refactor
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bfba653904
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@ -14,7 +14,7 @@ import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
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fun main () {
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// work in context with linear operations
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DoubleLinearOpsTensorAlgebra.invoke {
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DoubleLinearOpsTensorAlgebra {
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// set true value of x
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val trueX = fromArray(
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@ -19,7 +19,7 @@ fun main() {
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val randSeed = 100500L
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// work in context with linear operations
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DoubleLinearOpsTensorAlgebra.invoke {
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DoubleLinearOpsTensorAlgebra {
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// take coefficient vector from normal distribution
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val alpha = randomNormal(
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intArrayOf(5),
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@ -3,6 +3,9 @@ plugins {
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}
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kotlin.sourceSets {
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all {
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languageSettings.useExperimentalAnnotation("space.kscience.kmath.misc.UnstableKMathAPI")
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}
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commonMain {
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dependencies {
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api(project(":kmath-core"))
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@ -109,9 +109,13 @@ public fun Array<IntArray>.toIntTensor(): IntTensor {
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}
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public fun DoubleTensor.toDoubleArray(): DoubleArray {
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return tensor.mutableBuffer.array().drop(bufferStart).take(numElements).toDoubleArray()
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return DoubleArray(numElements) { i ->
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mutableBuffer[bufferStart + i]
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}
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}
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public fun IntTensor.toIntArray(): IntArray {
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return tensor.mutableBuffer.array().drop(bufferStart).take(numElements).toIntArray()
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return IntArray(numElements) { i ->
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mutableBuffer[bufferStart + i]
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}
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}
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@ -5,38 +5,35 @@ import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
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import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
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internal fun checkEmptyShape(shape: IntArray): Unit =
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internal fun checkEmptyShape(shape: IntArray) =
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check(shape.isNotEmpty()) {
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"Illegal empty shape provided"
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}
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internal fun checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
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internal fun checkEmptyDoubleBuffer(buffer: DoubleArray) =
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check(buffer.isNotEmpty()) {
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"Illegal empty buffer provided"
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}
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internal fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
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internal fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray) =
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check(buffer.size == shape.reduce(Int::times)) {
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"Inconsistent shape ${shape.toList()} for buffer of size ${buffer.size} provided"
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}
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internal fun <T> checkShapesCompatible(a: TensorStructure<T>, b: TensorStructure<T>): Unit =
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internal fun <T> checkShapesCompatible(a: TensorStructure<T>, b: TensorStructure<T>) =
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check(a.shape contentEquals b.shape) {
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"Incompatible shapes ${a.shape.toList()} and ${b.shape.toList()} "
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}
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internal fun checkTranspose(dim: Int, i: Int, j: Int): Unit =
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internal fun checkTranspose(dim: Int, i: Int, j: Int) =
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check((i < dim) and (j < dim)) {
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"Cannot transpose $i to $j for a tensor of dim $dim"
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}
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internal fun <T> checkView(a: TensorStructure<T>, shape: IntArray): Unit =
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internal fun <T> checkView(a: TensorStructure<T>, shape: IntArray) =
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check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
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internal fun checkSquareMatrix(shape: IntArray): Unit {
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internal fun checkSquareMatrix(shape: IntArray) {
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val n = shape.size
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check(n >= 2) {
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"Expected tensor with 2 or more dimensions, got size $n instead"
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@ -48,14 +45,12 @@ internal fun checkSquareMatrix(shape: IntArray): Unit {
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internal fun DoubleTensorAlgebra.checkSymmetric(
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tensor: TensorStructure<Double>, epsilon: Double = 1e-6
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): Unit =
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) =
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check(tensor.eq(tensor.transpose(), epsilon)) {
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"Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
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}
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internal fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(
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tensor: DoubleTensor, epsilon: Double = 1e-6
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): Unit {
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internal fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor, epsilon: Double = 1e-6) {
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checkSymmetric(tensor, epsilon)
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for (mat in tensor.matrixSequence())
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check(mat.asTensor().detLU().value() > 0.0) {
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@ -34,18 +34,6 @@ internal fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTensor<T>>
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}
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}
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internal inline fun <T> BufferedTensor<T>.forEachVector(vectorAction: (BufferedTensor<T>) -> Unit) {
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for (vector in vectorSequence()) {
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vectorAction(vector)
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}
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}
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internal inline fun <T> BufferedTensor<T>.forEachMatrix(matrixAction: (BufferedTensor<T>) -> Unit) {
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for (matrix in matrixSequence()) {
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matrixAction(matrix)
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}
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}
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internal fun dotHelper(
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a: MutableStructure2D<Double>,
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b: MutableStructure2D<Double>,
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@ -14,7 +14,6 @@ internal fun Buffer<Int>.array(): IntArray = when (this) {
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else -> this.toIntArray()
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}
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/**
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* Returns a reference to [DoubleArray] containing all of the elements of this [Buffer] or copy the data.
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*/
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@ -31,7 +30,7 @@ internal fun getRandomNormals(n: Int, seed: Long): DoubleArray {
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internal fun getRandomUnitVector(n: Int, seed: Long): DoubleArray {
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val unnorm = getRandomNormals(n, seed)
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val norm = sqrt(unnorm.map { it * it }.sum())
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val norm = sqrt(unnorm.sumOf { it * it })
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return unnorm.map { it / norm }.toDoubleArray()
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}
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@ -45,23 +44,33 @@ internal fun minusIndexFrom(n: Int, i: Int): Int = if (i >= 0) i else {
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internal fun <T> BufferedTensor<T>.minusIndex(i: Int): Int = minusIndexFrom(this.dimension, i)
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internal fun format(value: Double, digits: Int = 4): String {
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val ten = 10.0
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val approxOrder = if (value == 0.0) 0 else ceil(log10(abs(value))).toInt()
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val order = if (
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((value % ten) == 0.0) or
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(value == 1.0) or
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((1 / value) % ten == 0.0)
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) approxOrder else approxOrder - 1
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val lead = value / ten.pow(order)
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val leadDisplay = round(lead * ten.pow(digits)) / ten.pow(digits)
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val orderDisplay = if (order == 0) "" else if (order > 0) "E+$order" else "E$order"
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val valueDisplay = "$leadDisplay$orderDisplay"
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val res = if (value < 0.0) valueDisplay else " $valueDisplay"
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internal fun format(value: Double, digits: Int = 4): String = buildString {
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val res = buildString {
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val ten = 10.0
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val approxOrder = if (value == 0.0) 0 else ceil(log10(abs(value))).toInt()
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val order = if (
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((value % ten) == 0.0) ||
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(value == 1.0) ||
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((1 / value) % ten == 0.0)
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) approxOrder else approxOrder - 1
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val lead = value / ten.pow(order)
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if (value >= 0.0) append(' ')
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append(round(lead * ten.pow(digits)) / ten.pow(digits))
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when {
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order == 0 -> Unit
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order > 0 -> {
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append("e+")
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append(order)
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}
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else -> {
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append('e')
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append(order)
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}
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}
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}
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val fLength = digits + 6
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val endSpace = " ".repeat(fLength - res.length)
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return "$res$endSpace"
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append(res)
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repeat(fLength - res.length) { append(' ') }
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}
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internal fun DoubleTensor.toPrettyString(): String = buildString {
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