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Merge remote-tracking branch 'origin/feature/tensors-performance' into feature/tensors-performance

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margarita0303 2022-07-13 18:42:28 +03:00
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examples/src/main/kotlin/space/kscience/kmath/tensors/margarita.kt
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/*
* 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()
}