, DoubleArray(n))
+        multiIndexBroadCasting(tensor, resTensor, n)
+        resTensor
     }
 }
 
 internal fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
     val onlyTwoDims = tensors.asSequence().onEach {
         require(it.shape.size >= 2) {
-            throw RuntimeException("Tensors must have at least 2 dimensions")
+            "Tensors must have at least 2 dimensions"
         }
     }.any { it.shape.size != 2 }
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index 58d280307..88b9c6c5c 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -69,8 +69,7 @@ internal fun DoubleTensor.toPrettyString(): String = buildString {
     val shape = this@toPrettyString.shape
     val linearStructure = this@toPrettyString.linearStructure
     val vectorSize = shape.last()
-    val initString = "DoubleTensor(\n"
-    append(initString)
+    append("DoubleTensor(\n")
     var charOffset = 3
     for (vector in vectorSequence()) {
         repeat(charOffset) { append(' ') }
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 2282d7fcb..6689e893a 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -135,7 +135,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testCholesky() = DoubleLinearOpsTensorAlgebra.invoke {
-        val tensor = randNormal(intArrayOf(2, 5, 5), 0)
+        val tensor = randomNormal(intArrayOf(2, 5, 5), 0)
         val sigma = (tensor dot tensor.transpose()) + diagonalEmbedding(
             fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
         )
@@ -163,7 +163,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra.invoke {
-        val tensor = randNormal(intArrayOf(2, 5, 3), 0)
+        val tensor = randomNormal(intArrayOf(2, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
         val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
         assertTrue(tensor.eq(tensorSVD))
@@ -171,7 +171,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra.invoke {
-        val tensor = randNormal(shape = intArrayOf(2, 3, 3), 0)
+        val tensor = randomNormal(shape = intArrayOf(2, 3, 3), 0)
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
         val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose())

From ac6608b5b45ed7b59667216ec62b73380f60ef97 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Sat, 1 May 2021 20:45:23 +0300
Subject: [PATCH 145/178] refactor

---
 .../tensors/LinearSystemSolvingWithLUP.kt     |  2 +-
 .../kscience/kmath/tensors/OLSWithSVD.kt      |  2 +-
 kmath-tensors/build.gradle.kts                |  3 ++
 .../kmath/tensors/core/BufferedTensor.kt      |  8 +++-
 .../kscience/kmath/tensors/core/checks.kt     | 23 ++++------
 .../kscience/kmath/tensors/core/linUtils.kt   | 12 -----
 .../kscience/kmath/tensors/core/utils.kt      | 45 +++++++++++--------
 7 files changed, 47 insertions(+), 48 deletions(-)

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
index a84224bcf..bd8233ccc 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
@@ -14,7 +14,7 @@ import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 fun main () {
 
     // work in context with linear operations
-    DoubleLinearOpsTensorAlgebra.invoke {
+    DoubleLinearOpsTensorAlgebra {
 
         // set true value of x
         val trueX = fromArray(
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
index 095905f05..435af35f6 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
@@ -19,7 +19,7 @@ fun main() {
     val randSeed = 100500L
 
     // work in context with linear operations
-    DoubleLinearOpsTensorAlgebra.invoke {
+    DoubleLinearOpsTensorAlgebra {
         // take coefficient vector from normal distribution
         val alpha = randomNormal(
             intArrayOf(5),
diff --git a/kmath-tensors/build.gradle.kts b/kmath-tensors/build.gradle.kts
index af5116022..b7f24dc6a 100644
--- a/kmath-tensors/build.gradle.kts
+++ b/kmath-tensors/build.gradle.kts
@@ -3,6 +3,9 @@ plugins {
 }
 
 kotlin.sourceSets {
+    all {
+        languageSettings.useExperimentalAnnotation("space.kscience.kmath.misc.UnstableKMathAPI")
+    }
     commonMain {
         dependencies {
             api(project(":kmath-core"))
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 867b4fb7a..9541a97f9 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -109,9 +109,13 @@ public fun Array<IntArray>.toIntTensor(): IntTensor {
 }
 
 public fun DoubleTensor.toDoubleArray(): DoubleArray {
-    return tensor.mutableBuffer.array().drop(bufferStart).take(numElements).toDoubleArray()
+    return DoubleArray(numElements) { i ->
+        mutableBuffer[bufferStart + i]
+    }
 }
 
 public fun IntTensor.toIntArray(): IntArray {
-    return tensor.mutableBuffer.array().drop(bufferStart).take(numElements).toIntArray()
+    return IntArray(numElements) { i ->
+        mutableBuffer[bufferStart + i]
+    }
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index fd98be8b2..b1c12ccde 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -5,38 +5,35 @@ import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 
 
-internal fun checkEmptyShape(shape: IntArray): Unit =
+internal fun checkEmptyShape(shape: IntArray) =
     check(shape.isNotEmpty()) {
         "Illegal empty shape provided"
     }
 
-internal fun checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
+internal fun checkEmptyDoubleBuffer(buffer: DoubleArray) =
     check(buffer.isNotEmpty()) {
         "Illegal empty buffer provided"
     }
 
-internal fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
+internal fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray) =
     check(buffer.size == shape.reduce(Int::times)) {
         "Inconsistent shape ${shape.toList()} for buffer of size ${buffer.size} provided"
     }
 
-
-internal fun <T> checkShapesCompatible(a: TensorStructure<T>, b: TensorStructure<T>): Unit =
+internal fun <T> checkShapesCompatible(a: TensorStructure<T>, b: TensorStructure<T>) =
     check(a.shape contentEquals b.shape) {
         "Incompatible shapes ${a.shape.toList()} and ${b.shape.toList()} "
     }
 
-
-internal fun checkTranspose(dim: Int, i: Int, j: Int): Unit =
+internal fun checkTranspose(dim: Int, i: Int, j: Int) =
     check((i < dim) and (j < dim)) {
         "Cannot transpose $i to $j for a tensor of dim $dim"
     }
 
-internal fun <T> checkView(a: TensorStructure<T>, shape: IntArray): Unit =
+internal fun <T> checkView(a: TensorStructure<T>, shape: IntArray) =
     check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
 
-
-internal fun checkSquareMatrix(shape: IntArray): Unit {
+internal fun checkSquareMatrix(shape: IntArray) {
     val n = shape.size
     check(n >= 2) {
         "Expected tensor with 2 or more dimensions, got size $n instead"
@@ -48,14 +45,12 @@ internal fun checkSquareMatrix(shape: IntArray): Unit {
 
 internal fun DoubleTensorAlgebra.checkSymmetric(
     tensor: TensorStructure<Double>, epsilon: Double = 1e-6
-): Unit =
+) =
     check(tensor.eq(tensor.transpose(), epsilon)) {
         "Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
     }
 
-internal fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(
-    tensor: DoubleTensor, epsilon: Double = 1e-6
-): Unit {
+internal fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor, epsilon: Double = 1e-6) {
     checkSymmetric(tensor, epsilon)
     for (mat in tensor.matrixSequence())
         check(mat.asTensor().detLU().value() > 0.0) {
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
index e54cc4d26..ba8b823c9 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
@@ -34,18 +34,6 @@ internal fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTensor<T>>
     }
 }
 
-internal inline fun <T> BufferedTensor<T>.forEachVector(vectorAction: (BufferedTensor<T>) -> Unit) {
-    for (vector in vectorSequence()) {
-        vectorAction(vector)
-    }
-}
-
-internal inline fun <T> BufferedTensor<T>.forEachMatrix(matrixAction: (BufferedTensor<T>) -> Unit) {
-    for (matrix in matrixSequence()) {
-        matrixAction(matrix)
-    }
-}
-
 internal fun dotHelper(
     a: MutableStructure2D<Double>,
     b: MutableStructure2D<Double>,
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
index 88b9c6c5c..0211342bb 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
@@ -14,7 +14,6 @@ internal fun Buffer<Int>.array(): IntArray = when (this) {
     else -> this.toIntArray()
 }
 
-
 /**
  * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer] or copy the data.
  */
@@ -31,7 +30,7 @@ internal fun getRandomNormals(n: Int, seed: Long): DoubleArray {
 
 internal fun getRandomUnitVector(n: Int, seed: Long): DoubleArray {
     val unnorm = getRandomNormals(n, seed)
-    val norm = sqrt(unnorm.map { it * it }.sum())
+    val norm = sqrt(unnorm.sumOf { it * it })
     return unnorm.map { it / norm }.toDoubleArray()
 }
 
@@ -45,23 +44,33 @@ internal fun minusIndexFrom(n: Int, i: Int): Int = if (i >= 0) i else {
 
 internal fun <T> BufferedTensor<T>.minusIndex(i: Int): Int = minusIndexFrom(this.dimension, i)
 
-internal fun format(value: Double, digits: Int = 4): String {
-    val ten = 10.0
-    val approxOrder = if (value == 0.0) 0 else ceil(log10(abs(value))).toInt()
-    val order = if (
-        ((value % ten) == 0.0) or
-        (value == 1.0) or
-        ((1 / value) % ten == 0.0)
-    ) approxOrder else approxOrder - 1
-    val lead = value / ten.pow(order)
-    val leadDisplay = round(lead * ten.pow(digits)) / ten.pow(digits)
-    val orderDisplay = if (order == 0) "" else if (order > 0) "E+$order" else "E$order"
-    val valueDisplay = "$leadDisplay$orderDisplay"
-    val res = if (value < 0.0) valueDisplay else " $valueDisplay"
-
+internal fun format(value: Double, digits: Int = 4): String = buildString {
+    val res = buildString {
+        val ten = 10.0
+        val approxOrder = if (value == 0.0) 0 else ceil(log10(abs(value))).toInt()
+        val order = if (
+            ((value % ten) == 0.0) ||
+            (value == 1.0) ||
+            ((1 / value) % ten == 0.0)
+        ) approxOrder else approxOrder - 1
+        val lead = value / ten.pow(order)
+        if (value >= 0.0) append(' ')
+        append(round(lead * ten.pow(digits)) / ten.pow(digits))
+        when {
+            order == 0 -> Unit
+            order > 0 -> {
+                append("e+")
+                append(order)
+            }
+            else -> {
+                append('e')
+                append(order)
+            }
+        }
+    }
     val fLength = digits + 6
-    val endSpace = " ".repeat(fLength - res.length)
-    return "$res$endSpace"
+    append(res)
+    repeat(fLength - res.length) { append(' ') }
 }
 
 internal fun DoubleTensor.toPrettyString(): String = buildString {

From 48d86fac5620b5e663a5dc62618f4f2869e86521 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sat, 1 May 2021 19:55:48 +0100
Subject: [PATCH 146/178] invoke refactor

---
 .../core/algebras/TensorLinearStructure.kt    |  6 -----
 .../kscience/kmath/tensors/core/linUtils.kt   |  2 +-
 .../kmath/tensors/core/TestBroadcasting.kt    | 10 ++++----
 .../core/TestDoubleAnalyticTensorAlgebra.kt   |  2 +-
 .../core/TestDoubleLinearOpsAlgebra.kt        | 24 +++++++++----------
 .../kmath/tensors/core/TestDoubleTensor.kt    |  6 ++---
 .../tensors/core/TestDoubleTensorAlgebra.kt   | 18 +++++++-------
 7 files changed, 31 insertions(+), 37 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
index 08aab5175..5fbc7390f 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
@@ -9,12 +9,6 @@ import space.kscience.kmath.nd.Strides
 import kotlin.math.max
 
 
-internal fun offsetFromIndex(index: IntArray, shape: IntArray, strides: IntArray): Int =
-    index.mapIndexed { i, value ->
-        if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${shape[i]})")
-        value * strides[i]
-    }.sum()
-
 internal fun stridesFromShape(shape: IntArray): IntArray {
     val nDim = shape.size
     val res = IntArray(nDim)
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
index ba8b823c9..8adbfad39 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
@@ -241,7 +241,7 @@ internal fun DoubleLinearOpsTensorAlgebra.qrHelper(
                 }
             }
         }
-        r[j, j] = DoubleAnalyticTensorAlgebra.invoke { (v dot v).sqrt().value() }
+        r[j, j] = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
         for (i in 0 until n) {
             qM[i, j] = vv[i] / r[j, j]
         }
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
index 1070a1115..1564b85c9 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -9,7 +9,7 @@ import kotlin.test.assertTrue
 internal class TestBroadcasting {
 
     @Test
-    fun broadcastShapes() = DoubleTensorAlgebra.invoke {
+    fun broadcastShapes() = DoubleTensorAlgebra {
         assertTrue(
             broadcastShapes(
                 intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
@@ -24,7 +24,7 @@ internal class TestBroadcasting {
     }
 
     @Test
-    fun broadcastTo() = DoubleTensorAlgebra.invoke {
+    fun broadcastTo() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
 
@@ -34,7 +34,7 @@ internal class TestBroadcasting {
     }
 
     @Test
-    fun broadcastTensors() = DoubleTensorAlgebra.invoke {
+    fun broadcastTensors() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
@@ -51,7 +51,7 @@ internal class TestBroadcasting {
     }
 
     @Test
-    fun broadcastOuterTensors() = DoubleTensorAlgebra.invoke {
+    fun broadcastOuterTensors() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
@@ -68,7 +68,7 @@ internal class TestBroadcasting {
     }
 
     @Test
-    fun broadcastOuterTensorsShapes() = DoubleTensorAlgebra.invoke {
+    fun broadcastOuterTensorsShapes() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 1, 3, 2, 3), DoubleArray(2 * 1 * 3 * 2 * 3) {0.0})
         val tensor2 = fromArray(intArrayOf(4, 2, 5, 1, 3, 3), DoubleArray(4 * 2 * 5 * 1 * 3 * 3) {0.0})
         val tensor3 = fromArray(intArrayOf(1, 1), doubleArrayOf(500.0))
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
index 66959a0de..835b8a08a 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -27,7 +27,7 @@ internal class TestDoubleAnalyticTensorAlgebra {
     }
 
     @Test
-    fun testExp() = DoubleAnalyticTensorAlgebra.invoke {
+    fun testExp() = DoubleAnalyticTensorAlgebra {
         tensor.exp().let {
             assertTrue { shape contentEquals it.shape }
             assertTrue { buffer.fmap(::exp).epsEqual(it.mutableBuffer.array())}
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 6689e893a..65070af7f 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -10,7 +10,7 @@ import kotlin.test.assertTrue
 internal class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
-    fun testDetLU() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testDetLU() = DoubleLinearOpsTensorAlgebra {
         val tensor = fromArray(
             intArrayOf(2, 2, 2),
             doubleArrayOf(
@@ -35,7 +35,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testDet() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testDet() = DoubleLinearOpsTensorAlgebra {
         val expectedValue = 0.019827417
         val m = fromArray(
             intArrayOf(3, 3), doubleArrayOf(
@@ -49,7 +49,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testDetSingle() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testDetSingle() = DoubleLinearOpsTensorAlgebra {
         val expectedValue = 48.151623
         val m = fromArray(
             intArrayOf(1, 1), doubleArrayOf(
@@ -61,7 +61,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testInvLU() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testInvLU() = DoubleLinearOpsTensorAlgebra {
         val tensor = fromArray(
             intArrayOf(2, 2, 2),
             doubleArrayOf(
@@ -86,14 +86,14 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testScalarProduct() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testScalarProduct() = DoubleLinearOpsTensorAlgebra {
         val a = fromArray(intArrayOf(3), doubleArrayOf(1.8, 2.5, 6.8))
         val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4))
         assertEquals(a.dot(b).value(), 59.92)
     }
 
     @Test
-    fun testQR() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testQR() = DoubleLinearOpsTensorAlgebra {
         val shape = intArrayOf(2, 2, 2)
         val buffer = doubleArrayOf(
             1.0, 3.0,
@@ -114,7 +114,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testLU() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testLU() = DoubleLinearOpsTensorAlgebra {
         val shape = intArrayOf(2, 2, 2)
         val buffer = doubleArrayOf(
             1.0, 3.0,
@@ -134,7 +134,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testCholesky() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testCholesky() = DoubleLinearOpsTensorAlgebra {
         val tensor = randomNormal(intArrayOf(2, 5, 5), 0)
         val sigma = (tensor dot tensor.transpose()) + diagonalEmbedding(
             fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
@@ -145,7 +145,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testSVD1D() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testSVD1D() = DoubleLinearOpsTensorAlgebra {
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
 
         val res = svd1d(tensor2)
@@ -156,13 +156,13 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testSVD() = DoubleLinearOpsTensorAlgebra.invoke{
+    fun testSVD() = DoubleLinearOpsTensorAlgebra{
         testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
         testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
     }
 
     @Test
-    fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
         val tensor = randomNormal(intArrayOf(2, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
         val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
@@ -170,7 +170,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra.invoke {
+    fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra {
         val tensor = randomNormal(shape = intArrayOf(2, 3, 3), 0)
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 333a7bdec..ed5f8e780 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -15,14 +15,14 @@ import kotlin.test.assertTrue
 internal class TestDoubleTensor {
 
     @Test
-    fun valueTest() = DoubleTensorAlgebra.invoke {
+    fun valueTest() = DoubleTensorAlgebra {
         val value = 12.5
         val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
     }
 
     @Test
-    fun stridesTest() = DoubleTensorAlgebra.invoke {
+    fun stridesTest() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
         assertEquals(tensor[intArrayOf(0, 1)], 5.8)
         assertTrue(
@@ -31,7 +31,7 @@ internal class TestDoubleTensor {
     }
 
     @Test
-    fun getTest() = DoubleTensorAlgebra.invoke {
+    fun getTest() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(1, 2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
         val matrix = tensor[0].as2D()
         assertEquals(matrix[0, 1], 5.8)
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index a0efa4573..df2d21b96 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -10,21 +10,21 @@ import kotlin.test.assertTrue
 internal class TestDoubleTensorAlgebra {
 
     @Test
-    fun doublePlus() = DoubleTensorAlgebra.invoke {
+    fun doublePlus() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
         assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(11.0, 12.0))
     }
 
     @Test
-    fun doubleDiv() = DoubleTensorAlgebra.invoke {
+    fun doubleDiv() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(2.0, 4.0))
         val res = 2.0/tensor
         assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 0.5))
     }
 
     @Test
-    fun divDouble() = DoubleTensorAlgebra.invoke {
+    fun divDouble() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(10.0, 5.0))
         val res = tensor / 2.5
         assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(4.0, 2.0))
@@ -40,7 +40,7 @@ internal class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun transpose3x2() = DoubleTensorAlgebra.invoke {
+    fun transpose3x2() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res = tensor.transpose(1, 0)
 
@@ -49,7 +49,7 @@ internal class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun transpose1x2x3() = DoubleTensorAlgebra.invoke {
+    fun transpose1x2x3() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res01 = tensor.transpose(0, 1)
         val res02 = tensor.transpose(-3, 2)
@@ -65,7 +65,7 @@ internal class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun linearStructure() = DoubleTensorAlgebra.invoke {
+    fun linearStructure() = DoubleTensorAlgebra {
         val shape = intArrayOf(3)
         val tensorA = full(value = -4.5, shape = shape)
         val tensorB = full(value = 10.9, shape = shape)
@@ -97,7 +97,7 @@ internal class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun dot() = DoubleTensorAlgebra.invoke {
+    fun dot() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor11 = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
@@ -133,7 +133,7 @@ internal class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun diagonalEmbedding() = DoubleTensorAlgebra.invoke {
+    fun diagonalEmbedding() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor3 = zeros(intArrayOf(2, 3, 4, 5))
@@ -166,7 +166,7 @@ internal class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun testEq() = DoubleTensorAlgebra.invoke {
+    fun testEq() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor3 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0))

From 5aaba0dae443605b3dedb4bc4ee7733922b8e41f Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sun, 2 May 2021 16:19:05 +0100
Subject: [PATCH 147/178] TensorStructure to Tensor

---
 .../tensors/api/AnalyticTensorAlgebra.kt      | 36 ++++-----
 .../tensors/api/LinearOpsTensorAlgebra.kt     | 16 ++--
 .../api/{TensorStructure.kt => Tensor.kt}     |  2 +-
 .../kmath/tensors/api/TensorAlgebra.kt        | 52 ++++++------
 .../api/TensorPartialDivisionAlgebra.kt       | 12 +--
 .../kmath/tensors/core/BufferedTensor.kt      | 16 ++--
 .../algebras/BroadcastDoubleTensorAlgebra.kt  | 18 ++---
 .../algebras/DoubleAnalyticTensorAlgebra.kt   | 36 ++++-----
 .../algebras/DoubleLinearOpsTensorAlgebra.kt  | 36 ++++-----
 .../core/algebras/DoubleTensorAlgebra.kt      | 80 +++++++++----------
 .../kscience/kmath/tensors/core/checks.kt     |  8 +-
 11 files changed, 156 insertions(+), 156 deletions(-)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/{TensorStructure.kt => Tensor.kt} (60%)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
index 5c92c56c4..cd13e0752 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
@@ -6,7 +6,7 @@
 package space.kscience.kmath.tensors.api
 
 /**
- * Element-wise analytic operations on [TensorStructure].
+ * Element-wise analytic operations on [Tensor].
  *
  * @param T the type of items closed under analytic functions in the tensors.
  */
@@ -14,54 +14,54 @@ public interface AnalyticTensorAlgebra<T> :
     TensorPartialDivisionAlgebra<T> {
 
     //For information: https://pytorch.org/docs/stable/generated/torch.exp.html
-    public fun TensorStructure<T>.exp(): TensorStructure<T>
+    public fun Tensor<T>.exp(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.log.html
-    public fun TensorStructure<T>.log(): TensorStructure<T>
+    public fun Tensor<T>.log(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.sqrt.html
-    public fun TensorStructure<T>.sqrt(): TensorStructure<T>
+    public fun Tensor<T>.sqrt(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.acos.html#torch.cos
-    public fun TensorStructure<T>.cos(): TensorStructure<T>
+    public fun Tensor<T>.cos(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.acos.html#torch.acos
-    public fun TensorStructure<T>.acos(): TensorStructure<T>
+    public fun Tensor<T>.acos(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.cosh
-    public fun TensorStructure<T>.cosh(): TensorStructure<T>
+    public fun Tensor<T>.cosh(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.acosh
-    public fun TensorStructure<T>.acosh(): TensorStructure<T>
+    public fun Tensor<T>.acosh(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sin
-    public fun TensorStructure<T>.sin(): TensorStructure<T>
+    public fun Tensor<T>.sin(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asin
-    public fun TensorStructure<T>.asin(): TensorStructure<T>
+    public fun Tensor<T>.asin(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sinh
-    public fun TensorStructure<T>.sinh(): TensorStructure<T>
+    public fun Tensor<T>.sinh(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asinh
-    public fun TensorStructure<T>.asinh(): TensorStructure<T>
+    public fun Tensor<T>.asinh(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.atan.html#torch.tan
-    public fun TensorStructure<T>.tan(): TensorStructure<T>
+    public fun Tensor<T>.tan(): Tensor<T>
 
     //https://pytorch.org/docs/stable/generated/torch.atan.html#torch.atan
-    public fun TensorStructure<T>.atan(): TensorStructure<T>
+    public fun Tensor<T>.atan(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.tanh
-    public fun TensorStructure<T>.tanh(): TensorStructure<T>
+    public fun Tensor<T>.tanh(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.atanh
-    public fun TensorStructure<T>.atanh(): TensorStructure<T>
+    public fun Tensor<T>.atanh(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.ceil.html#torch.ceil
-    public fun TensorStructure<T>.ceil(): TensorStructure<T>
+    public fun Tensor<T>.ceil(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.floor.html#torch.floor
-    public fun TensorStructure<T>.floor(): TensorStructure<T>
+    public fun Tensor<T>.floor(): Tensor<T>
 
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
index bcbb52a1b..527e5d386 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
@@ -6,7 +6,7 @@
 package space.kscience.kmath.tensors.api
 
 /**
- * Common linear algebra operations. Operates on [TensorStructure].
+ * Common linear algebra operations. Operates on [Tensor].
  *
  * @param T the type of items closed under division in the tensors.
  */
@@ -19,7 +19,7 @@ public interface LinearOpsTensorAlgebra<T> :
      *
      * @return the determinant.
      */
-    public fun TensorStructure<T>.det(): TensorStructure<T>
+    public fun Tensor<T>.det(): Tensor<T>
 
     /**
      * Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input.
@@ -29,7 +29,7 @@ public interface LinearOpsTensorAlgebra<T> :
      *
      * @return the multiplicative inverse of a matrix.
      */
-    public fun TensorStructure<T>.inv(): TensorStructure<T>
+    public fun Tensor<T>.inv(): Tensor<T>
 
     /**
      * Cholesky decomposition.
@@ -44,7 +44,7 @@ public interface LinearOpsTensorAlgebra<T> :
      *
      * @return the batch of L matrices.
      */
-    public fun TensorStructure<T>.cholesky(): TensorStructure<T>
+    public fun Tensor<T>.cholesky(): Tensor<T>
 
     /**
      * QR decomposition.
@@ -57,7 +57,7 @@ public interface LinearOpsTensorAlgebra<T> :
      *
      * @return pair of Q and R tensors.
      */
-    public fun TensorStructure<T>.qr(): Pair<TensorStructure<T>, TensorStructure<T>>
+    public fun Tensor<T>.qr(): Pair<Tensor<T>, Tensor<T>>
 
     /**
      * LUP decomposition
@@ -70,7 +70,7 @@ public interface LinearOpsTensorAlgebra<T> :
      *
      * * @return triple of P, L and U tensors
      */
-    public fun TensorStructure<T>.lu(): Triple<TensorStructure<T>, TensorStructure<T>, TensorStructure<T>>
+    public fun Tensor<T>.lu(): Triple<Tensor<T>, Tensor<T>, Tensor<T>>
 
     /**
      * Singular Value Decomposition.
@@ -83,7 +83,7 @@ public interface LinearOpsTensorAlgebra<T> :
      *
      * @return the determinant.
      */
-    public fun TensorStructure<T>.svd(): Triple<TensorStructure<T>, TensorStructure<T>, TensorStructure<T>>
+    public fun Tensor<T>.svd(): Triple<Tensor<T>, Tensor<T>, Tensor<T>>
 
     /**
      * Returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices,
@@ -92,6 +92,6 @@ public interface LinearOpsTensorAlgebra<T> :
      *
      * @return a pair (eigenvalues, eigenvectors)
      */
-    public fun TensorStructure<T>.symEig(): Pair<TensorStructure<T>, TensorStructure<T>>
+    public fun Tensor<T>.symEig(): Pair<Tensor<T>, Tensor<T>>
 
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/Tensor.kt
similarity index 60%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorStructure.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/Tensor.kt
index edecd6383..179787684 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/Tensor.kt
@@ -2,4 +2,4 @@ package space.kscience.kmath.tensors.api
 
 import space.kscience.kmath.nd.MutableStructureND
 
-public typealias TensorStructure<T> = MutableStructureND<T>
+public typealias Tensor<T> = MutableStructureND<T>
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index 96d6985d8..b9c707c0b 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -8,19 +8,19 @@ package space.kscience.kmath.tensors.api
 import space.kscience.kmath.operations.Algebra
 
 /**
- * Algebra over a ring on [TensorStructure].
+ * Algebra over a ring on [Tensor].
  * For more information: https://proofwiki.org/wiki/Definition:Algebra_over_Ring
  *
  * @param T the type of items in the tensors.
  */
-public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
+public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
 
     /**
      * Returns a single tensor value of unit dimension. The tensor shape must be equal to [1].
      *
      * @return the value of a scalar tensor.
      */
-    public fun TensorStructure<T>.value(): T
+    public fun Tensor<T>.value(): T
 
     /**
      * Each element of the tensor [other] is added to this value.
@@ -29,7 +29,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param other tensor to be added.
      * @return the sum of this value and tensor [other].
      */
-    public operator fun T.plus(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun T.plus(other: Tensor<T>): Tensor<T>
 
     /**
      * Adds the scalar [value] to each element of this tensor and returns a new resulting tensor.
@@ -37,7 +37,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param value the number to be added to each element of this tensor.
      * @return the sum of this tensor and [value].
      */
-    public operator fun TensorStructure<T>.plus(value: T): TensorStructure<T>
+    public operator fun Tensor<T>.plus(value: T): Tensor<T>
 
     /**
      * Each element of the tensor [other] is added to each element of this tensor.
@@ -46,21 +46,21 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param other tensor to be added.
      * @return the sum of this tensor and [other].
      */
-    public operator fun TensorStructure<T>.plus(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun Tensor<T>.plus(other: Tensor<T>): Tensor<T>
 
     /**
      * Adds the scalar [value] to each element of this tensor.
      *
      * @param value the number to be added to each element of this tensor.
      */
-    public operator fun TensorStructure<T>.plusAssign(value: T): Unit
+    public operator fun Tensor<T>.plusAssign(value: T): Unit
 
     /**
      * Each element of the tensor [other] is added to each element of this tensor.
      *
      * @param other tensor to be added.
      */
-    public operator fun TensorStructure<T>.plusAssign(other: TensorStructure<T>): Unit
+    public operator fun Tensor<T>.plusAssign(other: Tensor<T>): Unit
 
 
     /**
@@ -70,7 +70,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param other tensor to be subtracted.
      * @return the difference between this value and tensor [other].
      */
-    public operator fun T.minus(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun T.minus(other: Tensor<T>): Tensor<T>
 
     /**
      * Subtracts the scalar [value] from each element of this tensor and returns a new resulting tensor.
@@ -78,7 +78,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param value the number to be subtracted from each element of this tensor.
      * @return the difference between this tensor and [value].
      */
-    public operator fun TensorStructure<T>.minus(value: T): TensorStructure<T>
+    public operator fun Tensor<T>.minus(value: T): Tensor<T>
 
     /**
      * Each element of the tensor [other] is subtracted from each element of this tensor.
@@ -87,21 +87,21 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param other tensor to be subtracted.
      * @return the difference between this tensor and [other].
      */
-    public operator fun TensorStructure<T>.minus(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun Tensor<T>.minus(other: Tensor<T>): Tensor<T>
 
     /**
      * Subtracts the scalar [value] from each element of this tensor.
      *
      * @param value the number to be subtracted from each element of this tensor.
      */
-    public operator fun TensorStructure<T>.minusAssign(value: T): Unit
+    public operator fun Tensor<T>.minusAssign(value: T): Unit
 
     /**
      * Each element of the tensor [other] is subtracted from each element of this tensor.
      *
      * @param other tensor to be subtracted.
      */
-    public operator fun TensorStructure<T>.minusAssign(other: TensorStructure<T>): Unit
+    public operator fun Tensor<T>.minusAssign(other: Tensor<T>): Unit
 
