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refactor

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This commit is contained in:
Andrei Kislitsyn 2021-05-01 17:47:12 +03:00
parent fe81dea243
commit bfba653904
6 changed files with 32 additions and 33 deletions
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6
examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
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@ -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()

8
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt
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@ -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()

18
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
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@ -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

20
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt
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@ -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) {
return tensors.map { tensor ->
val resTensor = DoubleTensor(totalShape, DoubleArray(n))
multiIndexBroadCasting(tensor, resTensor, n)
add(resTensor)
}
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 }

3
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
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@ -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(' ') }

6
kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
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@ -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())