teldufalsari/kmath
2
0
Fork 0
forked from kscience/kmath
Code Pull Requests 1 Activity

Replace complex access to tensor with faster access to buffer in Jacobi algorithm

Browse Source
This commit is contained in:
Ivan Kylchik 2022-02-20 02:44:27 +03:00
parent b13765ec19
commit dda6602ed4
1 changed files with 26 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

42
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
View File

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