Delete margarita.kt
deleted tmp file for testing during work
This commit is contained in:
parent
e7968f28f4
commit
b99d2e8486
@ -1,65 +0,0 @@
|
|||||||
/*
|
|
||||||
* Copyright 2018-2021 KMath contributors.
|
|
||||||
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
|
|
||||||
*/
|
|
||||||
|
|
||||||
package space.kscience.kmath.tensors
|
|
||||||
|
|
||||||
import space.kscience.kmath.linear.transpose
|
|
||||||
import space.kscience.kmath.misc.PerformancePitfall
|
|
||||||
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.tensorAlgebra
|
|
||||||
import kotlin.math.*
|
|
||||||
|
|
||||||
fun MutableStructure2D<Double>.print() {
|
|
||||||
val n = this.shape.component1()
|
|
||||||
val m = this.shape.component2()
|
|
||||||
for (i in 0 until n) {
|
|
||||||
for (j in 0 until m) {
|
|
||||||
val x = (this[i, j] * 100).roundToInt() / 100.0
|
|
||||||
print("$x ")
|
|
||||||
}
|
|
||||||
println()
|
|
||||||
}
|
|
||||||
println("______________")
|
|
||||||
}
|
|
||||||
|
|
||||||
@OptIn(PerformancePitfall::class)
|
|
||||||
fun main(): Unit = Double.tensorAlgebra.withBroadcast {
|
|
||||||
val shape = intArrayOf(5, 3)
|
|
||||||
val buffer = doubleArrayOf(
|
|
||||||
1.000000, 2.000000, 3.000000,
|
|
||||||
2.000000, 3.000000, 4.000000,
|
|
||||||
3.000000, 4.000000, 5.000000,
|
|
||||||
4.000000, 5.000000, 6.000000,
|
|
||||||
5.000000, 6.000000, 7.000000
|
|
||||||
)
|
|
||||||
val buffer2 = doubleArrayOf(
|
|
||||||
0.000000, 0.000000, 0.000000,
|
|
||||||
0.000000, 0.000000, 0.000000,
|
|
||||||
0.000000, 0.000000, 0.000000
|
|
||||||
)
|
|
||||||
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()
|
|
||||||
var ans = Pair(w, v)
|
|
||||||
tensor.svdGolabKahan(v, w)
|
|
||||||
|
|
||||||
println("u")
|
|
||||||
tensor.print()
|
|
||||||
println("w")
|
|
||||||
w.print()
|
|
||||||
println("v")
|
|
||||||
v.print()
|
|
||||||
|
|
||||||
|
|
||||||
}
|
|
Loading…
Reference in New Issue
Block a user