#include <mlpack/prereqs.hpp>#include <mlpack/core/util/io.hpp>#include <mlpack/core/util/mlpack_main.hpp>#include "pca.hpp"#include <mlpack/methods/pca/decomposition_policies/exact_svd_method.hpp>#include <mlpack/methods/pca/decomposition_policies/quic_svd_method.hpp>#include <mlpack/methods/pca/decomposition_policies/randomized_svd_method.hpp>#include <mlpack/methods/pca/decomposition_policies/randomized_block_krylov_method.hpp>
Include dependency graph for pca_main.cpp:
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Functions | |
| BINDING_NAME ("Principal Components Analysis") | |
| BINDING_SHORT_DESC ("An implementation of several strategies for principal components analysis " "(PCA), a common preprocessing step. Given a dataset and a desired new " "dimensionality, this can reduce the dimensionality of the data using the " "linear transformation determined by PCA.") | |
| BINDING_LONG_DESC ("This program performs principal components analysis on the given dataset " "using the exact, randomized, randomized block Krylov, or QUIC SVD method. " "It will transform the data onto its principal components, optionally " "performing dimensionality reduction by ignoring the principal components " "with the smallest eigenvalues." "\" "Use the "+PRINT_PARAM_STRING("input")+" parameter to specify the " "dataset to perform PCA on. A desired new dimensionality can be specified " "with the "+PRINT_PARAM_STRING("new_dimensionality")+" parameter, or " "the desired variance to retain can be specified with the "+PRINT_PARAM_STRING("var_to_retain")+" parameter. If desired, the " "dataset can be scaled before running PCA with the "+PRINT_PARAM_STRING("scale")+" parameter." "\" "Multiple different decomposition techniques can be used. The method to " "use can be specified with the "+PRINT_PARAM_STRING("decomposition_method")+" parameter, and it may take " "the values 'exact', 'randomized', or 'quic'.") | |
| BINDING_EXAMPLE ("For example, to reduce the dimensionality of the matrix "+PRINT_DATASET("data")+" to 5 dimensions using randomized SVD for the " "decomposition, storing the output matrix to "+PRINT_DATASET("data_mod")+", the following command can be used:" "\"+PRINT_CALL("pca", "input", "data", "new_dimensionality", 5, "decomposition_method", "randomized", "output", "data_mod")) | |
| BINDING_SEE_ALSO ("Principal component analysis on Wikipedia", "https://en.wikipedia.org/wiki/Principal_component_analysis") | |
| BINDING_SEE_ALSO ("mlpack::pca::PCA C++ class documentation", "@doxygen/classmlpack_1_1pca_1_1PCA.html") | |
| PARAM_MATRIX_IN_REQ ("input", "Input dataset to perform PCA on.", "i") | |
| PARAM_MATRIX_OUT ("output", "Matrix to save modified dataset to.", "o") | |
| PARAM_INT_IN ("new_dimensionality", "Desired dimensionality of output dataset. " "If 0, no dimensionality reduction is performed.", "d", 0) | |
| PARAM_DOUBLE_IN ("var_to_retain", "Amount of variance to retain; should be " "between 0 and 1. If 1, all variance is retained. Overrides -d.", "r", 0) | |
| PARAM_FLAG ("scale", "If set, the data will be scaled before running PCA, such " "that the variance of each feature is 1.", "s") | |
| PARAM_STRING_IN ("decomposition_method", "Method used for the principal " "components analysis: 'exact', 'randomized', 'randomized-block-krylov', " "'quic'.", "c", "exact") | |
| template<typename DecompositionPolicy > | |
| void | RunPCA (arma::mat &dataset, const size_t newDimension, const bool scale, const double varToRetain) |
| Run RunPCA on the specified dataset with the given decomposition method. | |
Detailed Description
Main executable to run PCA.
mlpack is free software; you may redistribute it and/or modify it under the terms of the 3-clause BSD license. You should have received a copy of the 3-clause BSD license along with mlpack. If not, see http://www.opensource.org/licenses/BSD-3-Clause for more information.
