refactor

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(

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),

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"))

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]
+    }
 }

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>) =
-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 fun checkTranspose(dim: Int, i: Int, j: Int) =
-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 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) {
-internal fun checkSquareMatrix(shape: IntArray): Unit {
     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(
+internal fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor, epsilon: Double = 1e-6) {
-    tensor: DoubleTensor, epsilon: Double = 1e-6
-): Unit {
     checkSymmetric(tensor, epsilon)
     for (mat in tensor.matrixSequence())
         check(mat.asTensor().detLU().value() > 0.0) {

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>,

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 {
+internal fun format(value: Double, digits: Int = 4): String = buildString {
-    val ten = 10.0
+    val res = buildString {
-    val approxOrder = if (value == 0.0) 0 else ceil(log10(abs(value))).toInt()
+        val ten = 10.0
-    val order = if (
+        val approxOrder = if (value == 0.0) 0 else ceil(log10(abs(value))).toInt()
-        ((value % ten) == 0.0) or
+        val order = if (
-        (value == 1.0) or
+            ((value % ten) == 0.0) ||
-        ((1 / value) % ten == 0.0)
+            (value == 1.0) ||
-    ) approxOrder else approxOrder - 1
+            ((1 / value) % ten == 0.0)
-    val lead = value / ten.pow(order)
+        ) approxOrder else approxOrder - 1
-    val leadDisplay = round(lead * ten.pow(digits)) / ten.pow(digits)
+        val lead = value / ten.pow(order)
-    val orderDisplay = if (order == 0) "" else if (order > 0) "E+$order" else "E$order"
+        if (value >= 0.0) append(' ')
-    val valueDisplay = "$leadDisplay$orderDisplay"
+        append(round(lead * ten.pow(digits)) / ten.pow(digits))
-    val res = if (value < 0.0) valueDisplay else " $valueDisplay"
+        when {
-
+            order == 0 -> Unit
+            order > 0 -> {
+                append("e+")
+                append(order)
+            }
+            else -> {
+                append('e')
+                append(order)
+            }
+        }
+    }
     val fLength = digits + 6
-    val endSpace = " ".repeat(fLength - res.length)
+    append(res)
-    return "$res$endSpace"
+    repeat(fLength - res.length) { append(' ') }
 }
 
 internal fun DoubleTensor.toPrettyString(): String = buildString {