More basic functionality, tests to come

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt

@@ -5,14 +5,107 @@
 
 package space.kscience.kmath.tensors.api
 
+
 /**
- * Element-wise analytic operations on [Tensor].
+ * Analytic operations on [Tensor].
  *
  * @param T the type of items closed under analytic functions in the tensors.
  */
 public interface AnalyticTensorAlgebra<T> :
     TensorPartialDivisionAlgebra<T> {
 
+
+    /**
+     * @return the minimum value of all elements in the input tensor.
+     */
+    public fun Tensor<T>.min(): T
+
+    /**
+     * Returns the minimum value of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the minimum value of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.min(dim: Int, keepDim: Boolean): Tensor<T>
+
+    /**
+     * @return the maximum value of all elements in the input tensor.
+     */
+    public fun Tensor<T>.max(): T
+
+    /**
+     * Returns the maximum value of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the maximum value of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T>
+
+
+    /**
+     * @return the mean of all elements in the input tensor.
+     */
+    public fun Tensor<T>.mean(): T
+
+    /**
+     * Returns the mean of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the mean of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.mean(dim: Int, keepDim: Boolean): Tensor<T>
+
+    /**
+     * @return the standard deviation of all elements in the input tensor.
+     */
+    public fun Tensor<T>.std(): T
+
+    /**
+     * Returns the standard deviation of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the standard deviation of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.std(dim: Int, keepDim: Boolean): Tensor<T>
+
+    /**
+     * @return the variance of all elements in the input tensor.
+     */
+    public fun Tensor<T>.variance(): T
+
+    /**
+     * Returns the variance of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the variance of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.variance(dim: Int, keepDim: Boolean): Tensor<T>
+
     //For information: https://pytorch.org/docs/stable/generated/torch.exp.html
     public fun Tensor<T>.exp(): Tensor<T>
 

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/StatisticTensorAlgebra.kt

@@ -1,124 +0,0 @@
-/*
- * 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.api
-
-import space.kscience.kmath.tensors.core.DoubleTensor
-
-/**
- * Common algebra with statistics methods. Operates on [Tensor].
- */
-
-public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
-
-    /**
-     * Returns the minimum value of all elements in the input tensor.
-     */
-    public fun Tensor<T>.min(): Double
-
-    /**
-     * Returns the minimum value of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the minimum value of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.min(dim: Int, keepDim: Boolean): DoubleTensor
-
-    /**
-     * Returns the maximum value of all elements in the input tensor.
-     */
-    public fun Tensor<T>.max(): Double
-
-    /**
-     * Returns the maximum value of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the maximum value of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.max(dim: Int, keepDim: Boolean): DoubleTensor
-
-    /**
-     * Returns the sum of all elements in the input tensor.
-     */
-    public fun Tensor<T>.sum(): Double
-
-    /**
-     * Returns the sum of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the sum of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.sum(dim: Int, keepDim: Boolean): DoubleTensor
-
-    /**
-     * Returns the mean of all elements in the input tensor.
-     */
-    public fun Tensor<T>.mean(): Double
-
-    /**
-     * Returns the mean of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the mean of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.mean(dim: Int, keepDim: Boolean): DoubleTensor
-
-    /**
-     * Returns the standard deviation of all elements in the input tensor.
-     */
-    public fun Tensor<T>.std(): Double
-
-    /**
-     * Returns the standard deviation of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the standard deviation of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.std(dim: Int, keepDim: Boolean): DoubleTensor
-
-    /**
-     * Returns the variance of all elements in the input tensor.
-     */
-    public fun Tensor<T>.variance(): Double
-
-    /**
-     * Returns the variance of each row of the input tensor in the given dimension [dim].
-     *
-     * If [keepDim] is true, the output tensor is of the same size as
-     * input except in the dimension [dim] where it is of size 1.
-     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
-     *
-     * @param dim the dimension to reduce.
-     * @param keepDim whether the output tensor has [dim] retained or not.
-     * @return the variance of each row of the input tensor in the given dimension [dim].
-     */
-    public fun Tensor<T>.variance(dim: Int, keepDim: Boolean): DoubleTensor
-
-}

