API cleanup

build.gradle.kts

@@ -19,7 +19,7 @@ allprojects {
     }
 
     group = "space.kscience"
-    version = "0.3.0-dev-14"
+    version = "0.3.0-dev-15"
 }
 
 subprojects {

examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt

@@ -5,19 +5,19 @@
 
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.tensors.core.tensorAlgebra
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.withBroadcast
 
 
 // simple PCA
 
-fun main(): Unit = BroadcastDoubleTensorAlgebra {  // work in context with broadcast methods
+fun main(): Unit = Double.tensorAlgebra.withBroadcast {  // work in context with broadcast methods
     val seed = 100500L
 
     // assume x is range from 0 until 10
     val x = fromArray(
         intArrayOf(10),
-        (0 until 10).toList().map { it.toDouble() }.toDoubleArray()
+        DoubleArray(10) { it.toDouble() }
     )
 
     // take y dependent on x with noise
@@ -62,7 +62,7 @@ fun main(): Unit = BroadcastDoubleTensorAlgebra {  // work in context with broad
     println("Eigenvector:\n$v")
 
     // reduce dimension of dataset
-    val datasetReduced  = v dot stack(listOf(xScaled, yScaled))
+    val datasetReduced = v dot stack(listOf(xScaled, yScaled))
     println("Reduced data:\n$datasetReduced")
 
     // we can restore original data from reduced data;

examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.kt

@@ -5,13 +5,13 @@
 
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.tensors.core.tensorAlgebra
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.withBroadcast
 
 
 // Dataset normalization
 
-fun main() = BroadcastDoubleTensorAlgebra {  // work in context with broadcast methods
+fun main() = Double.tensorAlgebra.withBroadcast {  // work in context with broadcast methods
     // take dataset of 5-element vectors from normal distribution
     val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
 

examples/src/main/kotlin/space/kscience/kmath/tensors/linearSystemSolvingWithLUP.kt

@@ -5,13 +5,13 @@
 
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.DoubleTensor
+import space.kscience.kmath.tensors.core.tensorAlgebra
+import space.kscience.kmath.tensors.core.withBroadcast
 
 // solving linear system with LUP decomposition
 
-fun main() = BroadcastDoubleTensorAlgebra {// work in context with linear operations
+fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear operations
 
     // set true value of x
     val trueX = fromArray(

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferedLinearSpace.kt

@@ -8,12 +8,14 @@ package space.kscience.kmath.linear
 import space.kscience.kmath.misc.PerformancePitfall
 import space.kscience.kmath.nd.BufferedRingND
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.nd.asND
 import space.kscience.kmath.nd.ndAlgebra
-import space.kscience.kmath.nd.unwrap
-import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.Ring
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.structures.*
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.BufferFactory
+import space.kscience.kmath.structures.VirtualBuffer
+import space.kscience.kmath.structures.indices
 
 
 public class BufferedLinearSpace<T, out A : Ring<T>>(
@@ -33,17 +35,17 @@ public class BufferedLinearSpace<T, out A : Ring<T>>(
         bufferFactory(size) { elementAlgebra.initializer(it) }
 
     override fun Matrix<T>.unaryMinus(): Matrix<T> = ndRing(rowNum, colNum).run {
-        unwrap().map { -it }.as2D()
+        asND().map { -it }.as2D()
     }
 
     override fun Matrix<T>.plus(other: Matrix<T>): Matrix<T> = ndRing(rowNum, colNum).run {
         require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
-        unwrap().plus(other.unwrap()).as2D()
+        asND().plus(other.asND()).as2D()
     }
 
     override fun Matrix<T>.minus(other: Matrix<T>): Matrix<T> = ndRing(rowNum, colNum).run {
         require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
-        unwrap().minus(other.unwrap()).as2D()
+        asND().minus(other.asND()).as2D()
     }
 
     private fun Buffer<T>.linearize() = if (this is VirtualBuffer) {
@@ -87,13 +89,10 @@ public class BufferedLinearSpace<T, out A : Ring<T>>(
     }
 
