1 | /*! |
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2 | * \file |
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3 | * \brief Definition of FastICA (Independent Component Analysis) for IT++ |
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4 | * \author Francois Cayre and Teddy Furon |
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5 | * |
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6 | * ------------------------------------------------------------------------- |
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7 | * |
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8 | * Copyright (C) 1995-2010 (see AUTHORS file for a list of contributors) |
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9 | * |
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10 | * This file is part of IT++ - a C++ library of mathematical, signal |
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11 | * processing, speech processing, and communications classes and functions. |
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12 | * |
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13 | * IT++ is free software: you can redistribute it and/or modify it under the |
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14 | * terms of the GNU General Public License as published by the Free Software |
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15 | * Foundation, either version 3 of the License, or (at your option) any |
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16 | * later version. |
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17 | * |
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18 | * IT++ is distributed in the hope that it will be useful, but WITHOUT ANY |
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19 | * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS |
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20 | * FOR A PARTICULAR PURPOSE. See the GNU General Public License for more |
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21 | * details. |
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22 | * |
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23 | * You should have received a copy of the GNU General Public License along |
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24 | * with IT++. If not, see <http://www.gnu.org/licenses/>. |
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25 | * |
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26 | * ------------------------------------------------------------------------- |
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27 | * |
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28 | * This is IT++ implementation of the original Matlab package FastICA. |
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29 | * |
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30 | * This code is Copyright (C) 2004 by: |
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31 | * Francois CAYRE and Teddy FURON |
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32 | * TEMICS Project |
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33 | * INRIA/Rennes (IRISA) |
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34 | * Campus Universitaire de Beaulieu |
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35 | * 35042 RENNES cedex FRANCE |
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36 | * |
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37 | * Email: firstname.lastname@irisa.fr |
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38 | * |
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39 | * Matlab package is Copyright (C) 1998 by: |
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40 | * Jarmo HURRI, Hugo GAVERT, Jaakko SARELA and Aapo HYVARINEN |
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41 | * Laboratory of Information and Computer Science |
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42 | * Helsinki University of Technology |
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43 | * |
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44 | * URL: http://www.cis.hut.fi/projects/ica/fastica/about.shtml |
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45 | * |
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46 | * If you use results given by this FastICA software in an article for |
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47 | * a scientific journal, conference proceedings or similar, please |
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48 | * include the following original reference in the bibliography: |
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49 | * |
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50 | * A. Hyvarinen, Fast and Robust Fixed-Point Algorithms for |
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51 | * Independent Component Analysis, IEEE Transactions on Neural |
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52 | * Networks 10(3):626-634, 1999 |
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53 | * |
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54 | * Differences with the original Matlab implementation: |
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55 | * - no GUI |
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56 | * - return something even in the case of a convergence problem |
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57 | * - optimization of SVD decomposition (performed 2 times in Matlab, |
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58 | * only 1 time in IT++) |
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59 | * - default approach is SYMM with non-linearity POW3 |
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60 | */ |
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61 | |
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62 | #ifndef FASTICA_H |
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63 | #define FASTICA_H |
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64 | |
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65 | #include <itpp/base/mat.h> |
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66 | |
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67 | |
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68 | //! Use deflation approach : compute IC one-by-one in a Gram-Schmidt-like fashion |
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69 | #define FICA_APPROACH_DEFL 2 |
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70 | //! Use symmetric approach : compute all ICs at a time |
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71 | #define FICA_APPROACH_SYMM 1 |
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72 | |
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73 | //! Use x^3 non-linearity |
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74 | #define FICA_NONLIN_POW3 10 |
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75 | //! Use tanh(x) non-linearity |
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76 | #define FICA_NONLIN_TANH 20 |
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77 | //! Use Gaussian non-linearity |
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78 | #define FICA_NONLIN_GAUSS 30 |
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79 | //! Use skew non-linearity |
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80 | #define FICA_NONLIN_SKEW 40 |
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81 | |
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82 | //! Set random start for Fast_ICA |
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83 | #define FICA_INIT_RAND 0 |
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84 | //! Set predefined start for Fast_ICA |
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85 | #define FICA_INIT_GUESS 1 |
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86 | |
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87 | //! Eigenvalues of the covariance matrix lower than FICA_TOL are discarded for analysis |
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88 | #define FICA_TOL 1e-9 |
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89 | |
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90 | namespace itpp |
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91 | { |
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92 | |
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93 | /*! |
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94 | \addtogroup fastica |
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95 | */ |
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96 | |
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97 | //---------------------- FastICA -------------------------------------- |
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98 | |
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99 | /*! |
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100 | \brief Fast_ICA Fast Independent Component Analysis (Fast ICA) |
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101 | \ingroup fastica |
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102 | |
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103 | The software is based upon original FastICA for Matlab from |
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104 | A. Hyvarinen. Fast and Robust Fixed-Point Algorithms for |
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105 | Independent Component Analysis. IEEE Transactions on Neural |
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106 | Networks, 10(3), pp. 626-634, 1999. |
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107 | |
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108 | Example: |
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109 | \code |
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110 | FastICA fastica(sources); |
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111 | fastica.set_nrof_independent_components(sources.rows()); |
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112 | fastica.set_non_linearity( FICA_NONLIN_TANH ); |
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113 | fastica.set_approach( FICA_APPROACH_DEFL ); |
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114 | fastica.separate(); |
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115 | mat ICs = fastica.get_independent_components(); |
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116 | \endcode |
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117 | */ |
