Implementation of the randomized SVD policy. More...

Public Member Functions
	RandomizedSVDPolicy (const size_t iteratedPower=0, const size_t maxIterations=2)
	Use randomized SVD method to perform the principal components analysis (PCA). More...

void	Apply (const arma::mat &data, const arma::mat &centeredData, arma::mat &transformedData, arma::vec &eigVal, arma::mat &eigvec, const size_t rank)
	Apply Principal Component Analysis to the provided data set using the randomized SVD. More...

size_t	IteratedPower () const
	Get the size of the normalized power iterations. More...

size_t &	IteratedPower ()
	Modify the size of the normalized power iterations. More...

size_t	MaxIterations () const
	Get the number of iterations for the power method. More...

size_t &	MaxIterations ()
	Modify the number of iterations for the power method. More...

Private Attributes
size_t	iteratedPower
	Locally stored size of the normalized power iterations. More...

size_t	maxIterations
	Locally stored number of iterations for the power method. More...

Detailed Description

Implementation of the randomized SVD policy.

Definition at line 27 of file randomized_svd_method.hpp.

Constructor & Destructor Documentation

mlpack::pca::RandomizedSVDPolicy::RandomizedSVDPolicy	(	const size_t	iteratedPower = `0`,
		const size_t	maxIterations = `2`
	)

inline

Use randomized SVD method to perform the principal components analysis (PCA).

Parameters

iteratedPower	Size of the normalized power iterations (Default: rank + 2).
maxIterations	Number of iterations for the power method (Default: 2).

Definition at line 39 of file randomized_svd_method.hpp.

void mlpack::pca::RandomizedSVDPolicy::Apply	(	const arma::mat &	data,
		const arma::mat &	centeredData,
		arma::mat &	transformedData,
		arma::vec &	eigVal,
		arma::mat &	eigvec,
		const size_t	rank
	)

inline

Apply Principal Component Analysis to the provided data set using the randomized SVD.

Parameters

data	Data matrix.
centeredData	Centered data matrix.
transformedData	Matrix to put results of PCA into.
eigVal	Vector to put eigenvalues into.
eigvec	Matrix to put eigenvectors (loadings) into.
rank	Rank of the decomposition.