 
     /**
@@ -111,7 +111,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param other tensor to be multiplied.
      * @return the product of this value and tensor [other].
      */
-    public operator fun T.times(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun T.times(other: Tensor<T>): Tensor<T>
 
     /**
      * Multiplies the scalar [value] by each element of this tensor and returns a new resulting tensor.
@@ -119,7 +119,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param value the number to be multiplied by each element of this tensor.
      * @return the product of this tensor and [value].
      */
-    public operator fun TensorStructure<T>.times(value: T): TensorStructure<T>
+    public operator fun Tensor<T>.times(value: T): Tensor<T>
 
     /**
      * Each element of the tensor [other] is multiplied by each element of this tensor.
@@ -128,28 +128,28 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param other tensor to be multiplied.
      * @return the product of this tensor and [other].
      */
-    public operator fun TensorStructure<T>.times(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun Tensor<T>.times(other: Tensor<T>): Tensor<T>
 
     /**
      * Multiplies the scalar [value] by each element of this tensor.
      *
      * @param value the number to be multiplied by each element of this tensor.
      */
-    public operator fun TensorStructure<T>.timesAssign(value: T): Unit
+    public operator fun Tensor<T>.timesAssign(value: T): Unit
 
     /**
      * Each element of the tensor [other] is multiplied by each element of this tensor.
      *
      * @param other tensor to be multiplied.
      */
-    public operator fun TensorStructure<T>.timesAssign(other: TensorStructure<T>): Unit
+    public operator fun Tensor<T>.timesAssign(other: Tensor<T>): Unit
 
     /**
      * Numerical negative, element-wise.
      *
      * @return tensor negation of the original tensor.
      */
-    public operator fun TensorStructure<T>.unaryMinus(): TensorStructure<T>
+    public operator fun Tensor<T>.unaryMinus(): Tensor<T>
 
     /**
      * Returns the tensor at index i
@@ -158,7 +158,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param i index of the extractable tensor
      * @return subtensor of the original tensor with index [i]
      */
-    public operator fun TensorStructure<T>.get(i: Int): TensorStructure<T>
+    public operator fun Tensor<T>.get(i: Int): Tensor<T>
 
     /**
      * Returns a tensor that is a transposed version of this tensor. The given dimensions [i] and [j] are swapped.
@@ -168,7 +168,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param j the second dimension to be transposed
      * @return transposed tensor
      */
-    public fun TensorStructure<T>.transpose(i: Int = -2, j: Int = -1): TensorStructure<T>
+    public fun Tensor<T>.transpose(i: Int = -2, j: Int = -1): Tensor<T>
 
     /**
      * Returns a new tensor with the same data as the self tensor but of a different shape.
@@ -178,7 +178,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param shape the desired size
      * @return tensor with new shape
      */
-    public fun TensorStructure<T>.view(shape: IntArray): TensorStructure<T>
+    public fun Tensor<T>.view(shape: IntArray): Tensor<T>
 
     /**
      * View this tensor as the same size as [other].
@@ -188,7 +188,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param other the result tensor has the same size as other.
      * @return the result tensor with the same size as other.
      */
-    public fun TensorStructure<T>.viewAs(other: TensorStructure<T>): TensorStructure<T>
+    public fun Tensor<T>.viewAs(other: Tensor<T>): Tensor<T>
 
     /**
      * Matrix product of two tensors.
@@ -219,7 +219,7 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * @param other tensor to be multiplied
      * @return mathematical product of two tensors
      */
-    public infix fun TensorStructure<T>.dot(other: TensorStructure<T>): TensorStructure<T>
+    public infix fun Tensor<T>.dot(other: Tensor<T>): Tensor<T>
 
     /**
      * Creates a tensor whose diagonals of certain 2D planes (specified by [dim1] and [dim2])
@@ -245,10 +245,10 @@ public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
      * are filled by [diagonalEntries]
      */
     public fun diagonalEmbedding(
-        diagonalEntries: TensorStructure<T>,
+        diagonalEntries: Tensor<T>,
         offset: Int = 0,
         dim1: Int = -2,
         dim2: Int = -1
-    ): TensorStructure<T>
+    ): Tensor<T>
 
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
index eccc6fa0d..921157963 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
@@ -6,7 +6,7 @@
 package space.kscience.kmath.tensors.api
 
 /**
- * Algebra over a field with partial division on [TensorStructure].
+ * Algebra over a field with partial division on [Tensor].
  * For more information: https://proofwiki.org/wiki/Definition:Division_Algebra
  *
  * @param T the type of items closed under division in the tensors.
@@ -21,7 +21,7 @@ public interface TensorPartialDivisionAlgebra<T> :
      * @param other tensor to divide by.
      * @return the division of this value by the tensor [other].
      */
-    public operator fun T.div(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun T.div(other: Tensor<T>): Tensor<T>
 
     /**
      * Divide by the scalar [value] each element of this tensor returns a new resulting tensor.
@@ -29,7 +29,7 @@ public interface TensorPartialDivisionAlgebra<T> :
      * @param value the number to divide by each element of this tensor.
      * @return the division of this tensor by the [value].
      */
-    public operator fun TensorStructure<T>.div(value: T): TensorStructure<T>
+    public operator fun Tensor<T>.div(value: T): Tensor<T>
 
     /**
      * Each element of the tensor [other] is divided by each element of this tensor.
@@ -38,19 +38,19 @@ public interface TensorPartialDivisionAlgebra<T> :
      * @param other tensor to be divided by.
      * @return the division of this tensor by [other].
      */
-    public operator fun TensorStructure<T>.div(other: TensorStructure<T>): TensorStructure<T>
+    public operator fun Tensor<T>.div(other: Tensor<T>): Tensor<T>
 
     /**
      * Divides by the scalar [value] each element of this tensor.
      *
      * @param value the number to divide by each element of this tensor.
      */
-    public operator fun TensorStructure<T>.divAssign(value: T)
+    public operator fun Tensor<T>.divAssign(value: T)
 
     /**
      * Each element of this tensor is divided by each element of the [other] tensor.
      *
      * @param other tensor to be divide by.
      */
-    public operator fun TensorStructure<T>.divAssign(other: TensorStructure<T>)
+    public operator fun Tensor<T>.divAssign(other: Tensor<T>)
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 9541a97f9..d0882efb8 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -2,7 +2,7 @@ package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.nd.MutableBufferND
 import space.kscience.kmath.structures.*
-import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
 
 
@@ -10,7 +10,7 @@ public open class BufferedTensor<T>(
     override val shape: IntArray,
     internal val mutableBuffer: MutableBuffer<T>,
     internal val bufferStart: Int
-) : TensorStructure<T> {
+) : Tensor<T> {
     public val linearStructure: TensorLinearStructure
         get() = TensorLinearStructure(shape)
 
@@ -53,33 +53,33 @@ internal fun BufferedTensor<Int>.asTensor(): IntTensor =
 internal fun BufferedTensor<Double>.asTensor(): DoubleTensor =
     DoubleTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
 
-internal fun <T> TensorStructure<T>.copyToBufferedTensor(): BufferedTensor<T> =
+internal fun <T> Tensor<T>.copyToBufferedTensor(): BufferedTensor<T> =
     BufferedTensor(
         this.shape,
         TensorLinearStructure(this.shape).indices().map(this::get).toMutableList().asMutableBuffer(), 0
     )
 
-internal fun <T> TensorStructure<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
+internal fun <T> Tensor<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
     is BufferedTensor<T> -> this
     is MutableBufferND<T> -> if (this.strides.strides contentEquals TensorLinearStructure(this.shape).strides)
         BufferedTensor(this.shape, this.mutableBuffer, 0) else this.copyToBufferedTensor()
     else -> this.copyToBufferedTensor()
 }
 
-internal val TensorStructure<Double>.tensor: DoubleTensor
+internal val Tensor<Double>.tensor: DoubleTensor
     get() = when (this) {
         is DoubleTensor -> this
         else -> this.toBufferedTensor().asTensor()
     }
 
-internal val TensorStructure<Int>.tensor: IntTensor
+internal val Tensor<Int>.tensor: IntTensor
     get() = when (this) {
         is IntTensor -> this
         else -> this.toBufferedTensor().asTensor()
     }
 
-public fun TensorStructure<Double>.toDoubleTensor(): DoubleTensor = this.tensor
-public fun TensorStructure<Int>.toIntTensor(): IntTensor = this.tensor
+public fun Tensor<Double>.toDoubleTensor(): DoubleTensor = this.tensor
+public fun Tensor<Int>.toIntTensor(): IntTensor = this.tensor
 
 public fun Array<DoubleArray>.toDoubleTensor(): DoubleTensor {
     val n = size
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
index 9b97d5ef2..873ec9027 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
@@ -5,7 +5,7 @@
 
 package space.kscience.kmath.tensors.core.algebras
 
-import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.*
 import space.kscience.kmath.tensors.core.broadcastTensors
 import space.kscience.kmath.tensors.core.broadcastTo
@@ -16,7 +16,7 @@ import space.kscience.kmath.tensors.core.broadcastTo
  */
 public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
-    override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
+    override fun Tensor<Double>.plus(other: Tensor<Double>): DoubleTensor {
         val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
@@ -26,7 +26,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
+    override fun Tensor<Double>.plusAssign(other: Tensor<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
         for (i in 0 until tensor.linearStructure.linearSize) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] +=
@@ -34,7 +34,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         }
     }
 
-    override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
+    override fun Tensor<Double>.minus(other: Tensor<Double>): DoubleTensor {
         val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
@@ -44,7 +44,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
+    override fun Tensor<Double>.minusAssign(other: Tensor<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
         for (i in 0 until tensor.linearStructure.linearSize) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] -=
@@ -52,7 +52,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         }
     }
 
-    override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
+    override fun Tensor<Double>.times(other: Tensor<Double>): DoubleTensor {
         val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
@@ -63,7 +63,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
+    override fun Tensor<Double>.timesAssign(other: Tensor<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
         for (i in 0 until tensor.linearStructure.linearSize) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] *=
@@ -71,7 +71,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         }
     }
 
-    override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
+    override fun Tensor<Double>.div(other: Tensor<Double>): DoubleTensor {
         val broadcast = broadcastTensors(tensor, other.tensor)
         val newThis = broadcast[0]
         val newOther = broadcast[1]
@@ -82,7 +82,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         return DoubleTensor(newThis.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
+    override fun Tensor<Double>.divAssign(other: Tensor<Double>) {
         val newOther = broadcastTo(other.tensor, tensor.shape)
         for (i in 0 until tensor.linearStructure.linearSize) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] /=
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
index 4a942df84..9aa6f093e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
@@ -6,7 +6,7 @@
 package space.kscience.kmath.tensors.core.algebras
 
 import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
-import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.tensor
 import kotlin.math.*
@@ -14,38 +14,38 @@ import kotlin.math.*
 public object DoubleAnalyticTensorAlgebra :
     AnalyticTensorAlgebra<Double>,
     DoubleTensorAlgebra() {
-    override fun TensorStructure<Double>.exp(): DoubleTensor = tensor.map(::exp)
+    override fun Tensor<Double>.exp(): DoubleTensor = tensor.map(::exp)
 
-    override fun TensorStructure<Double>.log(): DoubleTensor = tensor.map(::ln)
+    override fun Tensor<Double>.log(): DoubleTensor = tensor.map(::ln)
 
-    override fun TensorStructure<Double>.sqrt(): DoubleTensor = tensor.map(::sqrt)
+    override fun Tensor<Double>.sqrt(): DoubleTensor = tensor.map(::sqrt)
 
-    override fun TensorStructure<Double>.cos(): DoubleTensor = tensor.map(::cos)
+    override fun Tensor<Double>.cos(): DoubleTensor = tensor.map(::cos)
 
-    override fun TensorStructure<Double>.acos(): DoubleTensor = tensor.map(::acos)
+    override fun Tensor<Double>.acos(): DoubleTensor = tensor.map(::acos)
 
-    override fun TensorStructure<Double>.cosh(): DoubleTensor = tensor.map(::cosh)
+    override fun Tensor<Double>.cosh(): DoubleTensor = tensor.map(::cosh)
 
-    override fun TensorStructure<Double>.acosh(): DoubleTensor = tensor.map(::acosh)
+    override fun Tensor<Double>.acosh(): DoubleTensor = tensor.map(::acosh)
 
-    override fun TensorStructure<Double>.sin(): DoubleTensor = tensor.map(::sin)
+    override fun Tensor<Double>.sin(): DoubleTensor = tensor.map(::sin)
 
-    override fun TensorStructure<Double>.asin(): DoubleTensor = tensor.map(::asin)
+    override fun Tensor<Double>.asin(): DoubleTensor = tensor.map(::asin)
 
-    override fun TensorStructure<Double>.sinh(): DoubleTensor = tensor.map(::sinh)
+    override fun Tensor<Double>.sinh(): DoubleTensor = tensor.map(::sinh)
 
-    override fun TensorStructure<Double>.asinh(): DoubleTensor = tensor.map(::asinh)
+    override fun Tensor<Double>.asinh(): DoubleTensor = tensor.map(::asinh)
 
-    override fun TensorStructure<Double>.tan(): DoubleTensor = tensor.map(::tan)
+    override fun Tensor<Double>.tan(): DoubleTensor = tensor.map(::tan)
 
-    override fun TensorStructure<Double>.atan(): DoubleTensor = tensor.map(::atan)
+    override fun Tensor<Double>.atan(): DoubleTensor = tensor.map(::atan)
 
-    override fun TensorStructure<Double>.tanh(): DoubleTensor = tensor.map(::tanh)
+    override fun Tensor<Double>.tanh(): DoubleTensor = tensor.map(::tanh)
 
-    override fun TensorStructure<Double>.atanh(): DoubleTensor = tensor.map(::atanh)
+    override fun Tensor<Double>.atanh(): DoubleTensor = tensor.map(::atanh)
 
-    override fun TensorStructure<Double>.ceil(): DoubleTensor = tensor.map(::ceil)
+    override fun Tensor<Double>.ceil(): DoubleTensor = tensor.map(::ceil)
 
-    override fun TensorStructure<Double>.floor(): DoubleTensor = tensor.map(::floor)
+    override fun Tensor<Double>.floor(): DoubleTensor = tensor.map(::floor)
 
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
index dd5ad5a61..89345e315 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
@@ -8,7 +8,7 @@ package space.kscience.kmath.tensors.core.algebras
 import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.*
 import space.kscience.kmath.tensors.core.checkSquareMatrix
 import space.kscience.kmath.tensors.core.choleskyHelper
@@ -25,19 +25,19 @@ public object DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double>,
     DoubleTensorAlgebra() {
 
-    override fun TensorStructure<Double>.inv(): DoubleTensor = invLU(1e-9)
+    override fun Tensor<Double>.inv(): DoubleTensor = invLU(1e-9)
 
-    override fun TensorStructure<Double>.det(): DoubleTensor = detLU(1e-9)
+    override fun Tensor<Double>.det(): DoubleTensor = detLU(1e-9)
 
-    public fun TensorStructure<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
+    public fun Tensor<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
         computeLU(tensor, epsilon)
             ?: throw IllegalArgumentException("Tensor contains matrices which are singular at precision $epsilon")
 
-    public fun TensorStructure<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
+    public fun Tensor<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
 
     public fun luPivot(
-        luTensor: TensorStructure<Double>,
-        pivotsTensor: TensorStructure<Int>
+        luTensor: Tensor<Double>,
+        pivotsTensor: Tensor<Int>
     ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         checkSquareMatrix(luTensor.shape)
         check(
@@ -66,7 +66,7 @@ public object DoubleLinearOpsTensorAlgebra :
         return Triple(pTensor, lTensor, uTensor)
     }
 
-    public fun TensorStructure<Double>.cholesky(epsilon: Double): DoubleTensor {
+    public fun Tensor<Double>.cholesky(epsilon: Double): DoubleTensor {
         checkSquareMatrix(shape)
         checkPositiveDefinite(tensor, epsilon)
 
@@ -79,9 +79,9 @@ public object DoubleLinearOpsTensorAlgebra :
         return lTensor
     }
 
-    override fun TensorStructure<Double>.cholesky(): DoubleTensor = cholesky(1e-6)
+    override fun Tensor<Double>.cholesky(): DoubleTensor = cholesky(1e-6)
 
-    override fun TensorStructure<Double>.qr(): Pair<DoubleTensor, DoubleTensor> {
+    override fun Tensor<Double>.qr(): Pair<DoubleTensor, DoubleTensor> {
         checkSquareMatrix(shape)
         val qTensor = zeroesLike()
         val rTensor = zeroesLike()
@@ -95,10 +95,10 @@ public object DoubleLinearOpsTensorAlgebra :
         return qTensor to rTensor
     }
 
-    override fun TensorStructure<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
+    override fun Tensor<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
         svd(epsilon = 1e-10)
 
-    public fun TensorStructure<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+    public fun Tensor<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         val size = tensor.linearStructure.dim
         val commonShape = tensor.shape.sliceArray(0 until size - 2)
         val (n, m) = tensor.shape.sliceArray(size - 2 until size)
@@ -122,11 +122,11 @@ public object DoubleLinearOpsTensorAlgebra :
         return Triple(uTensor.transpose(), sTensor, vTensor.transpose())
     }
 
-    override fun TensorStructure<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> =
+    override fun Tensor<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> =
         symEig(epsilon = 1e-15)
 
     //For information: http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
-    public fun TensorStructure<Double>.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
+    public fun Tensor<Double>.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
         checkSymmetric(tensor, epsilon)
         val (u, s, v) = tensor.svd(epsilon)
         val shp = s.shape + intArrayOf(1)
@@ -139,7 +139,7 @@ public object DoubleLinearOpsTensorAlgebra :
         return eig to v
     }
 
-    public fun TensorStructure<Double>.detLU(epsilon: Double = 1e-9): DoubleTensor {
+    public fun Tensor<Double>.detLU(epsilon: Double = 1e-9): DoubleTensor {
 
         checkSquareMatrix(tensor.shape)
         val luTensor = tensor.copy()
@@ -164,7 +164,7 @@ public object DoubleLinearOpsTensorAlgebra :
         return detTensor
     }
 
-    public fun TensorStructure<Double>.invLU(epsilon: Double = 1e-9): DoubleTensor {
+    public fun Tensor<Double>.invLU(epsilon: Double = 1e-9): DoubleTensor {
         val (luTensor, pivotsTensor) = luFactor(epsilon)
         val invTensor = luTensor.zeroesLike()
 
@@ -177,11 +177,11 @@ public object DoubleLinearOpsTensorAlgebra :
         return invTensor
     }
 
-    public fun TensorStructure<Double>.lu(epsilon: Double = 1e-9): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+    public fun Tensor<Double>.lu(epsilon: Double = 1e-9): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         val (lu, pivots) = this.luFactor(epsilon)
         return luPivot(lu, pivots)
     }
 
-    override fun TensorStructure<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
+    override fun Tensor<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
 
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index d3e8bf175..4009f7b45 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -7,7 +7,7 @@ package space.kscience.kmath.tensors.core.algebras
 
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra
-import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.*
 import space.kscience.kmath.tensors.core.broadcastOuterTensors
 import space.kscience.kmath.tensors.core.checkBufferShapeConsistency
@@ -25,7 +25,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
     public companion object : DoubleTensorAlgebra()
 
-    override fun TensorStructure<Double>.value(): Double {
+    override fun Tensor<Double>.value(): Double {
         check(tensor.shape contentEquals intArrayOf(1)) {
             "Inconsistent value for tensor of shape ${shape.toList()}"
         }
@@ -39,7 +39,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return DoubleTensor(shape, buffer, 0)
     }
 
-    override operator fun TensorStructure<Double>.get(i: Int): DoubleTensor {
+    override operator fun Tensor<Double>.get(i: Int): DoubleTensor {
         val lastShape = tensor.shape.drop(1).toIntArray()
         val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
         val newStart = newShape.reduce(Int::times) * i + tensor.bufferStart
@@ -52,7 +52,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return DoubleTensor(shape, buffer)
     }
 
-    public fun TensorStructure<Double>.fullLike(value: Double): DoubleTensor {
+    public fun Tensor<Double>.fullLike(value: Double): DoubleTensor {
         val shape = tensor.shape
         val buffer = DoubleArray(tensor.numElements) { value }
         return DoubleTensor(shape, buffer)
@@ -60,11 +60,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
     public fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
 
-    public fun TensorStructure<Double>.zeroesLike(): DoubleTensor = tensor.fullLike(0.0)
+    public fun Tensor<Double>.zeroesLike(): DoubleTensor = tensor.fullLike(0.0)
 
     public fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
 
-    public fun TensorStructure<Double>.onesLike(): DoubleTensor = tensor.fullLike(1.0)
+    public fun Tensor<Double>.onesLike(): DoubleTensor = tensor.fullLike(1.0)
 
     public fun eye(n: Int): DoubleTensor {
         val shape = intArrayOf(n, n)
@@ -76,20 +76,20 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return res
     }
 
-    public fun TensorStructure<Double>.copy(): DoubleTensor {
+    public fun Tensor<Double>.copy(): DoubleTensor {
         return DoubleTensor(tensor.shape, tensor.mutableBuffer.array().copyOf(), tensor.bufferStart)
     }
 
-    override fun Double.plus(other: TensorStructure<Double>): DoubleTensor {
+    override fun Double.plus(other: Tensor<Double>): DoubleTensor {
         val resBuffer = DoubleArray(other.tensor.numElements) { i ->
             other.tensor.mutableBuffer.array()[other.tensor.bufferStart + i] + this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.plus(value: Double): DoubleTensor = value + tensor
+    override fun Tensor<Double>.plus(value: Double): DoubleTensor = value + tensor
 
-    override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
+    override fun Tensor<Double>.plus(other: Tensor<Double>): DoubleTensor {
         checkShapesCompatible(tensor, other.tensor)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
             tensor.mutableBuffer.array()[i] + other.tensor.mutableBuffer.array()[i]
@@ -97,13 +97,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.plusAssign(value: Double) {
+    override fun Tensor<Double>.plusAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] += value
         }
     }
 
-    override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
+    override fun Tensor<Double>.plusAssign(other: Tensor<Double>) {
         checkShapesCompatible(tensor, other.tensor)
         for (i in 0 until tensor.numElements) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] +=
@@ -111,21 +111,21 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         }
     }
 
-    override fun Double.minus(other: TensorStructure<Double>): DoubleTensor {
+    override fun Double.minus(other: Tensor<Double>): DoubleTensor {
         val resBuffer = DoubleArray(other.tensor.numElements) { i ->
             this - other.tensor.mutableBuffer.array()[other.tensor.bufferStart + i]
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.minus(value: Double): DoubleTensor {
+    override fun Tensor<Double>.minus(value: Double): DoubleTensor {
         val resBuffer = DoubleArray(tensor.numElements) { i ->
             tensor.mutableBuffer.array()[tensor.bufferStart + i] - value
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
+    override fun Tensor<Double>.minus(other: Tensor<Double>): DoubleTensor {
         checkShapesCompatible(tensor, other)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
             tensor.mutableBuffer.array()[i] - other.tensor.mutableBuffer.array()[i]
@@ -133,13 +133,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.minusAssign(value: Double) {
+    override fun Tensor<Double>.minusAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] -= value
         }
     }
 
-    override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
+    override fun Tensor<Double>.minusAssign(other: Tensor<Double>) {
         checkShapesCompatible(tensor, other)
         for (i in 0 until tensor.numElements) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] -=
@@ -147,16 +147,16 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         }
     }
 
-    override fun Double.times(other: TensorStructure<Double>): DoubleTensor {
+    override fun Double.times(other: Tensor<Double>): DoubleTensor {
         val resBuffer = DoubleArray(other.tensor.numElements) { i ->
             other.tensor.mutableBuffer.array()[other.tensor.bufferStart + i] * this
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.times(value: Double): DoubleTensor = value * tensor
+    override fun Tensor<Double>.times(value: Double): DoubleTensor = value * tensor
 
-    override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
+    override fun Tensor<Double>.times(other: Tensor<Double>): DoubleTensor {
         checkShapesCompatible(tensor, other)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
             tensor.mutableBuffer.array()[tensor.bufferStart + i] *
@@ -165,13 +165,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.timesAssign(value: Double) {
+    override fun Tensor<Double>.timesAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] *= value
         }
     }
 
-    override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
+    override fun Tensor<Double>.timesAssign(other: Tensor<Double>) {
         checkShapesCompatible(tensor, other)
         for (i in 0 until tensor.numElements) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] *=
@@ -179,21 +179,21 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         }
     }
 
-    override fun Double.div(other: TensorStructure<Double>): DoubleTensor {
+    override fun Double.div(other: Tensor<Double>): DoubleTensor {
         val resBuffer = DoubleArray(other.tensor.numElements) { i ->
             this / other.tensor.mutableBuffer.array()[other.tensor.bufferStart + i]
         }
         return DoubleTensor(other.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.div(value: Double): DoubleTensor {
+    override fun Tensor<Double>.div(value: Double): DoubleTensor {
         val resBuffer = DoubleArray(tensor.numElements) { i ->
             tensor.mutableBuffer.array()[tensor.bufferStart + i] / value
         }
         return DoubleTensor(shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
+    override fun Tensor<Double>.div(other: Tensor<Double>): DoubleTensor {
         checkShapesCompatible(tensor, other)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
             tensor.mutableBuffer.array()[other.tensor.bufferStart + i] /
@@ -202,13 +202,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.divAssign(value: Double) {
+    override fun Tensor<Double>.divAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] /= value
         }
     }
 
-    override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
+    override fun Tensor<Double>.divAssign(other: Tensor<Double>) {
         checkShapesCompatible(tensor, other)
         for (i in 0 until tensor.numElements) {
             tensor.mutableBuffer.array()[tensor.bufferStart + i] /=
@@ -216,14 +216,14 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         }
     }
 
-    override fun TensorStructure<Double>.unaryMinus(): DoubleTensor {
+    override fun Tensor<Double>.unaryMinus(): DoubleTensor {
         val resBuffer = DoubleArray(tensor.numElements) { i ->
             tensor.mutableBuffer.array()[tensor.bufferStart + i].unaryMinus()
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
-    override fun TensorStructure<Double>.transpose(i: Int, j: Int): DoubleTensor {
+    override fun Tensor<Double>.transpose(i: Int, j: Int): DoubleTensor {
         val ii = tensor.minusIndex(i)
         val jj = tensor.minusIndex(j)
         checkTranspose(tensor.dimension, ii, jj)
@@ -248,16 +248,16 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     }
 
 
-    override fun TensorStructure<Double>.view(shape: IntArray): DoubleTensor {
+    override fun Tensor<Double>.view(shape: IntArray): DoubleTensor {
         checkView(tensor, shape)
         return DoubleTensor(shape, tensor.mutableBuffer.array(), tensor.bufferStart)
     }
 
-    override fun TensorStructure<Double>.viewAs(other: TensorStructure<Double>): DoubleTensor {
+    override fun Tensor<Double>.viewAs(other: Tensor<Double>): DoubleTensor {
         return tensor.view(other.shape)
     }
 
-    override infix fun TensorStructure<Double>.dot(other: TensorStructure<Double>): DoubleTensor {
+    override infix fun Tensor<Double>.dot(other: Tensor<Double>): DoubleTensor {
         if (tensor.shape.size == 1 && other.shape.size == 1) {
             return DoubleTensor(intArrayOf(1), doubleArrayOf(tensor.times(other).tensor.mutableBuffer.array().sum()))
         }
@@ -309,7 +309,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return resTensor
     }
 
-    override fun diagonalEmbedding(diagonalEntries: TensorStructure<Double>, offset: Int, dim1: Int, dim2: Int):
+    override fun diagonalEmbedding(diagonalEntries: Tensor<Double>, offset: Int, dim1: Int, dim2: Int):
             DoubleTensor {
         val n = diagonalEntries.shape.size
         val d1 = minusIndexFrom(n + 1, dim1)
@@ -358,7 +358,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     }
 
 
-    public fun TensorStructure<Double>.map(transform: (Double) -> Double): DoubleTensor {
+    public fun Tensor<Double>.map(transform: (Double) -> Double): DoubleTensor {
         return DoubleTensor(
             tensor.shape,
             tensor.mutableBuffer.array().map { transform(it) }.toDoubleArray(),
@@ -366,14 +366,14 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         )
     }
 
-    public fun TensorStructure<Double>.eq(other: TensorStructure<Double>, epsilon: Double): Boolean {
+    public fun Tensor<Double>.eq(other: Tensor<Double>, epsilon: Double): Boolean {
         return tensor.eq(other) { x, y -> abs(x - y) < epsilon }
     }
 
-    public infix fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
+    public infix fun Tensor<Double>.eq(other: Tensor<Double>): Boolean = tensor.eq(other, 1e-5)
 
-    private fun TensorStructure<Double>.eq(
-        other: TensorStructure<Double>,
+    private fun Tensor<Double>.eq(
+        other: Tensor<Double>,
         eqFunction: (Double, Double) -> Boolean
     ): Boolean {
         checkShapesCompatible(tensor, other)
@@ -396,7 +396,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     public fun randomNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
         DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed))
 
-    public fun TensorStructure<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
+    public fun Tensor<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
     // stack tensors by axis 0
@@ -412,7 +412,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     }
 
     // build tensor from this rows by given indices
-    public fun TensorStructure<Double>.rowsByIndices(indices: IntArray): DoubleTensor {
+    public fun Tensor<Double>.rowsByIndices(indices: IntArray): DoubleTensor {
         return stack(indices.map { this[it] })
     }
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
index b1c12ccde..f8bd5027a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
@@ -1,6 +1,6 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 
@@ -20,7 +20,7 @@ internal fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray) =
         "Inconsistent shape ${shape.toList()} for buffer of size ${buffer.size} provided"
     }
 
-internal fun <T> checkShapesCompatible(a: TensorStructure<T>, b: TensorStructure<T>) =
+internal fun <T> checkShapesCompatible(a: Tensor<T>, b: Tensor<T>) =
     check(a.shape contentEquals b.shape) {
         "Incompatible shapes ${a.shape.toList()} and ${b.shape.toList()} "
     }
@@ -30,7 +30,7 @@ internal fun checkTranspose(dim: Int, i: Int, j: Int) =
         "Cannot transpose $i to $j for a tensor of dim $dim"
     }
 
