KMP library for tensors #300
@ -69,7 +69,7 @@ fun main () {
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val n = l.shape[0]
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val x = zeros(intArrayOf(n))
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for (i in 0 until n){
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x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / l[intArrayOf(i, i)]
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x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).valueOrNull()!!) / l[intArrayOf(i, i)]
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}
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return x
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}
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@ -60,7 +60,7 @@ fun main() {
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require(yTrue.shape contentEquals yPred.shape)
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val diff = yTrue - yPred
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return diff.dot(diff).sqrt().value()
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return diff.dot(diff).sqrt().valueOrNull()!!
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}
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println("MSE: ${mse(alpha, alphaOLS)}")
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@ -16,11 +16,12 @@ import space.kscience.kmath.operations.Algebra
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public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
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/**
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*
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* Returns a single tensor value of unit dimension. The tensor shape must be equal to [1].
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*
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* @return the value of a scalar tensor.
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*/
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public fun Tensor<T>.value(): T
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public fun Tensor<T>.valueOrNull(): T?
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/**
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* Each element of the tensor [other] is added to this value.
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@ -35,12 +35,10 @@ public open class DoubleTensorAlgebra :
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public companion object : DoubleTensorAlgebra()
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override fun Tensor<Double>.value(): Double {
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check(tensor.shape contentEquals intArrayOf(1)) {
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"Inconsistent value for tensor of shape ${shape.toList()}"
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}
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return tensor.mutableBuffer.array()[tensor.bufferStart]
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}
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override fun Tensor<Double>.valueOrNull(): Double? = if(tensor.shape contentEquals intArrayOf(1)) {
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// Inconsistent value for tensor of with this shape
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tensor.mutableBuffer.array()[tensor.bufferStart]
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} else null
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/**
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* Constructs a tensor with the specified shape and data.
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@ -58,7 +58,7 @@ internal fun DoubleTensorAlgebra.checkSymmetric(
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internal fun DoubleTensorAlgebra.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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"Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"
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check(mat.asTensor().detLU().valueOrNull()!! > 0.0) {
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"Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().valueOrNull()!!}"
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}
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}
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@ -12,6 +12,7 @@ import space.kscience.kmath.nd.as2D
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.tensors.core.*
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.valueOrNull
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import kotlin.math.abs
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import kotlin.math.min
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import kotlin.math.sign
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@ -239,14 +240,14 @@ internal fun DoubleTensorAlgebra.qrHelper(
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val vv = v.as1D()
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if (j > 0) {
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for (i in 0 until j) {
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r[i, j] = (qT[i] dot matrixT[j]).value()
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r[i, j] = (qT[i] dot matrixT[j]).valueOrNull()!!
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for (k in 0 until n) {
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val qTi = qT[i].as1D()
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vv[k] = vv[k] - r[i, j] * qTi[k]
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}
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}
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}
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r[j, j] = DoubleTensorAlgebra { (v dot v).sqrt().value() }
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r[j, j] = DoubleTensorAlgebra { (v dot v).sqrt().valueOrNull()!! }
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for (i in 0 until n) {
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qM[i, j] = vv[i] / r[j, j]
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}
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@ -269,9 +270,9 @@ internal fun DoubleTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10)
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while (true) {
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lastV = v
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v = b.dot(lastV)
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val norm = DoubleTensorAlgebra { (v dot v).sqrt().value() }
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val norm = DoubleTensorAlgebra { (v dot v).sqrt().valueOrNull()!! }
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v = v.times(1.0 / norm)
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if (abs(v.dot(lastV).value()) > 1 - epsilon) {
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if (abs(v.dot(lastV).valueOrNull()!!) > 1 - epsilon) {
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return v
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}
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}
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@ -292,7 +293,7 @@ internal fun DoubleTensorAlgebra.svdHelper(
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val outerProduct = DoubleArray(u.shape[0] * v.shape[0])
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for (i in 0 until u.shape[0]) {
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for (j in 0 until v.shape[0]) {
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outerProduct[i * v.shape[0] + j] = u[i].value() * v[j].value()
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outerProduct[i * v.shape[0] + j] = u[i].valueOrNull()!! * v[j].valueOrNull()!!
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}
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}
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a = a - singularValue.times(DoubleTensor(intArrayOf(u.shape[0], v.shape[0]), outerProduct))
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@ -303,12 +304,12 @@ internal fun DoubleTensorAlgebra.svdHelper(
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if (n > m) {
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v = svd1d(a, epsilon)
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u = matrix.dot(v)
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norm = DoubleTensorAlgebra { (u dot u).sqrt().value() }
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norm = DoubleTensorAlgebra { (u dot u).sqrt().valueOrNull()!! }
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u = u.times(1.0 / norm)
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} else {
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u = svd1d(a, epsilon)
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v = matrix.transpose(0, 1).dot(u)
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norm = DoubleTensorAlgebra { (v dot v).sqrt().value() }
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norm = DoubleTensorAlgebra { (v dot v).sqrt().valueOrNull()!! }
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v = v.times(1.0 / norm)
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}
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@ -46,7 +46,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
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)
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)
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assertTrue { abs(m.det().value() - expectedValue) < 1e-5 }
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assertTrue { abs(m.det().valueOrNull()!! - expectedValue) < 1e-5 }
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}
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@Test
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@ -58,7 +58,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
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)
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)
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assertTrue { abs(m.det().value() - expectedValue) < 1e-5 }
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assertTrue { abs(m.det().valueOrNull()!! - expectedValue) < 1e-5 }
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}
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@Test
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@ -90,7 +90,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
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fun testScalarProduct() = DoubleTensorAlgebra {
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val a = fromArray(intArrayOf(3), doubleArrayOf(1.8, 2.5, 6.8))
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val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4))
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assertEquals(a.dot(b).value(), 59.92)
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assertEquals(a.dot(b).valueOrNull()!!, 59.92)
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}
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@Test
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@ -21,7 +21,7 @@ internal class TestDoubleTensor {
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fun testValue() = DoubleTensorAlgebra {
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val value = 12.5
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val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
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assertEquals(tensor.value(), value)
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assertEquals(tensor.valueOrNull()!!, value)
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}
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@Test
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