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| 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...
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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...
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void | Apply (const arma::mat &data, arma::mat &transformedData, arma::vec &eigVal) const |
| Apply Principal Component Analysis to the provided data set. More...
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double | Apply (arma::mat &data, const size_t newDimension) const |
| Use PCA for dimensionality reduction on the given dataset. More...
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double | Apply (arma::mat &data, const int newDimension) const |
| This overload is here to make sure int gets casted right to size_t. More...
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double | Apply (arma::mat &data, const double varRetained) const |
| Use PCA for dimensionality reduction on the given dataset. More...
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bool | ScaleData () const |
| Get whether or not this PCA object will scale (by standard deviation) the data when PCA is performed. More...
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bool & | ScaleData () |
| Modify whether or not this PCA object will scale (by standard deviation) the data when PCA is performed. More...
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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 30 of file pca.hpp.
double mlpack::pca::PCA::Apply |
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arma::mat & |
data, |
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const double |
varRetained |
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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
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data | Data matrix. |
varRetained | Lower bound on amount of variance to retain; should be between 0 and 1. |
- Returns
- Actual amount of variance retained (between 0 and 1).