-internal fun <T> checkView(a: TensorStructure<T>, shape: IntArray) =
+internal fun <T> checkView(a: Tensor<T>, shape: IntArray) =
     check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
 
 internal fun checkSquareMatrix(shape: IntArray) {
@@ -44,7 +44,7 @@ internal fun checkSquareMatrix(shape: IntArray) {
 }
 
 internal fun DoubleTensorAlgebra.checkSymmetric(
-    tensor: TensorStructure<Double>, epsilon: Double = 1e-6
+    tensor: Tensor<Double>, epsilon: Double = 1e-6
 ) =
     check(tensor.eq(tensor.transpose(), epsilon)) {
         "Tensor is not symmetric about the last 2 dimensions at precision $epsilon"

From 8898f908ef6cd6a3ce9458af13cc9abe160e8649 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Mon, 3 May 2021 18:45:18 +0300
Subject: [PATCH 148/178] statistic algebra

---
 .../tensors/api/StatisticTensorAlgebra.kt     | 137 ++++++++++++++++++
 .../algebras/DoubleStatisticTensorAlgebra.kt  | 105 ++++++++++++++
 2 files changed, 242 insertions(+)
 create mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt
 create mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt
new file mode 100644
index 000000000..e3a2a1124
--- /dev/null
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt
@@ -0,0 +1,137 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.api
+
+import space.kscience.kmath.tensors.core.DoubleTensor
+
+/**
+ * Common algebra with statistics methods. Operates on [Tensor].
+ *
+ * @param T the type of items closed under division in the tensors.
+ */
+
+public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
+
+    /**
+     * Returns the minimum value of all elements in the input tensor.
+     *
+     * @return the minimum value of all elements in the input tensor.
+     */
+    public fun Tensor<T>.min(): Double
+
+    /**
+     * Returns the minimum value of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the minimum value of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.min(dim: Int, keepDim: Boolean): DoubleTensor
+
+    /**
+     * Returns the maximum value of all elements in the input tensor.
+     *
+     * @return the maximum value of all elements in the input tensor.
+     */
+    public fun Tensor<T>.max(): Double
+
+    /**
+     * Returns the maximum value of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the maximum value of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.max(dim: Int, keepDim: Boolean): DoubleTensor
+
+    /**
+     * Returns the sum of all elements in the input tensor.
+     *
+     * @return the sum of all elements in the input tensor.
+     */
+    public fun Tensor<T>.sum(): Double
+
+    /**
+     * Returns the sum of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the sum of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.sum(dim: Int, keepDim: Boolean): DoubleTensor
+
+    /**
+     * Returns the mean of all elements in the input tensor.
+     *
+     * @return the mean of all elements in the input tensor.
+     */
+    public fun Tensor<T>.mean(): Double
+
+    /**
+     * Returns the mean of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the mean of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.mean(dim: Int, keepDim: Boolean): DoubleTensor
+
+    /**
+     * Returns the standard deviation of all elements in the input tensor.
+     *
+     * @return the standard deviation of all elements in the input tensor.
+     */
+    public fun Tensor<T>.std(): Double
+
+    /**
+     * Returns the standard deviation of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the standard deviation of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.std(dim: Int, keepDim: Boolean): DoubleTensor
+
+    /**
+     * Returns the variance of all elements in the input tensor.
+     *
+     * @return the variance of all elements in the input tensor.
+     */
+    public fun Tensor<T>.variance(): Double
+
+    /**
+     * Returns the variance of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the variance of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.variance(dim: Int, keepDim: Boolean): DoubleTensor
+}
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt
new file mode 100644
index 000000000..3914a0dfb
--- /dev/null
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt
@@ -0,0 +1,105 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core.algebras
+
+import kotlin.math.sqrt
+
+import space.kscience.kmath.tensors.api.*
+import space.kscience.kmath.tensors.core.*
+import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.max
+import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.mean
+import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.min
+import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.sum
+import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.variance
+
+public object DoubleStatisticTensorAlgebra : StatisticTensorAlgebra<Double>, DoubleTensorAlgebra() {
+
+    private fun Tensor<Double>.fold(foldFunction: (DoubleArray) -> Double): Double {
+        return foldFunction(this.tensor.toDoubleArray())
+    }
+
+    private fun Tensor<Double>.foldDim(
+        foldFunction: (DoubleArray) -> Double,
+        dim: Int,
+        keepDim: Boolean
+    ): DoubleTensor {
+        check(dim < dimension) { "Dimension $dim out of range $dimension" }
+        val resShape = if (keepDim) {
+            shape.take(dim).toIntArray() + intArrayOf(1) + shape.takeLast(dimension - dim - 1).toIntArray()
+        } else {
+            shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray()
+        }
+        val resNumElements = resShape.reduce(Int::times)
+        val resTensor = DoubleTensor(resShape, DoubleArray(resNumElements) { 0.0 }, 0)
+        for (index in resTensor.linearStructure.indices()) {
+            val prefix = index.take(dim).toIntArray()
+            val suffix = index.takeLast(dimension - dim - 1).toIntArray()
+            resTensor[index] = foldFunction(DoubleArray(shape[dim]) { i ->
+                this[prefix + intArrayOf(i) + suffix]
+            })
+        }
+
+        return resTensor
+    }
+
+    override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
+
+    override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.minOrNull()!! }, dim, keepDim)
+
+    override fun Tensor<Double>.max(): Double = this.fold { it.maxOrNull()!! }
+
+    override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
+
+    override fun Tensor<Double>.sum(): Double = this.fold { it.sum() }
+
+    override fun Tensor<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.sum() }, dim, keepDim)
+
+    override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
+
+    override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim(
+            { arr ->
+                check(dim < dimension) { "Dimension $dim out of range $dimension" }
+                arr.sum() / shape[dim]
+            },
+            dim,
+            keepDim
+        )
+
+    override fun Tensor<Double>.std(): Double = this.fold { arr ->
+        val mean = arr.sum() / tensor.numElements
+        sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
+    }
+
+    override fun Tensor<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
+        { arr ->
+            check(dim < dimension) { "Dimension $dim out of range $dimension" }
+            val mean = arr.sum() / shape[dim]
+            sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
+        },
+        dim,
+        keepDim
+    )
+
+    override fun Tensor<Double>.variance(): Double = this.fold { arr ->
+        val mean = arr.sum() / tensor.numElements
+        arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
+    }
+
+    override fun Tensor<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
+        { arr ->
+            check(dim < dimension) { "Dimension $dim out of range $dimension" }
+            val mean = arr.sum() / shape[dim]
+            arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
+        },
+        dim,
+        keepDim
+    )
+
+}
\ No newline at end of file

From 7f8914d8eacb6fb09469fa18b7328fa16bfc2208 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Mon, 3 May 2021 20:42:18 +0300
Subject: [PATCH 149/178] fixes

---
 .../kmath/tensors/api/StatisticTensorAlgebra.kt | 17 ++---------------
 .../algebras/DoubleStatisticTensorAlgebra.kt    |  7 +++----
 2 files changed, 5 insertions(+), 19 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt
index e3a2a1124..d0b17af97 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt
@@ -9,16 +9,12 @@ import space.kscience.kmath.tensors.core.DoubleTensor
 
 /**
  * Common algebra with statistics methods. Operates on [Tensor].
- *
- * @param T the type of items closed under division in the tensors.
  */
 
 public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
 
     /**
      * Returns the minimum value of all elements in the input tensor.
-     *
-     * @return the minimum value of all elements in the input tensor.
      */
     public fun Tensor<T>.min(): Double
 
@@ -37,8 +33,6 @@ public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
 
     /**
      * Returns the maximum value of all elements in the input tensor.
-     *
-     * @return the maximum value of all elements in the input tensor.
      */
     public fun Tensor<T>.max(): Double
 
@@ -57,8 +51,6 @@ public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
 
     /**
      * Returns the sum of all elements in the input tensor.
-     *
-     * @return the sum of all elements in the input tensor.
      */
     public fun Tensor<T>.sum(): Double
 
@@ -77,8 +69,6 @@ public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
 
     /**
      * Returns the mean of all elements in the input tensor.
-     *
-     * @return the mean of all elements in the input tensor.
      */
     public fun Tensor<T>.mean(): Double
 
@@ -97,8 +87,6 @@ public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
 
     /**
      * Returns the standard deviation of all elements in the input tensor.
-     *
-     * @return the standard deviation of all elements in the input tensor.
      */
     public fun Tensor<T>.std(): Double
 
@@ -117,8 +105,6 @@ public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
 
     /**
      * Returns the variance of all elements in the input tensor.
-     *
-     * @return the variance of all elements in the input tensor.
      */
     public fun Tensor<T>.variance(): Double
 
@@ -134,4 +120,5 @@ public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
      * @return the variance of each row of the input tensor in the given dimension [dim].
      */
     public fun Tensor<T>.variance(dim: Int, keepDim: Boolean): DoubleTensor
-}
\ No newline at end of file
+
+}
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt
index 3914a0dfb..b455aff48 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt
@@ -17,9 +17,8 @@ import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.v
 
 public object DoubleStatisticTensorAlgebra : StatisticTensorAlgebra<Double>, DoubleTensorAlgebra() {
 
-    private fun Tensor<Double>.fold(foldFunction: (DoubleArray) -> Double): Double {
-        return foldFunction(this.tensor.toDoubleArray())
-    }
+    private fun Tensor<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
+        foldFunction(this.tensor.toDoubleArray())
 
     private fun Tensor<Double>.foldDim(
         foldFunction: (DoubleArray) -> Double,
@@ -102,4 +101,4 @@ public object DoubleStatisticTensorAlgebra : StatisticTensorAlgebra<Double>, Dou
         keepDim
     )
 
-}
\ No newline at end of file
+}

From b59e48410f07ad203820b9c581b5e9997bff9784 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Mon, 3 May 2021 19:49:23 +0100
Subject: [PATCH 150/178] More basic functionality, tests to come

---
 .../tensors/api/AnalyticTensorAlgebra.kt      |  95 +++++++++++++-
 .../tensors/api/StatisticTensorAlgebra.kt     | 124 ------------------
 .../kmath/tensors/api/TensorAlgebra.kt        |  20 +++
 .../algebras/DoubleAnalyticTensorAlgebra.kt   |  54 ++++++++
 .../algebras/DoubleStatisticTensorAlgebra.kt  | 104 ---------------
 .../core/algebras/DoubleTensorAlgebra.kt      |  36 ++++-
 6 files changed, 202 insertions(+), 231 deletions(-)
 delete mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt
 delete mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
index cd13e0752..f9b2df45c 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
@@ -5,14 +5,107 @@
 
 package space.kscience.kmath.tensors.api
 
+
 /**
- * Element-wise analytic operations on [Tensor].
+ * Analytic operations on [Tensor].
  *
  * @param T the type of items closed under analytic functions in the tensors.
  */
 public interface AnalyticTensorAlgebra<T> :
     TensorPartialDivisionAlgebra<T> {
 
+
+    /**
+     * @return the minimum value of all elements in the input tensor.
+     */
+    public fun Tensor<T>.min(): T
+
+    /**
+     * Returns the minimum value of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the minimum value of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.min(dim: Int, keepDim: Boolean): Tensor<T>
+
+    /**
+     * @return the maximum value of all elements in the input tensor.
+     */
+    public fun Tensor<T>.max(): T
+
+    /**
+     * Returns the maximum value of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the maximum value of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T>
+
+
+    /**
+     * @return the mean of all elements in the input tensor.
+     */
+    public fun Tensor<T>.mean(): T
+
+    /**
+     * Returns the mean of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the mean of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.mean(dim: Int, keepDim: Boolean): Tensor<T>
+
+    /**
+     * @return the standard deviation of all elements in the input tensor.
+     */
+    public fun Tensor<T>.std(): T
+
+    /**
+     * Returns the standard deviation of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the standard deviation of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.std(dim: Int, keepDim: Boolean): Tensor<T>
+
+    /**
+     * @return the variance of all elements in the input tensor.
+     */
+    public fun Tensor<T>.variance(): T
+
+    /**
+     * Returns the variance of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the variance of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.variance(dim: Int, keepDim: Boolean): Tensor<T>
+
     //For information: https://pytorch.org/docs/stable/generated/torch.exp.html
     public fun Tensor<T>.exp(): Tensor<T>
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt
deleted file mode 100644
index d0b17af97..000000000
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt
+++ /dev/null
@@ -1,124 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors.api
-
-import space.kscience.kmath.tensors.core.DoubleTensor
-
-/**
- * Common algebra with statistics methods. Operates on [Tensor].
- */
-
-public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
-
-    /**
-     * Returns the minimum value of all elements in the input tensor.
-     */
-    public fun Tensor<T>.min(): Double
-
-    /**
-     * Returns the minimum value of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the minimum value of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.min(dim: Int, keepDim: Boolean): DoubleTensor
-
-    /**
-     * Returns the maximum value of all elements in the input tensor.
-     */
-    public fun Tensor<T>.max(): Double
-
-    /**
-     * Returns the maximum value of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the maximum value of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.max(dim: Int, keepDim: Boolean): DoubleTensor
-
-    /**
-     * Returns the sum of all elements in the input tensor.
-     */
-    public fun Tensor<T>.sum(): Double
-
-    /**
-     * Returns the sum of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the sum of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.sum(dim: Int, keepDim: Boolean): DoubleTensor
-
-    /**
-     * Returns the mean of all elements in the input tensor.
-     */
-    public fun Tensor<T>.mean(): Double
-
-    /**
-     * Returns the mean of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the mean of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.mean(dim: Int, keepDim: Boolean): DoubleTensor
-
-    /**
-     * Returns the standard deviation of all elements in the input tensor.
-     */
-    public fun Tensor<T>.std(): Double
-
-    /**
-     * Returns the standard deviation of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the standard deviation of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.std(dim: Int, keepDim: Boolean): DoubleTensor
-
-    /**
-     * Returns the variance of all elements in the input tensor.
-     */
-    public fun Tensor<T>.variance(): Double
-
-    /**
-     * Returns the variance of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the variance of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.variance(dim: Int, keepDim: Boolean): DoubleTensor
-
-}
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index b9c707c0b..b99f79e9a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -251,4 +251,24 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
         dim2: Int = -1
     ): Tensor<T>
 
+    /**
+     * @return the sum of all elements in the input tensor.
+     */
+    public fun Tensor<T>.sum(): T
+
+    /**
+     * Returns the sum of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the sum of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.sum(dim: Int, keepDim: Boolean): Tensor<T>
+
+
+
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
index 9aa6f093e..547018498 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
@@ -14,6 +14,60 @@ import kotlin.math.*
 public object DoubleAnalyticTensorAlgebra :
     AnalyticTensorAlgebra<Double>,
     DoubleTensorAlgebra() {
+
+    override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
+
+    override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.minOrNull()!! }, dim, keepDim)
+
+    override fun Tensor<Double>.max(): Double = this.fold { it.maxOrNull()!! }
+
+    override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
+
+
+    override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
+
+    override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim(
+            { arr ->
+                check(dim < dimension) { "Dimension $dim out of range $dimension" }
+                arr.sum() / shape[dim]
+            },
+            dim,
+            keepDim
+        )
+
+    override fun Tensor<Double>.std(): Double = this.fold { arr ->
+        val mean = arr.sum() / tensor.numElements
+        sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
+    }
+
+    override fun Tensor<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
+        { arr ->
+            check(dim < dimension) { "Dimension $dim out of range $dimension" }
+            val mean = arr.sum() / shape[dim]
+            sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
+        },
+        dim,
+        keepDim
+    )
+
+    override fun Tensor<Double>.variance(): Double = this.fold { arr ->
+        val mean = arr.sum() / tensor.numElements
+        arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
+    }
+
+    override fun Tensor<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
+        { arr ->
+            check(dim < dimension) { "Dimension $dim out of range $dimension" }
+            val mean = arr.sum() / shape[dim]
+            arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
+        },
+        dim,
+        keepDim
+    )
+
     override fun Tensor<Double>.exp(): DoubleTensor = tensor.map(::exp)
 
     override fun Tensor<Double>.log(): DoubleTensor = tensor.map(::ln)
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt
deleted file mode 100644
index b455aff48..000000000
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt
+++ /dev/null
@@ -1,104 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors.core.algebras
-
-import kotlin.math.sqrt
-
-import space.kscience.kmath.tensors.api.*
-import space.kscience.kmath.tensors.core.*
-import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.max
-import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.mean
-import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.min
-import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.sum
-import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.variance
-
-public object DoubleStatisticTensorAlgebra : StatisticTensorAlgebra<Double>, DoubleTensorAlgebra() {
-
-    private fun Tensor<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
-        foldFunction(this.tensor.toDoubleArray())
-
-    private fun Tensor<Double>.foldDim(
-        foldFunction: (DoubleArray) -> Double,
-        dim: Int,
-        keepDim: Boolean
-    ): DoubleTensor {
-        check(dim < dimension) { "Dimension $dim out of range $dimension" }
-        val resShape = if (keepDim) {
-            shape.take(dim).toIntArray() + intArrayOf(1) + shape.takeLast(dimension - dim - 1).toIntArray()
-        } else {
-            shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray()
-        }
-        val resNumElements = resShape.reduce(Int::times)
-        val resTensor = DoubleTensor(resShape, DoubleArray(resNumElements) { 0.0 }, 0)
-        for (index in resTensor.linearStructure.indices()) {
-            val prefix = index.take(dim).toIntArray()
-            val suffix = index.takeLast(dimension - dim - 1).toIntArray()
-            resTensor[index] = foldFunction(DoubleArray(shape[dim]) { i ->
-                this[prefix + intArrayOf(i) + suffix]
-            })
-        }
-
-        return resTensor
-    }
-
-    override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
-
-    override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim({ x -> x.minOrNull()!! }, dim, keepDim)
-
-    override fun Tensor<Double>.max(): Double = this.fold { it.maxOrNull()!! }
-
-    override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
-
-    override fun Tensor<Double>.sum(): Double = this.fold { it.sum() }
-
-    override fun Tensor<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim({ x -> x.sum() }, dim, keepDim)
-
-    override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
-
-    override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim(
-            { arr ->
-                check(dim < dimension) { "Dimension $dim out of range $dimension" }
-                arr.sum() / shape[dim]
-            },
-            dim,
-            keepDim
-        )
-
-    override fun Tensor<Double>.std(): Double = this.fold { arr ->
-        val mean = arr.sum() / tensor.numElements
-        sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
-    }
-
-    override fun Tensor<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
-        { arr ->
-            check(dim < dimension) { "Dimension $dim out of range $dimension" }
-            val mean = arr.sum() / shape[dim]
-            sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
-        },
-        dim,
-        keepDim
-    )
-
-    override fun Tensor<Double>.variance(): Double = this.fold { arr ->
-        val mean = arr.sum() / tensor.numElements
-        arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
-    }
-
-    override fun Tensor<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
-        { arr ->
-            check(dim < dimension) { "Dimension $dim out of range $dimension" }
-            val mean = arr.sum() / shape[dim]
-            arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
-        },
-        dim,
-        keepDim
-    )
-
-}
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index 4009f7b45..c0a6312a9 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -284,7 +284,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         val m1 = newThis.shape[newThis.shape.size - 1]
         val m2 = newOther.shape[newOther.shape.size - 2]
         val n = newOther.shape[newOther.shape.size - 1]
-        check (m1 == m2) {
+        check(m1 == m2) {
             throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
         }
 
@@ -403,7 +403,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     public fun stack(tensors: List<DoubleTensor>): DoubleTensor {
         val shape = tensors.firstOrNull()?.shape
         check(shape != null) { "Collection must have at least 1 element" }
-        check(tensors.all { it.shape contentEquals shape }) {"Stacking tensors must have same shapes"}
+        check(tensors.all { it.shape contentEquals shape }) { "Stacking tensors must have same shapes" }
         val resShape = intArrayOf(tensors.size) + shape
         val resBuffer = tensors.flatMap {
             it.tensor.mutableBuffer.array().drop(it.bufferStart).take(it.numElements)
@@ -415,4 +415,36 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     public fun Tensor<Double>.rowsByIndices(indices: IntArray): DoubleTensor {
         return stack(indices.map { this[it] })
     }
+
+    internal fun Tensor<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
+        foldFunction(tensor.toDoubleArray())
+
+    internal fun Tensor<Double>.foldDim(
+        foldFunction: (DoubleArray) -> Double,
+        dim: Int,
+        keepDim: Boolean
+    ): DoubleTensor {
+        check(dim < dimension) { "Dimension $dim out of range $dimension" }
+        val resShape = if (keepDim) {
+            shape.take(dim).toIntArray() + intArrayOf(1) + shape.takeLast(dimension - dim - 1).toIntArray()
+        } else {
+            shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray()
+        }
+        val resNumElements = resShape.reduce(Int::times)
+        val resTensor = DoubleTensor(resShape, DoubleArray(resNumElements) { 0.0 }, 0)
+        for (index in resTensor.linearStructure.indices()) {
+            val prefix = index.take(dim).toIntArray()
+            val suffix = index.takeLast(dimension - dim - 1).toIntArray()
+            resTensor[index] = foldFunction(DoubleArray(shape[dim]) { i ->
+                tensor[prefix + intArrayOf(i) + suffix]
+            })
+        }
+
+        return resTensor
+    }
+
+    override fun Tensor<Double>.sum(): Double = tensor.fold { it.sum() }
+
+    override fun Tensor<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.sum() }, dim, keepDim)
 }

From d0281871fac1f59f4218ea1e4440f1991e742bb1 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Wed, 5 May 2021 14:27:01 +0300
Subject: [PATCH 151/178] analytic tests and examples

---
 .../kmath/tensors/DataSetNormalization.kt     |  46 ++++++
 .../tensors/api/AnalyticTensorAlgebra.kt      |   3 +-
 .../algebras/DoubleAnalyticTensorAlgebra.kt   |   3 +-
 .../core/TestDoubleAnalyticTensorAlgebra.kt   | 152 ++++++++++++++++--
 4 files changed, 186 insertions(+), 18 deletions(-)
 create mode 100644 examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt
new file mode 100644
index 000000000..4d53d940b
--- /dev/null
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt
@@ -0,0 +1,46 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
+
+// Dataset normalization
+
+fun main() {
+
+    // work in context with analytic methods
+    DoubleAnalyticTensorAlgebra {
+        // take dataset of 5-element vectors from normal distribution
+        val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
+        BroadcastDoubleTensorAlgebra {
+            dataset += fromArray(
+                intArrayOf(5),
+                doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
+            )
+        }
+
+        // find out mean and standard deviation of each column
+        val mean = dataset.mean(0, false)
+        val std = dataset.std(0, false)
+
+        println("Mean:\n$mean")
+        println("Standard deviation:\n$std")
+
+        // also we can calculate other statistic as minimum and maximum of rows
+        println("Minimum:\n${dataset.min(0, false)}")
+        println("Maximum:\n${dataset.max(0, false)}")
+
+        // now we can scale dataset with mean normalization
+        val datasetScaled = BroadcastDoubleTensorAlgebra { (dataset - mean) / std }
+
+        // find out mean and std of scaled dataset
+
+        println("Mean of scaled:\n${datasetScaled.mean(0, false)}")
+        println("Mean of scaled:\n${datasetScaled.std(0, false)}")
+    }
+}
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
index f9b2df45c..7784bfa45 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
@@ -51,7 +51,6 @@ public interface AnalyticTensorAlgebra<T> :
      */
     public fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T>
 
-
     /**
      * @return the mean of all elements in the input tensor.
      */
@@ -110,7 +109,7 @@ public interface AnalyticTensorAlgebra<T> :
     public fun Tensor<T>.exp(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.log.html
-    public fun Tensor<T>.log(): Tensor<T>
+    public fun Tensor<T>.ln(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.sqrt.html
     public fun Tensor<T>.sqrt(): Tensor<T>
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
index 547018498..5580f845f 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
@@ -25,7 +25,6 @@ public object DoubleAnalyticTensorAlgebra :
     override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
         foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
 
-
     override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
 
     override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
@@ -70,7 +69,7 @@ public object DoubleAnalyticTensorAlgebra :
 
     override fun Tensor<Double>.exp(): DoubleTensor = tensor.map(::exp)
 
-    override fun Tensor<Double>.log(): DoubleTensor = tensor.map(::ln)
+    override fun Tensor<Double>.ln(): DoubleTensor = tensor.map(::ln)
 
     override fun Tensor<Double>.sqrt(): DoubleTensor = tensor.map(::sqrt)
 
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
index 835b8a08a..bebd65dc5 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -2,35 +2,159 @@ package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
-import kotlin.math.abs
-import kotlin.math.exp
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra.tan
+import kotlin.math.*
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
 internal class TestDoubleAnalyticTensorAlgebra {
 
     val shape = intArrayOf(2, 1, 3, 2)
-    val buffer = doubleArrayOf(27.1, 20.0, 19.84, 23.123, 0.0, 1.0, 3.23, 133.7, 25.3, 100.3, 11.0, 12.012)
+    val buffer = doubleArrayOf(
+        27.1, 20.0, 19.84,
+        23.123, 3.0, 2.0,
+
+        3.23, 133.7, 25.3,
+        100.3, 11.0, 12.012
+    )
     val tensor = DoubleTensor(shape, buffer)
 
     fun DoubleArray.fmap(transform: (Double) -> Double): DoubleArray {
         return this.map(transform).toDoubleArray()
     }
 
-    fun DoubleArray.epsEqual(other: DoubleArray, eps: Double = 1e-5): Boolean {
-        for ((elem1, elem2) in this.asSequence().zip(other.asSequence())) {
-            if (abs(elem1 - elem2) > eps) {
-                return false
-            }
-        }
-        return true
+    fun expectedTensor(transform: (Double) -> Double): DoubleTensor {
+        return DoubleTensor(shape, buffer.fmap(transform))
     }
 
     @Test
     fun testExp() = DoubleAnalyticTensorAlgebra {
-        tensor.exp().let {
-            assertTrue { shape contentEquals it.shape }
-            assertTrue { buffer.fmap(::exp).epsEqual(it.mutableBuffer.array())}
-        }
+        assertTrue { tensor.exp() eq expectedTensor(::exp) }
     }
+
+    @Test
+    fun testLog() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.ln() eq expectedTensor(::ln) }
+    }
+
+    @Test
+    fun testSqrt() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.sqrt() eq expectedTensor(::sqrt) }
+    }
+
+    @Test
+    fun testCos() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.cos() eq expectedTensor(::cos) }
+    }
+
+
+    @Test
+    fun testCosh() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.cosh() eq expectedTensor(::cosh) }
+    }
+
+    @Test
+    fun testAcosh() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.acosh() eq expectedTensor(::acosh) }
+    }
+
+    @Test
+    fun testSin() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.sin() eq expectedTensor(::sin) }
+    }
+
+    @Test
+    fun testSinh() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.sinh() eq expectedTensor(::sinh) }
+    }
+
+    @Test
+    fun testAsinh() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.asinh() eq expectedTensor(::asinh) }
+    }
+
+    @Test
+    fun testTan() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.tan() eq expectedTensor(::tan) }
+    }
+
+    @Test
+    fun testAtan() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.atan() eq expectedTensor(::atan) }
+    }
+
+    @Test
+    fun testTanh() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.tanh() eq expectedTensor(::tanh) }
+    }
+
+    @Test
+    fun testCeil() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.ceil() eq expectedTensor(::ceil) }
+    }
+
+    @Test
+    fun testFloor() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.floor() eq expectedTensor(::floor) }
+    }
+
+    val shape2 = intArrayOf(2, 2)
+    val buffer2 = doubleArrayOf(
+        1.0, 2.0,
+        -3.0, 4.0
+    )
+    val tensor2 = DoubleTensor(shape2, buffer2)
+
+    @Test
+    fun testMin() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor2.min() == -3.0 }
+        assertTrue { tensor2.min(0, true) eq fromArray(
+            intArrayOf(1, 2),
+            doubleArrayOf(-3.0, 2.0)
+        )}
+        assertTrue { tensor2.min(1, false) eq fromArray(
+            intArrayOf(2),
+            doubleArrayOf(1.0, -3.0)
+        )}
+    }
+
+    @Test
+    fun testMax() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor2.max() == 4.0 }
+        assertTrue { tensor2.max(0, true) eq fromArray(
+            intArrayOf(1, 2),
+            doubleArrayOf(1.0, 4.0)
+        )}
+        assertTrue { tensor2.max(1, false) eq fromArray(
+            intArrayOf(2),
+            doubleArrayOf(2.0, 4.0)
+        )}
+    }
+
+    @Test
+    fun testSum() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor2.sum() == 4.0 }
+        assertTrue { tensor2.sum(0, true) eq fromArray(
+            intArrayOf(1, 2),
+            doubleArrayOf(-2.0, 6.0)
+        )}
+        assertTrue { tensor2.sum(1, false) eq fromArray(
+            intArrayOf(2),
+            doubleArrayOf(3.0, 1.0)
+        )}
+    }
+
+    @Test
+    fun testMean() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor2.mean() == 1.0 }
+        assertTrue { tensor2.mean(0, true) eq fromArray(
+            intArrayOf(1, 2),
+            doubleArrayOf(-1.0, 3.0)
+        )}
+        assertTrue { tensor2.mean(1, false) eq fromArray(
+            intArrayOf(2),
+            doubleArrayOf(1.5, 0.5)
+        )}
+    }
+
 }
\ No newline at end of file

From 218b81a242b79baba4cb8db82887e4fa90556b05 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 5 May 2021 16:11:46 +0100
Subject: [PATCH 152/178] Min max refactor

---
 .../tensors/api/AnalyticTensorAlgebra.kt      | 37 -------------------
 .../kmath/tensors/api/TensorAlgebra.kt        | 35 ++++++++++++++++++
 .../algebras/DoubleAnalyticTensorAlgebra.kt   | 10 -----
 .../core/algebras/DoubleTensorAlgebra.kt      | 14 +++++++
 .../core/TestDoubleAnalyticTensorAlgebra.kt   |  8 ++--
 5 files changed, 53 insertions(+), 51 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
index 7784bfa45..aa5678b31 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
@@ -14,43 +14,6 @@ package space.kscience.kmath.tensors.api
 public interface AnalyticTensorAlgebra<T> :
     TensorPartialDivisionAlgebra<T> {
 
-
-    /**
-     * @return the minimum value of all elements in the input tensor.
-     */
-    public fun Tensor<T>.min(): T
-
-    /**
-     * Returns the minimum value of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the minimum value of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.min(dim: Int, keepDim: Boolean): Tensor<T>
-
-    /**
-     * @return the maximum value of all elements in the input tensor.
-     */
-    public fun Tensor<T>.max(): T
-
-    /**
-     * Returns the maximum value of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the maximum value of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T>
-
     /**
      * @return the mean of all elements in the input tensor.
      */
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index b99f79e9a..b0a0f9618 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -269,6 +269,41 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
      */
     public fun Tensor<T>.sum(dim: Int, keepDim: Boolean): Tensor<T>
 
