/usr/include/mia-2.0/mia/core/ica_template.cxx is in libmia-2.0-dev 2.0.13-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | /* -*- mia-c++ -*-
*
* This file is part of MIA - a toolbox for medical image analysis
* Copyright (c) Leipzig, Madrid 1999-2013 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/>.
*
*/
#include <cassert>
#include <stdexcept>
#include <algorithm>
#include <iomanip>
#include <iostream>
#include <fstream>
#include <limits>
#include <numeric>
#include <mia/core/ica_template.hh>
NS_MIA_BEGIN
template <class Data>
TDataSeriesICA<Data>::TDataSeriesICA(const std::vector<Data>& initializer, bool strip_mean):
m_analysis(initializer.size(), initializer.empty() ? 0 : initializer[0].size())
{
TRACE_FUNCTION;
if (initializer.empty())
throw std::invalid_argument("TDataSeriesICA: empty series not supported");
m_size = initializer[0].get_size();
m_mean = Data(m_size);
if (strip_mean) {
for(size_t i = 0; i < initializer.size(); ++i) {
std::transform(initializer[i].begin(), initializer[i].end(),
m_mean.begin(), m_mean.begin(),
[](float x, float y){return x+y;});
}
float scale = 1.0f / initializer.size();
std::transform(m_mean.begin(), m_mean.end(), m_mean.begin(),
[&scale](float x){return x * scale;});
std::vector<float> help(initializer[0].size());
for(size_t i = 0; i < initializer.size(); ++i) {
std::transform(initializer[i].begin(), initializer[i].end(),
m_mean.begin(), help.begin(),
[](float x, float y){return x - y;});
m_analysis.set_row(i, help.begin(), help.end());
}
}else
for(size_t i = 0; i < initializer.size(); ++i)
m_analysis.set_row(i, initializer[i].begin(), initializer[i].end());
}
template <class Data>
TDataSeriesICA<Data>::~TDataSeriesICA()
{
}
template <class Data>
bool TDataSeriesICA<Data>::run(size_t ncomponents, bool strip_mean, bool ica_normalize,
std::vector<std::vector<float> > guess )
{
TRACE_FUNCTION;
bool result = m_analysis.run(ncomponents, guess);
if (result) {
if (strip_mean)
this->normalize_Mix();
if (ica_normalize)
this->normalize();
}
return result;
}
template <class Data>
const Data& TDataSeriesICA<Data>::get_mean_image() const
{
return m_mean;
}
template <class Data>
Data TDataSeriesICA<Data>::get_mix(size_t idx) const
{
TRACE_FUNCTION;
std::vector<float> mix = m_analysis.get_mix(idx);
Data result(m_size);
assert( result.size() == mix.size());
std::transform(mix.begin(), mix.end(), m_mean.begin(), result.begin(),
[](float x, float y){return x+y;});
return result;
}
template <class Data>
Data TDataSeriesICA<Data>::get_incomplete_mix(size_t idx, const IndexSet& skip) const
{
TRACE_FUNCTION;
std::vector<float> mix = m_analysis.get_incomplete_mix(idx, skip);
Data result(m_size);
assert( result.size() == mix.size());
std::transform(mix.begin(), mix.end(), m_mean.begin(), result.begin(),
[](float x, float y){return x+y;});
return result;
}
template <class Data>
Data TDataSeriesICA<Data>::get_partial_mix(size_t idx, const IndexSet& comps) const
{
TRACE_FUNCTION;
std::vector<float> mix = m_analysis.get_partial_mix(idx, comps);
Data result(m_size);
assert( result.size() == mix.size());
std::transform(mix.begin(), mix.end(), m_mean.begin(), result.begin(),
[](float x, float y){return x+y;});
return result;
}
template <class Data>
CSlopeColumns TDataSeriesICA<Data>::get_mixing_curves() const
{
TRACE_FUNCTION;
return m_analysis.get_mixing_curves();
}
template <class Data>
typename TDataSeriesICA<Data>::PData TDataSeriesICA<Data>::get_feature_image(size_t idx) const
{
Data *result = new Data(m_size);
PData presult(result);
const std::vector<float> feature = m_analysis.get_feature_row(idx);
std::copy(feature.begin(), feature.end(), result->begin());
return presult;
}
template <class Data>
typename TDataSeriesICA<Data>::PData TDataSeriesICA<Data>::get_delta_feature(const IndexSet& plus, const IndexSet& minus)const
{
Data *result = new Data(m_size);
PData presult(result);
const std::vector<float> feature = m_analysis.get_delta_feature(plus, minus);
std::copy(feature.begin(), feature.end(), result->begin());
return presult;
}
template <class Data>
void TDataSeriesICA<Data>::set_mixing_series(size_t index, const std::vector<float>& filtered_series)
{
m_analysis.set_mixing_series(index, filtered_series);
}
template <class Data>
void TDataSeriesICA<Data>::normalize()
{
m_analysis.normalize_ICs();
}
template <class Data>
void TDataSeriesICA<Data>::normalize_Mix()
{
std::vector<float> mean = m_analysis.normalize_Mix();
transform(m_mean.begin(), m_mean.end(), mean.begin(), m_mean.begin(),
[](float x, float y){return x+y;});
}
template <class Data>
size_t TDataSeriesICA<Data>::run_auto(int nica, int min_ica, float corr_thresh)
{
m_analysis.run_auto(nica, min_ica, corr_thresh);
return m_analysis.get_ncomponents();
}
template <class Data>
void TDataSeriesICA<Data>::set_max_iterations(int n)
{
m_analysis.set_max_iterations(n);
}
template <class Data>
void TDataSeriesICA<Data>::set_approach(int approach)
{
m_analysis.set_approach(approach);
}
NS_MIA_END
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