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/*!
* \file
* \brief FastICA test program
* \author Francois Cayre, Teddy Furon and Adam Piatyszek
*
* -------------------------------------------------------------------------
*
* Copyright (C) 1995-2010 (see AUTHORS file for a list of contributors)
*
* This file is part of IT++ - a C++ library of mathematical, signal
* processing, speech processing, and communications classes and functions.
*
* IT++ is free software: you can redistribute it and/or modify it under the
* terms of the GNU General Public License as published by the Free Software
* Foundation, either version 3 of the License, or (at your option) any
* later version.
*
* IT++ is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
* details.
*
* You should have received a copy of the GNU General Public License along
* with IT++. If not, see <http://www.gnu.org/licenses/>.
*
* -------------------------------------------------------------------------
*/
/*----------------------------------------------------------------------------------------*
* FastICA for IT++ *
*----------------------------------------------------------------------------------------*
* This code is Copyright (C) 2004 by Francois CAYRE and Teddy FURON *
* TEMICS Project *
* INRIA/Rennes (IRISA) *
* Campus Universitaire de Beaulieu *
* 35042 RENNES cedex FRANCE *
* *
* Email : firstname.lastname@irisa.fr *
* *
* This is the IT++ implementation of the original Matlab package FastICA. *
* *
* Matlab package is Copyright (C) 1998 by Jarmo HURRI, Hugo GAVERT, Jaakko SARELA and *
* Aapo HYVARINEN *
* Laboratory of Information and Computer Science *
* Helsinki University of Technology *
* *
* URL : http://www.cis.hut.fi/projects/ica/fastica/about.shtml *
* *
* If you use results given by this FastICA software in an article for a scientific *
* journal, conference proceedings or similar, please include the following original *
* reference in the bibliography : *
* *
* A. Hyvarinen. Fast and Robust Fixed-Point Algorithms for Independent Component *
* Analysis. IEEE Transactions on Neural Networks 10(3):626-634, 1999. *
*----------------------------------------------------------------------------------------*
* DISCLAIMER *
* *
* This software package is free software ; you can redistribute it and/or modify it *
* under the terms of the GNU General Public License as published by the Free Software *
* Foundation ; either version 2 of the License, or any later version. *
* *
* The software package is distributed in the hope that it will be useful, but WITHOUT *
* ANY WARRANTY ; without even the implied warranty of MERCHANTABILITY or FITNESS FOR *
* A PARTICULAR PURPOSE. *
* See the GNU General Public License for more details. *
* *
*----------------------------------------------------------------------------------------*
* Differences with the original Matlab implementation : *
* - no GUI *
* - return something even in the case of a convergence problem *
* - optimization of SVD decomposition (performed 2 times in Matlab, only 1 time in IT++) *
* - default approach is SYMM wit non-linearity POW3 *
*----------------------------------------------------------------------------------------*/
#include <itpp/itsignal.h>
#include <cstdio>
using namespace itpp;
using namespace std;
#if defined(FASTICA_TEST_FILE)
int main()
{
FILE * fpin = NULL;
float tmp = 0.0;
// Separate nrIC independent components in nrSamples samples
int nrSamples = 0, nrIC = 0;
fpin = fopen(FASTICA_TEST_FILE, "r");
if (fpin == 0) {
cerr << "Error: Could not open FASTICA_TEST_FILE for reading" << endl;
return 1;
}
cout << "=====================================" << endl;
cout << " Test program for FastICA / IT++ " << endl;
cout << "=====================================" << endl;
int ret = fscanf(fpin, "%d", &nrSamples);
ret = fscanf(fpin, "%d", &nrIC);
mat X = zeros(nrIC, nrSamples);
for (int i = 0; i < nrSamples; i++)
for (int j = 0; j < nrIC; j++) {
ret = fscanf(fpin , "%f", &tmp);
X(j, i) = tmp;
}
fclose(fpin);
// Instantiate an ICA object with default parameters : SYMM approach and
// POW3 non-linearity
// Be sure that :
// - nrSamples = number of samples = nb of columns of the input matrix
// - nrIC = number of sensors = nb of rows of the input matrix
cout << "\n==========================================================" << endl;
cout << "Use SYMM approach and POW3 non-linearity :" << endl;
Fast_ICA my_fastica(X);
// Set number of independent components to separate :
// By default, this value is taken from the dimension of
// the input data. This line is for illustration purposes.
// May help in some cases.
my_fastica.set_nrof_independent_components(nrIC);
// Perform ICA
bool result = my_fastica.separate();
if (result)
{
// Get results
cout << "Mixing matrix = " << my_fastica.get_mixing_matrix() << endl;
cout << "Separation matrix = " << my_fastica.get_separating_matrix() << endl;
cout << "Separated independent components = "
<< my_fastica.get_independent_components() << endl;
} else
{
cout << "Algorithm failed" << endl;
}
// Another test with other parameters
cout << "\n==========================================================" << endl;
cout << "Use Gaussian non-linearity and deflation approach :" << endl;
Fast_ICA my_fastica2(X);
// Set GAUSS non-linearity
my_fastica2.set_non_linearity(FICA_NONLIN_GAUSS);
// Use deflation approach : IC are computed one by one
my_fastica2.set_approach(FICA_APPROACH_DEFL);
// Perform ICA
result = my_fastica2.separate();
if (result)
{
// Get results
cout << "Mixing matrix = " << my_fastica.get_mixing_matrix() << endl;
cout << "Separation matrix = " << my_fastica.get_separating_matrix() << endl;
cout << "Separated independent components = "
<< my_fastica.get_independent_components() << endl;
} else
{
cout << "Algorithm failed" << endl;
}
// Another test which should fail
cout << "\n==========================================================" << endl;
cout << "Use Gaussian non-linearity and deflation approach :" << endl;
const int rows = 10;
const int comp = 3;
RNG_reset(1);
mat signal = randu(rows, 100);
mat guess = zeros(rows, comp);
Fast_ICA my_fastica3(signal);
// Use deflation approach : IC are computed one by one
my_fastica3.set_approach(FICA_APPROACH_DEFL);
my_fastica3.set_nrof_independent_components(comp);
my_fastica3.set_init_guess(guess);
my_fastica3.set_max_num_iterations(100);
// Perform ICA
result = my_fastica3.separate();
if (result)
{
// Get results
cout << "Mixing matrix = " << my_fastica.get_mixing_matrix() << endl;
cout << "Separation matrix = " << my_fastica.get_separating_matrix() << endl;
cout << "Separated independent components = "
<< my_fastica.get_independent_components() << endl;
} else
{
cout << "Algorithm failed" << endl;
}
cout << "\nEnd of Fast_ICA test execution. " << endl;
return 0;
}
#else
int main()
{
cerr << "FASTICA_TEST_FILE not defined. Test skipped." << endl;
return 1;
}
#endif // defined(FASTICA_TEST_FILE)
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