+    /**
+     * @return the minimum value of all elements in the input tensor.
+     */
+    public fun Tensor<T>.min(): T
+
+    /**
+     * Returns the minimum value of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the minimum value of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.min(dim: Int, keepDim: Boolean): Tensor<T>
+
+    /**
+     * @return the maximum value of all elements in the input tensor.
+     */
+    public fun Tensor<T>.max(): T
+
+    /**
+     * Returns the maximum value of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the maximum value of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T>
 
 
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
index 5580f845f..4a1f360e3 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
@@ -15,16 +15,6 @@ public object DoubleAnalyticTensorAlgebra :
     AnalyticTensorAlgebra<Double>,
     DoubleTensorAlgebra() {
 
-    override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
-
-    override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim({ x -> x.minOrNull()!! }, dim, keepDim)
-
-    override fun Tensor<Double>.max(): Double = this.fold { it.maxOrNull()!! }
-
-    override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
-
     override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
 
     override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index c0a6312a9..d220bdd9a 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -9,6 +9,8 @@ import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra
 import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.*
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra.fold
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra.foldDim
 import space.kscience.kmath.tensors.core.broadcastOuterTensors
 import space.kscience.kmath.tensors.core.checkBufferShapeConsistency
 import space.kscience.kmath.tensors.core.checkEmptyDoubleBuffer
@@ -447,4 +449,16 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
     override fun Tensor<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
         foldDim({ x -> x.sum() }, dim, keepDim)
+
+
+    override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
+
+    override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.minOrNull()!! }, dim, keepDim)
+
+    override fun Tensor<Double>.max(): Double = this.fold { it.maxOrNull()!! }
+
+    override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
+
 }
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
index bebd65dc5..3ea19da26 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -2,7 +2,7 @@ package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
-import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra.tan
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.math.*
 import kotlin.test.Test
 import kotlin.test.assertTrue
@@ -106,7 +106,7 @@ internal class TestDoubleAnalyticTensorAlgebra {
     val tensor2 = DoubleTensor(shape2, buffer2)
 
     @Test
-    fun testMin() = DoubleAnalyticTensorAlgebra {
+    fun testMin() = DoubleTensorAlgebra {
         assertTrue { tensor2.min() == -3.0 }
         assertTrue { tensor2.min(0, true) eq fromArray(
             intArrayOf(1, 2),
@@ -119,7 +119,7 @@ internal class TestDoubleAnalyticTensorAlgebra {
     }
 
     @Test
-    fun testMax() = DoubleAnalyticTensorAlgebra {
+    fun testMax() = DoubleTensorAlgebra {
         assertTrue { tensor2.max() == 4.0 }
         assertTrue { tensor2.max(0, true) eq fromArray(
             intArrayOf(1, 2),
@@ -132,7 +132,7 @@ internal class TestDoubleAnalyticTensorAlgebra {
     }
 
     @Test
-    fun testSum() = DoubleAnalyticTensorAlgebra {
+    fun testSum() = DoubleTensorAlgebra {
         assertTrue { tensor2.sum() == 4.0 }
         assertTrue { tensor2.sum(0, true) eq fromArray(
             intArrayOf(1, 2),

From 431db00f1ac9da54b65d00a09fa6f7b33c150942 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Wed, 5 May 2021 16:35:26 +0100
Subject: [PATCH 153/178] refactor tests

---
 .../kmath/tensors/core/TestBroadcasting.kt    | 12 ++++----
 .../kmath/tensors/core/TestDoubleTensor.kt    |  8 ++---
 .../tensors/core/TestDoubleTensorAlgebra.kt   | 30 ++++++-------------
 3 files changed, 19 insertions(+), 31 deletions(-)

diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
index 1564b85c9..6e3b4df60 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -9,7 +9,7 @@ import kotlin.test.assertTrue
 internal class TestBroadcasting {
 
     @Test
-    fun broadcastShapes() = DoubleTensorAlgebra {
+    fun testBroadcastShapes() = DoubleTensorAlgebra {
         assertTrue(
             broadcastShapes(
                 intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
@@ -24,7 +24,7 @@ internal class TestBroadcasting {
     }
 
     @Test
-    fun broadcastTo() = DoubleTensorAlgebra {
+    fun testBroadcastTo() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
 
@@ -34,7 +34,7 @@ internal class TestBroadcasting {
     }
 
     @Test
-    fun broadcastTensors() = DoubleTensorAlgebra {
+    fun testBroadcastTensors() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
@@ -51,7 +51,7 @@ internal class TestBroadcasting {
     }
 
     @Test
-    fun broadcastOuterTensors() = DoubleTensorAlgebra {
+    fun testBroadcastOuterTensors() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
@@ -68,7 +68,7 @@ internal class TestBroadcasting {
     }
 
     @Test
-    fun broadcastOuterTensorsShapes() = DoubleTensorAlgebra {
+    fun testBroadcastOuterTensorsShapes() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 1, 3, 2, 3), DoubleArray(2 * 1 * 3 * 2 * 3) {0.0})
         val tensor2 = fromArray(intArrayOf(4, 2, 5, 1, 3, 3), DoubleArray(4 * 2 * 5 * 1 * 3 * 3) {0.0})
         val tensor3 = fromArray(intArrayOf(1, 1), doubleArrayOf(500.0))
@@ -81,7 +81,7 @@ internal class TestBroadcasting {
     }
 
     @Test
-    fun minusTensor() = BroadcastDoubleTensorAlgebra.invoke {
+    fun testMinusTensor() = BroadcastDoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index ed5f8e780..132735cc7 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -15,14 +15,14 @@ import kotlin.test.assertTrue
 internal class TestDoubleTensor {
 
     @Test
-    fun valueTest() = DoubleTensorAlgebra {
+    fun testValue() = DoubleTensorAlgebra {
         val value = 12.5
         val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
     }
 
     @Test
-    fun stridesTest() = DoubleTensorAlgebra {
+    fun testStrides() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
         assertEquals(tensor[intArrayOf(0, 1)], 5.8)
         assertTrue(
@@ -31,7 +31,7 @@ internal class TestDoubleTensor {
     }
 
     @Test
-    fun getTest() = DoubleTensorAlgebra {
+    fun testGet() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(1, 2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
         val matrix = tensor[0].as2D()
         assertEquals(matrix[0, 1], 5.8)
@@ -55,7 +55,7 @@ internal class TestDoubleTensor {
     }
 
     @Test
-    fun noBufferProtocol() {
+    fun testNoBufferProtocol() {
 
         // create buffer
         val doubleArray = DoubleBuffer(doubleArrayOf(1.0, 2.0, 3.0))
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index df2d21b96..d782d78d9 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -10,28 +10,28 @@ import kotlin.test.assertTrue
 internal class TestDoubleTensorAlgebra {
 
     @Test
-    fun doublePlus() = DoubleTensorAlgebra {
+    fun testDoublePlus() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
         assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(11.0, 12.0))
     }
 
     @Test
-    fun doubleDiv() = DoubleTensorAlgebra {
+    fun TestDoubleDiv() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(2.0, 4.0))
         val res = 2.0/tensor
         assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 0.5))
     }
 
     @Test
-    fun divDouble() = DoubleTensorAlgebra {
+    fun testDivDouble() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(10.0, 5.0))
         val res = tensor / 2.5
         assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(4.0, 2.0))
     }
 
     @Test
-    fun transpose1x1() = DoubleTensorAlgebra {
+    fun testTranspose1x1() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(1), doubleArrayOf(0.0))
         val res = tensor.transpose(0, 0)
 
@@ -40,7 +40,7 @@ internal class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun transpose3x2() = DoubleTensorAlgebra {
+    fun testTranspose3x2() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res = tensor.transpose(1, 0)
 
@@ -49,7 +49,7 @@ internal class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun transpose1x2x3() = DoubleTensorAlgebra {
+    fun testTranspose1x2x3() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res01 = tensor.transpose(0, 1)
         val res02 = tensor.transpose(-3, 2)
@@ -65,7 +65,7 @@ internal class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun linearStructure() = DoubleTensorAlgebra {
+    fun testLinearStructure() = DoubleTensorAlgebra {
         val shape = intArrayOf(3)
         val tensorA = full(value = -4.5, shape = shape)
         val tensorB = full(value = 10.9, shape = shape)
@@ -97,7 +97,7 @@ internal class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun dot() = DoubleTensorAlgebra {
+    fun testDot() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor11 = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
@@ -118,22 +118,10 @@ internal class TestDoubleTensorAlgebra {
         val res11 = tensor1.dot(tensor11)
         assertTrue(res11.mutableBuffer.array() contentEquals doubleArrayOf(22.0, 28.0, 49.0, 64.0))
         assertTrue(res11.shape contentEquals intArrayOf(2, 2))
-
-        var tensor4 = fromArray(intArrayOf(10, 3, 4), DoubleArray(10 * 3 * 4) {0.0})
-        var tensor5 = fromArray(intArrayOf(10, 4, 5), DoubleArray(10 * 4 * 5) {0.0})
-        assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(10, 3, 5))
-
-        tensor4 = fromArray(intArrayOf(10, 3, 4), DoubleArray(10 * 3 * 4) {0.0})
-        tensor5 = fromArray(intArrayOf(4, 5), DoubleArray(4 * 5) {0.0})
-        assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(10, 3, 5))
-
-        tensor4 = fromArray(intArrayOf(4, 2, 1, 3, 8, 1), DoubleArray(4 * 2 * 1 * 3 * 8 * 1) {0.0})
-        tensor5 = fromArray(intArrayOf(5, 1, 2, 8, 3, 1, 5), DoubleArray(5 * 1 * 2 * 8 * 3 * 1 * 5) {0.0})
-        assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(5, 4, 2, 8, 3, 8, 5))
     }
 
     @Test
-    fun diagonalEmbedding() = DoubleTensorAlgebra {
+    fun testDiagonalEmbedding() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor3 = zeros(intArrayOf(2, 3, 4, 5))

From 229c1b57daa3727a6e59d16c6ed04786a83ae220 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Thu, 6 May 2021 10:27:47 +0300
Subject: [PATCH 154/178] add documentation to DoubleLinearOpsTensorAlgebra

---
 .../tensors/api/LinearOpsTensorAlgebra.kt     |  2 +-
 .../algebras/DoubleLinearOpsTensorAlgebra.kt  | 95 ++++++++++++++++++-
 2 files changed, 94 insertions(+), 3 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
index 527e5d386..ec070b6bd 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
@@ -81,7 +81,7 @@ public interface LinearOpsTensorAlgebra<T> :
      * If input is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as input.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
      *
-     * @return the determinant.
+     * @return triple `(U, S, V)`.
      */
     public fun Tensor<T>.svd(): Triple<Tensor<T>, Tensor<T>, Tensor<T>>
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
index 89345e315..d17dc70fe 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
@@ -20,7 +20,10 @@ import space.kscience.kmath.tensors.core.luPivotHelper
 import space.kscience.kmath.tensors.core.pivInit
 import kotlin.math.min
 
-
+/**
+ * Implementation of common linear algebra operations on double numbers.
+ * Implements the LinearOpsTensorAlgebra<Double> interface.
+ */
 public object DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double>,
     DoubleTensorAlgebra() {
@@ -29,12 +32,41 @@ public object DoubleLinearOpsTensorAlgebra :
 
     override fun Tensor<Double>.det(): DoubleTensor = detLU(1e-9)
 
+    /**
+     * Computes the LU factorization of a matrix or batches of matrices `input`.
+     * Returns a tuple containing the LU factorization and pivots of `input`.
+     *
+     * @param epsilon permissible error when comparing the determinant of a matrix with zero
+     * @return pair of `factorization` and `pivots`.
+     * The `factorization` has the shape ``(*, m, n)``, where``(*, m, n)`` is the shape of the `input` tensor.
+     * The `pivots`  has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows.
+     */
     public fun Tensor<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
         computeLU(tensor, epsilon)
             ?: throw IllegalArgumentException("Tensor contains matrices which are singular at precision $epsilon")
 
+    /**
+     * Computes the LU factorization of a matrix or batches of matrices `input`.
+     * Returns a tuple containing the LU factorization and pivots of `input`.
+     * Uses an error of ``1e-9`` when calculating whether a matrix is degenerate.
+     *
+     * @return pair of `factorization` and `pivots`.
+     * The `factorization` has the shape ``(*, m, n)``, where``(*, m, n)`` is the shape of the `input` tensor.
+     * The `pivots`  has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows.
+     */
     public fun Tensor<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
 
+    /**
+     * Unpacks the data and pivots from a LU factorization of a tensor.
+     * Given a tensor [luTensor], return tensors (P, L, U) satisfying ``P * luTensor = L * U``,
+     * with `P` being a permutation matrix or batch of matrices,
+     * `L` being a lower triangular matrix or batch of matrices,
+     * `U` being an upper triangular matrix or batch of matrices.
+     *
+     * @param luTensor the packed LU factorization data
+     * @param pivotsTensor the packed LU factorization pivots
+     * @return triple of P, L and U tensors
+     */
     public fun luPivot(
         luTensor: Tensor<Double>,
         pivotsTensor: Tensor<Int>
@@ -66,6 +98,18 @@ public object DoubleLinearOpsTensorAlgebra :
         return Triple(pTensor, lTensor, uTensor)
     }
 
+    /**
+     * QR decomposition.
+     *
+     * Computes the QR decomposition of a matrix or a batch of matrices, and returns a pair `(Q, R)` of tensors.
+     * Given a tensor `input`, return tensors (Q, R) satisfying ``input = Q * R``,
+     * with `Q` being an orthogonal matrix or batch of orthogonal matrices
+     * and `R` being an upper triangular matrix or batch of upper triangular matrices.
+     *
+     * @param epsilon permissible error when comparing tensors for equality.
+     * Used when checking the positive definiteness of the input matrix or matrices.
+     * @return pair of Q and R tensors.
+     */
     public fun Tensor<Double>.cholesky(epsilon: Double): DoubleTensor {
         checkSquareMatrix(shape)
         checkPositiveDefinite(tensor, epsilon)
@@ -98,6 +142,18 @@ public object DoubleLinearOpsTensorAlgebra :
     override fun Tensor<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
         svd(epsilon = 1e-10)
 
+    /**
+     * Singular Value Decomposition.
+     *
+     * Computes the singular value decomposition of either a matrix or batch of matrices `input`.
+     * The singular value decomposition is represented as a triple `(U, S, V)`,
+     * such that ``input = U.dot(diagonalEmbedding(S).dot(V.T))``.
+     * If input is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as input.
+     *
+     * @param epsilon permissible error when calculating the dot product of vectors,
+     * i.e. the precision with which the cosine approaches 1 in an iterative algorithm.
+     * @return triple `(U, S, V)`.
+     */
     public fun Tensor<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         val size = tensor.linearStructure.dim
         val commonShape = tensor.shape.sliceArray(0 until size - 2)
@@ -125,7 +181,14 @@ public object DoubleLinearOpsTensorAlgebra :
     override fun Tensor<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> =
         symEig(epsilon = 1e-15)
 
-    //For information: http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
+    /**
+     * Returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices,
+     * represented by a pair (eigenvalues, eigenvectors).
+     *
+     * @param epsilon permissible error when comparing tensors for equality
+     * and when the cosine approaches 1 in the SVD algorithm.
+     * @return a pair (eigenvalues, eigenvectors)
+     */
     public fun Tensor<Double>.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
         checkSymmetric(tensor, epsilon)
         val (u, s, v) = tensor.svd(epsilon)
@@ -139,6 +202,13 @@ public object DoubleLinearOpsTensorAlgebra :
         return eig to v
     }
 
+    /**
+     * Computes the determinant of a square matrix input, or of each square matrix in a batched input
+     * using LU factorization algorithm.
+     *
+     * @param epsilon error in the LU algorithm - permissible error when comparing the determinant of a matrix with zero
+     * @return the determinant.
+     */
     public fun Tensor<Double>.detLU(epsilon: Double = 1e-9): DoubleTensor {
 
         checkSquareMatrix(tensor.shape)
@@ -164,6 +234,15 @@ public object DoubleLinearOpsTensorAlgebra :
         return detTensor
     }
 
+    /**
+     * Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input
+     * using LU factorization algorithm.
+     * Given a square matrix `a`, return the matrix `aInv` satisfying
+     * ``a.dot(aInv) = aInv.dot(a) = eye(a.shape[0])``.
+     *
+     * @param epsilon error in the LU algorithm - permissible error when comparing the determinant of a matrix with zero
+     * @return the multiplicative inverse of a matrix.
+     */
     public fun Tensor<Double>.invLU(epsilon: Double = 1e-9): DoubleTensor {
         val (luTensor, pivotsTensor) = luFactor(epsilon)
         val invTensor = luTensor.zeroesLike()
@@ -177,6 +256,18 @@ public object DoubleLinearOpsTensorAlgebra :
         return invTensor
     }
 
+    /**
+     * LUP decomposition
+     *
+     * Computes the LUP decomposition of a matrix or a batch of matrices.
+     * Given a tensor `input`, return tensors (P, L, U) satisfying ``P * input = L * U``,
+     * with `P` being a permutation matrix or batch of matrices,
+     * `L` being a lower triangular matrix or batch of matrices,
+     * `U` being an upper triangular matrix or batch of matrices.
+     *
+     * @param epsilon permissible error when comparing the determinant of a matrix with zero
+     * @return triple of P, L and U tensors
+     */
     public fun Tensor<Double>.lu(epsilon: Double = 1e-9): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         val (lu, pivots) = this.luFactor(epsilon)
         return luPivot(lu, pivots)

From a1cbd7a457a93619ad3033c66248b522137e4c46 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Thu, 6 May 2021 09:48:43 +0100
Subject: [PATCH 155/178] TensorLinearStructure doc

---
 .../kmath/tensors/core/algebras/TensorLinearStructure.kt  | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
index 5fbc7390f..d69d342ad 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
@@ -56,7 +56,12 @@ internal fun stepIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
     return res
 }
 
-
+/**
+ * This [Strides] implemetation follow the last dimension first convention
+ * For more information: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html
+ *
+ * @param shape the shape of the tensor.
+ */
 public class TensorLinearStructure(override val shape: IntArray) : Strides
 {
     override val strides: IntArray
@@ -65,6 +70,7 @@ public class TensorLinearStructure(override val shape: IntArray) : Strides
     override fun index(offset: Int): IntArray =
         indexFromOffset(offset, strides, shape.size)
 
+    // TODO: documentation (Alya)
     public fun stepIndex(index: IntArray): IntArray =
         stepIndex(index, shape, shape.size)
 

From 477e64e4d396ebff3a66f487b2b0cab7f507e6bd Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Thu, 6 May 2021 09:51:59 +0100
Subject: [PATCH 156/178] Typos corrected

---
 .../kmath/tensors/core/algebras/TensorLinearStructure.kt        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
index d69d342ad..a5c01af55 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
@@ -57,7 +57,7 @@ internal fun stepIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
 }
 
 /**
- * This [Strides] implemetation follow the last dimension first convention
+ * This [Strides] implementation follows the last dimension first convention
  * For more information: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html
  *
  * @param shape the shape of the tensor.

From 16bed539977c516645d0a980f4f22578a6fbec11 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Thu, 6 May 2021 09:59:58 +0100
Subject: [PATCH 157/178] Drop unused functionality in TensorLinearStructure

---
 .../algebras/DoubleLinearOpsTensorAlgebra.kt  |  2 +-
 .../core/algebras/TensorLinearStructure.kt    | 27 +------------------
 2 files changed, 2 insertions(+), 27 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
index d17dc70fe..430482613 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
@@ -155,7 +155,7 @@ public object DoubleLinearOpsTensorAlgebra :
      * @return triple `(U, S, V)`.
      */
     public fun Tensor<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        val size = tensor.linearStructure.dim
+        val size = tensor.dimension
         val commonShape = tensor.shape.sliceArray(0 until size - 2)
         val (n, m) = tensor.shape.sliceArray(size - 2 until size)
         val uTensor = zeros(commonShape + intArrayOf(min(n, m), n))
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
index a5c01af55..68aa03311 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
@@ -39,45 +39,20 @@ internal fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArra
     return res
 }
 
-internal fun stepIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
-    val res = index.copyOf()
-    var current = nDim - 1
-    var carry = 0
-
-    do {
-        res[current]++
-        if (res[current] >= shape[current]) {
-            carry = 1
-            res[current] = 0
-        }
-        current--
-    } while (carry != 0 && current >= 0)
-
-    return res
-}
-
 /**
  * This [Strides] implementation follows the last dimension first convention
  * For more information: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html
  *
  * @param shape the shape of the tensor.
  */
-public class TensorLinearStructure(override val shape: IntArray) : Strides
-{
+public class TensorLinearStructure(override val shape: IntArray) : Strides {
     override val strides: IntArray
         get() = stridesFromShape(shape)
 
     override fun index(offset: Int): IntArray =
         indexFromOffset(offset, strides, shape.size)
 
-    // TODO: documentation (Alya)
-    public fun stepIndex(index: IntArray): IntArray =
-        stepIndex(index, shape, shape.size)
-
     override val linearSize: Int
         get() = shape.reduce(Int::times)
 
-    public val dim: Int
-        get() = shape.size
-
 }
\ No newline at end of file

From 499cf85ff08ac65e7343e33af2f4f65cd9967b3f Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Thu, 6 May 2021 12:30:13 +0300
Subject: [PATCH 158/178] refactor BT + docs

---
 .../kmath/tensors/core/BufferedTensor.kt      | 112 ++++--------------
 .../kmath/tensors/core/DoubleTensor.kt        |  19 +++
 .../kscience/kmath/tensors/core/IntTensor.kt  |  17 +++
 .../kmath/tensors/core/tensorCasts.kt         |  36 ++++++
 .../kmath/tensors/core/tensorCastsUtils.kt    |  42 +++++++
 5 files changed, 136 insertions(+), 90 deletions(-)
 create mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt
 create mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensor.kt
 create mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
 create mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index 22a1ef1bf..e4ffccd96 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -1,116 +1,48 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.nd.MutableBufferND
 import space.kscience.kmath.structures.*
 import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
 
-
-public open class BufferedTensor<T>(
+/**
+ * [Tensor] implementation provided with [MutableBuffer]
+ */
+public open class BufferedTensor<T> internal constructor(
     override val shape: IntArray,
     internal val mutableBuffer: MutableBuffer<T>,
     internal val bufferStart: Int
 ) : Tensor<T> {
+
+    /**
+     * [TensorLinearStructure] with the same shape
+     */
     public val linearStructure: TensorLinearStructure
         get() = TensorLinearStructure(shape)
 
+    /**
+     * Number of elements in tensor
+     */
     public val numElements: Int
         get() = linearStructure.linearSize
 
+    /**
+     * @param index [IntArray] with size equal to tensor dimension
+     * @return the element by multidimensional index
+     */
     override fun get(index: IntArray): T = mutableBuffer[bufferStart + linearStructure.offset(index)]
 
+    /**
+     * @param index the [IntArray] with size equal to tensor dimension
+     * @param value the value to set
+     */
     override fun set(index: IntArray, value: T) {
         mutableBuffer[bufferStart + linearStructure.offset(index)] = value
     }
 
+    /**
+     * @return the sequence of pairs multidimensional indices and values
+     */
     override fun elements(): Sequence<Pair<IntArray, T>> = linearStructure.indices().map {
         it to this[it]
     }
 }
-
-public class IntTensor internal constructor(
-    shape: IntArray,
-    buffer: IntArray,
-    offset: Int = 0
-) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset)
-
-public class DoubleTensor internal constructor(
-    shape: IntArray,
-    buffer: DoubleArray,
-    offset: Int = 0
-) : BufferedTensor<Double>(shape, DoubleBuffer(buffer), offset) {
-    override fun toString(): String = toPrettyString()
-}
-
-internal fun BufferedTensor<Int>.asTensor(): IntTensor =
-    IntTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
-
-internal fun BufferedTensor<Double>.asTensor(): DoubleTensor =
-    DoubleTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
-
-internal fun <T> Tensor<T>.copyToBufferedTensor(): BufferedTensor<T> =
-    BufferedTensor(
-        this.shape,
-        TensorLinearStructure(this.shape).indices().map(this::get).toMutableList().asMutableBuffer(), 0
-    )
-
-internal fun <T> Tensor<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
-    is BufferedTensor<T> -> this
-    is MutableBufferND<T> -> if (this.strides.strides contentEquals TensorLinearStructure(this.shape).strides)
-        BufferedTensor(this.shape, this.mutableBuffer, 0) else this.copyToBufferedTensor()
-    else -> this.copyToBufferedTensor()
-}
-
-internal val Tensor<Double>.tensor: DoubleTensor
-    get() = when (this) {
-        is DoubleTensor -> this
-        else -> this.toBufferedTensor().asTensor()
-    }
-
-internal val Tensor<Int>.tensor: IntTensor
-    get() = when (this) {
-        is IntTensor -> this
-        else -> this.toBufferedTensor().asTensor()
-    }
-
-public fun Tensor<Double>.toDoubleTensor(): DoubleTensor = this.tensor
-public fun Tensor<Int>.toIntTensor(): IntTensor = this.tensor
-
-public fun Array<DoubleArray>.toDoubleTensor(): DoubleTensor {
-    val n = size
-    check(n > 0) { "An empty array cannot be casted to tensor" }
-    val m = first().size
-    check(m > 0) { "Inner arrays must have at least 1 argument" }
-    check(all { size == m }) { "Inner arrays must be the same size" }
-
-    val shape = intArrayOf(n, m)
-    val buffer = this.flatMap { arr -> arr.map { it } }.toDoubleArray()
-
-    return DoubleTensor(shape, buffer, 0)
-}
-
-
-public fun Array<IntArray>.toIntTensor(): IntTensor {
-    val n = size
-    check(n > 0) { "An empty array cannot be casted to tensor" }
-    val m = first().size
-    check(m > 0) { "Inner arrays must have at least 1 argument" }
-    check(all { size == m }) { "Inner arrays must be the same size" }
-
-    val shape = intArrayOf(n, m)
-    val buffer = this.flatMap { arr -> arr.map { it } }.toIntArray()
-
-    return IntTensor(shape, buffer, 0)
-}
-
-public fun DoubleTensor.toDoubleArray(): DoubleArray {
-    return DoubleArray(numElements) { i ->
-        mutableBuffer[bufferStart + i]
-    }
-}
-
-public fun IntTensor.toIntArray(): IntArray {
-    return IntArray(numElements) { i ->
-        mutableBuffer[bufferStart + i]
-    }
-}
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt
new file mode 100644
index 000000000..e3143f5a7
--- /dev/null
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt
@@ -0,0 +1,19 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.structures.DoubleBuffer
+
+/**
+ * Default [BufferedTensor] implementation for [Double] values
+ */
+public class DoubleTensor internal constructor(
+    shape: IntArray,
+    buffer: DoubleArray,
+    offset: Int = 0
+) : BufferedTensor<Double>(shape, DoubleBuffer(buffer), offset) {
+    override fun toString(): String = toPrettyString()
+}
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensor.kt
new file mode 100644
index 000000000..ae1e6c8c8
--- /dev/null
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensor.kt
@@ -0,0 +1,17 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.structures.IntBuffer
+
+/**
+ * Default [BufferedTensor] implementation for [Int] values
+ */
+public class IntTensor internal constructor(
+    shape: IntArray,
+    buffer: IntArray,
+    offset: Int = 0
+) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset)
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
new file mode 100644
index 000000000..6254ce751
--- /dev/null
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
@@ -0,0 +1,36 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.api.Tensor
+
+/**
+ * Casts [Tensor<Double>] to [DoubleTensor]
+ */
+public fun Tensor<Double>.toDoubleTensor(): DoubleTensor = this.tensor
+
+/**
+ * Casts [Tensor<Int>] to [IntTensor]
+ */
+public fun Tensor<Int>.toIntTensor(): IntTensor = this.tensor
+
+/**
+ * @return [DoubleArray] of tensor elements
+ */
+public fun DoubleTensor.toDoubleArray(): DoubleArray {
+    return DoubleArray(numElements) { i ->
+        mutableBuffer[bufferStart + i]
+    }
+}
+
+/**
+ * @return [IntArray] of tensor elements
+ */
+public fun IntTensor.toIntArray(): IntArray {
+    return IntArray(numElements) { i ->
+        mutableBuffer[bufferStart + i]
+    }
+}
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt
new file mode 100644
index 000000000..31de6919f
--- /dev/null
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt
@@ -0,0 +1,42 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.nd.MutableBufferND
+import space.kscience.kmath.structures.asMutableBuffer
+import space.kscience.kmath.tensors.api.Tensor
+import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
+
+internal fun BufferedTensor<Int>.asTensor(): IntTensor =
+    IntTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
+
+internal fun BufferedTensor<Double>.asTensor(): DoubleTensor =
+    DoubleTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
+
+internal fun <T> Tensor<T>.copyToBufferedTensor(): BufferedTensor<T> =
+    BufferedTensor(
+        this.shape,
+        TensorLinearStructure(this.shape).indices().map(this::get).toMutableList().asMutableBuffer(), 0
+    )
+
+internal fun <T> Tensor<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
+    is BufferedTensor<T> -> this
+    is MutableBufferND<T> -> if (this.strides.strides contentEquals TensorLinearStructure(this.shape).strides)
+        BufferedTensor(this.shape, this.mutableBuffer, 0) else this.copyToBufferedTensor()
+    else -> this.copyToBufferedTensor()
+}
+
+internal val Tensor<Double>.tensor: DoubleTensor
+    get() = when (this) {
+        is DoubleTensor -> this
+        else -> this.toBufferedTensor().asTensor()
+    }
+
+internal val Tensor<Int>.tensor: IntTensor
+    get() = when (this) {
+        is IntTensor -> this
+        else -> this.toBufferedTensor().asTensor()
+    }
\ No newline at end of file

From 35928e7960bd5676820b751490be057fa62409ff Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Thu, 6 May 2021 10:52:37 +0100
Subject: [PATCH 159/178] minor corrections

---
 .../kscience/kmath/tensors/core/BufferedTensor.kt | 15 ++-------------
 1 file changed, 2 insertions(+), 13 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index e4ffccd96..e8c0556c2 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -5,7 +5,7 @@ import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
 
