renamed function and changed structure
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@ -11,28 +11,9 @@ import space.kscience.kmath.nd.MutableStructure2D
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import space.kscience.kmath.nd.Structure2D
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import space.kscience.kmath.nd.as2D
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import space.kscience.kmath.tensors.core.*
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.dot
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.mapIndexed
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.zeros
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.minus
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.sum
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import space.kscience.kmath.tensors.core.tensorAlgebra
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import kotlin.math.*
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fun DoubleArray.fmap(transform: (Double) -> Double): DoubleArray {
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return this.map(transform).toDoubleArray()
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}
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fun scalarProduct(v1: Structure2D<Double>, v2: Structure2D<Double>): Double {
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return v1.mapIndexed { index, d -> d * v2[index] }.sum()
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}
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internal fun diagonal(shape: IntArray, v: Double) : DoubleTensor {
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val matrix = zeros(shape)
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return matrix.mapIndexed { index, _ -> if (index.component1() == index.component2()) v else 0.0 }
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}
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fun MutableStructure2D<Double>.print() {
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val n = this.shape.component1()
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val m = this.shape.component2()
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@ -63,11 +44,22 @@ fun main(): Unit = Double.tensorAlgebra.withBroadcast {
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)
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val tensor = fromArray(shape, buffer).as2D()
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val v = fromArray(intArrayOf(3, 3), buffer2).as2D()
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val w_shape = intArrayOf(3, 1)
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var w_buffer = doubleArrayOf(0.000000)
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for (i in 0 until 3 - 1) {
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w_buffer += doubleArrayOf(0.000000)
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}
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val w = BroadcastDoubleTensorAlgebra.fromArray(w_shape, w_buffer).as2D()
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tensor.print()
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tensor.svdcmp(v)
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var ans = Pair(w, v)
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tensor.svdGolabKahan(v, w)
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println("u")
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tensor.print()
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println("w")
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w.print()
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println("v")
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v.print()
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}
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@ -1,7 +1,6 @@
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package space.kscience.kmath.tensors
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import space.kscience.kmath.nd.*
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
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import kotlin.math.abs
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import kotlin.math.max
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import kotlin.math.min
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@ -34,10 +33,12 @@ fun SIGN(a: Double, b: Double): Double {
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return -abs(a)
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}
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internal fun MutableStructure2D<Double>.svdcmp(v: MutableStructure2D<Double>) {
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// matrix v is not transposed at the output
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internal fun MutableStructure2D<Double>.svdGolabKahan(v: MutableStructure2D<Double>, w: MutableStructure2D<Double>) {
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val shape = this.shape
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val n = shape.component2()
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val m = shape.component1()
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val n = shape.component2()
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var f = 0.0
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val rv1 = DoubleArray(n)
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var s = 0.0
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@ -45,12 +46,6 @@ internal fun MutableStructure2D<Double>.svdcmp(v: MutableStructure2D<Double>) {
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var anorm = 0.0
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var g = 0.0
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var l = 0
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val w_shape = intArrayOf(n, 1)
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var w_buffer = doubleArrayOf(0.000000)
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for (i in 0 until n - 1) {
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w_buffer += doubleArrayOf(0.000000)
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}
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val w = BroadcastDoubleTensorAlgebra.fromArray(w_shape, w_buffer).as2D()
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for (i in 0 until n) {
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/* left-hand reduction */
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l = i + 1
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@ -212,6 +207,9 @@ internal fun MutableStructure2D<Double>.svdcmp(v: MutableStructure2D<Double>) {
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this[3, 2] = -0.297540
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this[4, 2] = 0.548193
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// задала правильные значения, чтобы проверить правильность кода дальше
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// дальше - все корректно
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var flag = 0
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var nm = 0
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var c = 0.0
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@ -268,9 +266,10 @@ internal fun MutableStructure2D<Double>.svdcmp(v: MutableStructure2D<Double>) {
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break
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}
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if (its == 30) {
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return
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}
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// надо придумать, что сделать - выкинуть ошибку?
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// if (its == 30) {
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// return
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// }
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x = w[l, 0]
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nm = k - 1
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@ -326,11 +325,4 @@ internal fun MutableStructure2D<Double>.svdcmp(v: MutableStructure2D<Double>) {
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w[k, 0] = x
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}
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}
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println("u")
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this.print()
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println("w")
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w.print()
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println("v")
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v.print()
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}
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