forked from kscience/kmath
More basic functionality, tests to come
This commit is contained in:
Roland Grinis
parent
7f8914d8ea
commit
b59e48410f
@ -5,14 +5,107 @@
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package space.kscience.kmath.tensors.api
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/**
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* Element-wise analytic operations on [Tensor].
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* Analytic operations on [Tensor].
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*
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* @param T the type of items closed under analytic functions in the tensors.
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*/
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public interface AnalyticTensorAlgebra<T> :
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TensorPartialDivisionAlgebra<T> {
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/**
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* @return the minimum value of all elements in the input tensor.
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*/
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public fun Tensor<T>.min(): T
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/**
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* Returns the minimum value of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the minimum value of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.min(dim: Int, keepDim: Boolean): Tensor<T>
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/**
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* @return the maximum value of all elements in the input tensor.
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*/
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public fun Tensor<T>.max(): T
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/**
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* Returns the maximum value of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the maximum value of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T>
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/**
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* @return the mean of all elements in the input tensor.
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*/
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public fun Tensor<T>.mean(): T
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/**
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* Returns the mean of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the mean of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.mean(dim: Int, keepDim: Boolean): Tensor<T>
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/**
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* @return the standard deviation of all elements in the input tensor.
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*/
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public fun Tensor<T>.std(): T
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/**
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* Returns the standard deviation of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the standard deviation of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.std(dim: Int, keepDim: Boolean): Tensor<T>
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/**
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* @return the variance of all elements in the input tensor.
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*/
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public fun Tensor<T>.variance(): T
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/**
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* Returns the variance of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the variance of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.variance(dim: Int, keepDim: Boolean): Tensor<T>
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//For information: https://pytorch.org/docs/stable/generated/torch.exp.html
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public fun Tensor<T>.exp(): Tensor<T>
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@ -1,124 +0,0 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.tensors.api
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import space.kscience.kmath.tensors.core.DoubleTensor
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/**
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* Common algebra with statistics methods. Operates on [Tensor].
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*/
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public interface StatisticTensorAlgebra<T>: TensorAlgebra<T> {
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/**
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* Returns the minimum value of all elements in the input tensor.
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*/
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public fun Tensor<T>.min(): Double
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/**
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* Returns the minimum value of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the minimum value of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.min(dim: Int, keepDim: Boolean): DoubleTensor
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/**
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* Returns the maximum value of all elements in the input tensor.
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*/
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public fun Tensor<T>.max(): Double
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/**
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* Returns the maximum value of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the maximum value of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.max(dim: Int, keepDim: Boolean): DoubleTensor
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/**
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* Returns the sum of all elements in the input tensor.
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*/
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public fun Tensor<T>.sum(): Double
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/**
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* Returns the sum of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the sum of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.sum(dim: Int, keepDim: Boolean): DoubleTensor
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/**
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* Returns the mean of all elements in the input tensor.
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*/
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public fun Tensor<T>.mean(): Double
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/**
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* Returns the mean of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the mean of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.mean(dim: Int, keepDim: Boolean): DoubleTensor
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/**
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* Returns the standard deviation of all elements in the input tensor.
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*/
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public fun Tensor<T>.std(): Double
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/**
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* Returns the standard deviation of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the standard deviation of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.std(dim: Int, keepDim: Boolean): DoubleTensor
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/**
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* Returns the variance of all elements in the input tensor.
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*/
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public fun Tensor<T>.variance(): Double
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/**
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* Returns the variance of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the variance of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.variance(dim: Int, keepDim: Boolean): DoubleTensor
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}
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@ -251,4 +251,24 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
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dim2: Int = -1
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): Tensor<T>
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/**
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* @return the sum of all elements in the input tensor.
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*/
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public fun Tensor<T>.sum(): T
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/**
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* Returns the sum of each row of the input tensor in the given dimension [dim].
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*
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* If [keepDim] is true, the output tensor is of the same size as
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* input except in the dimension [dim] where it is of size 1.
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* Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension.
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*
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* @param dim the dimension to reduce.
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* @param keepDim whether the output tensor has [dim] retained or not.
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* @return the sum of each row of the input tensor in the given dimension [dim].
