PCA Class Reference

Principal Component Analysis. More...

#include <pca.h>

List of all members.

Public Member Functions

 PCA (double **data=0, int rows=0, int cols=0)
virtual ~PCA ()
 Destructor.
void LoadData (double **data, int rows, int cols)
void PerformPCA (bool center=true, bool scale=false)
void GetPCScores (Matrix< double > *scores)
void GetPCLoadings (Matrix< double > *loadings)
void GetEigenvalues (vector< double > *eigenvalues)
void GetMean (vector< double > *mean)
void GetVariance (vector< double > *var)
void GetCumulativeVariance (vector< double > *cumvar)
void GetEigenvectors (vector< vector< double > > *eigenvectors)
bool IsCalculated ()


Detailed Description

Principal Component Analysis.

Constructor & Destructor Documentation

PCA::PCA ( double **  data = 0,
int  rows = 0,
int  cols = 0 
)

Constructor

Parameters:
data the data coordinates
rows number of rows
cols number of columns
datasets number of datasets


Member Function Documentation

void PCA::GetCumulativeVariance ( vector< double > *  cumvar  ) 

Get the vector with cumulative variance explained by each eigenvalue

Returns:
the vector of cumulative variance

void PCA::GetEigenvalues ( vector< double > *  eigenvalues  ) 

Get the vector with eigenvalues

Returns:
the eigenvalues

void PCA::GetEigenvectors ( vector< vector< double > > *  eigenvectors  ) 

Get the vector with eigenvectors

Returns:
the eigenvectors

void PCA::GetMean ( vector< double > *  mean  ) 

Get the vector with mean values

Returns:
the vector of means

void PCA::GetPCLoadings ( Matrix< double > *  loadings  ) 

Get PC loadings

Returns:
the PC loadings

void PCA::GetPCScores ( Matrix< double > *  scores  ) 

Get PC scores

Returns:
the PC scores

void PCA::GetVariance ( vector< double > *  var  ) 

Get the vector with variance explained by each eigenvalue

Returns:
the vector of variance

bool PCA::IsCalculated (  ) 

Has PCA been performed

Returns:
true if PCA analysis has been performed on our data

void PCA::LoadData ( double **  data,
int  rows,
int  cols 
)

LoadData

Parameters:
data the data coordinates
rows number of rows
cols number of columns

void PCA::PerformPCA ( bool  center = true,
bool  scale = false 
)

Perform Principal Component Analysis on our data

Parameters:
center center data, subtract the mean of each column
scale scale data, divide with root mean square of each column


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

Generated on Mon Jun 29 08:29:35 2009 for GMM for EVAN Toolkit by  doxygen 1.5.8