 /**
- * [Tensor] implementation provided with [MutableBuffer]
+ * Represents [Tensor] over a [MutableBuffer] intended to be used through [DoubleTensor] and [IntTensor]
  */
 public open class BufferedTensor<T> internal constructor(
     override val shape: IntArray,
@@ -14,7 +14,7 @@ public open class BufferedTensor<T> internal constructor(
 ) : Tensor<T> {
 
     /**
-     * [TensorLinearStructure] with the same shape
+     * Buffer strides based on [TensorLinearStructure] implementation
      */
     public val linearStructure: TensorLinearStructure
         get() = TensorLinearStructure(shape)
@@ -25,23 +25,12 @@ public open class BufferedTensor<T> internal constructor(
     public val numElements: Int
         get() = linearStructure.linearSize
 
-    /**
-     * @param index [IntArray] with size equal to tensor dimension
-     * @return the element by multidimensional index
-     */
     override fun get(index: IntArray): T = mutableBuffer[bufferStart + linearStructure.offset(index)]
 
-    /**
-     * @param index the [IntArray] with size equal to tensor dimension
-     * @param value the value to set
-     */
     override fun set(index: IntArray, value: T) {
         mutableBuffer[bufferStart + linearStructure.offset(index)] = value
     }
 
-    /**
-     * @return the sequence of pairs multidimensional indices and values
-     */
     override fun elements(): Sequence<Pair<IntArray, T>> = linearStructure.indices().map {
         it to this[it]
     }

From 8ac253b9fe112bfe64d467c07db9dc9b9ed74e52 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Thu, 6 May 2021 14:09:47 +0300
Subject: [PATCH 160/178] cov + docs

---
 .../tensors/api/AnalyticTensorAlgebra.kt      | 10 +++++++
 .../algebras/DoubleAnalyticTensorAlgebra.kt   | 24 ++++++++++++++++-
 .../core/algebras/DoubleTensorAlgebra.kt      | 27 +++++++++++++------
 3 files changed, 52 insertions(+), 9 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
index aa5678b31..69e88c28f 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
@@ -68,6 +68,16 @@ public interface AnalyticTensorAlgebra<T> :
      */
     public fun Tensor<T>.variance(dim: Int, keepDim: Boolean): Tensor<T>
 
+    /**
+     * Returns the covariance matrix M of given vectors.
+     *
+     * M[i, j] contains covariance of i-th and j-th given vectors
+     *
+     * @param tensors the [List] of 1-dimensional tensors with same shape
+     * @return the covariance matrix
+     */
+    public fun cov(tensors: List<Tensor<T>>): Tensor<T>
+
     //For information: https://pytorch.org/docs/stable/generated/torch.exp.html
     public fun Tensor<T>.exp(): Tensor<T>
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
index 4a1f360e3..23a2fa282 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
@@ -57,6 +57,28 @@ public object DoubleAnalyticTensorAlgebra :
         keepDim
     )
 
+    private fun cov(x: DoubleTensor, y:DoubleTensor): Double{
+        val n = x.shape[0]
+        return ((x - x.mean()) * (y - y.mean())).mean() * n / (n - 1)
+    }
+
+    override fun cov(tensors: List<Tensor<Double>>): DoubleTensor {
+        check(tensors.isNotEmpty()) { "List must have at least 1 element" }
+        val n = tensors.size
+        val m = tensors[0].shape[0]
+        check(tensors.all { it.shape contentEquals intArrayOf(m) }) { "Tensors must have same shapes" }
+        val resTensor = DoubleTensor(
+            intArrayOf(n, n),
+            DoubleArray(n * n) {0.0}
+        )
+        for (i in 0 until n){
+            for (j in 0 until n){
+                resTensor[intArrayOf(i, j)] = cov(tensors[i].tensor, tensors[j].tensor)
+            }
+        }
+        return resTensor
+    }
+
     override fun Tensor<Double>.exp(): DoubleTensor = tensor.map(::exp)
 
     override fun Tensor<Double>.ln(): DoubleTensor = tensor.map(::ln)
@@ -91,4 +113,4 @@ public object DoubleAnalyticTensorAlgebra :
 
     override fun Tensor<Double>.floor(): DoubleTensor = tensor.map(::floor)
 
-}
\ No newline at end of file
+}
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index d220bdd9a..74ef63ab7 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -401,19 +401,31 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     public fun Tensor<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
-    // stack tensors by axis 0
-    public fun stack(tensors: List<DoubleTensor>): DoubleTensor {
-        val shape = tensors.firstOrNull()?.shape
-        check(shape != null) { "Collection must have at least 1 element" }
-        check(tensors.all { it.shape contentEquals shape }) { "Stacking tensors must have same shapes" }
+    /**
+     * Concatenates a sequence of tensors along a new dimension.
+     *
+     * @param tensors the [List] of tensors with same shapes to concatenate
+     * @param dim the dimension to insert
+     * @return tensor with concatenation result
+     */
+    public fun stack(tensors: List<Tensor<Double>>, dim: Int = 0): DoubleTensor {
+        check(dim == 0) { "Stack by non-zero dimension not implemented yet" }
+        check(tensors.isNotEmpty()) { "List must have at least 1 element" }
+        val shape = tensors[0].shape
+        check(tensors.all { it.shape contentEquals shape }) { "Tensors must have same shapes" }
         val resShape = intArrayOf(tensors.size) + shape
         val resBuffer = tensors.flatMap {
-            it.tensor.mutableBuffer.array().drop(it.bufferStart).take(it.numElements)
+            it.tensor.mutableBuffer.array().drop(it.tensor.bufferStart).take(it.tensor.numElements)
         }.toDoubleArray()
         return DoubleTensor(resShape, resBuffer, 0)
     }
 
-    // build tensor from this rows by given indices
+    /**
+     * Build tensor from rows of input tensor
+     *
+     * @param indices the [IntArray] of 1-dimensional indices
+     * @return tensor with rows corresponding to rows by [indices]
+     */
     public fun Tensor<Double>.rowsByIndices(indices: IntArray): DoubleTensor {
         return stack(indices.map { this[it] })
     }
@@ -450,7 +462,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     override fun Tensor<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
         foldDim({ x -> x.sum() }, dim, keepDim)
 
-
     override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
 
     override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =

From dc22bd84986d14f6d82209a106d6413e3bd609e2 Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Thu, 6 May 2021 14:23:57 +0300
Subject: [PATCH 161/178] add documentation to DoubleTensorAlgebra

---
 .../core/algebras/DoubleTensorAlgebra.kt      | 57 ++++++++++++++++++-
 1 file changed, 56 insertions(+), 1 deletion(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index d220bdd9a..2cad85a09 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -23,6 +23,9 @@ import space.kscience.kmath.tensors.core.getRandomNormals
 import space.kscience.kmath.tensors.core.minusIndexFrom
 import kotlin.math.abs
 
+/**
+ * Implementation of basic operations over double tensors and basic algebra operations on them.
+ */
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
     public companion object : DoubleTensorAlgebra()
@@ -34,6 +37,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return tensor.mutableBuffer.array()[tensor.bufferStart]
     }
 
+    /**
+     * Constructs a tensor with the specified shape and data.
+     *
+     * @param shape the desired shape for the tensor.
+     * @param buffer one-dimensional data array.
+     * @return tensor with the [shape] shape and [buffer] data.
+     */
     public fun fromArray(shape: IntArray, buffer: DoubleArray): DoubleTensor {
         checkEmptyShape(shape)
         checkEmptyDoubleBuffer(buffer)
@@ -48,26 +58,67 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return DoubleTensor(newShape, tensor.mutableBuffer.array(), newStart)
     }
 
+    /**
+     * Creates a tensor of a given shape and fills all elements with a given value.
+     *
+     * @param value the value to fill the output tensor with.
+     * @param shape array of integers defining the shape of the output tensor.
+     * @return tensor with the [shape] shape and filled with [value].
+     */
     public fun full(value: Double, shape: IntArray): DoubleTensor {
         checkEmptyShape(shape)
         val buffer = DoubleArray(shape.reduce(Int::times)) { value }
         return DoubleTensor(shape, buffer)
     }
 
+    /**
+     * Returns a tensor with the same shape as `input` filled with [value].
+     *
+     * @param value the value to fill the output tensor with.
+     * @return tensor with the `input` tensor shape and filled with [value].
+     */
     public fun Tensor<Double>.fullLike(value: Double): DoubleTensor {
         val shape = tensor.shape
         val buffer = DoubleArray(tensor.numElements) { value }
         return DoubleTensor(shape, buffer)
     }
 
+    /**
+     * Returns a tensor filled with the scalar value 0.0, with the shape defined by the variable argument [shape].
+     *
+     * @param shape array of integers defining the shape of the output tensor.
+     * @return tensor filled with the scalar value 0.0, with the [shape] shape.
+     */
     public fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
 
+    /**
+     * Returns a tensor filled with the scalar value 0.0, with the same shape as a given array.
+     *
+     * @return tensor filled with the scalar value 0.0, with the same shape as `input` tensor.
+     */
     public fun Tensor<Double>.zeroesLike(): DoubleTensor = tensor.fullLike(0.0)
 
+    /**
+     * Returns a tensor filled with the scalar value 1.0, with the shape defined by the variable argument [shape].
+     *
+     * @param shape array of integers defining the shape of the output tensor.
+     * @return tensor filled with the scalar value 1.0, with the [shape] shape.
+     */
     public fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
 
+    /**
+     * Returns a tensor filled with the scalar value 1.0, with the same shape as a given array.
+     *
+     * @return tensor filled with the scalar value 1.0, with the same shape as `input` tensor.
+     */
     public fun Tensor<Double>.onesLike(): DoubleTensor = tensor.fullLike(1.0)
 
+    /**
+     * Returns a 2-D tensor with shape ([n], [n]), with ones on the diagonal and zeros elsewhere.
+     *
+     * @param n the number of rows and columns
+     * @return a 2-D tensor with ones on the diagonal and zeros elsewhere.
+     */
     public fun eye(n: Int): DoubleTensor {
         val shape = intArrayOf(n, n)
         val buffer = DoubleArray(n * n) { 0.0 }
@@ -78,6 +129,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return res
     }
 
+    /**
+     * Return a copy of the tensor.
+     *
+     * @return a copy of the `input` tensor with a copied buffer.
+     */
     public fun Tensor<Double>.copy(): DoubleTensor {
         return DoubleTensor(tensor.shape, tensor.mutableBuffer.array().copyOf(), tensor.bufferStart)
     }
@@ -359,7 +415,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return resTensor.tensor
     }
 
-
     public fun Tensor<Double>.map(transform: (Double) -> Double): DoubleTensor {
         return DoubleTensor(
             tensor.shape,

From 90149e396561004e781b390f87c51361e6a9e82f Mon Sep 17 00:00:00 2001
From: AlyaNovikova <aleksnovikova@gmail.com>
Date: Thu, 6 May 2021 14:42:15 +0300
Subject: [PATCH 162/178] add documentation to map and randomNormal function

---
 .../core/algebras/DoubleTensorAlgebra.kt      | 36 +++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index d8b59daa7..8c76de0de 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -415,6 +415,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return resTensor.tensor
     }
 
+    /**
+     * Applies the [transform] function to each element of the tensor and returns the resulting modified tensor.
+     *
+     * @param transform the function to be applied to each element of the tensor.
+     * @return the resulting tensor after applying the function.
+     */
     public fun Tensor<Double>.map(transform: (Double) -> Double): DoubleTensor {
         return DoubleTensor(
             tensor.shape,
@@ -423,10 +429,24 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         )
     }
 
+    /**
+     * Compares element-wise two tensors with a specified precision.
+     *
+     * @param other the tensor to compare with `input` tensor.
+     * @param epsilon permissible error when comparing two Double values.
+     * @return true if two tensors have the same shape and elements, false otherwise.
+     */
     public fun Tensor<Double>.eq(other: Tensor<Double>, epsilon: Double): Boolean {
         return tensor.eq(other) { x, y -> abs(x - y) < epsilon }
     }
 
+    /**
+     * Compares element-wise two tensors.
+     * Comparison of two Double values occurs with 1e-5 precision.
+     *
+     * @param other the tensor to compare with `input` tensor.
+     * @return true if two tensors have the same shape and elements, false otherwise.
+     */
     public infix fun Tensor<Double>.eq(other: Tensor<Double>): Boolean = tensor.eq(other, 1e-5)
 
     private fun Tensor<Double>.eq(
@@ -450,9 +470,25 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return true
     }
 
+    /**
+     * Returns a tensor of random numbers drawn from normal distributions with 0.0 mean and 1.0 standard deviation.
+     *
+     * @param shape the desired shape for the output tensor.
+     * @param seed the random seed of the pseudo-random number generator.
+     * @return tensor of a given shape filled with numbers from the normal distribution
+     * with 0.0 mean and 1.0 standard deviation.
+     */
     public fun randomNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
         DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed))
 
+    /**
+     * Returns a tensor with the same shape as `input` of random numbers drawn from normal distributions
+     * with 0.0 mean and 1.0 standard deviation.
+     *
+     * @param seed the random seed of the pseudo-random number generator.
+     * @return tensor with the same shape as `input` filled with numbers from the normal distribution
+     * with 0.0 mean and 1.0 standard deviation.
+     */
     public fun Tensor<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 

From 5fe1320855941167afba93fb0f3d0beb855c834e Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Thu, 6 May 2021 12:59:21 +0100
Subject: [PATCH 163/178] minor corrections

---
 .../kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index 8c76de0de..1c8eacf66 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -493,7 +493,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
     /**
-     * Concatenates a sequence of tensors along a new dimension.
+     * Concatenates a sequence of tensors along the first dimension.
      *
      * @param tensors the [List] of tensors with same shapes to concatenate
      * @param dim the dimension to insert

From db5378c9f4ab3ff4e84d815d9db4686015330bf9 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Thu, 6 May 2021 16:29:21 +0300
Subject: [PATCH 164/178] PCA example

---
 .../space/kscience/kmath/tensors/PCA.kt       | 78 +++++++++++++++++++
 1 file changed, 78 insertions(+)
 create mode 100644 examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
new file mode 100644
index 000000000..1d72b2326
--- /dev/null
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
@@ -0,0 +1,78 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+
+const val seed = 100500L
+
+// simple PCA
+
+fun main(){
+    DoubleAnalyticTensorAlgebra {
+
+        // assume x is range from 0 until 10
+        val x = fromArray(
+            intArrayOf(10),
+            (0 until 10).toList().map { it.toDouble() }.toDoubleArray()
+        )
+
+        // take y dependent on x with noise
+        val y = 2.0 * x + (3.0 + x.randomNormalLike(seed) * 1.5)
+
+        println("x:\n$x")
+        println("y:\n$y")
+
+        // stack them into single dataset
+        val dataset = stack(listOf(x, y)).transpose()
+
+        // normalize both x and y
+        val xMean = x.mean()
+        val yMean = y.mean()
+
+        val xStd = x.std()
+        val yStd = y.std()
+
+        val xScaled = (x - xMean) / xStd
+        val yScaled = (y - yMean) / yStd
+
+        // save means ans standard deviations for further recovery
+        val mean = fromArray(
+            intArrayOf(2),
+            doubleArrayOf(xMean, yMean)
+        )
+        println("Means:\n$mean")
+
+        val std = fromArray(
+            intArrayOf(2),
+            doubleArrayOf(xStd, yStd)
+        )
+        println("Standard deviations:\n$std")
+
+        // calculate the covariance matrix of scaled x and y
+        val covMatrix = cov(listOf(xScaled, yScaled))
+        println("Covariance matrix:\n$covMatrix")
+
+        // and find out eigenvector of it
+        val (_, evecs) = DoubleLinearOpsTensorAlgebra {covMatrix.symEig()}
+        val v = evecs[0]
+        println("Eigenvector:\n$v")
+
+        // reduce dimension of dataset
+        val datasetReduced  = v dot stack(listOf(xScaled, yScaled))
+        println("Reduced data:\n$datasetReduced")
+
+        // we can restore original data from reduced data.
+        // for example, find 7th element of dataset
+        val n = 7
+        val restored = BroadcastDoubleTensorAlgebra{(datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean}
+        println("Original value:\n${dataset[n]}")
+        println("Restored value:\n$restored")
+    }
+}

From febe526325150c2b716f233956a12e69fcb44804 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Thu, 6 May 2021 14:50:05 +0100
Subject: [PATCH 165/178] Update stack docs

---
 .../kmath/tensors/core/algebras/DoubleTensorAlgebra.kt      | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index 1c8eacf66..9500fbdec 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -493,14 +493,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
     /**
-     * Concatenates a sequence of tensors along the first dimension.
+     * Concatenates a sequence of tensors with equal shapes along the first dimension.
      *
      * @param tensors the [List] of tensors with same shapes to concatenate
-     * @param dim the dimension to insert
      * @return tensor with concatenation result
      */
-    public fun stack(tensors: List<Tensor<Double>>, dim: Int = 0): DoubleTensor {
-        check(dim == 0) { "Stack by non-zero dimension not implemented yet" }
+    public fun stack(tensors: List<Tensor<Double>>): DoubleTensor {
         check(tensors.isNotEmpty()) { "List must have at least 1 element" }
         val shape = tensors[0].shape
         check(tensors.all { it.shape contentEquals shape }) { "Tensors must have same shapes" }

From 1b1a078deaee681c99603737d11eecefe0411db4 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 7 May 2021 03:22:34 +0300
Subject: [PATCH 166/178] neural network!

---
 .../kscience/kmath/tensors/NeuralNetwork.kt   | 245 ++++++++++++++++++
 .../space/kscience/kmath/tensors/PCA.kt       |   5 +-
 .../kmath/tensors/api/TensorAlgebra.kt        |  13 +-
 .../core/algebras/DoubleTensorAlgebra.kt      |   5 +
 4 files changed, 266 insertions(+), 2 deletions(-)
 create mode 100644 examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt
new file mode 100644
index 000000000..ea863988c
--- /dev/null
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt
@@ -0,0 +1,245 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.tensors.core.DoubleTensor
+import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.toDoubleArray
+import kotlin.math.sqrt
+
+const val seed = 100500L
+
+// Simple feedforward neural network with backpropagation training
+
+// interface of network layer
+interface Layer {
+    fun forward(input: DoubleTensor): DoubleTensor
+    fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor
+}
+
+// activation layer
+open class Activation(
+    val activation: (DoubleTensor) -> DoubleTensor,
+    val activationDer: (DoubleTensor) -> DoubleTensor
+) : Layer {
+    override fun forward(input: DoubleTensor): DoubleTensor {
+        return activation(input)
+    }
+
+    override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor {
+        return DoubleTensorAlgebra { outputError * activationDer(input) }
+    }
+}
+
+fun relu(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
+    x.map { if (it > 0) it else 0.0 }
+}
+
+fun reluDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
+    x.map { if (it > 0) 1.0 else 0.0 }
+}
+
+// activation layer with relu activator
+class ReLU : Activation(::relu, ::reluDer)
+
+fun sigmoid(x: DoubleTensor): DoubleTensor = DoubleAnalyticTensorAlgebra {
+    1.0 / (1.0 + (-x).exp())
+}
+
+fun sigmoidDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
+    sigmoid(x) * (1.0 - sigmoid(x))
+}
+
+// activation layer with sigmoid activator
+class Sigmoid : Activation(::sigmoid, ::sigmoidDer)
+
+// dense layer
+class Dense(
+    private val inputUnits: Int,
+    private val outputUnits: Int,
+    private val learningRate: Double = 0.1
+) : Layer {
+
+    private val weights: DoubleTensor = DoubleTensorAlgebra {
+        randomNormal(
+            intArrayOf(inputUnits, outputUnits),
+            seed
+        ) * sqrt(2.0 / (inputUnits + outputUnits))
+    }
+
+    private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(intArrayOf(outputUnits)) }
+
+    override fun forward(input: DoubleTensor): DoubleTensor {
+        return BroadcastDoubleTensorAlgebra { (input dot weights) + bias }
+    }
+
+    override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
+        val gradInput = outputError dot weights.transpose()
+
+        val gradW = input.transpose() dot outputError
+        val gradBias = DoubleAnalyticTensorAlgebra {
+            outputError.mean(dim = 0, keepDim = false) * input.shape[0].toDouble()
+        }
+
+        weights -= learningRate * gradW
+        bias -= learningRate * gradBias
+
+        gradInput
+    }
+
+}
+
+// simple accuracy equal to the proportion of correct answers
+fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
+    check(yPred.shape contentEquals yTrue.shape)
+    val n = yPred.shape[0]
+    var correctCnt = 0
+    for (i in 0 until n) {
+        if (yPred[intArrayOf(i, 0)] == yTrue[intArrayOf(i, 0)]) {
+            correctCnt += 1
+        }
+    }
+    return correctCnt.toDouble() / n.toDouble()
+}
+
+// neural network class
+class NeuralNetwork(private val layers: List<Layer>) {
+    private fun softMaxLoss(yPred: DoubleTensor, yTrue: DoubleTensor): DoubleTensor = DoubleAnalyticTensorAlgebra {
+
+        val onesForAnswers = yPred.zeroesLike()
+        yTrue.toDoubleArray().forEachIndexed { index, labelDouble ->
+            val label = labelDouble.toInt()
+            onesForAnswers[intArrayOf(index, label)] = 1.0
+        }
+
+        val softmaxValue = BroadcastDoubleTensorAlgebra { yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true) }
+
+        (-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
+    }
+
+    @OptIn(ExperimentalStdlibApi::class)
+    private fun forward(x: DoubleTensor): List<DoubleTensor> {
+        var input = x
+
+        return buildList {
+            layers.forEach { layer ->
+                val output = layer.forward(input)
+                add(output)
+                input = output
+            }
+        }
+    }
+
+    @OptIn(ExperimentalStdlibApi::class)
+    private fun train(xTrain: DoubleTensor, yTrain: DoubleTensor) {
+        val layerInputs = buildList {
+            add(xTrain)
+            addAll(forward(xTrain))
+        }
+
+        var lossGrad = softMaxLoss(layerInputs.last(), yTrain)
+
+        layers.zip(layerInputs).reversed().forEach { (layer, input) ->
+            lossGrad = layer.backward(input, lossGrad)
+        }
+    }
+
+    fun fit(xTrain: DoubleTensor, yTrain: DoubleTensor, batchSize: Int, epochs: Int) = DoubleTensorAlgebra {
+        fun iterBatch(x: DoubleTensor, y: DoubleTensor): Sequence<Pair<DoubleTensor, DoubleTensor>> = sequence {
+            val n = x.shape[0]
+            val shuffledIndices = (0 until n).shuffled()
+            for (i in 0 until n step batchSize) {
+                val excerptIndices = shuffledIndices.drop(i).take(batchSize).toIntArray()
+                val batch = x.rowsByIndices(excerptIndices) to y.rowsByIndices(excerptIndices)
+                yield(batch)
+            }
+        }
+
+        for (epoch in 0 until epochs) {
+            println("Epoch ${epoch + 1}/$epochs")
+            for ((xBatch, yBatch) in iterBatch(xTrain, yTrain)) {
+                train(xBatch, yBatch)
+            }
+            println("Accuracy:${accuracy(yTrain, predict(xTrain).argMax(1, true))}")
+        }
+    }
+
+    fun predict(x: DoubleTensor): DoubleTensor {
+        return forward(x).last()
+    }
+
+}
+
+
+
+@OptIn(ExperimentalStdlibApi::class)
+fun main() {
+    DoubleTensorAlgebra {
+        val features = 5
+        val sampleSize = 250
+        val trainSize = 180
+        val testSize = sampleSize - trainSize
+
+        // take sample of features from normal distribution
+        val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
+        BroadcastDoubleTensorAlgebra {
+            x += fromArray(
+                intArrayOf(5),
+                doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // rows means
+            )
+        }
+
+        // define class like '1' if the sum of features > 0 and '0' otherwise
+        val y = fromArray(
+            intArrayOf(sampleSize, 1),
+            DoubleArray(sampleSize) { i ->
+                if (x[i].sum() > 0.0) {
+                    1.0
+                } else {
+                    0.0
+                }
+            }
+        )
+
+        // split train ans test
+        val trainIndices = (0 until trainSize).toList().toIntArray()
+        val testIndices = (trainSize until sampleSize).toList().toIntArray()
+
+        val xTrain = x.rowsByIndices(trainIndices)
+        val yTrain = y.rowsByIndices(trainIndices)
+
+        val xTest = x.rowsByIndices(testIndices)
+        val yTest = y.rowsByIndices(testIndices)
+
+        // build model
+        val layers = buildList {
+            add(Dense(features, 64))
+            add(ReLU())
+            add(Dense(64, 16))
+            add(ReLU())
+            add(Dense(16, 2))
+            add(Sigmoid())
+        }
+        val model = NeuralNetwork(layers)
+
+        // fit it with train data
+        model.fit(xTrain, yTrain, batchSize = 20, epochs = 10)
+
+        // make prediction
+        val prediction = model.predict(xTest)
+
+        // process raw prediction via argMax
+        val predictionLabels = prediction.argMax(1, true)
+
+        // find out accuracy
+        val acc = accuracy(yTest, predictionLabels)
+        println("Test accuracy:$acc")
+
+    }
+}
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
index 1d72b2326..ee25b63a3 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
@@ -10,11 +10,14 @@ import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 
-const val seed = 100500L
+
 
 // simple PCA
 
 fun main(){
+    val seed = 100500L
+
+    // work in context with analytic methods
     DoubleAnalyticTensorAlgebra {
 
         // assume x is range from 0 until 10
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index b0a0f9618..417cff87f 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -305,5 +305,16 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
      */
     public fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T>
 
-
+    /**
+     * Returns the index of maximum value of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the the index of maximum value of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.argMax(dim: Int, keepDim: Boolean): Tensor<T>
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index 9500fbdec..6fac0a0ec 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -561,4 +561,9 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
         foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
 
+    override fun Tensor<Double>.argMax(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x ->
+            x.withIndex().maxByOrNull { it.value }?.index!!.toDouble()
+        }, dim, keepDim)
+
 }

From 14ca7cdd31e14855c95a51b8143bf80aae02feb9 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 7 May 2021 13:00:20 +0300
Subject: [PATCH 167/178] fixes

---
 .../kmath/tensors/api/LinearOpsTensorAlgebra.kt   | 15 ++++++++-------
 .../kscience/kmath/tensors/api/TensorAlgebra.kt   |  2 +-
 .../tensors/core/algebras/DoubleTensorAlgebra.kt  |  9 ++++-----
 .../core/algebras/TensorLinearStructure.kt        |  3 +--
 .../kscience/kmath/tensors/core/tensorCasts.kt    |  2 +-
 .../kmath/tensors/core/tensorCastsUtils.kt        |  2 +-
 6 files changed, 16 insertions(+), 17 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
index ec070b6bd..4a325ab4e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
@@ -23,8 +23,8 @@ public interface LinearOpsTensorAlgebra<T> :
 
     /**
      * Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input.
-     * Given a square matrix `a`, return the matrix `aInv` satisfying
-     * ``a.dot(aInv) = aInv.dot(a) = eye(a.shape[0])``.
+     * Given a square matrix `A`, return the matrix `AInv` satisfying
+     * `A dot AInv  = AInv dot A = eye(a.shape[0])`.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
      *
      * @return the multiplicative inverse of a matrix.
@@ -37,7 +37,7 @@ public interface LinearOpsTensorAlgebra<T> :
      * Computes the Cholesky decomposition of a Hermitian (or symmetric for real-valued matrices)
      * positive-definite matrix or the Cholesky decompositions for a batch of such matrices.
      * Each decomposition has the form:
-     * Given a tensor `input`, return the tensor `L` satisfying ``input = L * L.H``,
+     * Given a tensor `input`, return the tensor `L` satisfying `input = L dot L.H`,
      * where L is a lower-triangular matrix and L.H is the conjugate transpose of L,
      * which is just a transpose for the case of real-valued input matrices.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.cholesky
@@ -50,7 +50,7 @@ public interface LinearOpsTensorAlgebra<T> :
      * QR decomposition.
      *
      * Computes the QR decomposition of a matrix or a batch of matrices, and returns a pair `(Q, R)` of tensors.
-     * Given a tensor `input`, return tensors (Q, R) satisfying ``input = Q * R``,
+     * Given a tensor `input`, return tensors (Q, R) satisfying ``input = Q dot R``,
      * with `Q` being an orthogonal matrix or batch of orthogonal matrices
      * and `R` being an upper triangular matrix or batch of upper triangular matrices.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
@@ -63,7 +63,7 @@ public interface LinearOpsTensorAlgebra<T> :
      * LUP decomposition
      *
      * Computes the LUP decomposition of a matrix or a batch of matrices.
-     * Given a tensor `input`, return tensors (P, L, U) satisfying ``P * input = L * U``,
+     * Given a tensor `input`, return tensors (P, L, U) satisfying `P dot input = L dot  U`,
      * with `P` being a permutation matrix or batch of matrices,
      * `L` being a lower triangular matrix or batch of matrices,
      * `U` being an upper triangular matrix or batch of matrices.
@@ -77,7 +77,8 @@ public interface LinearOpsTensorAlgebra<T> :
      *
      * Computes the singular value decomposition of either a matrix or batch of matrices `input`.
      * The singular value decomposition is represented as a triple `(U, S, V)`,
-     * such that ``input = U.dot(diagonalEmbedding(S).dot(V.T))``.
+     * such that `input = U dot diagonalEmbedding(S) dot  V.H`,
+     * where V.H is the conjugate transpose of V.
      * If input is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as input.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
      *
@@ -94,4 +95,4 @@ public interface LinearOpsTensorAlgebra<T> :
      */
     public fun Tensor<T>.symEig(): Pair<Tensor<T>, Tensor<T>>
 
-}
\ No newline at end of file
+}
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index 417cff87f..e7f8dc7ae 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -288,7 +288,7 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
     public fun Tensor<T>.min(dim: Int, keepDim: Boolean): Tensor<T>
 
     /**
-     * @return the maximum value of all elements in the input tensor.
+     * Returns the maximum value of all elements in the input tensor.
      */
     public fun Tensor<T>.max(): T
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index 6fac0a0ec..e5d41f856 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -343,7 +343,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         val m2 = newOther.shape[newOther.shape.size - 2]
         val n = newOther.shape[newOther.shape.size - 1]
         check(m1 == m2) {
-            throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
+            "Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)"
         }
 
         val resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
@@ -436,9 +436,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
      * @param epsilon permissible error when comparing two Double values.
      * @return true if two tensors have the same shape and elements, false otherwise.
      */
-    public fun Tensor<Double>.eq(other: Tensor<Double>, epsilon: Double): Boolean {
-        return tensor.eq(other) { x, y -> abs(x - y) < epsilon }
-    }
+    public fun Tensor<Double>.eq(other: Tensor<Double>, epsilon: Double): Boolean =
+        tensor.eq(other) { x, y -> abs(x - y) < epsilon }
 
