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fix bugs in svd

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This commit is contained in:
AlyaNovikova 2021-04-08 23:58:44 +03:00
parent 74dfca4e2e
commit ea4d6618b4
4 changed files with 30 additions and 11 deletions
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14
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
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@ -96,14 +96,20 @@ public class DoubleLinearOpsTensorAlgebra :
val size = this.shape.size val size = this.shape.size
val commonShape = this.shape.sliceArray(0 until size - 2) val commonShape = this.shape.sliceArray(0 until size - 2)
val (n, m) = this.shape.sliceArray(size - 2 until size) val (n, m) = this.shape.sliceArray(size - 2 until size)
val resU = zeros(commonShape + intArrayOf(n, min(n, m))) val resU = zeros(commonShape + intArrayOf(min(n, m), n))
val resS = zeros(commonShape + intArrayOf(min(n, m))) val resS = zeros(commonShape + intArrayOf(min(n, m)))
val resV = zeros(commonShape + intArrayOf(min(n, m), m)) val resV = zeros(commonShape + intArrayOf(min(n, m), m))
for ((matrix, USV) in this.matrixSequence() for ((matrix, USV) in this.matrixSequence()
.zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) .zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
svdHelper(matrix.asTensor(), USV, m, n) val size = matrix.shape.reduce { acc, i -> acc * i }
return Triple(resU, resS, resV.transpose(size - 2, size - 1)) val curMatrix = DoubleTensor(
matrix.shape,
matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + size).toDoubleArray()
)
svdHelper(curMatrix, USV, m, n)
}
return Triple(resU.transpose(size - 2, size - 1), resS, resV)
} }
override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> { override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {

16
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/linutils.kt
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@ -225,10 +225,10 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon:
val b: DoubleTensor val b: DoubleTensor
if (n > m) { if (n > m) {
b = a.transpose(0, 1).dot(a) b = a.transpose(0, 1).dot(a)
v = DoubleTensor(intArrayOf(m), getRandomNormals(m, 0)) v = DoubleTensor(intArrayOf(m), getRandomUnitVector(m, 0))
} else { } else {
b = a.dot(a.transpose(0, 1)) b = a.dot(a.transpose(0, 1))
v = DoubleTensor(intArrayOf(n), getRandomNormals(n, 0)) v = DoubleTensor(intArrayOf(n), getRandomUnitVector(n, 0))
} }
var lastV: DoubleTensor var lastV: DoubleTensor
@ -284,7 +284,13 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
val s = res.map { it.first }.toDoubleArray() val s = res.map { it.first }.toDoubleArray()
val uBuffer = res.map { it.second }.flatMap { it.buffer.array().toList() }.toDoubleArray() val uBuffer = res.map { it.second }.flatMap { it.buffer.array().toList() }.toDoubleArray()
val vBuffer = res.map { it.third }.flatMap { it.buffer.array().toList() }.toDoubleArray() val vBuffer = res.map { it.third }.flatMap { it.buffer.array().toList() }.toDoubleArray()
uBuffer.copyInto(matrixU.buffer.array()) for (i in uBuffer.indices) {
s.copyInto(matrixS.buffer.array()) matrixU.buffer.array()[matrixU.bufferStart + i] = uBuffer[i]
vBuffer.copyInto(matrixV.buffer.array()) }
for (i in s.indices) {
matrixS.buffer.array()[matrixS.bufferStart + i] = s[i]
}
for (i in vBuffer.indices) {
matrixV.buffer.array()[matrixV.bufferStart + i] = vBuffer[i]
}
} }

7
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt
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@ -3,6 +3,7 @@ package space.kscience.kmath.tensors.core
import space.kscience.kmath.samplers.GaussianSampler import space.kscience.kmath.samplers.GaussianSampler
import space.kscience.kmath.stat.RandomGenerator import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.structures.* import space.kscience.kmath.structures.*
import kotlin.math.sqrt
/** /**
* Returns a reference to [IntArray] containing all of the elements of this [Buffer]. * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
@ -42,6 +43,12 @@ internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
return distribution.sample(generator).nextBufferBlocking(n).toDoubleArray() return distribution.sample(generator).nextBufferBlocking(n).toDoubleArray()
} }
internal inline fun getRandomUnitVector(n: Int, seed: Long): DoubleArray {
val unnorm = getRandomNormals(n, seed)
val norm = sqrt(unnorm.map { it * it }.sum())
return unnorm.map { it / norm }.toDoubleArray()
}
internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else { internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
val ii = n + i val ii = n + i
check(ii >= 0) { check(ii >= 0) {

4
kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
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@ -126,9 +126,9 @@ class TestDoubleLinearOpsTensorAlgebra {
testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0))) testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
} }
@Test @Ignore @Test
fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra { fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
val tensor = randNormal(intArrayOf(7, 5, 3), 0) val tensor = randNormal(intArrayOf(1, 15, 4, 7, 5, 3), 0)
val (tensorU, tensorS, tensorV) = tensor.svd() val (tensorU, tensorS, tensorV) = tensor.svd()
val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV) val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV)
assertTrue(tensor.eq(tensorSVD)) assertTrue(tensor.eq(tensorSVD))