Zero mean and unity variance. More...

#include <yat/normalizer/Zscore.h>

Public Member Functions
	Zscore (double k=1.0)

double	k (void) const

template<class InputIterator , class OutputIterator >
void	operator() (InputIterator first, InputIterator last, OutputIterator result) const

Detailed Description

Zero mean and unity variance.

Shift and scale the values in a range as: $y_i = \frac{x_i-m}{k*s}$ where m is the mean, s is the standard deviation, and k is set in constructor (default: 1.0). After normalization, the range will have zero mean and variance = $ 1/k^2 $ .

Type Requirements:

InputIterator is Data Iterator
InputIterator is Forward Traversal Iterator
OutputIterator is Writable Iterator
OutputIterator is Forward Traversal Iterator

Since: New in yat 0.5

Constructor & Destructor Documentation

◆ Zscore()

theplu::yat::normalizer::Zscore::Zscore ( double k = 1.0 )

explicit

Parameters

k	In the scaling step elements are divided by `k` times the standard deviation

Since: New in yat 0.19

Member Function Documentation

◆ k()

double theplu::yat::normalizer::Zscore::k ( void ) const

Returns: the k-factor set in constructor

Since: new in yat 0.19

◆ operator()()

template<class InputIterator , class OutputIterator >

void theplu::yat::normalizer::Zscore::operator()	(	InputIterator	first,
		InputIterator	last,
		OutputIterator	result
	)		const

inline

The element in range [result, result + (last-first)) is calculated as result[i] = (first[i] - m) / s where m and std are the mean and standard deviation, respectively, of the range [first, last).

It is possible to centralize a range "in place"; it is permissible for the iterators first and result to be the same.

See also: std::transform

The documentation for this class was generated from the following file:

yat/normalizer/Zscore.h