     /**
      * Compares element-wise two tensors.
@@ -510,7 +509,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     }
 
     /**
-     * Build tensor from rows of input tensor
+     * Builds tensor from rows of input tensor
      *
      * @param indices the [IntArray] of 1-dimensional indices
      * @return tensor with rows corresponding to rows by [indices]
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
index 68aa03311..f65e2b955 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
@@ -23,7 +23,6 @@ internal fun stridesFromShape(shape: IntArray): IntArray {
         current--
     }
     return res
-
 }
 
 internal fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {
@@ -55,4 +54,4 @@ public class TensorLinearStructure(override val shape: IntArray) : Strides {
     override val linearSize: Int
         get() = shape.reduce(Int::times)
 
-}
\ No newline at end of file
+}
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
index 6254ce751..2743a5218 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
@@ -33,4 +33,4 @@ public fun IntTensor.toIntArray(): IntArray {
     return IntArray(numElements) { i ->
         mutableBuffer[bufferStart + i]
     }
-}
\ No newline at end of file
+}
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt
index 31de6919f..70e3b9c61 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt
@@ -39,4 +39,4 @@ internal val Tensor<Int>.tensor: IntTensor
     get() = when (this) {
         is IntTensor -> this
         else -> this.toBufferedTensor().asTensor()
-    }
\ No newline at end of file
+    }

From 0920e21d622e85e95e25b6d28d906309dab8d875 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 7 May 2021 12:52:17 +0100
Subject: [PATCH 168/178] Merging implementations together

---
 .../kmath/tensors/DataSetNormalization.kt     |  20 +-
 .../tensors/LinearSystemSolvingWithLUP.kt     |   4 +-
 .../kscience/kmath/tensors/NeuralNetwork.kt   |  26 +-
 .../kscience/kmath/tensors/OLSWithSVD.kt      |   9 +-
 .../space/kscience/kmath/tensors/PCA.kt       |  11 +-
 .../tensors/api/AnalyticTensorAlgebra.kt      |   3 +-
 .../tensors/api/LinearOpsTensorAlgebra.kt     |   3 +-
 .../api/TensorPartialDivisionAlgebra.kt       |   3 +-
 .../kmath/tensors/core/DoubleTensor.kt        |   1 +
 .../algebras/BroadcastDoubleTensorAlgebra.kt  |   6 +-
 .../algebras/DoubleAnalyticTensorAlgebra.kt   | 116 ------
 .../algebras/DoubleLinearOpsTensorAlgebra.kt  | 278 -------------
 .../core/algebras/DoubleTensorAlgebra.kt      | 385 +++++++++++++++++-
 .../core/{ => internal}/broadcastUtils.kt     |   8 +-
 .../tensors/core/{ => internal}/checks.kt     |  11 +-
 .../tensors/core/{ => internal}/linUtils.kt   |  27 +-
 .../core/{ => internal}/tensorCastsUtils.kt   |   5 +-
 .../tensors/core/{ => internal}/utils.kt      |   9 +-
 .../kmath/tensors/core/tensorCasts.kt         |   1 +
 .../kmath/tensors/core/TestBroadcasting.kt    |   1 +
 .../core/TestDoubleAnalyticTensorAlgebra.kt   |  31 +-
 .../core/TestDoubleLinearOpsAlgebra.kt        |  30 +-
 .../kmath/tensors/core/TestDoubleTensor.kt    |   4 +
 .../tensors/core/TestDoubleTensorAlgebra.kt   |   1 +
 24 files changed, 488 insertions(+), 505 deletions(-)
 delete mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
 delete mode 100644 kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{ => internal}/broadcastUtils.kt (94%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{ => internal}/checks.kt (83%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{ => internal}/linUtils.kt (91%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{ => internal}/tensorCastsUtils.kt (88%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{ => internal}/utils.kt (91%)

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt
index 4d53d940b..d029348f2 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt
@@ -7,22 +7,22 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
+
 
 // Dataset normalization
 
 fun main() {
 
-    // work in context with analytic methods
-    DoubleAnalyticTensorAlgebra {
+    // work in context with broadcast methods
+    BroadcastDoubleTensorAlgebra {
         // take dataset of 5-element vectors from normal distribution
         val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
-        BroadcastDoubleTensorAlgebra {
-            dataset += fromArray(
-                intArrayOf(5),
-                doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
-            )
-        }
+
+        dataset += fromArray(
+            intArrayOf(5),
+            doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
+        )
+
 
         // find out mean and standard deviation of each column
         val mean = dataset.mean(0, false)
@@ -36,7 +36,7 @@ fun main() {
         println("Maximum:\n${dataset.max(0, false)}")
 
         // now we can scale dataset with mean normalization
-        val datasetScaled = BroadcastDoubleTensorAlgebra { (dataset - mean) / std }
+        val datasetScaled = (dataset - mean) / std
 
         // find out mean and std of scaled dataset
 
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
index bd8233ccc..c0ece04ca 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
@@ -7,14 +7,14 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
 
 // solving linear system with LUP decomposition
 
 fun main () {
 
     // work in context with linear operations
-    DoubleLinearOpsTensorAlgebra {
+    BroadcastDoubleTensorAlgebra {
 
         // set true value of x
         val trueX = fromArray(
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt
index ea863988c..1998b8d16 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt
@@ -8,7 +8,6 @@ package space.kscience.kmath.tensors
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.toDoubleArray
 import kotlin.math.sqrt
@@ -48,7 +47,7 @@ fun reluDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
 // activation layer with relu activator
 class ReLU : Activation(::relu, ::reluDer)
 
-fun sigmoid(x: DoubleTensor): DoubleTensor = DoubleAnalyticTensorAlgebra {
+fun sigmoid(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
     1.0 / (1.0 + (-x).exp())
 }
 
@@ -83,9 +82,7 @@ class Dense(
         val gradInput = outputError dot weights.transpose()
 
         val gradW = input.transpose() dot outputError
-        val gradBias = DoubleAnalyticTensorAlgebra {
-            outputError.mean(dim = 0, keepDim = false) * input.shape[0].toDouble()
-        }
+        val gradBias = outputError.mean(dim = 0, keepDim = false) * input.shape[0].toDouble()
 
         weights -= learningRate * gradW
         bias -= learningRate * gradBias
@@ -110,7 +107,7 @@ fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
 
 // neural network class
 class NeuralNetwork(private val layers: List<Layer>) {
-    private fun softMaxLoss(yPred: DoubleTensor, yTrue: DoubleTensor): DoubleTensor = DoubleAnalyticTensorAlgebra {
+    private fun softMaxLoss(yPred: DoubleTensor, yTrue: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
 
         val onesForAnswers = yPred.zeroesLike()
         yTrue.toDoubleArray().forEachIndexed { index, labelDouble ->
@@ -118,7 +115,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
             onesForAnswers[intArrayOf(index, label)] = 1.0
         }
 
-        val softmaxValue = BroadcastDoubleTensorAlgebra { yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true) }
+        val softmaxValue =  yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
 
         (-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
     }
@@ -177,10 +174,9 @@ class NeuralNetwork(private val layers: List<Layer>) {
 }
 
 
-
 @OptIn(ExperimentalStdlibApi::class)
 fun main() {
-    DoubleTensorAlgebra {
+    BroadcastDoubleTensorAlgebra {
         val features = 5
         val sampleSize = 250
         val trainSize = 180
@@ -188,12 +184,12 @@ fun main() {
 
         // take sample of features from normal distribution
         val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
-        BroadcastDoubleTensorAlgebra {
-            x += fromArray(
-                intArrayOf(5),
-                doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // rows means
-            )
-        }
+
+        x += fromArray(
+            intArrayOf(5),
+            doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // rows means
+        )
+
 
         // define class like '1' if the sum of features > 0 and '0' otherwise
         val y = fromArray(
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
index 435af35f6..497f63b41 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
@@ -7,8 +7,7 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
-import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 
 import kotlin.math.abs
 
@@ -19,7 +18,7 @@ fun main() {
     val randSeed = 100500L
 
     // work in context with linear operations
-    DoubleLinearOpsTensorAlgebra {
+    DoubleTensorAlgebra {
         // take coefficient vector from normal distribution
         val alpha = randomNormal(
             intArrayOf(5),
@@ -56,12 +55,12 @@ fun main() {
                 "$alphaOLS")
 
         // figure out MSE of approximation
-        fun mse(yTrue: DoubleTensor, yPred: DoubleTensor): Double = DoubleAnalyticTensorAlgebra{
+        fun mse(yTrue: DoubleTensor, yPred: DoubleTensor): Double {
             require(yTrue.shape.size == 1)
             require(yTrue.shape contentEquals yPred.shape)
 
             val diff = yTrue - yPred
-            diff.dot(diff).sqrt().value()
+            return diff.dot(diff).sqrt().value()
         }
 
         println("MSE: ${mse(alpha, alphaOLS)}")
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
index ee25b63a3..d29dbb094 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
@@ -7,9 +7,6 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
-import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
-
 
 
 // simple PCA
@@ -17,8 +14,8 @@ import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 fun main(){
     val seed = 100500L
 
-    // work in context with analytic methods
-    DoubleAnalyticTensorAlgebra {
+    // work in context with broadcast methods
+    BroadcastDoubleTensorAlgebra {
 
         // assume x is range from 0 until 10
         val x = fromArray(
@@ -63,7 +60,7 @@ fun main(){
         println("Covariance matrix:\n$covMatrix")
 
         // and find out eigenvector of it
-        val (_, evecs) = DoubleLinearOpsTensorAlgebra {covMatrix.symEig()}
+        val (_, evecs) = covMatrix.symEig()
         val v = evecs[0]
         println("Eigenvector:\n$v")
 
@@ -74,7 +71,7 @@ fun main(){
         // we can restore original data from reduced data.
         // for example, find 7th element of dataset
         val n = 7
-        val restored = BroadcastDoubleTensorAlgebra{(datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean}
+        val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
         println("Original value:\n${dataset[n]}")
         println("Restored value:\n$restored")
     }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
index 69e88c28f..1db986e77 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt
@@ -11,8 +11,7 @@ package space.kscience.kmath.tensors.api
  *
  * @param T the type of items closed under analytic functions in the tensors.
  */
-public interface AnalyticTensorAlgebra<T> :
-    TensorPartialDivisionAlgebra<T> {
+public interface AnalyticTensorAlgebra<T> : TensorPartialDivisionAlgebra<T> {
 
     /**
      * @return the mean of all elements in the input tensor.
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
index 4a325ab4e..6bdecfa85 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
@@ -10,8 +10,7 @@ package space.kscience.kmath.tensors.api
  *
  * @param T the type of items closed under division in the tensors.
  */
-public interface LinearOpsTensorAlgebra<T> :
-    TensorPartialDivisionAlgebra<T> {
+public interface LinearOpsTensorAlgebra<T> : TensorPartialDivisionAlgebra<T> {
 
     /**
      * Computes the determinant of a square matrix input, or of each square matrix in a batched input.
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
index 921157963..02bf5415d 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt
@@ -11,8 +11,7 @@ package space.kscience.kmath.tensors.api
  *
  * @param T the type of items closed under division in the tensors.
  */
-public interface TensorPartialDivisionAlgebra<T> :
-    TensorAlgebra<T> {
+public interface TensorPartialDivisionAlgebra<T> : TensorAlgebra<T> {
 
     /**
      * Each element of the tensor [other] is divided by this value.
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt
index e3143f5a7..41df50cba 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt
@@ -6,6 +6,7 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.structures.DoubleBuffer
+import space.kscience.kmath.tensors.core.internal.toPrettyString
 
 /**
  * Default [BufferedTensor] implementation for [Double] values
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
index 873ec9027..bc7d90c28 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
@@ -7,8 +7,10 @@ package space.kscience.kmath.tensors.core.algebras
 
 import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.*
-import space.kscience.kmath.tensors.core.broadcastTensors
-import space.kscience.kmath.tensors.core.broadcastTo
+import space.kscience.kmath.tensors.core.internal.array
+import space.kscience.kmath.tensors.core.internal.broadcastTensors
+import space.kscience.kmath.tensors.core.internal.broadcastTo
+import space.kscience.kmath.tensors.core.internal.tensor
 
 /**
  * Basic linear algebra operations implemented with broadcasting.
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
deleted file mode 100644
index 23a2fa282..000000000
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt
+++ /dev/null
@@ -1,116 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors.core.algebras
-
-import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
-import space.kscience.kmath.tensors.api.Tensor
-import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.tensor
-import kotlin.math.*
-
-public object DoubleAnalyticTensorAlgebra :
-    AnalyticTensorAlgebra<Double>,
-    DoubleTensorAlgebra() {
-
-    override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
-
-    override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim(
-            { arr ->
-                check(dim < dimension) { "Dimension $dim out of range $dimension" }
-                arr.sum() / shape[dim]
-            },
-            dim,
-            keepDim
-        )
-
-    override fun Tensor<Double>.std(): Double = this.fold { arr ->
-        val mean = arr.sum() / tensor.numElements
-        sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
-    }
-
-    override fun Tensor<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
-        { arr ->
-            check(dim < dimension) { "Dimension $dim out of range $dimension" }
-            val mean = arr.sum() / shape[dim]
-            sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
-        },
-        dim,
-        keepDim
-    )
-
-    override fun Tensor<Double>.variance(): Double = this.fold { arr ->
-        val mean = arr.sum() / tensor.numElements
-        arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
-    }
-
-    override fun Tensor<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
-        { arr ->
-            check(dim < dimension) { "Dimension $dim out of range $dimension" }
-            val mean = arr.sum() / shape[dim]
-            arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
-        },
-        dim,
-        keepDim
-    )
-
-    private fun cov(x: DoubleTensor, y:DoubleTensor): Double{
-        val n = x.shape[0]
-        return ((x - x.mean()) * (y - y.mean())).mean() * n / (n - 1)
-    }
-
-    override fun cov(tensors: List<Tensor<Double>>): DoubleTensor {
-        check(tensors.isNotEmpty()) { "List must have at least 1 element" }
-        val n = tensors.size
-        val m = tensors[0].shape[0]
-        check(tensors.all { it.shape contentEquals intArrayOf(m) }) { "Tensors must have same shapes" }
-        val resTensor = DoubleTensor(
-            intArrayOf(n, n),
-            DoubleArray(n * n) {0.0}
-        )
-        for (i in 0 until n){
-            for (j in 0 until n){
-                resTensor[intArrayOf(i, j)] = cov(tensors[i].tensor, tensors[j].tensor)
-            }
-        }
-        return resTensor
-    }
-
-    override fun Tensor<Double>.exp(): DoubleTensor = tensor.map(::exp)
-
-    override fun Tensor<Double>.ln(): DoubleTensor = tensor.map(::ln)
-
-    override fun Tensor<Double>.sqrt(): DoubleTensor = tensor.map(::sqrt)
-
-    override fun Tensor<Double>.cos(): DoubleTensor = tensor.map(::cos)
-
-    override fun Tensor<Double>.acos(): DoubleTensor = tensor.map(::acos)
-
-    override fun Tensor<Double>.cosh(): DoubleTensor = tensor.map(::cosh)
-
-    override fun Tensor<Double>.acosh(): DoubleTensor = tensor.map(::acosh)
-
-    override fun Tensor<Double>.sin(): DoubleTensor = tensor.map(::sin)
-
-    override fun Tensor<Double>.asin(): DoubleTensor = tensor.map(::asin)
-
-    override fun Tensor<Double>.sinh(): DoubleTensor = tensor.map(::sinh)
-
-    override fun Tensor<Double>.asinh(): DoubleTensor = tensor.map(::asinh)
-
-    override fun Tensor<Double>.tan(): DoubleTensor = tensor.map(::tan)
-
-    override fun Tensor<Double>.atan(): DoubleTensor = tensor.map(::atan)
-
-    override fun Tensor<Double>.tanh(): DoubleTensor = tensor.map(::tanh)
-
-    override fun Tensor<Double>.atanh(): DoubleTensor = tensor.map(::atanh)
-
-    override fun Tensor<Double>.ceil(): DoubleTensor = tensor.map(::ceil)
-
-    override fun Tensor<Double>.floor(): DoubleTensor = tensor.map(::floor)
-
-}
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
deleted file mode 100644
index 430482613..000000000
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
+++ /dev/null
@@ -1,278 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors.core.algebras
-
-import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
-import space.kscience.kmath.nd.as1D
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.tensors.api.Tensor
-import space.kscience.kmath.tensors.core.*
-import space.kscience.kmath.tensors.core.checkSquareMatrix
-import space.kscience.kmath.tensors.core.choleskyHelper
-import space.kscience.kmath.tensors.core.cleanSymHelper
-import space.kscience.kmath.tensors.core.luHelper
-import space.kscience.kmath.tensors.core.luMatrixDet
-import space.kscience.kmath.tensors.core.luMatrixInv
-import space.kscience.kmath.tensors.core.luPivotHelper
-import space.kscience.kmath.tensors.core.pivInit
-import kotlin.math.min
-
-/**
- * Implementation of common linear algebra operations on double numbers.
- * Implements the LinearOpsTensorAlgebra<Double> interface.
- */
-public object DoubleLinearOpsTensorAlgebra :
-    LinearOpsTensorAlgebra<Double>,
-    DoubleTensorAlgebra() {
-
-    override fun Tensor<Double>.inv(): DoubleTensor = invLU(1e-9)
-
-    override fun Tensor<Double>.det(): DoubleTensor = detLU(1e-9)
-
-    /**
-     * Computes the LU factorization of a matrix or batches of matrices `input`.
-     * Returns a tuple containing the LU factorization and pivots of `input`.
-     *
-     * @param epsilon permissible error when comparing the determinant of a matrix with zero
-     * @return pair of `factorization` and `pivots`.
-     * The `factorization` has the shape ``(*, m, n)``, where``(*, m, n)`` is the shape of the `input` tensor.
-     * The `pivots`  has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows.
-     */
-    public fun Tensor<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
-        computeLU(tensor, epsilon)
-            ?: throw IllegalArgumentException("Tensor contains matrices which are singular at precision $epsilon")
-
-    /**
-     * Computes the LU factorization of a matrix or batches of matrices `input`.
-     * Returns a tuple containing the LU factorization and pivots of `input`.
-     * Uses an error of ``1e-9`` when calculating whether a matrix is degenerate.
-     *
-     * @return pair of `factorization` and `pivots`.
-     * The `factorization` has the shape ``(*, m, n)``, where``(*, m, n)`` is the shape of the `input` tensor.
-     * The `pivots`  has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows.
-     */
-    public fun Tensor<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
-
-    /**
-     * Unpacks the data and pivots from a LU factorization of a tensor.
-     * Given a tensor [luTensor], return tensors (P, L, U) satisfying ``P * luTensor = L * U``,
-     * with `P` being a permutation matrix or batch of matrices,
-     * `L` being a lower triangular matrix or batch of matrices,
-     * `U` being an upper triangular matrix or batch of matrices.
-     *
-     * @param luTensor the packed LU factorization data
-     * @param pivotsTensor the packed LU factorization pivots
-     * @return triple of P, L and U tensors
-     */
-    public fun luPivot(
-        luTensor: Tensor<Double>,
-        pivotsTensor: Tensor<Int>
-    ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        checkSquareMatrix(luTensor.shape)
-        check(
-            luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() ||
-                    luTensor.shape.last() == pivotsTensor.shape.last() - 1
-        ) { "Inappropriate shapes of input tensors" }
-
-        val n = luTensor.shape.last()
-        val pTensor = luTensor.zeroesLike()
-        pTensor
-            .matrixSequence()
-            .zip(pivotsTensor.tensor.vectorSequence())
-            .forEach { (p, pivot) -> pivInit(p.as2D(), pivot.as1D(), n) }
-
-        val lTensor = luTensor.zeroesLike()
-        val uTensor = luTensor.zeroesLike()
-
-        lTensor.matrixSequence()
-            .zip(uTensor.matrixSequence())
-            .zip(luTensor.tensor.matrixSequence())
-            .forEach { (pairLU, lu) ->
-                val (l, u) = pairLU
-                luPivotHelper(l.as2D(), u.as2D(), lu.as2D(), n)
-            }
-
-        return Triple(pTensor, lTensor, uTensor)
-    }
-
-    /**
-     * QR decomposition.
-     *
-     * Computes the QR decomposition of a matrix or a batch of matrices, and returns a pair `(Q, R)` of tensors.
-     * Given a tensor `input`, return tensors (Q, R) satisfying ``input = Q * R``,
-     * with `Q` being an orthogonal matrix or batch of orthogonal matrices
-     * and `R` being an upper triangular matrix or batch of upper triangular matrices.
-     *
-     * @param epsilon permissible error when comparing tensors for equality.
-     * Used when checking the positive definiteness of the input matrix or matrices.
-     * @return pair of Q and R tensors.
-     */
-    public fun Tensor<Double>.cholesky(epsilon: Double): DoubleTensor {
-        checkSquareMatrix(shape)
-        checkPositiveDefinite(tensor, epsilon)
-
-        val n = shape.last()
-        val lTensor = zeroesLike()
-
-        for ((a, l) in tensor.matrixSequence().zip(lTensor.matrixSequence()))
-            for (i in 0 until n) choleskyHelper(a.as2D(), l.as2D(), n)
-
-        return lTensor
-    }
-
-    override fun Tensor<Double>.cholesky(): DoubleTensor = cholesky(1e-6)
-
-    override fun Tensor<Double>.qr(): Pair<DoubleTensor, DoubleTensor> {
-        checkSquareMatrix(shape)
-        val qTensor = zeroesLike()
-        val rTensor = zeroesLike()
-        tensor.matrixSequence()
-            .zip((qTensor.matrixSequence()
-                .zip(rTensor.matrixSequence()))).forEach { (matrix, qr) ->
-            val (q, r) = qr
-            qrHelper(matrix.asTensor(), q.asTensor(), r.as2D())
-        }
-
-        return qTensor to rTensor
-    }
-
-    override fun Tensor<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
-        svd(epsilon = 1e-10)
-
-    /**
-     * Singular Value Decomposition.
-     *
-     * Computes the singular value decomposition of either a matrix or batch of matrices `input`.
-     * The singular value decomposition is represented as a triple `(U, S, V)`,
-     * such that ``input = U.dot(diagonalEmbedding(S).dot(V.T))``.
-     * If input is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as input.
-     *
-     * @param epsilon permissible error when calculating the dot product of vectors,
-     * i.e. the precision with which the cosine approaches 1 in an iterative algorithm.
-     * @return triple `(U, S, V)`.
-     */
-    public fun Tensor<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        val size = tensor.dimension
-        val commonShape = tensor.shape.sliceArray(0 until size - 2)
-        val (n, m) = tensor.shape.sliceArray(size - 2 until size)
-        val uTensor = zeros(commonShape + intArrayOf(min(n, m), n))
-        val sTensor = zeros(commonShape + intArrayOf(min(n, m)))
-        val vTensor = zeros(commonShape + intArrayOf(min(n, m), m))
-
-        tensor.matrixSequence()
-            .zip(uTensor.matrixSequence()
-                .zip(sTensor.vectorSequence()
-                    .zip(vTensor.matrixSequence()))).forEach { (matrix, USV) ->
-                val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
-                val curMatrix = DoubleTensor(
-                    matrix.shape,
-                    matrix.mutableBuffer.array().slice(matrix.bufferStart until matrix.bufferStart + matrixSize)
-                        .toDoubleArray()
-                )
-                svdHelper(curMatrix, USV, m, n, epsilon)
-            }
-
-        return Triple(uTensor.transpose(), sTensor, vTensor.transpose())
-    }
-
-    override fun Tensor<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> =
-        symEig(epsilon = 1e-15)
-
-    /**
-     * Returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices,
-     * represented by a pair (eigenvalues, eigenvectors).
-     *
-     * @param epsilon permissible error when comparing tensors for equality
-     * and when the cosine approaches 1 in the SVD algorithm.
-     * @return a pair (eigenvalues, eigenvectors)
-     */
-    public fun Tensor<Double>.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
-        checkSymmetric(tensor, epsilon)
-        val (u, s, v) = tensor.svd(epsilon)
-        val shp = s.shape + intArrayOf(1)
-        val utv = u.transpose() dot v
-        val n = s.shape.last()
-        for (matrix in utv.matrixSequence())
-            cleanSymHelper(matrix.as2D(), n)
-
-        val eig = (utv dot s.view(shp)).view(s.shape)
-        return eig to v
-    }
-
-    /**
-     * Computes the determinant of a square matrix input, or of each square matrix in a batched input
-     * using LU factorization algorithm.
-     *
-     * @param epsilon error in the LU algorithm - permissible error when comparing the determinant of a matrix with zero
-     * @return the determinant.
-     */
-    public fun Tensor<Double>.detLU(epsilon: Double = 1e-9): DoubleTensor {
-
-        checkSquareMatrix(tensor.shape)
-        val luTensor = tensor.copy()
-        val pivotsTensor = tensor.setUpPivots()
-
-        val n = shape.size
-
-        val detTensorShape = IntArray(n - 1) { i -> shape[i] }
-        detTensorShape[n - 2] = 1
-        val resBuffer = DoubleArray(detTensorShape.reduce(Int::times)) { 0.0 }
-
-        val detTensor = DoubleTensor(
-            detTensorShape,
-            resBuffer
-        )
-
-        luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).forEachIndexed { index, (lu, pivots) ->
-            resBuffer[index] = if (luHelper(lu.as2D(), pivots.as1D(), epsilon))
-                0.0 else luMatrixDet(lu.as2D(), pivots.as1D())
-        }
-
-        return detTensor
-    }
-
-    /**
-     * Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input
-     * using LU factorization algorithm.
-     * Given a square matrix `a`, return the matrix `aInv` satisfying
-     * ``a.dot(aInv) = aInv.dot(a) = eye(a.shape[0])``.
-     *
-     * @param epsilon error in the LU algorithm - permissible error when comparing the determinant of a matrix with zero
-     * @return the multiplicative inverse of a matrix.
-     */
-    public fun Tensor<Double>.invLU(epsilon: Double = 1e-9): DoubleTensor {
-        val (luTensor, pivotsTensor) = luFactor(epsilon)
-        val invTensor = luTensor.zeroesLike()
-
-        val seq = luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
-        for ((luP, invMatrix) in seq) {
-            val (lu, pivots) = luP
-            luMatrixInv(lu.as2D(), pivots.as1D(), invMatrix.as2D())
-        }
-
-        return invTensor
-    }
-
-    /**
-     * LUP decomposition
-     *
-     * Computes the LUP decomposition of a matrix or a batch of matrices.
-     * Given a tensor `input`, return tensors (P, L, U) satisfying ``P * input = L * U``,
-     * with `P` being a permutation matrix or batch of matrices,
-     * `L` being a lower triangular matrix or batch of matrices,
-     * `U` being an upper triangular matrix or batch of matrices.
-     *
-     * @param epsilon permissible error when comparing the determinant of a matrix with zero
-     * @return triple of P, L and U tensors
-     */
-    public fun Tensor<Double>.lu(epsilon: Double = 1e-9): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        val (lu, pivots) = this.luFactor(epsilon)
-        return luPivot(lu, pivots)
-    }
-
-    override fun Tensor<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
-
-}
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
index e5d41f856..cb06432d0 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
@@ -5,28 +5,34 @@
 
 package space.kscience.kmath.tensors.core.algebras
 
+import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
+import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
 import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra
 import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.*
-import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra.fold
-import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra.foldDim
-import space.kscience.kmath.tensors.core.broadcastOuterTensors
-import space.kscience.kmath.tensors.core.checkBufferShapeConsistency
-import space.kscience.kmath.tensors.core.checkEmptyDoubleBuffer
-import space.kscience.kmath.tensors.core.checkEmptyShape
-import space.kscience.kmath.tensors.core.checkShapesCompatible
-import space.kscience.kmath.tensors.core.checkTranspose
-import space.kscience.kmath.tensors.core.checkView
-import space.kscience.kmath.tensors.core.dotHelper
-import space.kscience.kmath.tensors.core.getRandomNormals
-import space.kscience.kmath.tensors.core.minusIndexFrom
-import kotlin.math.abs
+import space.kscience.kmath.tensors.core.internal.dotHelper
+import space.kscience.kmath.tensors.core.internal.getRandomNormals
+import space.kscience.kmath.tensors.core.internal.*
+import space.kscience.kmath.tensors.core.internal.broadcastOuterTensors
+import space.kscience.kmath.tensors.core.internal.checkBufferShapeConsistency
+import space.kscience.kmath.tensors.core.internal.checkEmptyDoubleBuffer
+import space.kscience.kmath.tensors.core.internal.checkEmptyShape
+import space.kscience.kmath.tensors.core.internal.checkShapesCompatible
+import space.kscience.kmath.tensors.core.internal.checkSquareMatrix
+import space.kscience.kmath.tensors.core.internal.checkTranspose
+import space.kscience.kmath.tensors.core.internal.checkView
+import space.kscience.kmath.tensors.core.internal.minusIndexFrom
+import kotlin.math.*
 
 /**
  * Implementation of basic operations over double tensors and basic algebra operations on them.
  */
-public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
+public open class DoubleTensorAlgebra :
+    TensorPartialDivisionAlgebra<Double>,
+    AnalyticTensorAlgebra<Double>,
+    LinearOpsTensorAlgebra<Double> {
 
     public companion object : DoubleTensorAlgebra()
 
@@ -311,9 +317,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return DoubleTensor(shape, tensor.mutableBuffer.array(), tensor.bufferStart)
     }
 
-    override fun Tensor<Double>.viewAs(other: Tensor<Double>): DoubleTensor {
-        return tensor.view(other.shape)
-    }
+    override fun Tensor<Double>.viewAs(other: Tensor<Double>): DoubleTensor =
+        tensor.view(other.shape)
 
     override infix fun Tensor<Double>.dot(other: Tensor<Double>): DoubleTensor {
         if (tensor.shape.size == 1 && other.shape.size == 1) {
@@ -565,4 +570,350 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
             x.withIndex().maxByOrNull { it.value }?.index!!.toDouble()
         }, dim, keepDim)
 
