mir.stat.transform

This module contains algorithms for transforming data that are useful in statistical applications.


Function Name	Description
center	Subtracts the mean (or using some other function) from each element of a slice.
robustScale	Subtracts the median and divides by the difference between a lower and upper quantile from each element of a slice.
scale	Subtracts the mean (or using some other function) and divides by the standard deviation (or using some other function) from each element of a slice.
sweep	Applies a function and an operation to each element of a slice.
zscore	Subtracts the mean and divides by the standard deviation from each element of a slice.

Members

Functions

scale auto scale(Slice!(Iterator, N, kind) slice, T m, U d)
auto scale(SliceLike x, T m, U d)

Templates

center template center(alias centralTendency = mean!(Summation.appropriate)): Centers slice, which must be a finite iterable.
robustScale template robustScale(F, QuantileAlgo quantileAlgo = QuantileAlgo.type7, bool allowModifySlice = false): Scales input using robust statistics.
robustScale template robustScale(QuantileAlgo quantileAlgo = QuantileAlgo.type7, bool allowModifySlice = false)
template robustScale(F, string quantileAlgo, bool allowModifySlice = false)
template robustScale(string quantileAlgo, bool allowModifySlice = false)
template robustScale(bool allowModifySlice)
scale template scale(alias centralTendency = mean!(Summation.appropriate), alias dispersion = standardDeviation!(VarianceAlgo.hybrid, Summation.appropriate)): Scales the input.
sweep template sweep(alias fun, string op): For each e of the input, applies e op m where m is the result of fun and op is an operation, such as "+", "-", "*", or "/". For instance, if op = "-", then this function computes e - m for each e of the input and where m is the result of applying fun to the input. Overloads are provided to directly provide m to the function, rather than calculate it using fun.
sweep template sweep(string op)
zscore template zscore(F, VarianceAlgo varianceAlgo = VarianceAlgo.hybrid, Summation summation = Summation.appropriate)
template zscore(VarianceAlgo varianceAlgo = VarianceAlgo.hybrid, Summation summation = Summation.appropriate)
template zscore(F, string varianceAlgo, string summation = "appropriate")
template zscore(string varianceAlgo, string summation = "appropriate"): Computes the Z-score of the input.

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mir.stat.transform

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