From cccfe611129d414221bc09f58e6b62be0c36296c Mon Sep 17 00:00:00 2001 From: margarita0303 Date: Tue, 26 Jul 2022 22:13:48 +0300 Subject: [PATCH] added some tests for svd --- .../core/TestDoubleLinearOpsAlgebra.kt | 147 ++++++++---------- 1 file changed, 63 insertions(+), 84 deletions(-) diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt index c0d241a31..d3e67fbdc 100644 --- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt +++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt @@ -161,76 +161,6 @@ internal class TestDoubleLinearOpsTensorAlgebra { assertTrue { abs(abs(res.mutableBuffer.array()[res.bufferStart + 1]) - 0.922) < 0.01 } } - @Test - fun testSVD() = DoubleTensorAlgebra{ - testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))) - testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0))) - } - -// @Test -// fun testSVDError() = DoubleTensorAlgebra{ -// val buffer = doubleArrayOf( -// 1.000000, 2.000000, 3.000000, -// 2.000000, 3.000000, 4.000000, -// 3.000000, 4.000000, 5.000000, -// 4.000000, 5.000000, 6.000000, -// 5.000000, 6.000000, 7.000000 -// ) -// testSVDFor(fromArray(intArrayOf(5, 3), buffer)) -// } - - @Test - fun testBatchedSVD() = DoubleTensorAlgebra { - 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)) - } - - @Test - fun testSVDGolabKahan() = DoubleTensorAlgebra{ - testSVDGolabKahanFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))) - testSVDGolabKahanFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0))) - val buffer = doubleArrayOf( - 1.000000, 2.000000, 3.000000, - 2.000000, 3.000000, 4.000000, - 3.000000, 4.000000, 5.000000, - 4.000000, 5.000000, 6.000000, - 5.000000, 6.000000, 7.000000 - ) - testSVDGolabKahanFor(fromArray(intArrayOf(5, 3), buffer)) - } - -// @Test -// fun testSVDGolabKahanError() = DoubleTensorAlgebra{ -// val buffer = doubleArrayOf( -// 1.0, 2.0, 3.0, 2.0, 3.0, -// 4.0, 3.0, 4.0, 5.0, 4.0, -// 5.0, 6.0, 5.0, 6.0, 7.0 -// ) -// testSVDGolabKahanFor(fromArray(intArrayOf(3, 5), buffer)) -// } - -// @Test -// fun testSVDGolabKahanBig() = DoubleTensorAlgebra{ -// val tensor = DoubleTensorAlgebra.randomNormal(intArrayOf(100, 100, 100), 0) -// testSVDGolabKahanFor(tensor) -// } -// -// @Test -// fun testSVDBig() = DoubleTensorAlgebra{ -// val tensor = DoubleTensorAlgebra.randomNormal(intArrayOf(100, 100, 100), 0) -// testSVDFor(tensor) -// } - - @Test - fun testBatchedSVDGolabKahan() = DoubleTensorAlgebra{ - val tensor = randomNormal(intArrayOf(2, 5, 3), 0) - val (tensorU, tensorS, tensorV) = tensor.svdGolabKahan() - val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose()) - assertTrue(tensor.eq(tensorSVD)) - } - @Test fun testBatchedSymEig() = DoubleTensorAlgebra { val tensor = randomNormal(shape = intArrayOf(2, 3, 3), 0) @@ -240,24 +170,63 @@ internal class TestDoubleLinearOpsTensorAlgebra { assertTrue(tensorSigma.eq(tensorSigmaCalc)) } - -} - - -private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10) { - val svd = tensor.svd() - - val tensorSVD = svd.first - .dot( - diagonalEmbedding(svd.second) - .dot(svd.third.transpose()) + @Test + fun testSVD() = DoubleTensorAlgebra{ + testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))) + testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0))) + val buffer1 = doubleArrayOf( + 1.000000, 2.000000, 3.000000, + 2.000000, 3.000000, 4.000000, + 3.000000, 4.000000, 5.000000, + 4.000000, 5.000000, 6.000000, + 5.000000, 6.000000, 7.000000 ) + testSVDFor(fromArray(intArrayOf(5, 3), buffer1)) + val buffer2 = doubleArrayOf( + 1.0, 2.0, 3.0, 2.0, 3.0, + 4.0, 3.0, 4.0, 5.0, 4.0, + 5.0, 6.0, 5.0, 6.0, 7.0 + ) + testSVDFor(fromArray(intArrayOf(3, 5), buffer2)) + } - assertTrue(tensor.eq(tensorSVD, epsilon)) + @Test + fun testBatchedSVD() = DoubleTensorAlgebra{ + val tensor1 = randomNormal(intArrayOf(2, 5, 3), 0) + testSVDFor(tensor1) + val tensor2 = DoubleTensorAlgebra.randomNormal(intArrayOf(30, 30, 30), 0) + testSVDFor(tensor2) + } + + @Test + fun testSVDPowerMethod() = DoubleTensorAlgebra{ + testSVDPowerMethodFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))) + testSVDPowerMethodFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0))) + } + + @Test + fun testBatchedSVDPowerMethod() = DoubleTensorAlgebra { + val tensor1 = randomNormal(intArrayOf(2, 5, 3), 0) + testSVDPowerMethodFor(tensor1) + val tensor2 = DoubleTensorAlgebra.randomNormal(intArrayOf(30, 30, 30), 0) + testSVDPowerMethodFor(tensor2) + } + +// @Test +// fun testSVDPowerMethodError() = DoubleTensorAlgebra{ +// val buffer = doubleArrayOf( +// 1.000000, 2.000000, 3.000000, +// 2.000000, 3.000000, 4.000000, +// 3.000000, 4.000000, 5.000000, +// 4.000000, 5.000000, 6.000000, +// 5.000000, 6.000000, 7.000000 +// ) +// testSVDPowerMethodFor(fromArray(intArrayOf(5, 3), buffer)) +// } } -private fun DoubleTensorAlgebra.testSVDGolabKahanFor(tensor: DoubleTensor) { - val svd = tensor.svdGolabKahan() +private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor) { + val svd = tensor.svd() val tensorSVD = svd.first .dot( @@ -268,4 +237,14 @@ private fun DoubleTensorAlgebra.testSVDGolabKahanFor(tensor: DoubleTensor) { assertTrue(tensor.eq(tensorSVD)) } +private fun DoubleTensorAlgebra.testSVDPowerMethodFor(tensor: DoubleTensor, epsilon: Double = 1e-10) { + val svd = tensor.svdPowerMethod() + val tensorSVD = svd.first + .dot( + diagonalEmbedding(svd.second) + .dot(svd.third.transpose()) + ) + + assertTrue(tensor.eq(tensorSVD, epsilon)) +} \ No newline at end of file