forked from kscience/kmath
add documentation to DoubleTensorAlgebra
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@ -23,6 +23,9 @@ import space.kscience.kmath.tensors.core.getRandomNormals
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import space.kscience.kmath.tensors.core.minusIndexFrom
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import kotlin.math.abs
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/**
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* Implementation of basic operations over double tensors and basic algebra operations on them.
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*/
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public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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public companion object : DoubleTensorAlgebra()
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@ -34,6 +37,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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return tensor.mutableBuffer.array()[tensor.bufferStart]
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}
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/**
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* Constructs a tensor with the specified shape and data.
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*
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* @param shape the desired shape for the tensor.
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* @param buffer one-dimensional data array.
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* @return tensor with the [shape] shape and [buffer] data.
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*/
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public fun fromArray(shape: IntArray, buffer: DoubleArray): DoubleTensor {
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checkEmptyShape(shape)
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checkEmptyDoubleBuffer(buffer)
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@ -48,26 +58,67 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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return DoubleTensor(newShape, tensor.mutableBuffer.array(), newStart)
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}
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/**
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* Creates a tensor of a given shape and fills all elements with a given value.
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*
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* @param value the value to fill the output tensor with.
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* @param shape array of integers defining the shape of the output tensor.
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* @return tensor with the [shape] shape and filled with [value].
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*/
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public fun full(value: Double, shape: IntArray): DoubleTensor {
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checkEmptyShape(shape)
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val buffer = DoubleArray(shape.reduce(Int::times)) { value }
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return DoubleTensor(shape, buffer)
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}
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/**
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* Returns a tensor with the same shape as `input` filled with [value].
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*
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* @param value the value to fill the output tensor with.
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* @return tensor with the `input` tensor shape and filled with [value].
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*/
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public fun Tensor<Double>.fullLike(value: Double): DoubleTensor {
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val shape = tensor.shape
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val buffer = DoubleArray(tensor.numElements) { value }
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return DoubleTensor(shape, buffer)
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}
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/**
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* Returns a tensor filled with the scalar value 0.0, with the shape defined by the variable argument [shape].
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*
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* @param shape array of integers defining the shape of the output tensor.
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* @return tensor filled with the scalar value 0.0, with the [shape] shape.
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*/
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public fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
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/**
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* Returns a tensor filled with the scalar value 0.0, with the same shape as a given array.
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*
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* @return tensor filled with the scalar value 0.0, with the same shape as `input` tensor.
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*/
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public fun Tensor<Double>.zeroesLike(): DoubleTensor = tensor.fullLike(0.0)
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/**
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* Returns a tensor filled with the scalar value 1.0, with the shape defined by the variable argument [shape].
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*
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* @param shape array of integers defining the shape of the output tensor.
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* @return tensor filled with the scalar value 1.0, with the [shape] shape.
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*/
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public fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
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/**
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* Returns a tensor filled with the scalar value 1.0, with the same shape as a given array.
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*
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* @return tensor filled with the scalar value 1.0, with the same shape as `input` tensor.
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*/
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public fun Tensor<Double>.onesLike(): DoubleTensor = tensor.fullLike(1.0)
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/**
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* Returns a 2-D tensor with shape ([n], [n]), with ones on the diagonal and zeros elsewhere.
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*
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* @param n the number of rows and columns
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* @return a 2-D tensor with ones on the diagonal and zeros elsewhere.
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*/
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public fun eye(n: Int): DoubleTensor {
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val shape = intArrayOf(n, n)
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val buffer = DoubleArray(n * n) { 0.0 }
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@ -78,6 +129,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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return res
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}
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/**
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* Return a copy of the tensor.
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*
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* @return a copy of the `input` tensor with a copied buffer.
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*/
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public fun Tensor<Double>.copy(): DoubleTensor {
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return DoubleTensor(tensor.shape, tensor.mutableBuffer.array().copyOf(), tensor.bufferStart)
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}
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@ -359,7 +415,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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return resTensor.tensor
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}
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public fun Tensor<Double>.map(transform: (Double) -> Double): DoubleTensor {
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return DoubleTensor(
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tensor.shape,
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