     override fun Matrix<T>.times(value: T): Matrix<T> = ndRing(rowNum, colNum).run {
-        unwrap().map { it * value }.as2D()
+        asND().map { it * value }.as2D()
     }
 }
 
 
 public fun <T, A : Ring<T>> A.linearSpace(bufferFactory: BufferFactory<T>): BufferedLinearSpace<T, A> =
     BufferedLinearSpace(this, bufferFactory)
-
-public val DoubleField.linearSpace: BufferedLinearSpace<Double, DoubleField>
-    get() = BufferedLinearSpace(this, ::DoubleBuffer)

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/DoubleLinearSpace.kt

@@ -0,0 +1,109 @@
+/*
+ * 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 file.
+ */
+
+package space.kscience.kmath.linear
+
+import space.kscience.kmath.misc.PerformancePitfall
+import space.kscience.kmath.nd.DoubleFieldND
+import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.nd.asND
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.DoubleBuffer
+import space.kscience.kmath.structures.indices
+
+public object DoubleLinearSpace : LinearSpace<Double, DoubleField> {
+
+    override val elementAlgebra: DoubleField get() = DoubleField
+
+    private fun ndRing(
+        rows: Int,
+        cols: Int,
+    ): DoubleFieldND = DoubleFieldND(intArrayOf(rows, cols))
+
+    override fun buildMatrix(
+        rows: Int,
+        columns: Int,
+        initializer: DoubleField.(i: Int, j: Int) -> Double
+    ): Matrix<Double> = ndRing(rows, columns).produce { (i, j) -> DoubleField.initializer(i, j) }.as2D()
+
+
+    override fun buildVector(size: Int, initializer: DoubleField.(Int) -> Double): DoubleBuffer =
+        DoubleBuffer(size) { DoubleField.initializer(it) }
+
+    override fun Matrix<Double>.unaryMinus(): Matrix<Double> = ndRing(rowNum, colNum).run {
+        asND().map { -it }.as2D()
+    }
+
+    override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = ndRing(rowNum, colNum).run {
+        require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
+        asND().plus(other.asND()).as2D()
+    }
+
+    override fun Matrix<Double>.minus(other: Matrix<Double>): Matrix<Double> = ndRing(rowNum, colNum).run {
+        require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
+        asND().minus(other.asND()).as2D()
+    }
+
+    // Create a continuous in-memory representation of this vector for better memory layout handling
+    private fun Buffer<Double>.linearize() = if (this is DoubleBuffer) {
+        this
+    } else {
+        DoubleBuffer(size) { get(it) }
+    }
+
+    @OptIn(PerformancePitfall::class)
+    override fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> {
+        require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
+        val rows = this@dot.rows.map { it.linearize() }
+        val columns = other.columns.map { it.linearize() }
+        return buildMatrix(rowNum, other.colNum) { i, j ->
+            val r = rows[i]
+            val c = columns[j]
+            var res = 0.0
+            for (l in r.indices) {
+                res += r[l] * c[l]
+            }
+            res
+        }
+    }
+
+    @OptIn(PerformancePitfall::class)
+    override fun Matrix<Double>.dot(vector: Point<Double>): DoubleBuffer {
+        require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
+        val rows = this@dot.rows.map { it.linearize() }
+        return DoubleBuffer(rowNum) { i ->
+            val r = rows[i]
+            var res = 0.0
+            for (j in r.indices) {
+                res += r[j] * vector[j]
+            }
+            res
+        }
+
+    }
+
+    override fun Matrix<Double>.times(value: Double): Matrix<Double> = ndRing(rowNum, colNum).run {
+        asND().map { it * value }.as2D()
+    }
+
+    public override fun Point<Double>.plus(other: Point<Double>): DoubleBuffer = DoubleBuffer(size) {
+        get(it) + other[it]
+    }
+
+    public override fun Point<Double>.minus(other: Point<Double>): DoubleBuffer = DoubleBuffer(size) {
+        get(it) - other[it]
+    }
+
+    public override fun Point<Double>.times(value: Double): DoubleBuffer = DoubleBuffer(size) { i -> get(i) * value }
+
+    public operator fun Point<Double>.div(value: Double): DoubleBuffer = DoubleBuffer(size) { i -> get(i) / value }
+
+    public override fun Double.times(v: Point<Double>): DoubleBuffer = v * this
+
+
+}
+
+public val DoubleField.linearSpace: DoubleLinearSpace get() = DoubleLinearSpace

