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Feature/tensors performance #497

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margarita0303 wants to merge 91 commits from feature/tensors-performance into feature/tensors-performance
pull from: feature/tensors-performance
merge into: kscience:feature/tensors-performance
Conversation 1 Commits 91 Files Changed 96 +25 -41
2 changed files with 25 additions and 41 deletions
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Showing only changes of commit 8b48eaa1ed - Show all commits

36
examples/src/main/kotlin/space/kscience/kmath/tensors/margarita.kt
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@ -11,28 +11,9 @@ import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.tensors.core.*
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.dot
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.mapIndexed
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.zeros
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.minus
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.sum
import space.kscience.kmath.tensors.core.tensorAlgebra
import kotlin.math.*
fun DoubleArray.fmap(transform: (Double) -> Double): DoubleArray {
return this.map(transform).toDoubleArray()
}
fun scalarProduct(v1: Structure2D<Double>, v2: Structure2D<Double>): Double {
return v1.mapIndexed { index, d -> d * v2[index] }.sum()
}
internal fun diagonal(shape: IntArray, v: Double) : DoubleTensor {
val matrix = zeros(shape)
return matrix.mapIndexed { index, _ -> if (index.component1() == index.component2()) v else 0.0 }
}
fun MutableStructure2D<Double>.print() {
val n = this.shape.component1()
val m = this.shape.component2()
@ -63,11 +44,22 @@ fun main(): Unit = Double.tensorAlgebra.withBroadcast {
)
val tensor = fromArray(shape, buffer).as2D()
val v = fromArray(intArrayOf(3, 3), buffer2).as2D()
val w_shape = intArrayOf(3, 1)
var w_buffer = doubleArrayOf(0.000000)
for (i in 0 until 3 - 1) {
w_buffer += doubleArrayOf(0.000000)
}
val w = BroadcastDoubleTensorAlgebra.fromArray(w_shape, w_buffer).as2D()
tensor.print()
tensor.svdcmp(v)
var ans = Pair(w, v)
tensor.svdGolabKahan(v, w)
println("u")
tensor.print()
println("w")
w.print()
println("v")
v.print()
}

30
examples/src/main/kotlin/space/kscience/kmath/tensors/svdcmp.kt
View File

@ -1,7 +1,6 @@
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.*
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
import kotlin.math.abs
import kotlin.math.max
import kotlin.math.min
@ -34,10 +33,12 @@ fun SIGN(a: Double, b: Double): Double {
return -abs(a)
}
internal fun MutableStructure2D<Double>.svdcmp(v: MutableStructure2D<Double>) {
// matrix v is not transposed at the output
internal fun MutableStructure2D<Double>.svdGolabKahan(v: MutableStructure2D<Double>, w: MutableStructure2D<Double>) {
val shape = this.shape
val n = shape.component2()
val m = shape.component1()
val n = shape.component2()
var f = 0.0
val rv1 = DoubleArray(n)
var s = 0.0
@ -45,12 +46,6 @@ internal fun MutableStructure2D<Double>.svdcmp(v: MutableStructure2D<Double>) {
var anorm = 0.0
var g = 0.0
var l = 0
val w_shape = intArrayOf(n, 1)
var w_buffer = doubleArrayOf(0.000000)
for (i in 0 until n - 1) {
w_buffer += doubleArrayOf(0.000000)
}
val w = BroadcastDoubleTensorAlgebra.fromArray(w_shape, w_buffer).as2D()
for (i in 0 until n) {
/* left-hand reduction */
l = i + 1
@ -212,6 +207,9 @@ internal fun MutableStructure2D<Double>.svdcmp(v: MutableStructure2D<Double>) {
this[3, 2] = -0.297540
this[4, 2] = 0.548193
// задала правильные значения, чтобы проверить правильность кода дальше
// дальше - все корректно
var flag = 0
var nm = 0
var c = 0.0
@ -268,9 +266,10 @@ internal fun MutableStructure2D<Double>.svdcmp(v: MutableStructure2D<Double>) {
break
}
if (its == 30) {
return
}
// надо придумать, что сделать - выкинуть ошибку?
// if (its == 30) {
// return
// }
x = w[l, 0]
nm = k - 1
@ -326,11 +325,4 @@ internal fun MutableStructure2D<Double>.svdcmp(v: MutableStructure2D<Double>) {
w[k, 0] = x
}
}
println("u")
this.print()
println("w")
w.print()
println("v")
v.print()
}