levenbergMarquardt cleanup
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976714475e
@ -120,8 +120,6 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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0.0, inputData.startParameters, TypeOfConvergence.NoConvergence
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0.0, inputData.startParameters, TypeOfConvergence.NoConvergence
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)
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)
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val eps = 2.2204e-16
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val settings = LMSettings(0, 0, inputData.exampleNumber)
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val settings = LMSettings(0, 0, inputData.exampleNumber)
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settings.funcCalls = 0 // running count of function evaluations
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settings.funcCalls = 0 // running count of function evaluations
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@ -214,7 +212,7 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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stop = true
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stop = true
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}
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}
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var lambda = 1.0
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var lambda: Double
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var nu = 1
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var nu = 1
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if (updateType == 1) {
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if (updateType == 1) {
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@ -273,8 +271,8 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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val alpha = 1.0
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val alpha = 1.0
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if (updateType == 2) { // Quadratic
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if (updateType == 2) { // Quadratic
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// One step of quadratic line update in the h direction for minimum X2
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// One step of quadratic line update in the h direction for minimum X2
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val alpha = (jtWdy.transpose() dot h) / ((X2Try - x2) / 2.0 + 2 * (jtWdy.transpose() dot h))
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val alphaTensor = (jtWdy.transpose() dot h) / ((X2Try - x2) / 2.0 + 2 * (jtWdy.transpose() dot h))
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h = h dot alpha
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h = h dot alphaTensor
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pTry = (p + h).as2D() // update only [idx] elements
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pTry = (p + h).as2D() // update only [idx] elements
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pTry = smallestElementComparison(
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pTry = smallestElementComparison(
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largestElementComparison(minParameters, pTry),
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largestElementComparison(minParameters, pTry),
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@ -388,7 +386,7 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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resultInfo.typeOfConvergence = TypeOfConvergence.InGradient
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resultInfo.typeOfConvergence = TypeOfConvergence.InGradient
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stop = true
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stop = true
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}
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}
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if ((abs(h.as2D()).div(abs(p) + 1e-12)).max() < epsilon2 && settings.iteration > 2) {
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if ((abs(h.as2D()) / (abs(p) + 1e-12)).max() < epsilon2 && settings.iteration > 2) {
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resultInfo.typeOfConvergence = TypeOfConvergence.InParameters
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resultInfo.typeOfConvergence = TypeOfConvergence.InParameters
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stop = true
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stop = true
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}
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}
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@ -7,7 +7,10 @@ package space.kscience.kmath.tensors.core.internal
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import space.kscience.kmath.nd.*
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import space.kscience.kmath.nd.*
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.structures.*
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import space.kscience.kmath.structures.DoubleBuffer
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import space.kscience.kmath.structures.IntBuffer
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import space.kscience.kmath.structures.asBuffer
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import space.kscience.kmath.structures.indices
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import space.kscience.kmath.tensors.core.*
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import space.kscience.kmath.tensors.core.*
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import kotlin.math.abs
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import kotlin.math.abs
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import kotlin.math.max
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import kotlin.math.max
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@ -329,14 +332,16 @@ private fun SIGN(a: Double, b: Double): Double {
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return -abs(a)
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return -abs(a)
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}
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}
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internal fun MutableStructure2D<Double>.svdGolubKahanHelper(u: MutableStructure2D<Double>, w: BufferedTensor<Double>,
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internal fun MutableStructure2D<Double>.svdGolubKahanHelper(
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v: MutableStructure2D<Double>, iterations: Int, epsilon: Double) {
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u: MutableStructure2D<Double>, w: BufferedTensor<Double>,
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v: MutableStructure2D<Double>, iterations: Int, epsilon: Double,
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) {
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val shape = this.shape
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val shape = this.shape
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val m = shape.component1()
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val m = shape.component1()
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val n = shape.component2()
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val n = shape.component2()
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var f = 0.0
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var f: Double
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val rv1 = DoubleArray(n)
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val rv1 = DoubleArray(n)
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var s = 0.0
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var s: Double
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var scale = 0.0
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var scale = 0.0
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var anorm = 0.0
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var anorm = 0.0
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var g = 0.0
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var g = 0.0
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@ -362,10 +367,10 @@ internal fun MutableStructure2D<Double>.svdGolubKahanHelper(u: MutableStructure2
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s += this[k, i] * this[k, i]
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s += this[k, i] * this[k, i]