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt

@@ -136,7 +136,7 @@ public fun <T> Buffer<T>.asND(): Structure1D<T> = Buffer1DWrapper(this)
 /**
  * Expose inner buffer of this [Structure1D] if possible
  */
-internal fun <T : Any> Structure1D<T>.unwrap(): Buffer<T> = when {
+internal fun <T : Any> Structure1D<T>.asND(): Buffer<T> = when {
     this is Buffer1DWrapper<T> -> buffer
     this is Structure1DWrapper && structure is BufferND<T> -> structure.buffer
     else -> this

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt

@@ -160,10 +160,10 @@ public fun <T> MutableStructureND<T>.as2D(): MutableStructure2D<T> = this as? Mu
 /**
  * Expose inner [StructureND] if possible
  */
-internal fun <T> Structure2D<T>.unwrap(): StructureND<T> =
+internal fun <T> Structure2D<T>.asND(): StructureND<T> =
     if (this is Structure2DWrapper) structure
     else this
 
-internal fun <T> MutableStructure2D<T>.unwrap(): MutableStructureND<T> =
+internal fun <T> MutableStructure2D<T>.asND(): MutableStructureND<T> =
     if (this is MutableStructure2DWrapper) structure else this
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BigInt.kt

@@ -532,7 +532,7 @@ public val BigInt.algebra: BigIntField get() = BigIntField
 public inline fun Buffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
     boxing(size, initializer)
 
-public inline fun BigInt.buffer(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
+public inline fun BigInt.Companion.buffer(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
     Buffer.boxing(size, initializer)
 
 @Deprecated("Use BigInt::mutableBuffer")
@@ -543,4 +543,4 @@ public inline fun BigInt.mutableBuffer(size: Int, initializer: (Int) -> BigInt):
     Buffer.boxing(size, initializer)
 
 public fun BigIntField.nd(vararg shape: Int): BufferedRingND<BigInt, BigIntField> =
-    BufferedRingND(shape, BigIntField, Buffer.Companion::bigInt)
+    BufferedRingND(shape, BigIntField, BigInt::buffer)

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/DoubleBuffer.kt

@@ -25,6 +25,12 @@ public value class DoubleBuffer(public val array: DoubleArray) : MutableBuffer<D
     override operator fun iterator(): DoubleIterator = array.iterator()
 
     override fun copy(): DoubleBuffer = DoubleBuffer(array.copyOf())
+
+    override fun toString(): String = Buffer.toString(this)
+
+    public companion object{
+        public fun zero(size: Int): DoubleBuffer = DoubleArray(size).asBuffer()
+    }
 }
 
 /**

kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/DoubleLUSolverTest.kt

@@ -8,6 +8,7 @@ package space.kscience.kmath.linear
 import space.kscience.kmath.misc.PerformancePitfall
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.StructureND
+import space.kscience.kmath.operations.algebra
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
@@ -21,39 +22,37 @@ fun <T : Any> assertMatrixEquals(expected: StructureND<T>, actual: StructureND<T
 class DoubleLUSolverTest {
 
     @Test
-    fun testInvertOne() {
+    fun testInvertOne() = Double.algebra.linearSpace.run{
-        val matrix = LinearSpace.double.one(2, 2)
+        val matrix = one(2, 2)
-        val inverted = LinearSpace.double.lupSolver().inverse(matrix)
+        val inverted = lupSolver().inverse(matrix)
         assertMatrixEquals(matrix, inverted)
     }
 
