forked from kscience/kmath
matrixhelper
This commit is contained in:
Andrei Kislitsyn
parent
4e4690e510
commit
2d2c5aa684
@ -13,6 +13,7 @@ public open class BufferedTensor<T>(
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TensorStrides(shape),
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buffer
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) {
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/*
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public operator fun get(i: Int, j: Int): T {
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check(this.dimension == 2) { "Not matrix" }
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@ -24,8 +25,60 @@ public open class BufferedTensor<T>(
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this[intArrayOf(i, j)] = value
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}
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*/
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}
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//todo make generator mb nextMatrixIndex?
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public class InnerMatrix<T>(private val tensor: BufferedTensor<T>){
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private var offset: Int = 0
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private val n : Int = tensor.shape.size
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//stride?
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private val step = tensor.shape[n - 1] * tensor.shape[n - 2]
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public operator fun get(i: Int, j: Int): T {
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val index = tensor.strides.index(offset)
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index[n - 2] = i
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index[n - 1] = j
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return tensor[index]
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}
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public operator fun set(i: Int, j: Int, value: T): Unit {
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val index = tensor.strides.index(offset)
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index[n - 2] = i
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index[n - 1] = j
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tensor[index] = value
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}
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public fun makeStep(){
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offset += step
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}
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}
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public class InnerVector<T>(private val tensor: BufferedTensor<T>){
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private var offset: Int = 0
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private val n : Int = tensor.shape.size
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//stride?
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private val step = tensor.shape[n - 1]
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public operator fun get(i: Int): T {
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val index = tensor.strides.index(offset)
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index[n - 1] = i
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return tensor[index]
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}
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public operator fun set(i: Int, value: T): Unit {
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val index = tensor.strides.index(offset)
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index[n - 1] = i
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tensor[index] = value
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}
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public fun makeStep(){
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offset += step
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}
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}
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//todo default buffer = arrayOf(0)???
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public class IntTensor(
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shape: IntArray,
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buffer: IntArray
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@ -2,8 +2,7 @@ package space.kscience.kmath.tensors
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public class RealLinearOpsTensorAlgebra :
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LinearOpsTensorAlgebra<Double, RealTensor>,
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RealTensorAlgebra()
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{
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RealTensorAlgebra() {
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override fun eye(n: Int): RealTensor {
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val shape = intArrayOf(n, n)
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val buffer = DoubleArray(n * n) { 0.0 }
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@ -26,55 +25,71 @@ public class RealLinearOpsTensorAlgebra :
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override fun RealTensor.lu(): Pair<RealTensor, IntTensor> {
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// todo checks
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val lu = this.copy()
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val m = this.shape[0]
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val pivot = IntArray(m)
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// Initialize permutation array and parity
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for (row in 0 until m) pivot[row] = row
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var even = true
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for (i in 0 until m) {
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var maxA = -1.0
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var iMax = i
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for (k in i until m) {
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val absA = kotlin.math.abs(lu[k, i])
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if (absA > maxA) {
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maxA = absA
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iMax = k
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}
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}
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//todo check singularity
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if (iMax != i) {
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val j = pivot[i]
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pivot[i] = pivot[iMax]
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pivot[iMax] = j
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even != even
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for (k in 0 until m) {
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val tmp = lu[i, k]
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lu[i, k] = lu[iMax, k]
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lu[iMax, k] = tmp
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}
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}
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for (j in i + 1 until m) {
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lu[j, i] /= lu[i, i]
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for (k in i + 1 until m) {
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lu[j, k] -= lu[j, i] * lu[i, k]
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}
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}
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val luTensor = this.copy()
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val lu = InnerMatrix(luTensor)
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//stride TODO!!! move to generator?
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var matCnt = 1
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for (i in 0 until this.shape.size - 2) {
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matCnt *= this.shape[i]
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}
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return Pair(lu, IntTensor(intArrayOf(m), pivot))
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val n = this.shape.size
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val m = this.shape[n - 1]
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val pivotsShape = IntArray(n - 1) { i ->
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this.shape[i]
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}
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val pivotsTensor = IntTensor(
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pivotsShape,
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IntArray(matCnt * m) { 0 }
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)
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val pivot = InnerVector(pivotsTensor)
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for (i in 0 until matCnt) {
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for (row in 0 until m) pivot[row] = row
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for (i in 0 until m) {
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var maxA = -1.0
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var iMax = i
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for (k in i until m) {
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val absA = kotlin.math.abs(lu[k, i])
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if (absA > maxA) {
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maxA = absA
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iMax = k
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}
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}
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//todo check singularity
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if (iMax != i) {
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val j = pivot[i]
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pivot[i] = pivot[iMax]
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pivot[iMax] = j
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for (k in 0 until m) {
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val tmp = lu[i, k]
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lu[i, k] = lu[iMax, k]
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lu[iMax, k] = tmp
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}
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}
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for (j in i + 1 until m) {
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lu[j, i] /= lu[i, i]
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for (k in i + 1 until m) {
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lu[j, k] -= lu[j, i] * lu[i, k]
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}
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}
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}
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lu.makeStep()
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pivot.makeStep()
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}
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return Pair(luTensor, pivotsTensor)
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}
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override fun luPivot(lu: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
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TODO()
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/*
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// todo checks
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val n = lu.shape[0]
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val p = lu.zeroesLike()
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@ -97,9 +112,12 @@ public class RealLinearOpsTensorAlgebra :
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}
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}
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}
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return Triple(p, l, u)
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return Triple(p, l, u)*/
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}
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override fun RealTensor.det(): RealTensor {
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TODO("Not yet implemented")
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}
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@ -127,5 +145,5 @@ public class RealLinearOpsTensorAlgebra :
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}
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public inline fun <R> RealLinearOpsTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
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public inline fun <R> RealLinearOpsTensorAlgebra(block: RealLinearOpsTensorAlgebra.() -> R): R =
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RealLinearOpsTensorAlgebra().block()
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