MLPACK  1.0.10
mlpack::pca::PCA Class Reference

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

Public Member Functions

 PCA (const bool scaleData=false)
 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) const
 Apply Principal Component Analysis to the provided data set. More...

 
void Apply (const arma::mat &data, arma::mat &transformedData, arma::vec &eigVal) const
 Apply Principal Component Analysis to the provided data set. More...

 
double Apply (arma::mat &data, const size_t newDimension) const
 Use PCA for dimensionality reduction on the given dataset. More...

 
double Apply (arma::mat &data, const int newDimension) const
 This overload is here to make sure int gets casted right to size_t. More...

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

 
std::string ToString () const
 

Private Attributes

bool scaleData
 Whether or not the data will be scaled by standard deviation when PCA is performed. More...

 

Detailed Description

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 38 of file pca.hpp.

Constructor & Destructor Documentation

◆ PCA()

mlpack::pca::PCA::PCA ( const bool  scaleData = false)

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

Parameters
scaleDataWhether or not to scale the data.

Member Function Documentation

◆ Apply() [1/5]

void mlpack::pca::PCA::Apply ( const arma::mat &  data,
arma::mat &  transformedData,
arma::vec &  eigval,
arma::mat &  eigvec 
) const

Apply Principal Component Analysis to the provided data set.

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

Parameters
dataData matrix.
transformedDataMatrix to put results of PCA into.
eigvalVector to put eigenvalues into.
eigvecMatrix to put eigenvectors (loadings) into.

Referenced by Apply().

◆ Apply() [2/5]

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

Apply Principal Component Analysis to the provided data set.

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

Parameters
dataData matrix.
transformedDataMatrix to store results of PCA in.
eigvalVector to put eigenvalues into.

◆ Apply() [3/5]

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

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
dataData matrix.
newDimensionNew dimension of the data.
Returns
Amount of the variance of the data retained (between 0 and 1).

◆ Apply() [4/5]

double mlpack::pca::PCA::Apply ( arma::mat &  data,
const int  newDimension 
) const
inline

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

Definition at line 89 of file pca.hpp.

References Apply().

◆ Apply() [5/5]

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

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
dataData matrix.
varRetainedLower 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 mlpack::pca::PCA::ScaleData ( ) const
inline

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

Definition at line 113 of file pca.hpp.

References scaleData.

◆ ScaleData() [2/2]

bool& mlpack::pca::PCA::ScaleData ( )
inline

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

Definition at line 116 of file pca.hpp.

References scaleData, and ToString().

◆ ToString()

std::string mlpack::pca::PCA::ToString ( ) const

Referenced by ScaleData().

Member Data Documentation

◆ scaleData

bool mlpack::pca::PCA::scaleData
private

Whether or not the data will be scaled by standard deviation when PCA is performed.

Definition at line 124 of file pca.hpp.

Referenced by ScaleData().


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