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complete lu and matrix mapping

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This commit is contained in:
Andrei Kislitsyn 2021-03-17 17:53:14 +03:00
parent efb23591a9
commit 1fa0da2810
3 changed files with 85 additions and 95 deletions
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95
kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/BufferedTensor.kt
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@ -1,5 +1,7 @@
package space.kscience.kmath.tensors
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.nd.*
import space.kscience.kmath.structures.*
@ -29,59 +31,50 @@ public open class BufferedTensor<T>(
override fun hashCode(): Int = 0
// todo rename to vector
public inline fun forEachVector(vectorAction : (MutableStructure1D<T>) -> Unit): Unit {
check(shape.size >= 1) {"todo"}
val vectorOffset = strides.strides[0]
val vectorShape = intArrayOf(shape.last())
for (offset in 0 until numel step vectorOffset) {
val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
vectorAction(vector)
}
}
public inline fun forEachMatrix(matrixAction : (MutableStructure2D<T>) -> Unit): Unit {
check(shape.size >= 2) {"todo"}
val matrixOffset = strides.strides[1]
val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
for (offset in 0 until numel step matrixOffset) {
val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
matrixAction(matrix)
}
}
// todo remove code copy-pasting
public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
check(shape.size >= 1) {"todo"}
val vectorOffset = strides.strides[0]
val vectorShape = intArrayOf(shape.last())
for (offset in 0 until numel step vectorOffset) {
val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
yield(vector)
}
}
public fun matrixSequence(): Sequence<MutableStructure2D<T>> = sequence {
check(shape.size >= 2) {"todo"}
val matrixOffset = strides.strides[1]
val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
for (offset in 0 until numel step matrixOffset) {
val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
yield(matrix)
}
}
}
/*
//todo make generator mb nextMatrixIndex?
public class InnerMatrix<T>(private val tensor: BufferedTensor<T>){
private var offset: Int = 0
private val n : Int = tensor.shape.size
//stride?
private val step = tensor.shape[n - 1] * tensor.shape[n - 2]
public operator fun get(i: Int, j: Int): T {
val index = tensor.strides.index(offset)
index[n - 2] = i
index[n - 1] = j
return tensor[index]
}
public operator fun set(i: Int, j: Int, value: T): Unit {
val index = tensor.strides.index(offset)
index[n - 2] = i
index[n - 1] = j
tensor[index] = value
}
public fun makeStep(){
offset += step
}
}
public class InnerVector<T>(private val tensor: BufferedTensor<T>){
private var offset: Int = 0
private val n : Int = tensor.shape.size
//stride?
private val step = tensor.shape[n - 1]
public operator fun get(i: Int): T {
val index = tensor.strides.index(offset)
index[n - 1] = i
return tensor[index]
}
public operator fun set(i: Int, value: T): Unit {
val index = tensor.strides.index(offset)
index[n - 1] = i
tensor[index] = value
}
public fun makeStep(){
offset += step
}
}
//todo default buffer = arrayOf(0)???
*/
public class IntTensor(
shape: IntArray,

63
kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleLinearOpsTensorAlgebra.kt
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@ -9,27 +9,21 @@ public class DoubleLinearOpsTensorAlgebra :
}
override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
/*
// todo checks
val luTensor = this.copy()
val lu = InnerMatrix(luTensor)
//stride TODO!!! move to generator?
var matCnt = 1
for (i in 0 until this.shape.size - 2) {
matCnt *= this.shape[i]
}
val n = this.shape.size
val m = this.shape[n - 1]
val pivotsShape = IntArray(n - 1) { i ->
this.shape[i]
}
val m = this.shape.last()
val pivotsShape = IntArray(n - 1) { i -> this.shape[i] }
val pivotsTensor = IntTensor(
pivotsShape,
IntArray(matCnt * m) { 0 }
IntArray(pivotsShape.reduce(Int::times)) { 0 } //todo default???
)
val pivot = InnerVector(pivotsTensor)
for (i in 0 until matCnt) {
for (row in 0 until m) pivot[row] = row
for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
for (row in 0 until m) pivots[row] = row
for (i in 0 until m) {
var maxA = -1.0
@ -47,9 +41,9 @@ public class DoubleLinearOpsTensorAlgebra :
if (iMax != i) {
val j = pivot[i]
pivot[i] = pivot[iMax]
pivot[iMax] = j
val j = pivots[i]
pivots[i] = pivots[iMax]
pivots[iMax] = j
for (k in 0 until m) {
val tmp = lu[i, k]
@ -66,26 +60,28 @@ public class DoubleLinearOpsTensorAlgebra :
}
}
}
lu.makeStep()
pivot.makeStep()
}
return Pair(luTensor, pivotsTensor)*/
TODO("Andrei, use view, get, as2D, as1D")
return Pair(luTensor, pivotsTensor)
}
override fun luPivot(lu: DoubleTensor, pivots: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
/*
// todo checks
val n = lu.shape[0]
val p = lu.zeroesLike()
pivots.buffer.unsafeToIntArray().forEachIndexed { i, pivot ->
p[i, pivot] = 1.0
override fun luPivot(luTensor: DoubleTensor, pivotsTensor: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
//todo checks
val n = luTensor.shape.last()
val pTensor = luTensor.zeroesLike()
for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
for (i in 0 until n){
p[i, pivot[i]] = 1.0
}
}
val l = lu.zeroesLike()
val u = lu.zeroesLike()
val lTensor = luTensor.zeroesLike()
val uTensor = luTensor.zeroesLike()
for ((pairLU, lu) in lTensor.matrixSequence().zip(uTensor.matrixSequence()).zip(luTensor.matrixSequence())){
val (l, u) = pairLU
for (i in 0 until n) {
for (j in 0 until n) {
if (i == j) {
@ -99,9 +95,10 @@ public class DoubleLinearOpsTensorAlgebra :
}
}
}
}
return Triple(pTensor, lTensor, uTensor)
return Triple(p, l, u)*/
TODO("Andrei, first we need implement get(Int)")
}
override fun DoubleTensor.cholesky(): DoubleTensor {

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt
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@ -5,7 +5,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
public fun zeros(shape: IntArray): TensorType
public fun TensorType.zeroesLike(): TensorType
public fun TensorType.zeroesLike(): TensorType // mb it shouldn't be tensor but algebra method (like in numpy/torch) ?
public fun ones(shape: IntArray): TensorType
public fun TensorType.onesLike(): TensorType