altavir/diff #494
@ -953,23 +953,33 @@ public open class DoubleTensorAlgebra :
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maxIteration: Int,
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epsilon: Double
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): Pair<Structure1D<Double>, Structure2D<Double>> {
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val A_ = this.copy().as2D()
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val V = eye(this.shape[0]).as2D()
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val D = DoubleTensor(intArrayOf(this.shape[0]), (0 until this.rowNum).map { this[it, it] }.toDoubleArray()).as1D()
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val B = DoubleTensor(intArrayOf(this.shape[0]), (0 until this.rowNum).map { this[it, it] }.toDoubleArray()).as1D()
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val Z = zeros(intArrayOf(this.shape[0])).as1D()
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val n = this.shape[0]
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val A_ = this.copy()
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val V = eye(n)
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val D = DoubleTensor(intArrayOf(n), (0 until this.rowNum).map { this[it, it] }.toDoubleArray()).as1D()
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val B = DoubleTensor(intArrayOf(n), (0 until this.rowNum).map { this[it, it] }.toDoubleArray()).as1D()
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val Z = zeros(intArrayOf(n)).as1D()
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fun maxOffDiagonal(matrix: MutableStructure2D<Double>): Double {
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// assume that buffered tensor is square matrix
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operator fun BufferedTensor<Double>.get(i: Int, j: Int): Double {
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return this.mutableBuffer.array()[bufferStart + i * this.shape[0] + j]
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}
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operator fun BufferedTensor<Double>.set(i: Int, j: Int, value: Double) {
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this.mutableBuffer.array()[bufferStart + i * this.shape[0] + j] = value
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}
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fun maxOffDiagonal(matrix: BufferedTensor<Double>): Double {
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var maxOffDiagonalElement = 0.0
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for (i in 0 until matrix.rowNum - 1) {
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for (j in i + 1 until matrix.colNum) {
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for (i in 0 until n - 1) {
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for (j in i + 1 until n) {
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maxOffDiagonalElement = max(maxOffDiagonalElement, abs(matrix[i, j]))
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}
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}
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return maxOffDiagonalElement
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}
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fun rotate(a: MutableStructure2D<Double>, s: Double, tau: Double, i: Int, j: Int, k: Int, l: Int) {
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fun rotate(a: BufferedTensor<Double>, s: Double, tau: Double, i: Int, j: Int, k: Int, l: Int) {
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val g = a[i, j]
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val h = a[k, l]
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a[i, j] = g - s * (h + g * tau)
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@ -977,13 +987,13 @@ public open class DoubleTensorAlgebra :
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}
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fun jacobiIteration(
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a: MutableStructure2D<Double>,
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v: MutableStructure2D<Double>,
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a: BufferedTensor<Double>,
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v: BufferedTensor<Double>,
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d: MutableStructure1D<Double>,
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z: MutableStructure1D<Double>,
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) {
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for (ip in 0 until a.rowNum - 1) {
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for (iq in ip + 1 until a.colNum) {
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for (ip in 0 until n - 1) {
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for (iq in ip + 1 until n) {
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val g = 100.0 * abs(a[ip, iq])
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if (g <= epsilon * abs(d[ip]) && g <= epsilon * abs(d[iq])) {
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@ -1017,10 +1027,10 @@ public open class DoubleTensorAlgebra :
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for (j in (ip + 1) until iq) {
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rotate(a, s, tau, ip, j, j, iq)
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}
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for (j in (iq + 1) until a.rowNum) {
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for (j in (iq + 1) until n) {
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rotate(a, s, tau, ip, j, iq, j)
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}
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for (j in 0 until a.rowNum) {
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for (j in 0 until n) {
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rotate(v, s, tau, j, ip, j, iq)
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}
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}
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@ -1051,7 +1061,7 @@ public open class DoubleTensorAlgebra :
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}
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// TODO sort eigenvalues
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return D to V
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return D to V.as2D()
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}
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/**
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