forked from kscience/kmath
Drop unused functionality in TensorLinearStructure
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477e64e4d3
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@ -155,7 +155,7 @@ public object DoubleLinearOpsTensorAlgebra :
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* @return triple `(U, S, V)`.
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*/
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public fun Tensor<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
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val size = tensor.linearStructure.dim
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val size = tensor.dimension
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val commonShape = tensor.shape.sliceArray(0 until size - 2)
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val (n, m) = tensor.shape.sliceArray(size - 2 until size)
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val uTensor = zeros(commonShape + intArrayOf(min(n, m), n))
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@ -39,45 +39,20 @@ internal fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArra
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return res
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}
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internal fun stepIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
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val res = index.copyOf()
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var current = nDim - 1
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var carry = 0
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do {
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res[current]++
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if (res[current] >= shape[current]) {
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carry = 1
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res[current] = 0
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}
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current--
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} while (carry != 0 && current >= 0)
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return res
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}
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/**
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* This [Strides] implementation follows the last dimension first convention
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* For more information: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html
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*
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* @param shape the shape of the tensor.
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*/
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public class TensorLinearStructure(override val shape: IntArray) : Strides
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{
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public class TensorLinearStructure(override val shape: IntArray) : Strides {
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override val strides: IntArray
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get() = stridesFromShape(shape)
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override fun index(offset: Int): IntArray =
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indexFromOffset(offset, strides, shape.size)
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// TODO: documentation (Alya)
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public fun stepIndex(index: IntArray): IntArray =
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stepIndex(index, shape, shape.size)
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override val linearSize: Int
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get() = shape.reduce(Int::times)
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public val dim: Int
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get() = shape.size
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}
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