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt

@@ -251,4 +251,24 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
         dim2: Int = -1
     ): Tensor<T>
 
+    /**
+     * @return the sum of all elements in the input tensor.
+     */
+    public fun Tensor<T>.sum(): T
+
+    /**
+     * Returns the sum of each row of the input tensor in the given dimension [dim].
+     *
+     * If [keepDim] is true, the output tensor is of the same size as
+     * input except in the dimension [dim] where it is of size 1.
+     * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
+     *
+     * @param dim the dimension to reduce.
+     * @param keepDim whether the output tensor has [dim] retained or not.
+     * @return the sum of each row of the input tensor in the given dimension [dim].
+     */
+    public fun Tensor<T>.sum(dim: Int, keepDim: Boolean): Tensor<T>
+
+
+
 }

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleAnalyticTensorAlgebra.kt

@@ -14,6 +14,60 @@ import kotlin.math.*
 public object DoubleAnalyticTensorAlgebra :
     AnalyticTensorAlgebra<Double>,
     DoubleTensorAlgebra() {
+
+    override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
+
+    override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.minOrNull()!! }, dim, keepDim)
+
+    override fun Tensor<Double>.max(): Double = this.fold { it.maxOrNull()!! }
+
+    override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
+
+
+    override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
+
+    override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim(
+            { arr ->
+                check(dim < dimension) { "Dimension $dim out of range $dimension" }
+                arr.sum() / shape[dim]
+            },
+            dim,
+            keepDim
+        )
+
+    override fun Tensor<Double>.std(): Double = this.fold { arr ->
+        val mean = arr.sum() / tensor.numElements
+        sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
+    }
+
+    override fun Tensor<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
+        { arr ->
+            check(dim < dimension) { "Dimension $dim out of range $dimension" }
+            val mean = arr.sum() / shape[dim]
+            sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
+        },
+        dim,
+        keepDim
+    )
+
+    override fun Tensor<Double>.variance(): Double = this.fold { arr ->
+        val mean = arr.sum() / tensor.numElements
+        arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
+    }
+
+    override fun Tensor<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
+        { arr ->
+            check(dim < dimension) { "Dimension $dim out of range $dimension" }
+            val mean = arr.sum() / shape[dim]
+            arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
+        },
+        dim,
+        keepDim
+    )
+
     override fun Tensor<Double>.exp(): DoubleTensor = tensor.map(::exp)
 
     override fun Tensor<Double>.log(): DoubleTensor = tensor.map(::ln)

