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. | |
double | Apply (arma::mat &data, const double varRetained) const |
Use PCA for dimensionality reduction on the given dataset. | |
double | Apply (arma::mat &data, const int newDimension) const |
This overload is here to make sure int gets casted right to size_t. | |
double | Apply (arma::mat &data, const size_t newDimension) const |
Use PCA for dimensionality reduction on the given dataset. | |
void | Apply (const arma::mat &data, arma::mat &transformedData, arma::vec &eigVal) const |
Apply Principal Component Analysis to the provided data set. | |
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. | |
bool & | ScaleData () |
Modify whether or not this PCA object will scale (by standard deviation) the data when PCA is performed. | |
bool | ScaleData () const |
Get whether or not this PCA object will scale (by standard deviation) the data when PCA is performed. | |
Private Attributes | |
bool | scaleData |
Whether or not the data will be scaled by standard deviation when PCA is performed. |
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.
mlpack::pca::PCA::PCA | ( | const bool | scaleData = false |
) |
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.
data | Data matrix. | |
varRetained | Lower bound on amount of variance to retain; should be between 0 and 1. |
double mlpack::pca::PCA::Apply | ( | arma::mat & | data, | |
const int | newDimension | |||
) | const [inline] |
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.
void mlpack::pca::PCA::Apply | ( | const arma::mat & | data, | |
arma::mat & | transformedData, | |||
arma::vec & | eigVal | |||
) | const |
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.
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 Apply().
bool& mlpack::pca::PCA::ScaleData | ( | ) | [inline] |
bool mlpack::pca::PCA::ScaleData | ( | ) | const [inline] |
bool mlpack::pca::PCA::scaleData [private] |
Whether or not the data will be scaled by standard deviation when PCA is performed.
Definition at line 121 of file pca.hpp.
Referenced by ScaleData().