+
+    override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
+
+    override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim(
+            { arr ->
+                check(dim < dimension) { "Dimension $dim out of range $dimension" }
+                arr.sum() / shape[dim]
+            },
+            dim,
+            keepDim
+        )
+
+    override fun Tensor<Double>.std(): Double = this.fold { arr ->
+        val mean = arr.sum() / tensor.numElements
+        sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
+    }
+
+    override fun Tensor<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
+        { arr ->
+            check(dim < dimension) { "Dimension $dim out of range $dimension" }
+            val mean = arr.sum() / shape[dim]
+            sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
+        },
+        dim,
+        keepDim
+    )
+
+    override fun Tensor<Double>.variance(): Double = this.fold { arr ->
+        val mean = arr.sum() / tensor.numElements
+        arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
+    }
+
+    override fun Tensor<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
+        { arr ->
+            check(dim < dimension) { "Dimension $dim out of range $dimension" }
+            val mean = arr.sum() / shape[dim]
+            arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
+        },
+        dim,
+        keepDim
+    )
+
+    private fun cov(x: DoubleTensor, y:DoubleTensor): Double{
+        val n = x.shape[0]
+        return ((x - x.mean()) * (y - y.mean())).mean() * n / (n - 1)
+    }
+
+    override fun cov(tensors: List<Tensor<Double>>): DoubleTensor {
+        check(tensors.isNotEmpty()) { "List must have at least 1 element" }
+        val n = tensors.size
+        val m = tensors[0].shape[0]
+        check(tensors.all { it.shape contentEquals intArrayOf(m) }) { "Tensors must have same shapes" }
+        val resTensor = DoubleTensor(
+            intArrayOf(n, n),
+            DoubleArray(n * n) {0.0}
+        )
+        for (i in 0 until n){
+            for (j in 0 until n){
+                resTensor[intArrayOf(i, j)] = cov(tensors[i].tensor, tensors[j].tensor)
+            }
+        }
+        return resTensor
+    }
+
+    override fun Tensor<Double>.exp(): DoubleTensor = tensor.map(::exp)
+
+    override fun Tensor<Double>.ln(): DoubleTensor = tensor.map(::ln)
+
+    override fun Tensor<Double>.sqrt(): DoubleTensor = tensor.map(::sqrt)
+
+    override fun Tensor<Double>.cos(): DoubleTensor = tensor.map(::cos)
+
+    override fun Tensor<Double>.acos(): DoubleTensor = tensor.map(::acos)
+
+    override fun Tensor<Double>.cosh(): DoubleTensor = tensor.map(::cosh)
+
+    override fun Tensor<Double>.acosh(): DoubleTensor = tensor.map(::acosh)
+
+    override fun Tensor<Double>.sin(): DoubleTensor = tensor.map(::sin)
+
+    override fun Tensor<Double>.asin(): DoubleTensor = tensor.map(::asin)
+
+    override fun Tensor<Double>.sinh(): DoubleTensor = tensor.map(::sinh)
+
+    override fun Tensor<Double>.asinh(): DoubleTensor = tensor.map(::asinh)
+
+    override fun Tensor<Double>.tan(): DoubleTensor = tensor.map(::tan)
+
+    override fun Tensor<Double>.atan(): DoubleTensor = tensor.map(::atan)
+
+    override fun Tensor<Double>.tanh(): DoubleTensor = tensor.map(::tanh)
+
+    override fun Tensor<Double>.atanh(): DoubleTensor = tensor.map(::atanh)
+
+    override fun Tensor<Double>.ceil(): DoubleTensor = tensor.map(::ceil)
+
+    override fun Tensor<Double>.floor(): DoubleTensor = tensor.map(::floor)
+
+    override fun Tensor<Double>.inv(): DoubleTensor = invLU(1e-9)
+
+    override fun Tensor<Double>.det(): DoubleTensor = detLU(1e-9)
+
+    /**
+     * Computes the LU factorization of a matrix or batches of matrices `input`.
+     * Returns a tuple containing the LU factorization and pivots of `input`.
+     *
+     * @param epsilon permissible error when comparing the determinant of a matrix with zero
+     * @return pair of `factorization` and `pivots`.
+     * The `factorization` has the shape ``(*, m, n)``, where``(*, m, n)`` is the shape of the `input` tensor.
+     * The `pivots`  has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows.
+     */
+    public fun Tensor<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
+        computeLU(tensor, epsilon)
+            ?: throw IllegalArgumentException("Tensor contains matrices which are singular at precision $epsilon")
+
+    /**
+     * Computes the LU factorization of a matrix or batches of matrices `input`.
+     * Returns a tuple containing the LU factorization and pivots of `input`.
+     * Uses an error of ``1e-9`` when calculating whether a matrix is degenerate.
+     *
+     * @return pair of `factorization` and `pivots`.
+     * The `factorization` has the shape ``(*, m, n)``, where``(*, m, n)`` is the shape of the `input` tensor.
+     * The `pivots`  has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows.
+     */
+    public fun Tensor<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
+
+    /**
+     * Unpacks the data and pivots from a LU factorization of a tensor.
+     * Given a tensor [luTensor], return tensors (P, L, U) satisfying ``P * luTensor = L * U``,
+     * with `P` being a permutation matrix or batch of matrices,
+     * `L` being a lower triangular matrix or batch of matrices,
+     * `U` being an upper triangular matrix or batch of matrices.
+     *
+     * @param luTensor the packed LU factorization data
+     * @param pivotsTensor the packed LU factorization pivots
+     * @return triple of P, L and U tensors
+     */
+    public fun luPivot(
+        luTensor: Tensor<Double>,
+        pivotsTensor: Tensor<Int>
+    ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+        checkSquareMatrix(luTensor.shape)
+        check(
+            luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() ||
+                    luTensor.shape.last() == pivotsTensor.shape.last() - 1
+        ) { "Inappropriate shapes of input tensors" }
+
+        val n = luTensor.shape.last()
+        val pTensor = luTensor.zeroesLike()
+        pTensor
+            .matrixSequence()
+            .zip(pivotsTensor.tensor.vectorSequence())
+            .forEach { (p, pivot) -> pivInit(p.as2D(), pivot.as1D(), n) }
+
+        val lTensor = luTensor.zeroesLike()
+        val uTensor = luTensor.zeroesLike()
+
+        lTensor.matrixSequence()
+            .zip(uTensor.matrixSequence())
+            .zip(luTensor.tensor.matrixSequence())
+            .forEach { (pairLU, lu) ->
+                val (l, u) = pairLU
+                luPivotHelper(l.as2D(), u.as2D(), lu.as2D(), n)
+            }
+
+        return Triple(pTensor, lTensor, uTensor)
+    }
+
+    /**
+     * QR decomposition.
+     *
+     * Computes the QR decomposition of a matrix or a batch of matrices, and returns a pair `(Q, R)` of tensors.
+     * Given a tensor `input`, return tensors (Q, R) satisfying ``input = Q * R``,
+     * with `Q` being an orthogonal matrix or batch of orthogonal matrices
+     * and `R` being an upper triangular matrix or batch of upper triangular matrices.
+     *
+     * @param epsilon permissible error when comparing tensors for equality.
+     * Used when checking the positive definiteness of the input matrix or matrices.
+     * @return pair of Q and R tensors.
+     */
+    public fun Tensor<Double>.cholesky(epsilon: Double): DoubleTensor {
+        checkSquareMatrix(shape)
+        checkPositiveDefinite(tensor, epsilon)
+
+        val n = shape.last()
+        val lTensor = zeroesLike()
+
+        for ((a, l) in tensor.matrixSequence().zip(lTensor.matrixSequence()))
+            for (i in 0 until n) choleskyHelper(a.as2D(), l.as2D(), n)
+
+        return lTensor
+    }
+
+    override fun Tensor<Double>.cholesky(): DoubleTensor = cholesky(1e-6)
+
+    override fun Tensor<Double>.qr(): Pair<DoubleTensor, DoubleTensor> {
+        checkSquareMatrix(shape)
+        val qTensor = zeroesLike()
+        val rTensor = zeroesLike()
+        tensor.matrixSequence()
+            .zip((qTensor.matrixSequence()
+                .zip(rTensor.matrixSequence()))).forEach { (matrix, qr) ->
+                val (q, r) = qr
+                qrHelper(matrix.asTensor(), q.asTensor(), r.as2D())
+            }
+
+        return qTensor to rTensor
+    }
+
+    override fun Tensor<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
+        svd(epsilon = 1e-10)
+
+    /**
+     * Singular Value Decomposition.
+     *
+     * Computes the singular value decomposition of either a matrix or batch of matrices `input`.
+     * The singular value decomposition is represented as a triple `(U, S, V)`,
+     * such that ``input = U.dot(diagonalEmbedding(S).dot(V.T))``.
+     * If input is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as input.
+     *
+     * @param epsilon permissible error when calculating the dot product of vectors,
+     * i.e. the precision with which the cosine approaches 1 in an iterative algorithm.
+     * @return triple `(U, S, V)`.
+     */
+    public fun Tensor<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+        val size = tensor.dimension
+        val commonShape = tensor.shape.sliceArray(0 until size - 2)
+        val (n, m) = tensor.shape.sliceArray(size - 2 until size)
+        val uTensor = zeros(commonShape + intArrayOf(min(n, m), n))
+        val sTensor = zeros(commonShape + intArrayOf(min(n, m)))
+        val vTensor = zeros(commonShape + intArrayOf(min(n, m), m))
+
+        tensor.matrixSequence()
+            .zip(uTensor.matrixSequence()
+                .zip(sTensor.vectorSequence()
+                    .zip(vTensor.matrixSequence()))).forEach { (matrix, USV) ->
+                val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
+                val curMatrix = DoubleTensor(
+                    matrix.shape,
+                    matrix.mutableBuffer.array().slice(matrix.bufferStart until matrix.bufferStart + matrixSize)
+                        .toDoubleArray()
+                )
+                svdHelper(curMatrix, USV, m, n, epsilon)
+            }
+
+        return Triple(uTensor.transpose(), sTensor, vTensor.transpose())
+    }
+
+    override fun Tensor<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> =
+        symEig(epsilon = 1e-15)
+
+    /**
+     * Returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices,
+     * represented by a pair (eigenvalues, eigenvectors).
+     *
+     * @param epsilon permissible error when comparing tensors for equality
+     * and when the cosine approaches 1 in the SVD algorithm.
+     * @return a pair (eigenvalues, eigenvectors)
+     */
+    public fun Tensor<Double>.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
+        checkSymmetric(tensor, epsilon)
+        val (u, s, v) = tensor.svd(epsilon)
+        val shp = s.shape + intArrayOf(1)
+        val utv = u.transpose() dot v
+        val n = s.shape.last()
+        for (matrix in utv.matrixSequence())
+            cleanSymHelper(matrix.as2D(), n)
+
+        val eig = (utv dot s.view(shp)).view(s.shape)
+        return eig to v
+    }
+
+    /**
+     * Computes the determinant of a square matrix input, or of each square matrix in a batched input
+     * using LU factorization algorithm.
+     *
+     * @param epsilon error in the LU algorithm - permissible error when comparing the determinant of a matrix with zero
+     * @return the determinant.
+     */
+    public fun Tensor<Double>.detLU(epsilon: Double = 1e-9): DoubleTensor {
+
+        checkSquareMatrix(tensor.shape)
+        val luTensor = tensor.copy()
+        val pivotsTensor = tensor.setUpPivots()
+
+        val n = shape.size
+
+        val detTensorShape = IntArray(n - 1) { i -> shape[i] }
+        detTensorShape[n - 2] = 1
+        val resBuffer = DoubleArray(detTensorShape.reduce(Int::times)) { 0.0 }
+
+        val detTensor = DoubleTensor(
+            detTensorShape,
+            resBuffer
+        )
+
+        luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).forEachIndexed { index, (lu, pivots) ->
+            resBuffer[index] = if (luHelper(lu.as2D(), pivots.as1D(), epsilon))
+                0.0 else luMatrixDet(lu.as2D(), pivots.as1D())
+        }
+
+        return detTensor
+    }
+
+    /**
+     * Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input
+     * using LU factorization algorithm.
+     * Given a square matrix `a`, return the matrix `aInv` satisfying
+     * ``a.dot(aInv) = aInv.dot(a) = eye(a.shape[0])``.
+     *
+     * @param epsilon error in the LU algorithm - permissible error when comparing the determinant of a matrix with zero
+     * @return the multiplicative inverse of a matrix.
+     */
+    public fun Tensor<Double>.invLU(epsilon: Double = 1e-9): DoubleTensor {
+        val (luTensor, pivotsTensor) = luFactor(epsilon)
+        val invTensor = luTensor.zeroesLike()
+
+        val seq = luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
+        for ((luP, invMatrix) in seq) {
+            val (lu, pivots) = luP
+            luMatrixInv(lu.as2D(), pivots.as1D(), invMatrix.as2D())
+        }
+
+        return invTensor
+    }
+
+    /**
+     * LUP decomposition
+     *
+     * Computes the LUP decomposition of a matrix or a batch of matrices.
+     * Given a tensor `input`, return tensors (P, L, U) satisfying ``P * input = L * U``,
+     * with `P` being a permutation matrix or batch of matrices,
+     * `L` being a lower triangular matrix or batch of matrices,
+     * `U` being an upper triangular matrix or batch of matrices.
+     *
+     * @param epsilon permissible error when comparing the determinant of a matrix with zero
+     * @return triple of P, L and U tensors
+     */
+    public fun Tensor<Double>.lu(epsilon: Double = 1e-9): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+        val (lu, pivots) = this.luFactor(epsilon)
+        return luPivot(lu, pivots)
+    }
+
+    override fun Tensor<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
+
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/broadcastUtils.kt
similarity index 94%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/broadcastUtils.kt
index dfac054b5..6324dc242 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/broadcastUtils.kt
@@ -1,5 +1,11 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
 
+package space.kscience.kmath.tensors.core.internal
+
+import space.kscience.kmath.tensors.core.DoubleTensor
 import kotlin.math.max
 
 internal fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: DoubleTensor, linearSize: Int) {
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
similarity index 83%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
index f8bd5027a..0221c961e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
@@ -1,7 +1,12 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core.internal
 
 import space.kscience.kmath.tensors.api.Tensor
-import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 
 
@@ -50,7 +55,7 @@ internal fun DoubleTensorAlgebra.checkSymmetric(
         "Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
     }
 
-internal fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor, epsilon: Double = 1e-6) {
+internal fun DoubleTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor, epsilon: Double = 1e-6) {
     checkSymmetric(tensor, epsilon)
     for (mat in tensor.matrixSequence())
         check(mat.asTensor().detLU().value() > 0.0) {
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
similarity index 91%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
index 8adbfad39..23909f81e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
@@ -1,12 +1,17 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core.internal
 
 import space.kscience.kmath.nd.MutableStructure1D
 import space.kscience.kmath.nd.MutableStructure2D
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
-import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+import space.kscience.kmath.tensors.core.*
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.math.abs
 import kotlin.math.min
 import kotlin.math.sign
@@ -114,7 +119,7 @@ internal fun <T> BufferedTensor<T>.setUpPivots(): IntTensor {
     )
 }
 
-internal fun DoubleLinearOpsTensorAlgebra.computeLU(
+internal fun DoubleTensorAlgebra.computeLU(
     tensor: DoubleTensor,
     epsilon: Double
 ): Pair<DoubleTensor, IntTensor>? {
@@ -218,7 +223,7 @@ internal fun luMatrixInv(
     }
 }
 
-internal fun DoubleLinearOpsTensorAlgebra.qrHelper(
+internal fun DoubleTensorAlgebra.qrHelper(
     matrix: DoubleTensor,
     q: DoubleTensor,
     r: MutableStructure2D<Double>
@@ -241,14 +246,14 @@ internal fun DoubleLinearOpsTensorAlgebra.qrHelper(
                 }
             }
         }
-        r[j, j] = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
+        r[j, j] = DoubleTensorAlgebra { (v dot v).sqrt().value() }
         for (i in 0 until n) {
             qM[i, j] = vv[i] / r[j, j]
         }
     }
 }
 
-internal fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10): DoubleTensor {
+internal fun DoubleTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10): DoubleTensor {
     val (n, m) = a.shape
     var v: DoubleTensor
     val b: DoubleTensor
@@ -264,7 +269,7 @@ internal fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double
     while (true) {
         lastV = v
         v = b.dot(lastV)
-        val norm = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
+        val norm = DoubleTensorAlgebra { (v dot v).sqrt().value() }
         v = v.times(1.0 / norm)
         if (abs(v.dot(lastV).value()) > 1 - epsilon) {
             return v
@@ -272,7 +277,7 @@ internal fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double
     }
 }
 
-internal fun DoubleLinearOpsTensorAlgebra.svdHelper(
+internal fun DoubleTensorAlgebra.svdHelper(
     matrix: DoubleTensor,
     USV: Pair<BufferedTensor<Double>, Pair<BufferedTensor<Double>, BufferedTensor<Double>>>,
     m: Int, n: Int, epsilon: Double
@@ -298,12 +303,12 @@ internal fun DoubleLinearOpsTensorAlgebra.svdHelper(
         if (n > m) {
             v = svd1d(a, epsilon)
             u = matrix.dot(v)
-            norm = DoubleAnalyticTensorAlgebra { (u dot u).sqrt().value() }
+            norm = DoubleTensorAlgebra { (u dot u).sqrt().value() }
             u = u.times(1.0 / norm)
         } else {
             u = svd1d(a, epsilon)
             v = matrix.transpose(0, 1).dot(u)
-            norm = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
+            norm = DoubleTensorAlgebra { (v dot v).sqrt().value() }
             v = v.times(1.0 / norm)
         }
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/tensorCastsUtils.kt
similarity index 88%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/tensorCastsUtils.kt
index 70e3b9c61..67cb0b842 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCastsUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/tensorCastsUtils.kt
@@ -3,11 +3,14 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
-package space.kscience.kmath.tensors.core
+package space.kscience.kmath.tensors.core.internal
 
 import space.kscience.kmath.nd.MutableBufferND
 import space.kscience.kmath.structures.asMutableBuffer
 import space.kscience.kmath.tensors.api.Tensor
+import space.kscience.kmath.tensors.core.BufferedTensor
+import space.kscience.kmath.tensors.core.DoubleTensor
+import space.kscience.kmath.tensors.core.IntTensor
 import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
 
 internal fun BufferedTensor<Int>.asTensor(): IntTensor =
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/utils.kt
similarity index 91%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/utils.kt
index 0211342bb..0ffaf39e7 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/utils.kt
@@ -1,9 +1,16 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.core.internal
 
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.samplers.GaussianSampler
 import space.kscience.kmath.stat.RandomGenerator
 import space.kscience.kmath.structures.*
+import space.kscience.kmath.tensors.core.BufferedTensor
+import space.kscience.kmath.tensors.core.DoubleTensor
 import kotlin.math.*
 
 /**
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
index 2743a5218..814a1bb9b 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
@@ -6,6 +6,7 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.api.Tensor
+import space.kscience.kmath.tensors.core.internal.tensor
 
 /**
  * Casts [Tensor<Double>] to [DoubleTensor]
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
index 6e3b4df60..80c7ab13a 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -3,6 +3,7 @@ package space.kscience.kmath.tensors.core
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.internal.*
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
index 3ea19da26..4dcc367ca 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -1,7 +1,6 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.math.*
 import kotlin.test.Test
@@ -28,73 +27,73 @@ internal class TestDoubleAnalyticTensorAlgebra {
     }
 
     @Test
-    fun testExp() = DoubleAnalyticTensorAlgebra {
+    fun testExp() = DoubleTensorAlgebra {
         assertTrue { tensor.exp() eq expectedTensor(::exp) }
     }
 
     @Test
-    fun testLog() = DoubleAnalyticTensorAlgebra {
+    fun testLog() = DoubleTensorAlgebra {
         assertTrue { tensor.ln() eq expectedTensor(::ln) }
     }
 
     @Test
-    fun testSqrt() = DoubleAnalyticTensorAlgebra {
+    fun testSqrt() = DoubleTensorAlgebra {
         assertTrue { tensor.sqrt() eq expectedTensor(::sqrt) }
     }
 
     @Test
-    fun testCos() = DoubleAnalyticTensorAlgebra {
+    fun testCos() = DoubleTensorAlgebra {
         assertTrue { tensor.cos() eq expectedTensor(::cos) }
     }
 
 
     @Test
-    fun testCosh() = DoubleAnalyticTensorAlgebra {
+    fun testCosh() = DoubleTensorAlgebra {
         assertTrue { tensor.cosh() eq expectedTensor(::cosh) }
     }
 
     @Test
-    fun testAcosh() = DoubleAnalyticTensorAlgebra {
+    fun testAcosh() = DoubleTensorAlgebra {
         assertTrue { tensor.acosh() eq expectedTensor(::acosh) }
     }
 
     @Test
-    fun testSin() = DoubleAnalyticTensorAlgebra {
+    fun testSin() = DoubleTensorAlgebra {
         assertTrue { tensor.sin() eq expectedTensor(::sin) }
     }
 
     @Test
-    fun testSinh() = DoubleAnalyticTensorAlgebra {
+    fun testSinh() = DoubleTensorAlgebra {
         assertTrue { tensor.sinh() eq expectedTensor(::sinh) }
     }
 
     @Test
-    fun testAsinh() = DoubleAnalyticTensorAlgebra {
+    fun testAsinh() = DoubleTensorAlgebra {
         assertTrue { tensor.asinh() eq expectedTensor(::asinh) }
     }
 
     @Test
-    fun testTan() = DoubleAnalyticTensorAlgebra {
+    fun testTan() = DoubleTensorAlgebra {
         assertTrue { tensor.tan() eq expectedTensor(::tan) }
     }
 
     @Test
-    fun testAtan() = DoubleAnalyticTensorAlgebra {
+    fun testAtan() = DoubleTensorAlgebra {
         assertTrue { tensor.atan() eq expectedTensor(::atan) }
     }
 
     @Test
-    fun testTanh() = DoubleAnalyticTensorAlgebra {
+    fun testTanh() = DoubleTensorAlgebra {
         assertTrue { tensor.tanh() eq expectedTensor(::tanh) }
     }
 
     @Test
-    fun testCeil() = DoubleAnalyticTensorAlgebra {
+    fun testCeil() = DoubleTensorAlgebra {
         assertTrue { tensor.ceil() eq expectedTensor(::ceil) }
     }
 
     @Test
-    fun testFloor() = DoubleAnalyticTensorAlgebra {
+    fun testFloor() = DoubleTensorAlgebra {
         assertTrue { tensor.floor() eq expectedTensor(::floor) }
     }
 
@@ -145,7 +144,7 @@ internal class TestDoubleAnalyticTensorAlgebra {
     }
 
     @Test
-    fun testMean() = DoubleAnalyticTensorAlgebra {
+    fun testMean() = DoubleTensorAlgebra {
         assertTrue { tensor2.mean() == 1.0 }
         assertTrue { tensor2.mean(0, true) eq fromArray(
             intArrayOf(1, 2),
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 65070af7f..77748b15e 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -1,7 +1,9 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.internal.array
+import space.kscience.kmath.tensors.core.internal.svd1d
 import kotlin.math.abs
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -10,7 +12,7 @@ import kotlin.test.assertTrue
 internal class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
-    fun testDetLU() = DoubleLinearOpsTensorAlgebra {
+    fun testDetLU() = DoubleTensorAlgebra {
         val tensor = fromArray(
             intArrayOf(2, 2, 2),
             doubleArrayOf(
@@ -35,7 +37,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testDet() = DoubleLinearOpsTensorAlgebra {
+    fun testDet() = DoubleTensorAlgebra {
         val expectedValue = 0.019827417
         val m = fromArray(
             intArrayOf(3, 3), doubleArrayOf(
@@ -49,7 +51,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testDetSingle() = DoubleLinearOpsTensorAlgebra {
+    fun testDetSingle() = DoubleTensorAlgebra {
         val expectedValue = 48.151623
         val m = fromArray(
             intArrayOf(1, 1), doubleArrayOf(
@@ -61,7 +63,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testInvLU() = DoubleLinearOpsTensorAlgebra {
+    fun testInvLU() = DoubleTensorAlgebra {
         val tensor = fromArray(
             intArrayOf(2, 2, 2),
             doubleArrayOf(
@@ -86,14 +88,14 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testScalarProduct() = DoubleLinearOpsTensorAlgebra {
+    fun testScalarProduct() = DoubleTensorAlgebra {
         val a = fromArray(intArrayOf(3), doubleArrayOf(1.8, 2.5, 6.8))
         val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4))
         assertEquals(a.dot(b).value(), 59.92)
     }
 
     @Test
-    fun testQR() = DoubleLinearOpsTensorAlgebra {
+    fun testQR() = DoubleTensorAlgebra {
         val shape = intArrayOf(2, 2, 2)
         val buffer = doubleArrayOf(
             1.0, 3.0,
@@ -114,7 +116,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testLU() = DoubleLinearOpsTensorAlgebra {
+    fun testLU() = DoubleTensorAlgebra {
         val shape = intArrayOf(2, 2, 2)
         val buffer = doubleArrayOf(
             1.0, 3.0,
@@ -134,7 +136,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testCholesky() = DoubleLinearOpsTensorAlgebra {
+    fun testCholesky() = DoubleTensorAlgebra {
         val tensor = randomNormal(intArrayOf(2, 5, 5), 0)
         val sigma = (tensor dot tensor.transpose()) + diagonalEmbedding(
             fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
@@ -145,7 +147,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testSVD1D() = DoubleLinearOpsTensorAlgebra {
+    fun testSVD1D() = DoubleTensorAlgebra {
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
 
         val res = svd1d(tensor2)
@@ -156,13 +158,13 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testSVD() = DoubleLinearOpsTensorAlgebra{
+    fun testSVD() = DoubleTensorAlgebra{
         testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
         testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
     }
 
     @Test
-    fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
+    fun testBatchedSVD() = DoubleTensorAlgebra {
         val tensor = randomNormal(intArrayOf(2, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
         val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
@@ -170,7 +172,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra {
+    fun testBatchedSymEig() = DoubleTensorAlgebra {
         val tensor = randomNormal(shape = intArrayOf(2, 3, 3), 0)
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
@@ -182,7 +184,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 }
 
 
-private fun DoubleLinearOpsTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10): Unit {
+private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10): Unit {
     val svd = tensor.svd()
 
     val tensorSVD = svd.first
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 132735cc7..c4f9f94b0 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -8,6 +8,10 @@ import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.structures.DoubleBuffer
 import space.kscience.kmath.structures.toDoubleArray
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.internal.array
+import space.kscience.kmath.tensors.core.internal.asTensor
+import space.kscience.kmath.tensors.core.internal.matrixSequence
+import space.kscience.kmath.tensors.core.internal.toBufferedTensor
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index d782d78d9..ed858259e 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -3,6 +3,7 @@ package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.internal.array
 import kotlin.test.Test
 import kotlin.test.assertFalse
 import kotlin.test.assertTrue

From 0ef64130aeae0deb5543b9c6f73af3a46f465394 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 7 May 2021 13:00:30 +0100
Subject: [PATCH 169/178] TensorLinearStructure made internal

---
 .../space/kscience/kmath/tensors/DataSetNormalization.kt     | 2 +-
 .../kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt     | 2 +-
 .../kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt     | 4 ++--
 .../main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt   | 2 +-
 examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt | 2 +-
 .../core/{algebras => }/BroadcastDoubleTensorAlgebra.kt      | 3 +--
 .../space/kscience/kmath/tensors/core/BufferedTensor.kt      | 5 +++--
 .../kmath/tensors/core/{algebras => }/DoubleTensorAlgebra.kt | 3 +--
 .../core/{algebras => internal}/TensorLinearStructure.kt     | 4 ++--
 .../space/kscience/kmath/tensors/core/internal/checks.kt     | 2 +-
 .../space/kscience/kmath/tensors/core/internal/linUtils.kt   | 2 +-
 .../kscience/kmath/tensors/core/internal/tensorCastsUtils.kt | 1 -
 .../space/kscience/kmath/tensors/core/TestBroadcasting.kt    | 2 --
 .../kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt    | 1 -
 .../kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt         | 1 -
 .../space/kscience/kmath/tensors/core/TestDoubleTensor.kt    | 1 -
 .../kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt   | 1 -
 17 files changed, 15 insertions(+), 23 deletions(-)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{algebras => }/BroadcastDoubleTensorAlgebra.kt (97%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{algebras => }/DoubleTensorAlgebra.kt (99%)
 rename kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/{algebras => internal}/TensorLinearStructure.kt (91%)

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt
index d029348f2..6fbf16a91 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt
@@ -6,7 +6,7 @@
 package space.kscience.kmath.tensors
 
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
 
 
 // Dataset normalization
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
index c0ece04ca..78370b517 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
@@ -7,7 +7,7 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
 
 // solving linear system with LUP decomposition
 
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt
index 1998b8d16..874ac8034 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt
@@ -7,8 +7,8 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.toDoubleArray
 import kotlin.math.sqrt
 
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
index 497f63b41..42a0a4ba1 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
@@ -7,7 +7,7 @@ package space.kscience.kmath.tensors
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 
 import kotlin.math.abs
 
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
index d29dbb094..f8ac13d3f 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
@@ -6,7 +6,7 @@
 package space.kscience.kmath.tensors
 
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
 
 
 // simple PCA
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
similarity index 97%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
index bc7d90c28..b8530f637 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
@@ -3,10 +3,9 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
-package space.kscience.kmath.tensors.core.algebras
+package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.api.Tensor
-import space.kscience.kmath.tensors.core.*
 import space.kscience.kmath.tensors.core.internal.array
 import space.kscience.kmath.tensors.core.internal.broadcastTensors
 import space.kscience.kmath.tensors.core.internal.broadcastTo
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
index e8c0556c2..858532abc 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
@@ -1,8 +1,9 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.nd.Strides
 import space.kscience.kmath.structures.*
 import space.kscience.kmath.tensors.api.Tensor
-import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
+import space.kscience.kmath.tensors.core.internal.TensorLinearStructure
 
 /**
  * Represents [Tensor] over a [MutableBuffer] intended to be used through [DoubleTensor] and [IntTensor]
@@ -16,7 +17,7 @@ public open class BufferedTensor<T> internal constructor(
     /**
      * Buffer strides based on [TensorLinearStructure] implementation
      */
-    public val linearStructure: TensorLinearStructure
+    public val linearStructure: Strides
         get() = TensorLinearStructure(shape)
 