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleStatisticTensorAlgebra.kt

@@ -1,104 +0,0 @@
-/*
- * 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.core.algebras
-
-import kotlin.math.sqrt
-
-import space.kscience.kmath.tensors.api.*
-import space.kscience.kmath.tensors.core.*
-import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.max
-import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.mean
-import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.min
-import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.sum
-import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.variance
-
-public object DoubleStatisticTensorAlgebra : StatisticTensorAlgebra<Double>, DoubleTensorAlgebra() {
-
-    private fun Tensor<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
-        foldFunction(this.tensor.toDoubleArray())
-
-    private fun Tensor<Double>.foldDim(
-        foldFunction: (DoubleArray) -> Double,
-        dim: Int,
-        keepDim: Boolean
-    ): DoubleTensor {
-        check(dim < dimension) { "Dimension $dim out of range $dimension" }
-        val resShape = if (keepDim) {
-            shape.take(dim).toIntArray() + intArrayOf(1) + shape.takeLast(dimension - dim - 1).toIntArray()
-        } else {
-            shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray()
-        }
-        val resNumElements = resShape.reduce(Int::times)
-        val resTensor = DoubleTensor(resShape, DoubleArray(resNumElements) { 0.0 }, 0)
-        for (index in resTensor.linearStructure.indices()) {
-            val prefix = index.take(dim).toIntArray()
-            val suffix = index.takeLast(dimension - dim - 1).toIntArray()
-            resTensor[index] = foldFunction(DoubleArray(shape[dim]) { i ->
-                this[prefix + intArrayOf(i) + suffix]
-            })
-        }
-
-        return resTensor
-    }
-
-    override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
-
-    override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim({ x -> x.minOrNull()!! }, dim, keepDim)
-
-    override fun Tensor<Double>.max(): Double = this.fold { it.maxOrNull()!! }
-
-    override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
-
-    override fun Tensor<Double>.sum(): Double = this.fold { it.sum() }
-
-    override fun Tensor<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim({ x -> x.sum() }, dim, keepDim)
-
-    override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
-
-    override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
-        foldDim(
-            { arr ->
-                check(dim < dimension) { "Dimension $dim out of range $dimension" }
-                arr.sum() / shape[dim]
-            },
-            dim,
-            keepDim
-        )
-
-    override fun Tensor<Double>.std(): Double = this.fold { arr ->
-        val mean = arr.sum() / tensor.numElements
-        sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
-    }
-
-    override fun Tensor<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
-        { arr ->
-            check(dim < dimension) { "Dimension $dim out of range $dimension" }
-            val mean = arr.sum() / shape[dim]
-            sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
-        },
-        dim,
-        keepDim
-    )
-
-    override fun Tensor<Double>.variance(): Double = this.fold { arr ->
-        val mean = arr.sum() / tensor.numElements
-        arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
-    }
-
-    override fun Tensor<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
-        { arr ->
-            check(dim < dimension) { "Dimension $dim out of range $dimension" }
-            val mean = arr.sum() / shape[dim]
-            arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
-        },
-        dim,
-        keepDim
-    )
-
-}

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt

@@ -284,7 +284,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         val m1 = newThis.shape[newThis.shape.size - 1]
         val m2 = newOther.shape[newOther.shape.size - 2]
         val n = newOther.shape[newOther.shape.size - 1]
-        check (m1 == m2) {
+        check(m1 == m2) {
             throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
         }
 
@@ -403,7 +403,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     public fun stack(tensors: List<DoubleTensor>): DoubleTensor {
         val shape = tensors.firstOrNull()?.shape
         check(shape != null) { "Collection must have at least 1 element" }
-        check(tensors.all { it.shape contentEquals shape }) {"Stacking tensors must have same shapes"}
+        check(tensors.all { it.shape contentEquals shape }) { "Stacking tensors must have same shapes" }
         val resShape = intArrayOf(tensors.size) + shape
         val resBuffer = tensors.flatMap {
             it.tensor.mutableBuffer.array().drop(it.bufferStart).take(it.numElements)
@@ -415,4 +415,36 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     public fun Tensor<Double>.rowsByIndices(indices: IntArray): DoubleTensor {
         return stack(indices.map { this[it] })
     }
+
+    internal fun Tensor<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
+        foldFunction(tensor.toDoubleArray())
+
+    internal fun Tensor<Double>.foldDim(
+        foldFunction: (DoubleArray) -> Double,
+        dim: Int,
+        keepDim: Boolean
+    ): DoubleTensor {
+        check(dim < dimension) { "Dimension $dim out of range $dimension" }
+        val resShape = if (keepDim) {
+            shape.take(dim).toIntArray() + intArrayOf(1) + shape.takeLast(dimension - dim - 1).toIntArray()
+        } else {
+            shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray()
+        }
+        val resNumElements = resShape.reduce(Int::times)
+        val resTensor = DoubleTensor(resShape, DoubleArray(resNumElements) { 0.0 }, 0)
+        for (index in resTensor.linearStructure.indices()) {
+            val prefix = index.take(dim).toIntArray()
+            val suffix = index.takeLast(dimension - dim - 1).toIntArray()
+            resTensor[index] = foldFunction(DoubleArray(shape[dim]) { i ->
+                tensor[prefix + intArrayOf(i) + suffix]
+            })
+        }
+
+        return resTensor
+    }
+
+    override fun Tensor<Double>.sum(): Double = tensor.fold { it.sum() }
+
+    override fun Tensor<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
+        foldDim({ x -> x.sum() }, dim, keepDim)
 }