basic implements & refactor tensor structure & and new tensor algebra methods
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90f4ff06fd
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@ -3,6 +3,8 @@ package space.kscience.kmath.tensors
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import space.kscience.kmath.nd.MutableNDBuffer
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import space.kscience.kmath.structures.RealBuffer
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import space.kscience.kmath.structures.array
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import kotlin.js.JsName
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import kotlin.math.abs
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public class RealTensor(
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@ -13,46 +15,80 @@ public class RealTensor(
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MutableNDBuffer<Double>(
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TensorStrides(shape),
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RealBuffer(buffer)
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) {
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/*
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* TODO: Andrei remove item()
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*/
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override fun item(): Double {
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check(buffer.size > 0) { "The tensor is empty" }
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return buffer[0]
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}
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}
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)
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public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
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//rename to item?
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override fun RealTensor.value(): Double {
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TODO("Andrei")
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check(this.dimension == 0) {
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// todo change message
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"This tensor has shape ${shape.toList()}"
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}
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return this.buffer.array[0]
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}
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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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val res = RealTensor(shape, buffer)
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for (i in 0 until n) {
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res[intArrayOf(i, i)] = 1.0
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}
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return res
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}
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override fun zeros(shape: IntArray): RealTensor {
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TODO("Not yet implemented")
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}
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override fun zeroesLike(other: RealTensor): RealTensor {
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TODO("Not yet implemented")
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}
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override fun ones(shape: IntArray): RealTensor {
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TODO("Not yet implemented")
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}
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override fun onesLike(shape: IntArray): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.copy(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun Double.plus(other: RealTensor): RealTensor {
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val n = other.buffer.size
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val arr = other.buffer.array
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val res = DoubleArray(n)
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for (i in 1..n)
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res[i - 1] = arr[i - 1] + this
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return RealTensor(other.shape, res)
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//todo should be change with broadcasting
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val resBuffer = DoubleArray(other.buffer.size) { i ->
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other.buffer.array[i] + this
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}
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return RealTensor(other.shape, resBuffer)
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}
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override fun RealTensor.plus(value: Double): RealTensor {
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TODO("Andrei")
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}
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//todo should be change with broadcasting
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override fun RealTensor.plus(value: Double): RealTensor = value + this
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override fun RealTensor.plus(other: RealTensor): RealTensor {
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TODO("Andrei")
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//todo should be change with broadcasting
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val resBuffer = DoubleArray(this.buffer.size) { i ->
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this.buffer.array[i] + other.buffer.array[i]
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}
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return RealTensor(this.shape, resBuffer)
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}
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override fun RealTensor.plusAssign(value: Double) {
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TODO("Andrei")
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//todo should be change with broadcasting
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for (i in this.buffer.array.indices) {
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this.buffer.array[i] += value
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}
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}
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override fun RealTensor.plusAssign(other: RealTensor) {
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TODO("Andrei")
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//todo should be change with broadcasting
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for (i in this.buffer.array.indices) {
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this.buffer.array[i] += other.buffer.array[i]
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}
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}
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override fun Double.minus(other: RealTensor): RealTensor {
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@ -76,27 +112,43 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
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}
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override fun Double.times(other: RealTensor): RealTensor {
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TODO("Andrei")
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//todo should be change with broadcasting
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val resBuffer = DoubleArray(other.buffer.size) { i ->
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other.buffer.array[i] * this
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}
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return RealTensor(other.shape, resBuffer)
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}
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override fun RealTensor.times(value: Double): RealTensor {
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TODO("Andrei")
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}
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//todo should be change with broadcasting
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override fun RealTensor.times(value: Double): RealTensor = value * this
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override fun RealTensor.times(other: RealTensor): RealTensor {
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TODO("Andrei")
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//todo should be change with broadcasting
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val resBuffer = DoubleArray(this.buffer.size) { i ->
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this.buffer.array[i] * other.buffer.array[i]
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}
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return RealTensor(this.shape, resBuffer)
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}
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override fun RealTensor.timesAssign(value: Double) {
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TODO("Andrei")
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//todo should be change with broadcasting
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for (i in this.buffer.array.indices) {
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this.buffer.array[i] *= value
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}
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}
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override fun RealTensor.timesAssign(other: RealTensor) {
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TODO("Andrei")
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//todo should be change with broadcasting
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for (i in this.buffer.array.indices) {
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this.buffer.array[i] *= other.buffer.array[i]
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}
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}
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override fun RealTensor.unaryMinus(): RealTensor {
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TODO("Andrei")
