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mlpack
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This class implements principal components analysis (PCA). More...
Public Member Functions | |
| PCAType (const bool scaleData=false, const DecompositionPolicy &decomposition=DecompositionPolicy()) | |
| 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) |
| Apply Principal Component Analysis to the provided data set. More... | |
| void | Apply (const arma::mat &data, arma::mat &transformedData, arma::vec &eigVal) |
| Apply Principal Component Analysis to the provided data set. More... | |
| double | Apply (arma::mat &data, const size_t newDimension) |
| Use PCA for dimensionality reduction on the given dataset. More... | |
| double | Apply (arma::mat &data, const int newDimension) |
| This overload is here to make sure int gets casted right to size_t. More... | |
| double | Apply (arma::mat &data, const double varRetained) |
| 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... | |
Private Member Functions | |
| void | ScaleData (arma::mat ¢eredData) |
| Scaling the data is when we reduce the variance of each dimension to 1. More... | |
Private Attributes | |
| DecompositionPolicy | decomposition |
| Decomposition method used to perform principal components analysis. More... | |
| bool | scaleData |
| Whether or not the data will be scaled by standard deviation when PCA is performed. More... | |
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. Note this class will be changed to have the name PCA in mlpack 3.0.0
| mlpack::pca::PCAType< DecompositionPolicy >::PCAType | ( | const bool | scaleData = false, |
| const DecompositionPolicy & | decomposition = DecompositionPolicy() |
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| ) |
Create the PCA object, specifying if the data should be scaled in each dimension by standard deviation when PCA is performed.
| scaleData | Whether or not to scale the data. |
| void mlpack::pca::PCAType< DecompositionPolicy >::Apply | ( | const arma::mat & | data, |
| arma::mat & | transformedData, | ||
| arma::vec & | eigVal, | ||
| arma::mat & | eigvec | ||
| ) |
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 mlpack::pca::PCAType< DecompositionPolicy >::Apply().
| void mlpack::pca::PCAType< DecompositionPolicy >::Apply | ( | const arma::mat & | data, |
| arma::mat & | transformedData, | ||
| arma::vec & | eigVal | ||
| ) |
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 store results of PCA in. |
| eigVal | Vector to put eigenvalues into. |
| double mlpack::pca::PCAType< DecompositionPolicy >::Apply | ( | arma::mat & | data, |
| const size_t | newDimension | ||
| ) |
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.
| data | Data matrix. |
| newDimension | New dimension of the data. |
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This overload is here to make sure int gets casted right to size_t.
Definition at line 86 of file pca.hpp.
References mlpack::pca::PCAType< DecompositionPolicy >::Apply().
| double mlpack::pca::PCAType< DecompositionPolicy >::Apply | ( | arma::mat & | data, |
| const double | varRetained | ||
| ) |
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. |
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Get whether or not this PCA object will scale (by standard deviation) the data when PCA is performed.
Definition at line 110 of file pca.hpp.
References mlpack::pca::PCAType< DecompositionPolicy >::scaleData.
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Modify 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 mlpack::pca::PCAType< DecompositionPolicy >::scaleData.
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Scaling the data is when we reduce the variance of each dimension to 1.
Definition at line 117 of file pca.hpp.
References mlpack::pca::PCAType< DecompositionPolicy >::scaleData.
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Whether or not the data will be scaled by standard deviation when PCA is performed.
Definition at line 138 of file pca.hpp.
Referenced by mlpack::pca::PCAType< DecompositionPolicy >::ScaleData().
1.8.11