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118 | class Fast_ICA |
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119 | { |
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120 | |
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121 | public: |
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122 | |
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123 | /*! |
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124 | \brief Constructor |
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125 | |
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126 | Construct a Fast_ICA object with mixed signals to separate. |
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127 | |
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128 | \param ma_mixed_sig (Input) Mixed signals to separate |
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129 | */ |
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130 | Fast_ICA(mat ma_mixed_sig); |
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131 | |
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132 | /*! |
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133 | \brief Explicit launch of main FastICA function |
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134 | |
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135 | Explicit launch of the Fast_ICA algorithm. |
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136 | */ |
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137 | void separate(void); |
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138 | |
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139 | /*! |
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140 | \brief Set approach : FICA_APPROACH_DEFL or FICA_APPROACH_SYMM (default) |
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141 | |
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142 | Set approach to use : FICA_APPROACH_SYMM (symmetric) or FICA_APPROACH_DEFL (deflation). The symmetric approach computes all ICs at a time, whereas the deflation approach computes them one by one. |
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143 | |
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144 | \param in_approach (Input) Type of approach to use |
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145 | */ |
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146 | void set_approach(int in_approach); |
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147 | |
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148 | /*! |
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149 | \brief Set number of independent components to separate |
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150 | |
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151 | Set the number of ICs to compute. |
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152 | |
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153 | \param in_nrIC (Input) Number of ICs to compute |
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154 | */ |
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155 | void set_nrof_independent_components(int in_nrIC); |
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156 | |
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157 | /*! |
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158 | \brief Set non-linearity |
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159 | |
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160 | Set non-linearity to use : FICA_NONLIN_POW3 (default), FICA_NONLIN_TANH, FICA_NONLIN_GAUSS, FICA_NONLIN_SKEW |
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161 | |
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162 | \param in_g (Input) Non-linearity. Can be selected from FICA_NONLIN_POW3, FICA_NONLIN_TANH, FICA_NONLIN_GAUSS or FICA_NONLIN_SKEW |
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163 | */ |
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164 | void set_non_linearity(int in_g); |
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165 | |
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166 | /*! |
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167 | \brief Set fine tuning |
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168 | |
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169 | Set fine tuning true or false. |
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170 | |
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171 | \param in_finetune (Input) Boolean (true or false) |
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172 | */ |
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173 | void set_fine_tune(bool in_finetune); |
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174 | |
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175 | /*! |
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176 | \brief Set \f$a_1\f$ parameter |
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177 | |
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178 | Set internal parameter \f$a_1\f$ of Fast_ICA (See reference paper). |
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179 | |
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180 | \param fl_a1 (Input) Parameter \f$a_1\f$ from reference paper |
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181 | */ |
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182 | void set_a1(double fl_a1); |
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183 | |
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184 | /*! |
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185 | \brief Set \f$a_2\f$ parameter |
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186 | |
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187 | Set internal parameter \f$a_2\f$ of Fast_ICA (See reference paper). |
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188 | |
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189 | \param fl_a2 (Input) Parameter \f$a_2\f$ from reference paper |
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190 | */ |
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191 | void set_a2(double fl_a2); |
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192 | |
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193 | /*! |
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194 | \brief Set \f$\mu\f$ parameter |
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195 | |
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196 | Set internal parameter \f$\mu\f$ of Fast_ICA (See reference paper). |
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197 | |
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198 | \param fl_mu (Input) Parameter \f$\mu\f$ from reference paper |
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199 | */ |
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200 | void set_mu(double fl_mu); |
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201 | |
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202 | /*! |
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203 | \brief Set convergence parameter \f$\epsilon\f$ |
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204 | |
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205 | Set \f$\epsilon\f$ parameter for convergence precision. |
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206 | |
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207 | \param fl_epsilon (Input) \f$\epsilon\f$ is convergence precision |
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208 | */ |
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209 | void set_epsilon(double fl_epsilon); |
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210 | |
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211 | /*! |
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212 | \brief Set sample size |
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213 | |
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214 | Set the percentage of samples to take into account at every iteration. |
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215 | |
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216 | \param fl_sampleSize (Input) Percentage of data to take into account at every iteration |
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217 | */ |
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218 | void set_sample_size(double fl_sampleSize); |
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219 | |
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220 | /*! |
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221 | \brief Set stabilization mode true or off |
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222 | |
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223 | Set stabilization mode. |
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224 | |
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225 | \param in_stabilization (Input) Set stabilization true or false |
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226 | */ |
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227 | void set_stabilization(bool in_stabilization); |
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228 | |
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229 | /*! |
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230 | \brief Set maximum number of iterations |
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231 | |
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232 | Set maximum number of iterations for Fast_ICA. |
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233 | |
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234 | \param in_maxNumIterations (Input) Maximum number of iterations to go through |
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235 | */ |
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236 | void set_max_num_iterations(int in_maxNumIterations); |
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237 | |
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238 | /*! |
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239 | \brief Set maximum number of iterations for fine tuning |
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240 | |
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241 | Set maximum numberr of iterations for fine tuning. |
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242 | |
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243 | \param in_maxFineTune (Input) Maximum number of iterations for fine tuning stage |
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244 | */ |