     @Test
-    fun testDecomposition() {
+    fun testDecomposition() = Double.algebra.linearSpace.run {
-        LinearSpace.double.run {
+        val matrix = matrix(2, 2)(
-            val matrix = matrix(2, 2)(
+            3.0, 1.0,
-                3.0, 1.0,
+            2.0, 3.0
-                2.0, 3.0
+        )
-            )
 
-            val lup = lup(matrix)
+        val lup = lup(matrix)
 
-            //Check determinant
+        //Check determinant
-            assertEquals(7.0, lup.determinant)
+        assertEquals(7.0, lup.determinant)
 
-            assertMatrixEquals(lup.p dot matrix, lup.l dot lup.u)
+        assertMatrixEquals(lup.p dot matrix, lup.l dot lup.u)
-        }
     }
 
     @Test
-    fun testInvert() {
+    fun testInvert()  = Double.algebra.linearSpace.run{
-        val matrix = LinearSpace.double.matrix(2, 2)(
+        val matrix = matrix(2, 2)(
             3.0, 1.0,
             1.0, 3.0
         )
 
-        val inverted = LinearSpace.double.lupSolver().inverse(matrix)
+        val inverted = lupSolver().inverse(matrix)
 
-        val expected = LinearSpace.double.matrix(2, 2)(
+        val expected = matrix(2, 2)(
             0.375, -0.125,
             -0.125, 0.375
         )

kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt

@@ -9,6 +9,7 @@ import space.kscience.kmath.misc.PerformancePitfall
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.StructureND
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.operations.algebra
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
@@ -19,15 +20,15 @@ import kotlin.test.assertTrue
 class MatrixTest {
 
     @Test
-    fun testTranspose() {
+    fun testTranspose() = Double.algebra.linearSpace.run {
-        val matrix = LinearSpace.double.one(3, 3)
+        val matrix = one(3, 3)
         val transposed = matrix.transpose()
         assertTrue { StructureND.contentEquals(matrix, transposed) }
     }
 
     @Test
-    fun testBuilder() {
+    fun testBuilder() = Double.algebra.linearSpace.run {
-        val matrix = LinearSpace.double.matrix(2, 3)(
+        val matrix = matrix(2, 3)(
             1.0, 0.0, 0.0,
             0.0, 1.0, 2.0
         )
@@ -36,7 +37,7 @@ class MatrixTest {
     }
 
     @Test
-    fun testMatrixExtension() {
+    fun testMatrixExtension() = Double.algebra.linearSpace.run {
         val transitionMatrix: Matrix<Double> = VirtualMatrix(6, 6) { row, col ->
             when {
                 col == 0 -> .50
@@ -49,7 +50,7 @@ class MatrixTest {
         infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
             var res = this
             repeat(power - 1) {
-                res = LinearSpace.double.run { res dot this@pow }
+                res =  res dot this@pow
             }
             return res
         }
@@ -58,19 +59,18 @@ class MatrixTest {
     }
 
     @Test
-    fun test2DDot() {
+    fun test2DDot() = Double.algebra.linearSpace.run {
         val firstMatrix = StructureND.auto(2, 3) { (i, j) -> (i + j).toDouble() }.as2D()
         val secondMatrix = StructureND.auto(3, 2) { (i, j) -> (i + j).toDouble() }.as2D()
 
