#include <mlpack/prereqs.hpp>#include <mlpack/core/util/io.hpp>#include <mlpack/core/util/mlpack_main.hpp>#include <mlpack/core/math/random.hpp>#include "radical.hpp"
Include dependency graph for radical_main.cpp:
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Functions | |
| BINDING_NAME ("RADICAL") | |
| BINDING_SHORT_DESC ("An implementation of RADICAL, a method for independent component analysis " "(ICA). Given a dataset, this can decompose the dataset into an unmixing " "matrix and an independent component matrix; this can be useful for " "preprocessing.") | |
| BINDING_LONG_DESC ("An implementation of RADICAL, a method for independent component analysis " "(ICA). Assuming that we have an input matrix X, the goal is to find a " "square unmixing matrix W such that Y = W * X and the dimensions of Y are " "independent components. If the algorithm is running particularly slowly, " "try reducing the number of replicates." "\" "The input matrix to perform ICA on should be specified with the "+PRINT_PARAM_STRING("input")+" parameter. The output matrix Y may be " "saved with the "+PRINT_PARAM_STRING("output_ic")+" output parameter, " "and the output unmixing matrix W may be saved with the "+PRINT_PARAM_STRING("output_unmixing")+" output parameter.") | |
| BINDING_EXAMPLE ("For example, to perform ICA on the matrix "+PRINT_DATASET("X")+" with " "40 replicates, saving the independent components to "+PRINT_DATASET("ic")+", the following command may be used: " "\"+PRINT_CALL("radical", "input", "X", "replicates", 40, "output_ic", "ic")) | |
| BINDING_SEE_ALSO ("Independent component analysis on Wikipedia", "https://en.wikipedia.org/wiki/Independent_component_analysis") | |
| BINDING_SEE_ALSO ("ICA using spacings estimates of entropy (pdf)", "http://www.jmlr.org/papers/volume4/learned-miller03a/" "learned-miller03a.pdf") | |
| BINDING_SEE_ALSO ("mlpack::radical::Radical C++ class documentation", "@doxygen/classmlpack_1_1radical_1_1Radical.html") | |
| PARAM_MATRIX_IN_REQ ("input", "Input dataset for ICA.", "i") | |
| PARAM_MATRIX_OUT ("output_ic", "Matrix to save independent components to.", "o") | |
| PARAM_MATRIX_OUT ("output_unmixing", "Matrix to save unmixing matrix to.", "u") | |
| PARAM_DOUBLE_IN ("noise_std_dev", "Standard deviation of Gaussian noise.", "n", 0.175) | |
| PARAM_INT_IN ("replicates", "Number of Gaussian-perturbed replicates to use " "(per point) in Radical2D.", "r", 30) | |
| PARAM_INT_IN ("angles", "Number of angles to consider in brute-force search " "during Radical2D.", "a", 150) | |
| PARAM_INT_IN ("sweeps", "Number of sweeps; each sweep calls Radical2D once for " "each pair of dimensions.", "S", 0) | |
| PARAM_INT_IN ("seed", "Random seed. If 0, 'std::time(NULL)' is used.", "s", 0) | |
| PARAM_FLAG ("objective", "If set, an estimate of the final objective function " "is printed.", "O") | |
Detailed Description
Executable for RADICAL. RADICAL is Robust, Accurate, Direct ICA aLgorithm.
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.
