ica_template.hh

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/* -*- mia-c++  -*-
 *
 * This file is part of MIA - a toolbox for medical image analysis 
 * Copyright (c) Leipzig, Madrid 1999-2017 Gert Wollny
 *
 * MIA 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.
 *
 * This program 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 MIA; if not, see <http://www.gnu.org/licenses/>.
 *
 */

#ifndef mia_core_ica_template_hh
#define mia_core_ica_template_hh

#include <mia/core/ica.hh>
#include <vector>


#ifndef EXPORT_TDataSeriesICA
#ifdef WIN32
#  define EXPORT_TDataSeriesICA __declspec(dllimport)
#else
# ifdef __GNUC__
#   define EXPORT_TDataSeriesICA __attribute__((visibility("default")))
# else
#   define EXPORT_TDataSeriesICA
# endif
#endif
#endif 

NS_MIA_BEGIN

template <class Data> 
class  EXPORT_TDataSeriesICA TDataSeriesICA {
public:
        typedef CIndepCompAnalysis::IndexSet IndexSet;

        typedef typename Data::Pointer PData; 

        TDataSeriesICA(const CIndepCompAnalysisFactory&  icatool, const std::vector<Data>& initializer, bool strip_mean);
        
        bool run(size_t ncomponents, bool strip_mean, bool ica_normalize, 
                 std::vector<std::vector<float> >  guess = std::vector<std::vector<float> >());


        
        void normalize();
        
        void normalize_Mix();
        

        Data get_mix(size_t idx) const;

        const Data&  get_mean_image() const;

        Data get_incomplete_mix(size_t idx, const IndexSet& skip) const;

        Data get_partial_mix(size_t idx, const IndexSet& comps) const;
        
        CSlopeColumns get_mixing_curves() const;

        std::vector<float> get_mixing_curve(unsigned idx) const;

        PData get_feature_image(size_t idx) const; 

        PData get_delta_feature(const IndexSet& plus, const IndexSet& minus)const; 
        
        void set_mixing_series(size_t index, const std::vector<float>& series);


        void set_max_iterations(int n); 

        void set_approach(CIndepCompAnalysis::EApproach approach);
        

        ~TDataSeriesICA();
private:
        PIndepCompAnalysis m_analysis;
        typedef typename Data::dimsize_type dimsize_type; 
        dimsize_type m_size;
        Data m_mean;

};

NS_MIA_END

#endif