forked from kscience/kmath
Create object with factory method for generating RealNDElement.
This commit is contained in:
Mikhail Zelenyy
parent
2b4419823b
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36609827dd
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package scientifik.kmath.structures
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import scientifik.kmath.operations.RealField
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import scientifik.kmath.operations.RealField.power
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import kotlin.math.*
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object RealFactories{
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/**
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* Create a NDArray filled with ones
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*/
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fun ones(vararg shape: Int) = NDElement.real(shape){1.0}
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/**
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* Create a 2D NDArray, with ones on the diagonal and zeros elsewhere.
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*
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* @param offset Index of the diagonal: 0 (the default) refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal.
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*/
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fun eye(dim1: Int, dim2: Int, offset : Int = 0) = NDElement.real2D(dim1, dim2){i, j -> if (i == j + offset) 1.0 else 0.0}
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/**
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* An array with ones at and below the given diagonal and zeros elsewhere.
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* T[i,j] == 1 for i <= j + offset
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*
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* @param offset Index of the diagonal: 0 (the default) refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal.
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*/
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fun triangle(dim1: Int, dim2: Int, offset : Int = 0) = NDElement.real2D(dim1, dim2){i, j -> if (i <= j + offset) 1.0 else 0.0}
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/**
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* Return evenly spaced values within a given interval.
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*
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* Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop).
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* @param range use it like:
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* (start..stop) to step
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*/
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fun range(range : Pair<ClosedFloatingPointRange<Double>,Double>) = NDElement.real1D(ceil((range.first.endInclusive - range.first.start)/range.second).toInt()){i-> range.first.start + i*range.second}
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/**
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* Return evenly spaced numbers over a specified interval.
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* @param range use it like:
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* (start..stop) to number
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* start is starting value, finaly value depend from endPoint parameter
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* @param endPoint If True, right boundary of range is the last sample. Otherwise, it is not included.
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*/
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fun linspace(range : Pair<ClosedFloatingPointRange<Double>,Int>, endPoint: Boolean = true): Pair<RealNDElement, Double> {
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val div = if (endPoint) (range.second - 1) else range.second
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val delta = range.first.start - range.first.endInclusive
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if (range.second > 1){
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val step = delta/div
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if (step == 0.0){ error("Bad ranges: step = $step")}
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val result = NDElement.real1D(range.second){
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if ( endPoint and (it == range.second - 1) ){ range.first.endInclusive}
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range.first.start + it*step
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}
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return result to step
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}
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else{
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val step = Double.NaN
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return NDElement.real1D(1){range.first.start} to step
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}
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}
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/**
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* Return numbers spaced evenly on a log scale.
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* @param range use it like:
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* (start..stop) to number
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* power(base,start) is starting value, endvalue depend from endPoint parameter
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* @param endPoint If True, power(base,stop) is the last sample. Otherwise, it is not included.
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* @param base - The base of the log space.
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*/
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fun logspace(range : Pair<ClosedFloatingPointRange<Double>,Int>, endPoint: Boolean = true, base : Double = 10.0) : RealNDElement {
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val lin = linspace(range, endPoint).first
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val fun_ = {x: Double -> power(base, x)}
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return fun_(lin) // FIXME: RealNDElement.map return not suitable type ( `linspace(range, endPoint).first.map{power(base, it}`)
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}
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/**
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* Return numbers spaced evenly on a log scale (a geometric progression).
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*
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* This is similar to [logspace], but with endpoints specified directly. Each output sample is a constant multiple of the previous.
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* @param range use it like:
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* (start..stop) to number
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* start is starting value, finaly value depend from endPoint parameter
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* @param endPoint If True, right boundary of range is the last sample. Otherwise, it is not included.
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*/
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fun geomspace(range : Pair<ClosedFloatingPointRange<Double>,Int>, endPoint: Boolean = true) : RealNDElement{
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var start = range.first.start
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var stop = range.first.endInclusive
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val num = range.second
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if ( start == 0.0 || stop == 0.0){
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error("Geometric sequence cannot include zero")
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}
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var outSign = 1.0
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if (sign(start) == -1.0 && sign(stop) == -1.0){
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start = -start
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stop = -stop
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outSign = -outSign
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}
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val log_ = logspace((log(start, 10.0)..log(stop, 10.0) to num), endPoint=endPoint)
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val fun_ = {x:Double -> outSign*x}
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return fun_(log_) // FIXME: `outSign*log_` --- don't define times operator
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}
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/**
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* Return specified diagonals of 2D NDArray.
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*
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* @param offset Index of the diagonal: 0 (the default) refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal.
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*/
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fun extractDiagonal(array : RealNDElement, offset: Int = 0): RealNDElement{
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if (array.dimension != 2){
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error("Input must be 2D NDArray")}
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val size = min(array.shape[0], array.shape[0])
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if (offset>=0){
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return NDElement.real1D(size){i -> array[i, i+offset]}
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}
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else{
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return NDElement.real1D(size){i -> array[i-offset, i]}
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}
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}
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/**
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* Return a 2-D array with [array] on the [offset] diagonal.
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*
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* @param offset Index of the diagonal: 0 (the default) refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal.
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*/
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fun fromDiagonal(array : RealNDElement, offset: Int = 0): RealNDElement{
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if (array.dimension != 1){
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error("Input must be 1D NDArray")}
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val size = array.shape[0]
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if (offset>=0){
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return NDElement.real2D(size, size+offset){
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i, j -> if (i == j+offset) array[i] else 0.0
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}
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}
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else{
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return NDElement.real2D(size-offset, size){
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i, j -> if (i-offset == j) array[j] else 0.0
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}
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}
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}
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/**
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* Generate a [Vandermonde matrix](https://en.wikipedia.org/wiki/Vandermonde_matrix).
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*
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* @param nCols --- number of columns, as default using length of [array]
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* @param increasing --- Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed. FIXME: Default order like numpy
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*/
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fun vandermonde(array : RealNDElement, nCols: Int = 0, increasing: Boolean =false): RealNDElement{
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if (array.dimension != 1){
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error("Input must be 1D NDArray")}
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var size = if (nCols ==0) array.shape[0] else nCols
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if (increasing){
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return NDElement.real2D(array.shape[0], size){
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i, j -> power(array[i], j)
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}
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}else{
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return NDElement.real2D(array.shape[0], size){
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i, j -> power(array[i], size - j - 1)
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}
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}
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}
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}
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