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*/
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public fun Tensor<T>.sum(dim: Int, keepDim: Boolean): Tensor<T>
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}
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@ -14,6 +14,60 @@ import kotlin.math.*
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public object DoubleAnalyticTensorAlgebra :
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AnalyticTensorAlgebra<Double>,
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DoubleTensorAlgebra() {
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override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
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override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim({ x -> x.minOrNull()!! }, dim, keepDim)
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override fun Tensor<Double>.max(): Double = this.fold { it.maxOrNull()!! }
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override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
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override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
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override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim(
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{ arr ->
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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arr.sum() / shape[dim]
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},
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dim,
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keepDim
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)
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override fun Tensor<Double>.std(): Double = this.fold { arr ->
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val mean = arr.sum() / tensor.numElements
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sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
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}
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override fun Tensor<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
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{ arr ->
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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val mean = arr.sum() / shape[dim]
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sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
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},
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dim,
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keepDim
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)
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override fun Tensor<Double>.variance(): Double = this.fold { arr ->
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val mean = arr.sum() / tensor.numElements
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arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
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}
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override fun Tensor<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
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{ arr ->
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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val mean = arr.sum() / shape[dim]
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arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
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},
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dim,
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keepDim
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)
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override fun Tensor<Double>.exp(): DoubleTensor = tensor.map(::exp)
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override fun Tensor<Double>.log(): DoubleTensor = tensor.map(::ln)
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@ -1,104 +0,0 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.tensors.core.algebras
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import kotlin.math.sqrt
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import space.kscience.kmath.tensors.api.*
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import space.kscience.kmath.tensors.core.*
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import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.max
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import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.mean
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import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.min
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import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.sum
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import space.kscience.kmath.tensors.core.algebras.DoubleStatisticTensorAlgebra.variance
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public object DoubleStatisticTensorAlgebra : StatisticTensorAlgebra<Double>, DoubleTensorAlgebra() {
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private fun Tensor<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
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foldFunction(this.tensor.toDoubleArray())
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private fun Tensor<Double>.foldDim(
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foldFunction: (DoubleArray) -> Double,
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dim: Int,
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keepDim: Boolean
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): DoubleTensor {
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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val resShape = if (keepDim) {
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shape.take(dim).toIntArray() + intArrayOf(1) + shape.takeLast(dimension - dim - 1).toIntArray()
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} else {
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shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray()
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}
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val resNumElements = resShape.reduce(Int::times)
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val resTensor = DoubleTensor(resShape, DoubleArray(resNumElements) { 0.0 }, 0)
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for (index in resTensor.linearStructure.indices()) {
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val prefix = index.take(dim).toIntArray()
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val suffix = index.takeLast(dimension - dim - 1).toIntArray()
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resTensor[index] = foldFunction(DoubleArray(shape[dim]) { i ->
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this[prefix + intArrayOf(i) + suffix]
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})
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}
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return resTensor
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}
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override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
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override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim({ x -> x.minOrNull()!! }, dim, keepDim)
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override fun Tensor<Double>.max(): Double = this.fold { it.maxOrNull()!! }
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override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
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override fun Tensor<Double>.sum(): Double = this.fold { it.sum() }
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override fun Tensor<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim({ x -> x.sum() }, dim, keepDim)
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override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
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override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim(
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{ arr ->
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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arr.sum() / shape[dim]
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},
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dim,
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keepDim
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)
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override fun Tensor<Double>.std(): Double = this.fold { arr ->
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val mean = arr.sum() / tensor.numElements
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sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
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}
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override fun Tensor<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
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{ arr ->
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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val mean = arr.sum() / shape[dim]
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sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
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},
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dim,
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keepDim
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)
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override fun Tensor<Double>.variance(): Double = this.fold { arr ->
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val mean = arr.sum() / tensor.numElements
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arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
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}
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override fun Tensor<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
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{ arr ->
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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val mean = arr.sum() / shape[dim]
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arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
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},
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dim,
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keepDim
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)
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}
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@ -284,7 +284,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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val m1 = newThis.shape[newThis.shape.size - 1]
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val m2 = newOther.shape[newOther.shape.size - 2]
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val n = newOther.shape[newOther.shape.size - 1]
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check (m1 == m2) {
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check(m1 == m2) {
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throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
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}
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@ -403,7 +403,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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public fun stack(tensors: List<DoubleTensor>): DoubleTensor {
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val shape = tensors.firstOrNull()?.shape
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check(shape != null) { "Collection must have at least 1 element" }
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check(tensors.all { it.shape contentEquals shape }) {"Stacking tensors must have same shapes"}
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check(tensors.all { it.shape contentEquals shape }) { "Stacking tensors must have same shapes" }
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val resShape = intArrayOf(tensors.size) + shape
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val resBuffer = tensors.flatMap {
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it.tensor.mutableBuffer.array().drop(it.bufferStart).take(it.numElements)
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@ -415,4 +415,36 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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public fun Tensor<Double>.rowsByIndices(indices: IntArray): DoubleTensor {
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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)
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user