     /**
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
similarity index 99%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index cb06432d0..d4a0f1433 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -3,7 +3,7 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
-package space.kscience.kmath.tensors.core.algebras
+package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
@@ -11,7 +11,6 @@ import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
 import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
 import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra
 import space.kscience.kmath.tensors.api.Tensor
-import space.kscience.kmath.tensors.core.*
 import space.kscience.kmath.tensors.core.internal.dotHelper
 import space.kscience.kmath.tensors.core.internal.getRandomNormals
 import space.kscience.kmath.tensors.core.internal.*
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/TensorLinearStructure.kt
similarity index 91%
rename from kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
rename to kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/TensorLinearStructure.kt
index f65e2b955..4bdd987db 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/TensorLinearStructure.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/TensorLinearStructure.kt
@@ -3,7 +3,7 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
-package space.kscience.kmath.tensors.core.algebras
+package space.kscience.kmath.tensors.core.internal
 
 import space.kscience.kmath.nd.Strides
 import kotlin.math.max
@@ -44,7 +44,7 @@ internal fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArra
  *
  * @param shape the shape of the tensor.
  */
-public class TensorLinearStructure(override val shape: IntArray) : Strides {
+internal class TensorLinearStructure(override val shape: IntArray) : Strides {
     override val strides: IntArray
         get() = stridesFromShape(shape)
 
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
index 0221c961e..f1c158770 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
@@ -7,7 +7,7 @@ package space.kscience.kmath.tensors.core.internal
 
 import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 
 
 internal fun checkEmptyShape(shape: IntArray) =
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
index 23909f81e..ff78ca64c 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
@@ -11,7 +11,7 @@ import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.*
-import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import kotlin.math.abs
 import kotlin.math.min
 import kotlin.math.sign
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/tensorCastsUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/tensorCastsUtils.kt
index 67cb0b842..d965b6bcd 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/tensorCastsUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/tensorCastsUtils.kt
@@ -11,7 +11,6 @@ import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.BufferedTensor
 import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.IntTensor
-import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
 
 internal fun BufferedTensor<Int>.asTensor(): IntTensor =
     IntTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
index 80c7ab13a..672089a3c 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -1,8 +1,6 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.internal.*
 import kotlin.test.Test
 import kotlin.test.assertTrue
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
index 4dcc367ca..05fedaa09 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -1,7 +1,6 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.math.*
 import kotlin.test.Test
 import kotlin.test.assertTrue
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 77748b15e..fddb37251 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -1,7 +1,6 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.internal.array
 import space.kscience.kmath.tensors.core.internal.svd1d
 import kotlin.math.abs
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index c4f9f94b0..d39b5c365 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -7,7 +7,6 @@ import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.structures.DoubleBuffer
 import space.kscience.kmath.structures.toDoubleArray
-import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.internal.array
 import space.kscience.kmath.tensors.core.internal.asTensor
 import space.kscience.kmath.tensors.core.internal.matrixSequence
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index ed858259e..354840719 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -2,7 +2,6 @@ package space.kscience.kmath.tensors.core
 
 
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.internal.array
 import kotlin.test.Test
 import kotlin.test.assertFalse

From d31726a0d90de41ced32d211d3d929dd46f5091c Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 7 May 2021 13:12:18 +0100
Subject: [PATCH 170/178] produce routine

---
 .../kmath/tensors/core/DoubleTensorAlgebra.kt         | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index d4a0f1433..052d8d97e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -56,6 +56,17 @@ public open class DoubleTensorAlgebra :
         return DoubleTensor(shape, buffer, 0)
     }
 
+    /**
+     * Constructs a tensor with the specified shape and initializer.
+     *
+     * @param shape the desired shape for the tensor.
+     * @param initializer mapping tensor indices to values.
+     * @return tensor with the [shape] shape and data generated by initializer.
+     */
+    public fun produce(shape: IntArray, initializer: (IntArray) -> Double): DoubleTensor =
+        fromArray(shape,
+            TensorLinearStructure(shape).indices().map(initializer).toMutableList().toDoubleArray())
+
     override operator fun Tensor<Double>.get(i: Int): DoubleTensor {
         val lastShape = tensor.shape.drop(1).toIntArray()
         val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)

From e80eefa90f5832de6b3c81487b27e7a355d38358 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 7 May 2021 13:12:55 +0100
Subject: [PATCH 171/178] typo corrected

---
 .../space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 052d8d97e..5baf6c7ad 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -61,7 +61,7 @@ public open class DoubleTensorAlgebra :
      *
      * @param shape the desired shape for the tensor.
      * @param initializer mapping tensor indices to values.
-     * @return tensor with the [shape] shape and data generated by initializer.
+     * @return tensor with the [shape] shape and data generated by the [initializer].
      */
     public fun produce(shape: IntArray, initializer: (IntArray) -> Double): DoubleTensor =
         fromArray(shape,

From d73d03c0558348af0d8de282664206b99fccf681 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 7 May 2021 13:36:15 +0100
Subject: [PATCH 172/178] valueOrNull added

---
 .../kmath/tensors/LinearSystemSolvingWithLUP.kt   |  2 +-
 .../space/kscience/kmath/tensors/OLSWithSVD.kt    |  2 +-
 .../kscience/kmath/tensors/api/TensorAlgebra.kt   |  3 ++-
 .../kmath/tensors/core/DoubleTensorAlgebra.kt     | 10 ++++------
 .../kmath/tensors/core/internal/checks.kt         |  4 ++--
 .../kmath/tensors/core/internal/linUtils.kt       | 15 ++++++++-------
 .../tensors/core/TestDoubleLinearOpsAlgebra.kt    |  6 +++---
 .../kmath/tensors/core/TestDoubleTensor.kt        |  2 +-
 8 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
index 78370b517..4494d6799 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
@@ -69,7 +69,7 @@ fun main () {
             val n = l.shape[0]
             val x = zeros(intArrayOf(n))
             for (i in 0 until n){
-                x[intArrayOf(i)] = (b[intArrayOf(i)] -  l[i].dot(x).value()) / l[intArrayOf(i, i)]
+                x[intArrayOf(i)] = (b[intArrayOf(i)] -  l[i].dot(x).valueOrNull()!!) / l[intArrayOf(i, i)]
             }
             return x
         }
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
index 42a0a4ba1..5d1883e7c 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
@@ -60,7 +60,7 @@ fun main() {
             require(yTrue.shape contentEquals yPred.shape)
 
             val diff = yTrue - yPred
-            return diff.dot(diff).sqrt().value()
+            return diff.dot(diff).sqrt().valueOrNull()!!
         }
 
         println("MSE: ${mse(alpha, alphaOLS)}")
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index e7f8dc7ae..78a36e229 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -16,11 +16,12 @@ import space.kscience.kmath.operations.Algebra
 public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
 
     /**
+     *
      * Returns a single tensor value of unit dimension. The tensor shape must be equal to [1].
      *
      * @return the value of a scalar tensor.
      */
-    public fun Tensor<T>.value(): T
+    public fun Tensor<T>.valueOrNull(): T?
 
     /**
      * Each element of the tensor [other] is added to this value.
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 5baf6c7ad..bf27a2690 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -35,12 +35,10 @@ public open class DoubleTensorAlgebra :
 
     public companion object : DoubleTensorAlgebra()
 
-    override fun Tensor<Double>.value(): Double {
-        check(tensor.shape contentEquals intArrayOf(1)) {
-            "Inconsistent value for tensor of shape ${shape.toList()}"
-        }
-        return tensor.mutableBuffer.array()[tensor.bufferStart]
-    }
+    override fun Tensor<Double>.valueOrNull(): Double? = if(tensor.shape contentEquals intArrayOf(1)) {
+            // Inconsistent value for tensor of with this shape
+            tensor.mutableBuffer.array()[tensor.bufferStart]
+        } else null
 
     /**
      * Constructs a tensor with the specified shape and data.
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
index f1c158770..bfbc6334d 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
@@ -58,7 +58,7 @@ internal fun DoubleTensorAlgebra.checkSymmetric(
 internal fun DoubleTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor, epsilon: Double = 1e-6) {
     checkSymmetric(tensor, epsilon)
     for (mat in tensor.matrixSequence())
-        check(mat.asTensor().detLU().value() > 0.0) {
-            "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"
+        check(mat.asTensor().detLU().valueOrNull()!! > 0.0) {
+            "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().valueOrNull()!!}"
         }
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
index ff78ca64c..5c120c05f 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
@@ -12,6 +12,7 @@ import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.*
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.valueOrNull
 import kotlin.math.abs
 import kotlin.math.min
 import kotlin.math.sign
@@ -239,14 +240,14 @@ internal fun DoubleTensorAlgebra.qrHelper(
         val vv = v.as1D()
         if (j > 0) {
             for (i in 0 until j) {
-                r[i, j] = (qT[i] dot matrixT[j]).value()
+                r[i, j] = (qT[i] dot matrixT[j]).valueOrNull()!!
                 for (k in 0 until n) {
                     val qTi = qT[i].as1D()
                     vv[k] = vv[k] - r[i, j] * qTi[k]
                 }
             }
         }
-        r[j, j] = DoubleTensorAlgebra { (v dot v).sqrt().value() }
+        r[j, j] = DoubleTensorAlgebra { (v dot v).sqrt().valueOrNull()!! }
         for (i in 0 until n) {
             qM[i, j] = vv[i] / r[j, j]
         }
@@ -269,9 +270,9 @@ internal fun DoubleTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10)
     while (true) {
         lastV = v
         v = b.dot(lastV)
-        val norm = DoubleTensorAlgebra { (v dot v).sqrt().value() }
+        val norm = DoubleTensorAlgebra { (v dot v).sqrt().valueOrNull()!! }
         v = v.times(1.0 / norm)
-        if (abs(v.dot(lastV).value()) > 1 - epsilon) {
+        if (abs(v.dot(lastV).valueOrNull()!!) > 1 - epsilon) {
             return v
         }
     }
@@ -292,7 +293,7 @@ internal fun DoubleTensorAlgebra.svdHelper(
             val outerProduct = DoubleArray(u.shape[0] * v.shape[0])
             for (i in 0 until u.shape[0]) {
                 for (j in 0 until v.shape[0]) {
-                    outerProduct[i * v.shape[0] + j] = u[i].value() * v[j].value()
+                    outerProduct[i * v.shape[0] + j] = u[i].valueOrNull()!! * v[j].valueOrNull()!!
                 }
             }
             a = a - singularValue.times(DoubleTensor(intArrayOf(u.shape[0], v.shape[0]), outerProduct))
@@ -303,12 +304,12 @@ internal fun DoubleTensorAlgebra.svdHelper(
         if (n > m) {
             v = svd1d(a, epsilon)
             u = matrix.dot(v)
-            norm = DoubleTensorAlgebra { (u dot u).sqrt().value() }
+            norm = DoubleTensorAlgebra { (u dot u).sqrt().valueOrNull()!! }
             u = u.times(1.0 / norm)
         } else {
             u = svd1d(a, epsilon)
             v = matrix.transpose(0, 1).dot(u)
-            norm = DoubleTensorAlgebra { (v dot v).sqrt().value() }
+            norm = DoubleTensorAlgebra { (v dot v).sqrt().valueOrNull()!! }
             v = v.times(1.0 / norm)
         }
 
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index fddb37251..99f00edbf 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -46,7 +46,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
             )
         )
 
-        assertTrue { abs(m.det().value() - expectedValue) < 1e-5 }
+        assertTrue { abs(m.det().valueOrNull()!! - expectedValue) < 1e-5 }
     }
 
     @Test
@@ -58,7 +58,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
             )
         )
 
-        assertTrue { abs(m.det().value() - expectedValue) < 1e-5 }
+        assertTrue { abs(m.det().valueOrNull()!! - expectedValue) < 1e-5 }
     }
 
     @Test
@@ -90,7 +90,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     fun testScalarProduct() = DoubleTensorAlgebra {
         val a = fromArray(intArrayOf(3), doubleArrayOf(1.8, 2.5, 6.8))
         val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4))
-        assertEquals(a.dot(b).value(), 59.92)
+        assertEquals(a.dot(b).valueOrNull()!!, 59.92)
     }
 
     @Test
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index d39b5c365..1afdb2263 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -21,7 +21,7 @@ internal class TestDoubleTensor {
     fun testValue() = DoubleTensorAlgebra {
         val value = 12.5
         val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
-        assertEquals(tensor.value(), value)
+        assertEquals(tensor.valueOrNull()!!, value)
     }
 
     @Test

From 5b725a087b81b8c4f9206a4f9098b2cc01c4cd54 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 7 May 2021 15:53:09 +0300
Subject: [PATCH 173/178] new cast

---
 .../kmath/tensors/core/tensorCasts.kt         | 32 +++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
index 814a1bb9b..921bcf97e 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
@@ -35,3 +35,35 @@ public fun IntTensor.toIntArray(): IntArray {
         mutableBuffer[bufferStart + i]
     }
 }
+
+/**
+ * Casts [Array<DoubleArray>] to [DoubleTensor]
+ */
+public fun Array<DoubleArray>.toDoubleTensor(): DoubleTensor {
+    val n = size
+    check(n > 0) { "An empty array cannot be casted to tensor" }
+    val m = first().size
+    check(m > 0) { "Inner arrays must have at least 1 argument" }
+    check(all { size == m }) { "Inner arrays must be the same size" }
+
+    val shape = intArrayOf(n, m)
+    val buffer = this.flatMap { arr -> arr.map { it } }.toDoubleArray()
+
+    return DoubleTensor(shape, buffer, 0)
+}
+
+/**
+ * Casts [Array<IntArray>] to [IntTensor]
+ */
+public fun Array<IntArray>.toIntTensor(): IntTensor {
+    val n = size
+    check(n > 0) { "An empty array cannot be casted to tensor" }
+    val m = first().size
+    check(m > 0) { "Inner arrays must have at least 1 argument" }
+    check(all { size == m }) { "Inner arrays must be the same size" }
+
+    val shape = intArrayOf(n, m)
+    val buffer = this.flatMap { arr -> arr.map { it } }.toIntArray()
+
+    return IntTensor(shape, buffer, 0)
+}

From 21b5d45b96878e89c464020994b3c7d931464c7f Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 7 May 2021 14:13:07 +0100
Subject: [PATCH 174/178] Throwable value method

---
 .../tensors/LinearSystemSolvingWithLUP.kt     |  2 +-
 .../kscience/kmath/tensors/OLSWithSVD.kt      |  2 +-
 .../kmath/tensors/api/TensorAlgebra.kt        | 10 ++++-
 .../kmath/tensors/core/DoubleTensorAlgebra.kt | 44 ++++++++++++-------
 .../kmath/tensors/core/internal/checks.kt     |  4 +-
 .../kmath/tensors/core/internal/linUtils.kt   | 14 +++---
 .../core/TestDoubleLinearOpsAlgebra.kt        |  6 +--
 .../kmath/tensors/core/TestDoubleTensor.kt    |  2 +-
 8 files changed, 51 insertions(+), 33 deletions(-)

diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
index 4494d6799..78370b517 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
@@ -69,7 +69,7 @@ fun main () {
             val n = l.shape[0]
             val x = zeros(intArrayOf(n))
             for (i in 0 until n){
-                x[intArrayOf(i)] = (b[intArrayOf(i)] -  l[i].dot(x).valueOrNull()!!) / l[intArrayOf(i, i)]
+                x[intArrayOf(i)] = (b[intArrayOf(i)] -  l[i].dot(x).value()) / l[intArrayOf(i, i)]
             }
             return x
         }
diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
index 5d1883e7c..42a0a4ba1 100644
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
@@ -60,7 +60,7 @@ fun main() {
             require(yTrue.shape contentEquals yPred.shape)
 
             val diff = yTrue - yPred
-            return diff.dot(diff).sqrt().valueOrNull()!!
+            return diff.dot(diff).sqrt().value()
         }
 
         println("MSE: ${mse(alpha, alphaOLS)}")
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index 78a36e229..4718b6a58 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -15,13 +15,21 @@ import space.kscience.kmath.operations.Algebra
  */
 public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
 
+    /**
+     *
+     * Returns a single tensor value of unit dimension if tensor shape equals to [1].
+     *
+     * @return a nullable value of a potentially scalar tensor.
+     */
+    public fun Tensor<T>.valueOrNull(): T?
+
     /**
      *
      * Returns a single tensor value of unit dimension. The tensor shape must be equal to [1].
      *
      * @return the value of a scalar tensor.
      */
-    public fun Tensor<T>.valueOrNull(): T?
+    public fun Tensor<T>.value(): T
 
     /**
      * Each element of the tensor [other] is added to this value.
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index bf27a2690..c1694644f 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -35,10 +35,11 @@ public open class DoubleTensorAlgebra :
 
     public companion object : DoubleTensorAlgebra()
 
-    override fun Tensor<Double>.valueOrNull(): Double? = if(tensor.shape contentEquals intArrayOf(1)) {
-            // Inconsistent value for tensor of with this shape
-            tensor.mutableBuffer.array()[tensor.bufferStart]
-        } else null
+    override fun Tensor<Double>.valueOrNull(): Double? = if (tensor.shape contentEquals intArrayOf(1))
+        tensor.mutableBuffer.array()[tensor.bufferStart] else null
+
+    override fun Tensor<Double>.value(): Double =
+        valueOrNull() ?: throw IllegalArgumentException("Inconsistent value for tensor of with $shape shape")
 
     /**
      * Constructs a tensor with the specified shape and data.
@@ -62,8 +63,10 @@ public open class DoubleTensorAlgebra :
      * @return tensor with the [shape] shape and data generated by the [initializer].
      */
     public fun produce(shape: IntArray, initializer: (IntArray) -> Double): DoubleTensor =
-        fromArray(shape,
-            TensorLinearStructure(shape).indices().map(initializer).toMutableList().toDoubleArray())
+        fromArray(
+            shape,
+            TensorLinearStructure(shape).indices().map(initializer).toMutableList().toDoubleArray()
+        )
 
     override operator fun Tensor<Double>.get(i: Int): DoubleTensor {
         val lastShape = tensor.shape.drop(1).toIntArray()
@@ -621,7 +624,7 @@ public open class DoubleTensorAlgebra :
         keepDim
     )
 
-    private fun cov(x: DoubleTensor, y:DoubleTensor): Double{
+    private fun cov(x: DoubleTensor, y: DoubleTensor): Double {
         val n = x.shape[0]
         return ((x - x.mean()) * (y - y.mean())).mean() * n / (n - 1)
     }
@@ -633,10 +636,10 @@ public open class DoubleTensorAlgebra :
         check(tensors.all { it.shape contentEquals intArrayOf(m) }) { "Tensors must have same shapes" }
         val resTensor = DoubleTensor(
             intArrayOf(n, n),
-            DoubleArray(n * n) {0.0}
+            DoubleArray(n * n) { 0.0 }
         )
-        for (i in 0 until n){
-            for (j in 0 until n){
+        for (i in 0 until n) {
+            for (j in 0 until n) {
                 resTensor[intArrayOf(i, j)] = cov(tensors[i].tensor, tensors[j].tensor)
             }
         }
@@ -779,8 +782,10 @@ public open class DoubleTensorAlgebra :
         val qTensor = zeroesLike()
         val rTensor = zeroesLike()
         tensor.matrixSequence()
-            .zip((qTensor.matrixSequence()
-                .zip(rTensor.matrixSequence()))).forEach { (matrix, qr) ->
+            .zip(
+                (qTensor.matrixSequence()
+                    .zip(rTensor.matrixSequence()))
+            ).forEach { (matrix, qr) ->
                 val (q, r) = qr
                 qrHelper(matrix.asTensor(), q.asTensor(), r.as2D())
             }
@@ -812,9 +817,13 @@ public open class DoubleTensorAlgebra :
         val vTensor = zeros(commonShape + intArrayOf(min(n, m), m))
 
         tensor.matrixSequence()
-            .zip(uTensor.matrixSequence()
-                .zip(sTensor.vectorSequence()
-                    .zip(vTensor.matrixSequence()))).forEach { (matrix, USV) ->
+            .zip(
+                uTensor.matrixSequence()
+                    .zip(
+                        sTensor.vectorSequence()
+                            .zip(vTensor.matrixSequence())
+                    )
+            ).forEach { (matrix, USV) ->
                 val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
                 val curMatrix = DoubleTensor(
                     matrix.shape,
@@ -918,10 +927,11 @@ public open class DoubleTensorAlgebra :
      * @return triple of P, L and U tensors
      */
     public fun Tensor<Double>.lu(epsilon: Double = 1e-9): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        val (lu, pivots) = this.luFactor(epsilon)
+        val (lu, pivots) = tensor.luFactor(epsilon)
         return luPivot(lu, pivots)
     }
 
     override fun Tensor<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
-
 }
+
+
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
index bfbc6334d..f1c158770 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
@@ -58,7 +58,7 @@ internal fun DoubleTensorAlgebra.checkSymmetric(
 internal fun DoubleTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor, epsilon: Double = 1e-6) {
     checkSymmetric(tensor, epsilon)
     for (mat in tensor.matrixSequence())
-        check(mat.asTensor().detLU().valueOrNull()!! > 0.0) {
-            "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().valueOrNull()!!}"
+        check(mat.asTensor().detLU().value() > 0.0) {
+            "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"
         }
 }
\ No newline at end of file
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
index 5c120c05f..7d3617547 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
@@ -240,14 +240,14 @@ internal fun DoubleTensorAlgebra.qrHelper(
         val vv = v.as1D()
         if (j > 0) {
             for (i in 0 until j) {
-                r[i, j] = (qT[i] dot matrixT[j]).valueOrNull()!!
+                r[i, j] = (qT[i] dot matrixT[j]).value()
                 for (k in 0 until n) {
                     val qTi = qT[i].as1D()
                     vv[k] = vv[k] - r[i, j] * qTi[k]
                 }
             }
         }
-        r[j, j] = DoubleTensorAlgebra { (v dot v).sqrt().valueOrNull()!! }
+        r[j, j] = DoubleTensorAlgebra { (v dot v).sqrt().value() }
         for (i in 0 until n) {
             qM[i, j] = vv[i] / r[j, j]
         }
@@ -270,9 +270,9 @@ internal fun DoubleTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10)
     while (true) {
         lastV = v
         v = b.dot(lastV)
-        val norm = DoubleTensorAlgebra { (v dot v).sqrt().valueOrNull()!! }
+        val norm = DoubleTensorAlgebra { (v dot v).sqrt().value() }
         v = v.times(1.0 / norm)
-        if (abs(v.dot(lastV).valueOrNull()!!) > 1 - epsilon) {
+        if (abs(v.dot(lastV).value()) > 1 - epsilon) {
             return v
         }
     }
@@ -293,7 +293,7 @@ internal fun DoubleTensorAlgebra.svdHelper(
             val outerProduct = DoubleArray(u.shape[0] * v.shape[0])
             for (i in 0 until u.shape[0]) {
                 for (j in 0 until v.shape[0]) {
-                    outerProduct[i * v.shape[0] + j] = u[i].valueOrNull()!! * v[j].valueOrNull()!!
+                    outerProduct[i * v.shape[0] + j] = u[i].value() * v[j].value()
                 }
             }
             a = a - singularValue.times(DoubleTensor(intArrayOf(u.shape[0], v.shape[0]), outerProduct))
@@ -304,12 +304,12 @@ internal fun DoubleTensorAlgebra.svdHelper(
         if (n > m) {
             v = svd1d(a, epsilon)
             u = matrix.dot(v)
-            norm = DoubleTensorAlgebra { (u dot u).sqrt().valueOrNull()!! }
+            norm = DoubleTensorAlgebra { (u dot u).sqrt().value() }
             u = u.times(1.0 / norm)
         } else {
             u = svd1d(a, epsilon)
             v = matrix.transpose(0, 1).dot(u)
-            norm = DoubleTensorAlgebra { (v dot v).sqrt().valueOrNull()!! }
+            norm = DoubleTensorAlgebra { (v dot v).sqrt().value() }
             v = v.times(1.0 / norm)
         }
 
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index 99f00edbf..fddb37251 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -46,7 +46,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
             )
         )
 
-        assertTrue { abs(m.det().valueOrNull()!! - expectedValue) < 1e-5 }
+        assertTrue { abs(m.det().value() - expectedValue) < 1e-5 }
     }
 
     @Test
@@ -58,7 +58,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
             )
         )
 
-        assertTrue { abs(m.det().valueOrNull()!! - expectedValue) < 1e-5 }
+        assertTrue { abs(m.det().value() - expectedValue) < 1e-5 }
     }
 
     @Test
@@ -90,7 +90,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     fun testScalarProduct() = DoubleTensorAlgebra {
         val a = fromArray(intArrayOf(3), doubleArrayOf(1.8, 2.5, 6.8))
         val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4))
-        assertEquals(a.dot(b).valueOrNull()!!, 59.92)
+        assertEquals(a.dot(b).value(), 59.92)
     }
 
     @Test
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index 1afdb2263..d39b5c365 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -21,7 +21,7 @@ internal class TestDoubleTensor {
     fun testValue() = DoubleTensorAlgebra {
         val value = 12.5
         val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
-        assertEquals(tensor.valueOrNull()!!, value)
+        assertEquals(tensor.value(), value)
     }
 
     @Test

From a5b72f201617752033941b50c1789cd5598902ec Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Fri, 7 May 2021 14:15:48 +0100
Subject: [PATCH 175/178] Doc typo

---
 .../kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt    | 2 --
 1 file changed, 2 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
index 4718b6a58..2eb18ada6 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
@@ -16,7 +16,6 @@ import space.kscience.kmath.operations.Algebra
 public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
 
     /**
-     *
      * Returns a single tensor value of unit dimension if tensor shape equals to [1].
      *
      * @return a nullable value of a potentially scalar tensor.
@@ -24,7 +23,6 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
     public fun Tensor<T>.valueOrNull(): T?
 
     /**
-     *
      * Returns a single tensor value of unit dimension. The tensor shape must be equal to [1].
      *
      * @return the value of a scalar tensor.

From dde1db7b0afa27162988fcb1b3543afe6187a4d6 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 7 May 2021 23:05:37 +0300
Subject: [PATCH 176/178] fixes

---
 .../kmath/tensors/core/tensorCasts.kt         | 40 ++-----------------
 1 file changed, 4 insertions(+), 36 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
index 921bcf97e..4e43ed1e1 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
@@ -9,17 +9,17 @@ import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.internal.tensor
 
 /**
- * Casts [Tensor<Double>] to [DoubleTensor]
+ * Casts [Tensor] iof [Double] to [DoubleTensor]
  */
 public fun Tensor<Double>.toDoubleTensor(): DoubleTensor = this.tensor
 
 /**
- * Casts [Tensor<Int>] to [IntTensor]
+ * Casts [Tensor] iof [Int] to [IntTensor]
  */
 public fun Tensor<Int>.toIntTensor(): IntTensor = this.tensor
 
 /**
- * @return [DoubleArray] of tensor elements
+ * Returns [DoubleArray] of tensor elements
  */
 public fun DoubleTensor.toDoubleArray(): DoubleArray {
     return DoubleArray(numElements) { i ->
@@ -28,42 +28,10 @@ public fun DoubleTensor.toDoubleArray(): DoubleArray {
 }
 
 /**
- * @return [IntArray] of tensor elements
+ * Returns [IntArray] of tensor elements
  */
 public fun IntTensor.toIntArray(): IntArray {
     return IntArray(numElements) { i ->
         mutableBuffer[bufferStart + i]
     }
 }
-
-/**
- * Casts [Array<DoubleArray>] to [DoubleTensor]
- */
-public fun Array<DoubleArray>.toDoubleTensor(): DoubleTensor {
-    val n = size
-    check(n > 0) { "An empty array cannot be casted to tensor" }
-    val m = first().size
-    check(m > 0) { "Inner arrays must have at least 1 argument" }
-    check(all { size == m }) { "Inner arrays must be the same size" }
-
-    val shape = intArrayOf(n, m)
-    val buffer = this.flatMap { arr -> arr.map { it } }.toDoubleArray()
-
-    return DoubleTensor(shape, buffer, 0)
-}
-
-/**
- * Casts [Array<IntArray>] to [IntTensor]
- */
-public fun Array<IntArray>.toIntTensor(): IntTensor {
-    val n = size
-    check(n > 0) { "An empty array cannot be casted to tensor" }
-    val m = first().size
-    check(m > 0) { "Inner arrays must have at least 1 argument" }
-    check(all { size == m }) { "Inner arrays must be the same size" }
-
-    val shape = intArrayOf(n, m)
-    val buffer = this.flatMap { arr -> arr.map { it } }.toIntArray()
-
-    return IntTensor(shape, buffer, 0)
-}

From 11a1624e64d2b779e84d25bf120698b49b400cc1 Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 7 May 2021 23:06:41 +0300
Subject: [PATCH 177/178] fixes

---
 .../kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
index 4e43ed1e1..142cb2156 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt
@@ -9,12 +9,12 @@ import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.internal.tensor
 
 /**
- * Casts [Tensor] iof [Double] to [DoubleTensor]
+ * Casts [Tensor] of [Double] to [DoubleTensor]
  */
 public fun Tensor<Double>.toDoubleTensor(): DoubleTensor = this.tensor
 
 /**
- * Casts [Tensor] iof [Int] to [IntTensor]
+ * Casts [Tensor] of [Int] to [IntTensor]
  */
 public fun Tensor<Int>.toIntTensor(): IntTensor = this.tensor
 

From 33e27cd99262d64533ccde72bbdb10b47c75d9fe Mon Sep 17 00:00:00 2001
From: Andrei Kislitsyn <kisandy@yandex.ru>
Date: Fri, 7 May 2021 23:07:02 +0300
Subject: [PATCH 178/178] fixed

---
 .../space/kscience/kmath/tensors/core/TestBroadcasting.kt       | 2 +-
 .../kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt       | 2 +-
 .../kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt   | 2 --
 .../space/kscience/kmath/tensors/core/TestDoubleTensor.kt       | 2 +-
 .../kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt      | 2 +-
 5 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
index 672089a3c..35e605fd9 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
@@ -102,4 +102,4 @@ internal class TestBroadcasting {
         assertTrue(tensor32.mutableBuffer.array() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
     }
 
-}
\ No newline at end of file
+}
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
index 05fedaa09..3b4c615b4 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
@@ -155,4 +155,4 @@ internal class TestDoubleAnalyticTensorAlgebra {
         )}
     }
 
-}
\ No newline at end of file
+}
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index fddb37251..347bb683f 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -194,5 +194,3 @@ private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double
 
     assertTrue(tensor.eq(tensorSVD, epsilon))
 }
-
-
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
index d39b5c365..a6c6a7618 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
@@ -86,4 +86,4 @@ internal class TestDoubleTensor {
         assertEquals(ndArray[intArrayOf(0)], 1.0)
 
     }
-}
\ No newline at end of file
+}
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index 354840719..e7e898008 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -164,4 +164,4 @@ internal class TestDoubleTensorAlgebra {
         assertFalse(tensor1.eq(tensor3))
 
     }
-}
\ No newline at end of file
+}