theplu::yat::normalizer Namespace Reference

Normalization of data. More...

Classes
class	ColumnNormalizer
	Using a functor T to normalize each column. More...
class	Gauss
	Gaussian Normalizer. More...
class	qQuantileNormalizer
	Perform Q-quantile normalization. More...
class	QuantileNormalizer
	Perform quantile normalization. More...
class	QuantileNormalizer2
	Perform quantile normalization. More...
class	RangeNormalizer
class	RowNormalizer
	Using a functor T to normalize each column. More...
class	Spearman
	Replace elements with normalized rank. More...
class	Zscore
	Zero mean and unity variance. More...

Typedefs
template<typename T = statistics::Average>
using	Centralizer = RangeNormalizer< T, std::minus< double > >
	Centralize a range. More...
using	UnityScaler = RangeNormalizer< detail::UnityScalerFactor, std::multiplies< double > >
	Scale a range to unity. More...

Detailed Description

Normalization of data.

Typedef Documentation

Centralizer

template<typename T = statistics::Average>

using theplu::yat::normalizer::Centralizer = typedef RangeNormalizer<T, std::minus<double> >

Centralize a range.

The class centralizes a range [first, last) in two steps. First, the center value is calculaterd using the functor UnaryFunction to calculate the center. Second, the center value is subtracted from each element in range [first, last) . UnaryFunction must be a functor that has an operator:

return_type operator()(InputIterator, InputIterator) const

where return_type must be convertible to value_type of InputIterator. By default the center value is calculated as the arithmetic mean via class statistics::Average, but this can be changed using an alternative functor such as statistics::Percentiler.

Since

New in yat 0.5

UnityScaler

using theplu::yat::normalizer::UnityScaler = typedef RangeNormalizer<detail::UnityScalerFactor, std::multiplies<double> >

Scale a range to unity.

The sum of input range is calculated. If the input range is unweighted: ; if the input range is weighted: and the data value in the result range is calculated as the result[i] = input[i] / sum . Consequently, the sum of elements in the resulting range is unity, except in the case when the input range is weighted and the result range is unweighed the information about the weights gets lost.

Since

New in yat 0.18