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245 | void set_max_fine_tune(int in_maxFineTune); |
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246 | |
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247 | /*! |
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248 | \brief Set first eigenvalue index to take into account |
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249 | |
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250 | Set first eigenvalue index to take into account. |
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251 | |
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252 | \param in_firstEig (Input) First eigenvalue index to take into account |
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253 | */ |
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254 | void set_first_eig(int in_firstEig); |
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255 | |
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256 | /*! |
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257 | \brief Set last eigenvalue index to take into account |
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258 | |
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259 | Set last eigenvalue index to take into account. |
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260 | |
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261 | \param in_lastEig (Input) Last eigenvalue index to take into account |
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262 | */ |
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263 | void set_last_eig(int in_lastEig); |
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264 | |
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265 | /*! |
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266 | \brief If true, only perform Principal Component Analysis (default = false) |
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267 | |
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268 | Wether to perform PCA only or PCA+ICA. |
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269 | |
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270 | \param in_PCAonly (Input) True = PCA only, false = PCA+ICA (default) |
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271 | */ |
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272 | void set_pca_only(bool in_PCAonly); |
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273 | |
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274 | /*! |
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275 | \brief Set initial guess matrix instead of random (default) |
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276 | |
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277 | Set initial matrix instead of random matrix. |
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278 | |
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279 | \param ma_initGuess (Input) Initial guess matrix |
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280 | */ |
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281 | void set_init_guess(mat ma_initGuess); |
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282 | |
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283 | |
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284 | /*! |
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285 | \brief Get mixing matrix |
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286 | |
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287 | Return mixing matrix. |
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288 | |
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289 | \return Mixing matrix |
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290 | */ |
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291 | mat get_mixing_matrix(); |
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292 | |
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293 | /*! |
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294 | \brief Get separating matrix |
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295 | |
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296 | Return separating matrix. |
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297 | |
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298 | \return Separating matrix |
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299 | */ |
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300 | mat get_separating_matrix(); |
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301 | |
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302 | /*! |
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303 | \brief Get separated signals |
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304 | |
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305 | Return separated signals (Independent Components). |
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306 | |
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307 | \return ICs |
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308 | */ |
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309 | mat get_independent_components(); |
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310 | |
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311 | /*! |
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312 | \brief Get number of independent components |
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313 | |
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314 | Return number of ICs. |
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315 | |
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316 | \return Number of ICs |
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317 | */ |
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318 | int get_nrof_independent_components(); |
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319 | |
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320 | /*! |
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321 | \brief Get nrIC first columns of the de-whitening matrix |
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322 | |
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323 | Return principal eigenvectors. |
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324 | |
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325 | \return Principal eigenvectors |
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326 | */ |
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327 | mat get_principal_eigenvectors(); |
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328 | |
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329 | /*! |
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330 | \brief Get the whitening matrix |
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331 | |
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332 | Return whitening matrix. |
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333 | |
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334 | \return Whitening matrix |
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335 | */ |
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336 | mat get_whitening_matrix(); |
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337 | |
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338 | /*! |
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339 | \brief Get the de-whitening matrix |
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340 | |
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341 | Return dewhitening matrix. |
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342 | |
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343 | \return Dewhitening matrix |
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344 | */ |
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345 | mat get_dewhitening_matrix(); |
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346 | |
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347 | /*! |
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348 | \brief Get whitened signals |
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349 | |
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350 | Return whitened signals. |
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351 | |
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352 | \return Whitened signals |
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353 | */ |
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354 | mat get_white_sig(); |
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355 | |
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356 | private: |
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357 | |
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358 | int approach, numOfIC, g, initState; |
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359 | bool finetune, stabilization, PCAonly; |
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360 | double a1, a2, mu, epsilon, sampleSize; |
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361 | int maxNumIterations, maxFineTune; |
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362 | |
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363 | int firstEig, lastEig; |
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364 | |
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365 | mat initGuess; |
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366 | |
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367 | mat mixedSig, A, W, icasig; |
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368 | |
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369 | mat whiteningMatrix; |
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370 | mat dewhiteningMatrix; |
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371 | mat whitesig; |
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372 | |
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373 | mat E, VecPr; |
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374 | vec D; |
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375 | |
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376 | }; // class Fast_ICA |
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377 | |
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378 | } // namespace itpp |
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379 | |
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380 | |
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381 | #endif // #ifndef FASTICA_H |
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