teldufalsari/kmath
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fixes

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This commit is contained in:
Andrei Kislitsyn 2021-03-24 18:42:41 +03:00
parent 96a755071f
commit ab81370001
2 changed files with 9 additions and 15 deletions
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22
kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
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@ -10,9 +10,9 @@ public class DoubleLinearOpsTensorAlgebra :
LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>, LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
DoubleTensorAlgebra() { DoubleTensorAlgebra() {
override fun DoubleTensor.inv(): DoubleTensor { override fun DoubleTensor.inv(): DoubleTensor = invLU()
TODO("ANDREI")
} override fun DoubleTensor.det(): DoubleTensor = detLU()
override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> { override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
@ -156,15 +156,11 @@ public class DoubleLinearOpsTensorAlgebra :
private fun luMatrixDet(lu: Structure2D<Double>, pivots: Structure1D<Int>): Double { private fun luMatrixDet(lu: Structure2D<Double>, pivots: Structure1D<Int>): Double {
val m = lu.shape[0] val m = lu.shape[0]
val sign = if((pivots[m] - m) % 2 == 0) 1.0 else -1.0 val sign = if((pivots[m] - m) % 2 == 0) 1.0 else -1.0
var det = sign return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
for (i in 0 until m){
det *= lu[i, i]
}
return det
} }
public fun DoubleTensor.detLU(): DoubleTensor { public fun DoubleTensor.detLU(): DoubleTensor {
val (luTensor, pivotsTensor) = this.lu() val (luTensor, pivotsTensor) = lu()
val n = shape.size val n = shape.size
val detTensorShape = IntArray(n - 1) { i -> shape[i] } val detTensorShape = IntArray(n - 1) { i -> shape[i] }
@ -211,14 +207,12 @@ public class DoubleLinearOpsTensorAlgebra :
} }
public fun DoubleTensor.invLU(): DoubleTensor { public fun DoubleTensor.invLU(): DoubleTensor {
val (luTensor, pivotsTensor) = this.lu() val (luTensor, pivotsTensor) = lu()
val n = shape.size val n = shape.size
val invTensor = luTensor.zeroesLike() val invTensor = luTensor.zeroesLike()
for ( val seq = luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
(luP, invMatrix) in for ((luP, invMatrix) in seq) {
luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
) {
val (lu, pivots) = luP val (lu, pivots) = luP
luMatrixInv(lu, pivots, invMatrix) luMatrixInv(lu, pivots, invMatrix)
} }