forked from kscience/kmath
refactor
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fe81dea243
commit
bfba653904
@ -21,7 +21,7 @@ fun main() {
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// work in context with linear operations
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DoubleLinearOpsTensorAlgebra.invoke {
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// take coefficient vector from normal distribution
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val alpha = randNormal(
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val alpha = randomNormal(
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intArrayOf(5),
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randSeed
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) + fromArray(
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@ -32,14 +32,14 @@ fun main() {
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println("Real alpha:\n$alpha")
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// also take sample of size 20 from normal distribution for x
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val x = randNormal(
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val x = randomNormal(
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intArrayOf(20, 5),
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randSeed
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)
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// calculate y and add gaussian noise (N(0, 0.05))
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val y = x dot alpha
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y += y.randNormalLike(randSeed) * 0.05
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y += y.randomNormalLike(randSeed) * 0.05
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// now restore the coefficient vector with OSL estimator with SVD
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val (u, singValues, v) = x.svd()
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@ -31,7 +31,7 @@ public object DoubleLinearOpsTensorAlgebra :
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public fun TensorStructure<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
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computeLU(tensor, epsilon)
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?: throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
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?: throw IllegalArgumentException("Tensor contains matrices which are singular at precision $epsilon")
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public fun TensorStructure<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
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@ -47,8 +47,10 @@ public object DoubleLinearOpsTensorAlgebra :
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val n = luTensor.shape.last()
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val pTensor = luTensor.zeroesLike()
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for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.tensor.vectorSequence()))
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pivInit(p.as2D(), pivot.as1D(), n)
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pTensor
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.matrixSequence()
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.zip(pivotsTensor.tensor.vectorSequence())
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.forEach { (p, pivot) -> pivInit(p.as2D(), pivot.as1D(), n) }
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val lTensor = luTensor.zeroesLike()
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val uTensor = luTensor.zeroesLike()
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@ -284,7 +284,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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val m1 = newThis.shape[newThis.shape.size - 1]
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val m2 = newOther.shape[newOther.shape.size - 2]
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val n = newOther.shape[newOther.shape.size - 1]
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if (m1 != m2) {
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check (m1 == m2) {
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throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
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}
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@ -315,11 +315,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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val d1 = minusIndexFrom(n + 1, dim1)
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val d2 = minusIndexFrom(n + 1, dim2)
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if (d1 == d2) {
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throw RuntimeException("Diagonal dimensions cannot be identical $d1, $d2")
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check(d1 != d2) {
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"Diagonal dimensions cannot be identical $d1, $d2"
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}
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if (d1 > n || d2 > n) {
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throw RuntimeException("Dimension out of range")
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check(d1 <= n && d2 <= n) {
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"Dimension out of range"
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}
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var lessDim = d1
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@ -366,8 +366,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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)
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}
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public fun TensorStructure<Double>.eq(other: TensorStructure<Double>, delta: Double): Boolean {
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return tensor.eq(other) { x, y -> abs(x - y) < delta }
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public fun TensorStructure<Double>.eq(other: TensorStructure<Double>, epsilon: Double): Boolean {
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return tensor.eq(other) { x, y -> abs(x - y) < epsilon }
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}
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public infix fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
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@ -393,10 +393,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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return true
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}
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public fun randNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
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public fun randomNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
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DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed))
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public fun TensorStructure<Double>.randNormalLike(seed: Long = 0): DoubleTensor =
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public fun TensorStructure<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
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DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
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// stack tensors by axis 0
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@ -42,8 +42,8 @@ internal fun broadcastShapes(vararg shapes: IntArray): IntArray {
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for (i in shape.indices) {
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val curDim = shape[i]
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val offset = totalDim - shape.size
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if (curDim != 1 && totalShape[i + offset] != curDim) {
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throw RuntimeException("Shapes are not compatible and cannot be broadcast")
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check(curDim == 1 || totalShape[i + offset] == curDim) {
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"Shapes are not compatible and cannot be broadcast"
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}
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}
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}
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@ -52,8 +52,8 @@ internal fun broadcastShapes(vararg shapes: IntArray): IntArray {
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}
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internal fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor {
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if (tensor.shape.size > newShape.size) {
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throw RuntimeException("Tensor is not compatible with the new shape")
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require(tensor.shape.size <= newShape.size) {
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"Tensor is not compatible with the new shape"
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}
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val n = newShape.reduce { acc, i -> acc * i }
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@ -62,8 +62,8 @@ internal fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor
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for (i in tensor.shape.indices) {
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val curDim = tensor.shape[i]
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val offset = newShape.size - tensor.shape.size
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if (curDim != 1 && newShape[i + offset] != curDim) {
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throw RuntimeException("Tensor is not compatible with the new shape and cannot be broadcast")
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check(curDim == 1 || newShape[i + offset] == curDim) {
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"Tensor is not compatible with the new shape and cannot be broadcast"
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}
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}
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@ -75,19 +75,17 @@ internal fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleTensor>
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val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
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val n = totalShape.reduce { acc, i -> acc * i }
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return buildList {
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for (tensor in tensors) {
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val resTensor = DoubleTensor(totalShape, DoubleArray(n))
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multiIndexBroadCasting(tensor, resTensor, n)
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add(resTensor)
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}
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return tensors.map { tensor ->
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val resTensor = DoubleTensor(totalShape, DoubleArray(n))
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multiIndexBroadCasting(tensor, resTensor, n)
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resTensor
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}
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}
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internal fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
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val onlyTwoDims = tensors.asSequence().onEach {
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require(it.shape.size >= 2) {
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throw RuntimeException("Tensors must have at least 2 dimensions")
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"Tensors must have at least 2 dimensions"
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}
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}.any { it.shape.size != 2 }
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@ -69,8 +69,7 @@ internal fun DoubleTensor.toPrettyString(): String = buildString {
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val shape = this@toPrettyString.shape
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val linearStructure = this@toPrettyString.linearStructure
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val vectorSize = shape.last()
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val initString = "DoubleTensor(\n"
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append(initString)
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append("DoubleTensor(\n")
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var charOffset = 3
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for (vector in vectorSequence()) {
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repeat(charOffset) { append(' ') }
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@ -135,7 +135,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
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@Test
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fun testCholesky() = DoubleLinearOpsTensorAlgebra.invoke {
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val tensor = randNormal(intArrayOf(2, 5, 5), 0)
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val tensor = randomNormal(intArrayOf(2, 5, 5), 0)
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val sigma = (tensor dot tensor.transpose()) + diagonalEmbedding(
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fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
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)
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@ -163,7 +163,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
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@Test
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fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra.invoke {
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val tensor = randNormal(intArrayOf(2, 5, 3), 0)
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val tensor = randomNormal(intArrayOf(2, 5, 3), 0)
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val (tensorU, tensorS, tensorV) = tensor.svd()
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val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
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assertTrue(tensor.eq(tensorSVD))
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@ -171,7 +171,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
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@Test
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fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra.invoke {
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val tensor = randNormal(shape = intArrayOf(2, 3, 3), 0)
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val tensor = randomNormal(shape = intArrayOf(2, 3, 3), 0)
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val tensorSigma = tensor + tensor.transpose()
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val (tensorS, tensorV) = tensorSigma.symEig()
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val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
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