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val resBuffer = DoubleArray(this.buffer.size) { i ->
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this.buffer.array[i].unaryMinus()
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}
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return RealTensor(this.shape, resBuffer)
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}
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override fun RealTensor.dot(other: RealTensor): RealTensor {
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@ -124,11 +176,11 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
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}
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override fun RealTensor.view(shape: IntArray): RealTensor {
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TODO("Andrei")
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return RealTensor(shape, this.buffer.array)
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}
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override fun RealTensor.view_as(other: RealTensor): RealTensor {
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TODO("Andrei")
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override fun RealTensor.viewAs(other: RealTensor): RealTensor {
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return this.view(other.shape)
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}
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override fun RealTensor.abs(): RealTensor {
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@ -147,10 +199,18 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
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TODO("Not yet implemented")
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}
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override fun RealTensor.div(value: Double): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.div(other: RealTensor): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.divAssign(value: Double) {
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TODO("Not yet implemented")
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}
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override fun RealTensor.divAssign(other: RealTensor) {
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TODO("Not yet implemented")
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}
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@ -172,15 +232,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
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}
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override fun RealTensor.lu(): Pair<RealTensor, RealTensor> {
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/**
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* Main first task for @AndreiKingsley
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* Compare with the implementation of [LupDecomposition]
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* and provide a common API
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*/
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TODO("Not yet implemented")
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TODO()
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}
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override fun lu_unpack(A_LU: RealTensor, pivots: RealTensor): Triple<RealTensor, RealTensor, RealTensor> {
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override fun luUnpack(A_LU: RealTensor, pivots: RealTensor): Triple<RealTensor, RealTensor, RealTensor> {
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TODO("Not yet implemented")
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}
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@ -5,6 +5,14 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
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public fun TensorType.value(): T
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public fun eye(n: Int): TensorType
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public fun zeros(shape: IntArray): TensorType
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public fun zeroesLike(other: TensorType): TensorType
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public fun ones(shape: IntArray): TensorType
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public fun onesLike(shape: IntArray): TensorType
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public fun TensorType.copy(): TensorType
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public operator fun T.plus(other: TensorType): TensorType
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public operator fun TensorType.plus(value: T): TensorType
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public operator fun TensorType.plus(other: TensorType): TensorType
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@ -42,7 +50,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
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//https://pytorch.org/docs/stable/tensor_view.html
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public fun TensorType.view(shape: IntArray): TensorType
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public fun TensorType.view_as(other: TensorType): TensorType
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public fun TensorType.viewAs(other: TensorType): TensorType
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//https://pytorch.org/docs/stable/generated/torch.abs.html
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public fun TensorType.abs(): TensorType
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@ -57,7 +65,9 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
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public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
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TensorAlgebra<T, TensorType> {
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public operator fun TensorType.div(value: T): TensorType
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public operator fun TensorType.div(other: TensorType): TensorType
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public operator fun TensorType.divAssign(value: T)
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public operator fun TensorType.divAssign(other: TensorType)
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//https://pytorch.org/docs/stable/generated/torch.exp.html
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@ -72,7 +82,7 @@ public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>
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public fun TensorType.lu(): Pair<TensorType, TensorType>
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//https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
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public fun lu_unpack(A_LU: TensorType, pivots: TensorType): Triple<TensorType, TensorType, TensorType>
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public fun luUnpack(A_LU: TensorType, pivots: TensorType): Triple<TensorType, TensorType, TensorType>
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//https://pytorch.org/docs/stable/generated/torch.svd.html
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public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
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@ -5,7 +5,7 @@ import space.kscience.kmath.nd.offsetFromIndex
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import kotlin.math.max
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inline public fun stridesFromShape(shape: IntArray): IntArray {
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public inline fun stridesFromShape(shape: IntArray): IntArray {
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val nDim = shape.size
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val res = IntArray(nDim)
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if (nDim == 0)
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@ -22,7 +22,7 @@ inline public fun stridesFromShape(shape: IntArray): IntArray {
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}
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inline public fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {
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public inline fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {
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val res = IntArray(nDim)
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var current = offset
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var strideIndex = 0
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@ -2,26 +2,5 @@ package space.kscience.kmath.tensors
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import space.kscience.kmath.nd.MutableNDStructure
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public interface TensorStructure<T> : MutableNDStructure<T> {
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public typealias TensorStructure<T> = MutableNDStructure<T>
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/*
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* TODO: Andrei remove item() and value()
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*/
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public fun item(): T
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// A tensor can have empty shape, in which case it represents just a value
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public fun value(): T {
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checkIsValue()
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return item()
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}
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}
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public inline fun <T> TensorStructure<T>.isValue(): Boolean {
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return (dimension == 0)
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}
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public inline fun <T> TensorStructure<T>.isNotValue(): Boolean = !this.isValue()
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public inline fun <T> TensorStructure<T>.checkIsValue(): Unit = check(this.isValue()) {
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"This tensor has shape ${shape.toList()}"
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}
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