Throwable value method
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@ -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).valueOrNull()!!) / l[intArrayOf(i, i)]
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x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / 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().valueOrNull()!!
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return diff.dot(diff).sqrt().value()
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}
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println("MSE: ${mse(alpha, alphaOLS)}")
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@ -15,13 +15,21 @@ import space.kscience.kmath.operations.Algebra
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*/
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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 if tensor shape equals to [1].
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*
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* @return a nullable value of a potentially scalar tensor.
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*/
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public fun Tensor<T>.valueOrNull(): 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>.valueOrNull(): T?
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public fun Tensor<T>.value(): T
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/**
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* Each element of the tensor [other] is added to this value.
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@ -35,10 +35,11 @@ public open class DoubleTensorAlgebra :
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public companion object : DoubleTensorAlgebra()
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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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override fun Tensor<Double>.valueOrNull(): Double? = if (tensor.shape contentEquals intArrayOf(1))
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tensor.mutableBuffer.array()[tensor.bufferStart] else null
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override fun Tensor<Double>.value(): Double =
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valueOrNull() ?: throw IllegalArgumentException("Inconsistent value for tensor of with $shape shape")
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/**
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* Constructs a tensor with the specified shape and data.
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@ -62,8 +63,10 @@ public open class DoubleTensorAlgebra :
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* @return tensor with the [shape] shape and data generated by the [initializer].
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*/
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public fun produce(shape: IntArray, initializer: (IntArray) -> Double): DoubleTensor =
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fromArray(shape,
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TensorLinearStructure(shape).indices().map(initializer).toMutableList().toDoubleArray())
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fromArray(
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shape,
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TensorLinearStructure(shape).indices().map(initializer).toMutableList().toDoubleArray()
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)
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override operator fun Tensor<Double>.get(i: Int): DoubleTensor {
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val lastShape = tensor.shape.drop(1).toIntArray()
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@ -621,7 +624,7 @@ public open class DoubleTensorAlgebra :
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keepDim
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)
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private fun cov(x: DoubleTensor, y:DoubleTensor): Double{
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private fun cov(x: DoubleTensor, y: DoubleTensor): Double {
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val n = x.shape[0]
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return ((x - x.mean()) * (y - y.mean())).mean() * n / (n - 1)
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}
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@ -633,10 +636,10 @@ public open class DoubleTensorAlgebra :
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check(tensors.all { it.shape contentEquals intArrayOf(m) }) { "Tensors must have same shapes" }
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val resTensor = DoubleTensor(
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intArrayOf(n, n),
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DoubleArray(n * n) {0.0}
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DoubleArray(n * n) { 0.0 }
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)
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for (i in 0 until n){
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for (j in 0 until n){
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for (i in 0 until n) {
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for (j in 0 until n) {
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resTensor[intArrayOf(i, j)] = cov(tensors[i].tensor, tensors[j].tensor)
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}
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}
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@ -779,8 +782,10 @@ public open class DoubleTensorAlgebra :
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val qTensor = zeroesLike()
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val rTensor = zeroesLike()
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tensor.matrixSequence()
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.zip((qTensor.matrixSequence()
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.zip(rTensor.matrixSequence()))).forEach { (matrix, qr) ->
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.zip(
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(qTensor.matrixSequence()
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.zip(rTensor.matrixSequence()))
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).forEach { (matrix, qr) ->
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val (q, r) = qr
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qrHelper(matrix.asTensor(), q.asTensor(), r.as2D())
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}
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@ -812,9 +817,13 @@ public open class DoubleTensorAlgebra :
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val vTensor = zeros(commonShape + intArrayOf(min(n, m), m))
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tensor.matrixSequence()
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.zip(uTensor.matrixSequence()
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.zip(sTensor.vectorSequence()
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.zip(vTensor.matrixSequence()))).forEach { (matrix, USV) ->
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.zip(
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uTensor.matrixSequence()
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.zip(
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sTensor.vectorSequence()
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.zip(vTensor.matrixSequence())
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)
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).forEach { (matrix, USV) ->
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val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
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val curMatrix = DoubleTensor(
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matrix.shape,
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@ -918,10 +927,11 @@ public open class DoubleTensorAlgebra :
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* @return triple of P, L and U tensors
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*/
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public fun Tensor<Double>.lu(epsilon: Double = 1e-9): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
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val (lu, pivots) = this.luFactor(epsilon)
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val (lu, pivots) = tensor.luFactor(epsilon)
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return luPivot(lu, pivots)
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}
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override fun Tensor<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
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}
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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().valueOrNull()!! > 0.0) {
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"Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().valueOrNull()!!}"
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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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}
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}
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@ -240,14 +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]).valueOrNull()!!
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r[i, j] = (qT[i] dot matrixT[j]).value()
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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().valueOrNull()!! }
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r[j, j] = DoubleTensorAlgebra { (v dot v).sqrt().value() }
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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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@ -270,9 +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().valueOrNull()!! }
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val norm = DoubleTensorAlgebra { (v dot v).sqrt().value() }
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v = v.times(1.0 / norm)
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if (abs(v.dot(lastV).valueOrNull()!!) > 1 - epsilon) {
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if (abs(v.dot(lastV).value()) > 1 - epsilon) {
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return v
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}
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}
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@ -293,7 +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].valueOrNull()!! * v[j].valueOrNull()!!
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outerProduct[i * v.shape[0] + j] = u[i].value() * v[j].value()
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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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@ -304,12 +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().valueOrNull()!! }
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norm = DoubleTensorAlgebra { (u dot u).sqrt().value() }
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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().valueOrNull()!! }
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norm = DoubleTensorAlgebra { (v dot v).sqrt().value() }
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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().valueOrNull()!! - expectedValue) < 1e-5 }
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assertTrue { abs(m.det().value() - 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().valueOrNull()!! - expectedValue) < 1e-5 }
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assertTrue { abs(m.det().value() - 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).valueOrNull()!!, 59.92)
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assertEquals(a.dot(b).value(), 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.valueOrNull()!!, value)
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assertEquals(tensor.value(), value)
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}
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@Test
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