PCA< DecompositionPolicy > Class Template Reference

This class implements principal components analysis (PCA). More...

Public Member Functions
	PCA (const bool scaleData=false, const DecompositionPolicy &decomposition=DecompositionPolicy())
	Create the PCA object, specifying if the data should be scaled in each dimension by standard deviation when PCA is performed. More...
void	Apply (const arma::mat &data, arma::mat &transformedData, arma::vec &eigVal, arma::mat &eigvec)
	Apply Principal Component Analysis to the provided data set. More...
void	Apply (const arma::mat &data, arma::mat &transformedData, arma::vec &eigVal)
	Apply Principal Component Analysis to the provided data set. More...
void	Apply (const arma::mat &data, arma::mat &transformedData)
	Apply Principal Component Analysis to the provided data set. More...
double	Apply (arma::mat &data, const size_t newDimension)
	Use PCA for dimensionality reduction on the given dataset. More...
double	Apply (arma::mat &data, const int newDimension)
	This overload is here to make sure int gets casted right to size_t. More...
double	Apply (arma::mat &data, const double varRetained)
	Use PCA for dimensionality reduction on the given dataset. More...
bool	ScaleData () const
	Get whether or not this PCA object will scale (by standard deviation) the data when PCA is performed. More...
bool &	ScaleData ()
	Modify whether or not this PCA object will scale (by standard deviation) the data when PCA is performed. More...

Detailed Description

template

<

typename

DecompositionPolicy

=

ExactSVDPolicy

>

class mlpack::pca::PCA< DecompositionPolicy >

This class implements principal components analysis (PCA).

This is a common, widely-used technique that is often used for either dimensionality reduction or transforming data into a better basis. Further information on PCA can be found in almost any statistics or machine learning textbook, and all over the internet.

Definition at line 33 of file pca.hpp.

Constructor & Destructor Documentation

PCA()

PCA	(	const bool	scaleData = false,
		const DecompositionPolicy &	decomposition = DecompositionPolicy()
	)

Create the PCA object, specifying if the data should be scaled in each dimension by standard deviation when PCA is performed.

Parameters

scaleData	Whether or not to scale the data.
decomposition	Decomposition policy to use.

Member Function Documentation

Apply() [1/6]

void Apply	(	const arma::mat &	data,
		arma::mat &	transformedData,
		arma::vec &	eigVal,
		arma::mat &	eigvec
	)

Apply Principal Component Analysis to the provided data set.

It is safe to pass the same matrix reference for both data and transformedData.

Parameters

data	Data matrix.
transformedData	Matrix to put results of PCA into.
eigVal	Vector to put eigenvalues into.
eigvec	Matrix to put eigenvectors (loadings) into.

Referenced by PCA< DecompositionPolicy >::Apply().

Apply() [2/6]

void Apply	(	const arma::mat &	data,
		arma::mat &	transformedData,
		arma::vec &	eigVal
	)

Apply Principal Component Analysis to the provided data set.

It is safe to pass the same matrix reference for both data and transformedData.

Parameters

data	Data matrix.
transformedData	Matrix to store results of PCA in.
eigVal	Vector to put eigenvalues into.

Apply() [3/6]

void Apply	(	const arma::mat &	data,
		arma::mat &	transformedData
	)

Apply Principal Component Analysis to the provided data set.

It is safe to pass the same matrix reference for both data and transformedData.

Parameters

data	Data matrix.
transformedData	Matrix to store results of PCA in.

Apply() [4/6]

double Apply	(	arma::mat &	data,
		const size_t	newDimension
	)

Use PCA for dimensionality reduction on the given dataset.

This will save the newDimension largest principal components of the data and remove the rest. The parameter returned is the amount of variance of the data that is retained; this is a value between 0 and 1. For instance, a value of 0.9 indicates that 90% of the variance present in the data was retained.

Parameters

data	Data matrix.
newDimension	New dimension of the data.

Returns

Amount of the variance of the data retained (between 0 and 1).

Apply() [5/6]

double Apply ( arma::mat &  data, const int  newDimension  )

inline

This overload is here to make sure int gets casted right to size_t.

Definition at line 94 of file pca.hpp.

References PCA< DecompositionPolicy >::Apply().

Apply() [6/6]

double Apply	(	arma::mat &	data,
		const double	varRetained
	)

Use PCA for dimensionality reduction on the given dataset.

This will save as many dimensions as necessary to retain at least the given amount of variance (specified by parameter varRetained). The amount should be between 0 and 1; if the amount is 0, then only 1 dimension will be retained. If the amount is 1, then all dimensions will be retained.

The method returns the actual amount of variance retained, which will always be greater than or equal to the varRetained parameter.

Parameters

data	Data matrix.
varRetained	Lower bound on amount of variance to retain; should be between 0 and 1.

Returns

Actual amount of variance retained (between 0 and 1).

ScaleData() [1/2]

bool ScaleData ( ) const

inline

Get whether or not this PCA object will scale (by standard deviation) the data when PCA is performed.

Definition at line 118 of file pca.hpp.

Referenced by PCA< DecompositionPolicy >::ScaleData().

ScaleData() [2/2]

bool& ScaleData ( )

inline

Modify whether or not this PCA object will scale (by standard deviation) the data when PCA is performed.

Definition at line 121 of file pca.hpp.

References PCA< DecompositionPolicy >::ScaleData().

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

• /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/pca/pca.hpp