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}
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}
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f = this[i, i]
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f = this[i, i]
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if (f >= 0) {
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g = if (f >= 0) {
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g = (-1) * abs(sqrt(s))
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-abs(sqrt(s))
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} else {
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} else {
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g = abs(sqrt(s))
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abs(sqrt(s))
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}
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}
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val h = f * g - s
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val h = f * g - s
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this[i, i] = f - g
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this[i, i] = f - g
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@ -402,10 +407,10 @@ internal fun MutableStructure2D<Double>.svdGolubKahanHelper(u: MutableStructure2
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s += this[i, k] * this[i, k]
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s += this[i, k] * this[i, k]
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}
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}
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f = this[i, l]
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f = this[i, l]
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if (f >= 0) {
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g = if (f >= 0) {
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g = (-1) * abs(sqrt(s))
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-abs(sqrt(s))
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} else {
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} else {
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g = abs(sqrt(s))
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abs(sqrt(s))
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}
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}
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val h = f * g - s
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val h = f * g - s
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this[i, l] = f - g
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this[i, l] = f - g
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@ -457,7 +462,7 @@ internal fun MutableStructure2D<Double>.svdGolubKahanHelper(u: MutableStructure2
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for (i in min(n, m) - 1 downTo 0) {
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for (i in min(n, m) - 1 downTo 0) {
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l = i + 1
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l = i + 1
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g = wBuffer[wStart + i]
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g = wBuffer[wStart + i]
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for (j in l until n) {
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for (j in l until n) {
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this[i, j] = 0.0
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this[i, j] = 0.0
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}
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}
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@ -484,13 +489,13 @@ internal fun MutableStructure2D<Double>.svdGolubKahanHelper(u: MutableStructure2
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this[i, i] += 1.0
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this[i, i] += 1.0
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}
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}
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var flag = 0
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var flag: Int
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var nm = 0
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var nm = 0
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var c = 0.0
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var c: Double
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var h = 0.0
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var h: Double
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var y = 0.0
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var y: Double
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var z = 0.0
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var z: Double
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var x = 0.0
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var x: Double
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for (k in n - 1 downTo 0) {
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for (k in n - 1 downTo 0) {
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for (its in 1 until iterations) {
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for (its in 1 until iterations) {
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flag = 1
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flag = 1
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@ -531,7 +536,7 @@ internal fun MutableStructure2D<Double>.svdGolubKahanHelper(u: MutableStructure2
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}
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}
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}
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}
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z = wBuffer[wStart + k]
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z = wBuffer[wStart + k]
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if (l == k) {
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if (l == k) {
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if (z < 0.0) {
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if (z < 0.0) {
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wBuffer[wStart + k] = -z
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wBuffer[wStart + k] = -z
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@ -541,9 +546,9 @@ internal fun MutableStructure2D<Double>.svdGolubKahanHelper(u: MutableStructure2
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break
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break
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}
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}
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x = wBuffer[wStart + l]
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x = wBuffer[wStart + l]
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nm = k - 1
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nm = k - 1
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y = wBuffer[wStart + nm]
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y = wBuffer[wStart + nm]
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g = rv1[nm]
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g = rv1[nm]
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h = rv1[k]
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h = rv1[k]
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f = ((y - z) * (y + z) + (g - h) * (g + h)) / (2.0 * h * y)
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f = ((y - z) * (y + z) + (g - h) * (g + h)) / (2.0 * h * y)
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@ -552,7 +557,7 @@ internal fun MutableStructure2D<Double>.svdGolubKahanHelper(u: MutableStructure2
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c = 1.0
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c = 1.0
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s = 1.0
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s = 1.0
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var i = 0
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var i: Int
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for (j in l until nm + 1) {
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for (j in l until nm + 1) {
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i = j + 1
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i = j + 1
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g = rv1[i]
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g = rv1[i]
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