-        LinearSpace.double.run {
 //            val firstMatrix = produce(2, 3) { i, j -> (i + j).toDouble() }
 //            val secondMatrix = produce(3, 2) { i, j -> (i + j).toDouble() }
-            val result = firstMatrix dot secondMatrix
+        val result = firstMatrix dot secondMatrix
-            assertEquals(2, result.rowNum)
+        assertEquals(2, result.rowNum)
-            assertEquals(2, result.colNum)
+        assertEquals(2, result.colNum)
-            assertEquals(8.0, result[0, 1])
+        assertEquals(8.0, result[0, 1])
-            assertEquals(8.0, result[1, 0])
+        assertEquals(8.0, result[1, 0])
-            assertEquals(14.0, result[1, 1])
+        assertEquals(14.0, result[1, 1])
-        }
+
     }
 }

kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/NumberNDFieldTest.kt

@@ -5,7 +5,7 @@
 
 package space.kscience.kmath.structures
 
-import space.kscience.kmath.linear.LinearSpace
+import space.kscience.kmath.linear.linearSpace
 import space.kscience.kmath.misc.PerformancePitfall
 import space.kscience.kmath.nd.StructureND
 import space.kscience.kmath.nd.combine
@@ -13,6 +13,7 @@ import space.kscience.kmath.nd.get
 import space.kscience.kmath.nd.ndAlgebra
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.Norm
+import space.kscience.kmath.operations.algebra
 import space.kscience.kmath.operations.invoke
 import kotlin.math.abs
 import kotlin.math.pow
@@ -42,17 +43,18 @@ class NumberNDFieldTest {
     }
 
     @Test
-    fun testGeneration() {
+    fun testGeneration() = Double.algebra.linearSpace.run {
 
-        val array = LinearSpace.double.buildMatrix(3, 3) { i, j ->
+        val array = buildMatrix(3, 3) { i, j ->
             (i * 10 + j).toDouble()
         }
 
-        for (i in 0..2)
+        for (i in 0..2) {
             for (j in 0..2) {
                 val expected = (i * 10 + j).toDouble()
                 assertEquals(expected, array[i, j], "Error at index [$i, $j]")
             }
+        }
     }
 
     @Test

kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Wrappers.kt

@@ -5,13 +5,11 @@
 
 package space.kscience.kmath.dimensions
 
-import space.kscience.kmath.linear.LinearSpace
+import space.kscience.kmath.linear.*
-import space.kscience.kmath.linear.Matrix
-import space.kscience.kmath.linear.Point
-import space.kscience.kmath.linear.transpose
 import space.kscience.kmath.nd.Structure2D
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.Ring
+import space.kscience.kmath.operations.algebra
 import kotlin.jvm.JvmInline
 
 /**
@@ -151,7 +149,7 @@ public value class DMatrixContext<T : Any, out A : Ring<T>>(public val context:
         context.run { (this@transpose as Matrix<T>).transpose() }.coerce()
 
     public companion object {
-        public val real: DMatrixContext<Double, DoubleField> = DMatrixContext(LinearSpace.double)
+        public val real: DMatrixContext<Double, DoubleField> = DMatrixContext(Double.algebra.linearSpace)
     }
 }
 

kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleMatrixTest.kt

@@ -5,12 +5,12 @@
 
 package space.kscience.kmath.real
 
-import space.kscience.kmath.linear.LinearSpace
+import space.kscience.kmath.linear.linearSpace
 import space.kscience.kmath.linear.matrix
 import space.kscience.kmath.misc.PerformancePitfall
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.StructureND
-import space.kscience.kmath.real.*
+import space.kscience.kmath.operations.algebra
 import space.kscience.kmath.structures.contentEquals
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -59,13 +59,13 @@ internal class DoubleMatrixTest {
     }
 
     @Test
-    fun testMatrixAndDouble() {
+    fun testMatrixAndDouble() = Double.algebra.linearSpace.run {
         val matrix1 = realMatrix(2, 3)(
             1.0, 0.0, 3.0,
             4.0, 6.0, 2.0
         )
         val matrix2 = (matrix1 * 2.5 + 1.0 - 2.0) / 2.0
-        val expectedResult = LinearSpace.double.matrix(2, 3)(
+        val expectedResult = matrix(2, 3)(
             0.75, -0.5, 3.25,
             4.5, 7.0, 2.0
         )
@@ -159,8 +159,8 @@ internal class DoubleMatrixTest {
     }
 
