.TH "mlpack::pca::PCA" 3 "Tue Sep 9 2014" "Version 1.0.10" "MLPACK" \" -*- nroff -*-
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.SH NAME
mlpack::pca::PCA \- 
.PP
This class implements principal components analysis (\fBPCA\fP)\&.  

.SH SYNOPSIS
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.PP
.SS "Public Member Functions"

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.RI "\fBPCA\fP (const bool \fBscaleData\fP=false)"
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.RI "\fICreate the \fBPCA\fP object, specifying if the data should be scaled in each dimension by standard deviation when \fBPCA\fP is performed\&. \fP"
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.RI "void \fBApply\fP (const arma::mat &data, arma::mat &transformedData, arma::vec &eigval, arma::mat &eigvec) const "
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.RI "\fIApply Principal Component Analysis to the provided data set\&. \fP"
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.RI "void \fBApply\fP (const arma::mat &data, arma::mat &transformedData, arma::vec &eigVal) const "
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.RI "\fIApply Principal Component Analysis to the provided data set\&. \fP"
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.RI "double \fBApply\fP (arma::mat &data, const size_t newDimension) const "
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.RI "\fIUse \fBPCA\fP for dimensionality reduction on the given dataset\&. \fP"
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.RI "double \fBApply\fP (arma::mat &data, const int newDimension) const "
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.RI "\fIThis overload is here to make sure int gets casted right to size_t\&. \fP"
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.RI "double \fBApply\fP (arma::mat &data, const double varRetained) const "
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.RI "\fIUse \fBPCA\fP for dimensionality reduction on the given dataset\&. \fP"
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.RI "bool \fBScaleData\fP () const "
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.RI "\fIGet whether or not this \fBPCA\fP object will scale (by standard deviation) the data when \fBPCA\fP is performed\&. \fP"
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.RI "bool & \fBScaleData\fP ()"
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.RI "\fIModify whether or not this \fBPCA\fP object will scale (by standard deviation) the data when \fBPCA\fP is performed\&. \fP"
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.RI "std::string \fBToString\fP () const "
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.SS "Private Attributes"

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.RI "bool \fBscaleData\fP"
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.RI "\fIWhether or not the data will be scaled by standard deviation when \fBPCA\fP is performed\&. \fP"
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.SH "Detailed Description"
.PP 
This class implements principal components analysis (\fBPCA\fP)\&. 

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 \fBPCA\fP can be found in almost any statistics or machine learning textbook, and all over the internet\&. 
.PP
Definition at line 38 of file pca\&.hpp\&.
.SH "Constructor & Destructor Documentation"
.PP 
.SS "mlpack::pca::PCA::PCA (const boolscaleData = \fCfalse\fP)"

.PP
Create the \fBPCA\fP object, specifying if the data should be scaled in each dimension by standard deviation when \fBPCA\fP is performed\&. 
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\fBParameters:\fP
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\fIscaleData\fP Whether or not to scale the data\&. 
.RE
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.SH "Member Function Documentation"
.PP 
.SS "void mlpack::pca::PCA::Apply (const arma::mat &data, arma::mat &transformedData, arma::vec &eigval, arma::mat &eigvec) const"

.PP
Apply Principal Component Analysis to the provided data set\&. It is safe to pass the same matrix reference for both data and transformedData\&.
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\fBParameters:\fP
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\fIdata\fP Data matrix\&. 
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\fItransformedData\fP Matrix to put results of \fBPCA\fP into\&. 
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\fIeigval\fP Vector to put eigenvalues into\&. 
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\fIeigvec\fP Matrix to put eigenvectors (loadings) into\&. 
.RE
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.PP
Referenced by Apply()\&.
.SS "void mlpack::pca::PCA::Apply (const arma::mat &data, arma::mat &transformedData, arma::vec &eigVal) const"

.PP
Apply Principal Component Analysis to the provided data set\&. It is safe to pass the same matrix reference for both data and transformedData\&.
.PP
\fBParameters:\fP
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\fIdata\fP Data matrix\&. 
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\fItransformedData\fP Matrix to store results of \fBPCA\fP in\&. 
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\fIeigval\fP Vector to put eigenvalues into\&. 
.RE
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.SS "double mlpack::pca::PCA::Apply (arma::mat &data, const size_tnewDimension) const"

.PP
Use \fBPCA\fP 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\&.
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\fBParameters:\fP
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\fIdata\fP Data matrix\&. 
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\fInewDimension\fP New dimension of the data\&. 
.RE
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\fBReturns:\fP
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Amount of the variance of the data retained (between 0 and 1)\&. 
.RE
.PP

.SS "double mlpack::pca::PCA::Apply (arma::mat &data, const intnewDimension) const\fC [inline]\fP"

.PP
This overload is here to make sure int gets casted right to size_t\&. 
.PP
Definition at line 89 of file pca\&.hpp\&.
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References Apply()\&.
.SS "double mlpack::pca::PCA::Apply (arma::mat &data, const doublevarRetained) const"

.PP
Use \fBPCA\fP 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\&.
.PP
The method returns the actual amount of variance retained, which will always be greater than or equal to the varRetained parameter\&.
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\fBParameters:\fP
.RS 4
\fIdata\fP Data matrix\&. 
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\fIvarRetained\fP Lower bound on amount of variance to retain; should be between 0 and 1\&. 
.RE
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\fBReturns:\fP
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Actual amount of variance retained (between 0 and 1)\&. 
.RE
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.SS "bool mlpack::pca::PCA::ScaleData () const\fC [inline]\fP"

.PP
Get whether or not this \fBPCA\fP object will scale (by standard deviation) the data when \fBPCA\fP is performed\&. 
.PP
Definition at line 113 of file pca\&.hpp\&.
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References scaleData\&.
.SS "bool& mlpack::pca::PCA::ScaleData ()\fC [inline]\fP"

.PP
Modify whether or not this \fBPCA\fP object will scale (by standard deviation) the data when \fBPCA\fP is performed\&. 
.PP
Definition at line 116 of file pca\&.hpp\&.
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References scaleData\&.
.SS "std::string mlpack::pca::PCA::ToString () const"

.SH "Member Data Documentation"
.PP 
.SS "bool mlpack::pca::PCA::scaleData\fC [private]\fP"

.PP
Whether or not the data will be scaled by standard deviation when \fBPCA\fP is performed\&. 
.PP
Definition at line 124 of file pca\&.hpp\&.
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Referenced by ScaleData()\&.

.SH "Author"
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Generated automatically by Doxygen for MLPACK from the source code\&.