     @Test
-    fun testAllElementOperations() {
+    fun testAllElementOperations() = Double.algebra.linearSpace.run {
-        val matrix1 = LinearSpace.double.matrix(2, 4)(
+        val matrix1 = matrix(2, 4)(
             -1.0, 0.0, 3.0, 15.0,
             4.0, -6.0, 7.0, -11.0
         )

kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleVectorTest.kt

@@ -5,10 +5,10 @@
 
 package space.kscience.kmath.real
 
-import space.kscience.kmath.linear.LinearSpace
 import space.kscience.kmath.linear.asMatrix
+import space.kscience.kmath.linear.linearSpace
 import space.kscience.kmath.linear.transpose
-import space.kscience.kmath.real.plus
+import space.kscience.kmath.operations.algebra
 import space.kscience.kmath.structures.DoubleBuffer
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -30,12 +30,12 @@ internal class DoubleVectorTest {
     }
 
     @Test
-    fun testDot() {
+    fun testDot() = Double.algebra.linearSpace.run {
         val vector1 = DoubleBuffer(5) { it.toDouble() }
         val vector2 = DoubleBuffer(5) { 5 - it.toDouble() }
         val matrix1 = vector1.asMatrix()
         val matrix2 = vector2.asMatrix().transpose()
-        val product = LinearSpace.double.run { matrix1 dot matrix2 }
+        val product = matrix1 dot matrix2
         assertEquals(5.0, product[1, 0])
         assertEquals(6.0, product[2, 2])
     }

kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/QowOptimizer.kt

@@ -13,6 +13,7 @@ import space.kscience.kmath.linear.*
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.misc.log
 import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.algebra
 import space.kscience.kmath.structures.DoubleBuffer
 import space.kscience.kmath.structures.DoubleL2Norm
 
@@ -32,7 +33,7 @@ public class QowRuns(public val runs: Int) : OptimizationFeature {
 @UnstableKMathAPI
 public object QowOptimizer : Optimizer<Double, XYFit> {
 
-    private val linearSpace: LinearSpace<Double, DoubleField> = LinearSpace.double
+    private val linearSpace: LinearSpace<Double, DoubleField> = Double.algebra.linearSpace
     private val solver: LinearSolver<Double> = linearSpace.lupSolver()
 
     @OptIn(UnstableKMathAPI::class)

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt

@@ -5,6 +5,7 @@
 
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.internal.array
 import space.kscience.kmath.tensors.core.internal.broadcastTensors
@@ -91,3 +92,11 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         }
     }
 }
+
+
+/**
+ * Compute a value using broadcast double tensor algebra
+ */
+@UnstableKMathAPI
+public fun <R> DoubleTensorAlgebra.withBroadcast(block: BroadcastDoubleTensorAlgebra.() -> R): R =
+    BroadcastDoubleTensorAlgebra.block()

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt

@@ -949,4 +949,6 @@ public open class DoubleTensorAlgebra :
     override fun Tensor<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
 }
 
+public val Double.Companion.tensorAlgebra: DoubleTensorAlgebra.Companion get() = DoubleTensorAlgebra
+
 

settings.gradle.kts

@@ -5,11 +5,11 @@ pluginManagement {
         gradlePluginPortal()
     }
 
-    val kotlinVersion = "1.5.30"
+    val kotlinVersion = "1.6.0-M1"
 
     plugins {
         id("org.jetbrains.kotlinx.benchmark") version "0.3.1"
-        id("ru.mipt.npm.gradle.project") version "0.10.3"
+        id("ru.mipt.npm.gradle.project") version "0.10.4"
         kotlin("multiplatform") version kotlinVersion
         kotlin("plugin.allopen") version kotlinVersion
     }
@@ -17,6 +17,9 @@ pluginManagement {
 
 rootProject.name = "kmath"
 
+enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
+enableFeaturePreview("VERSION_CATALOGS")
+
 include(
     ":kmath-memory",
     ":kmath-complex",