/usr/include/mia-2.0/mia/core/kmeans.hh 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 | /* -*- 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/>.
*
*/
#ifndef __mia_core_kmeans_hh
#define __mia_core_kmeans_hh
#include <vector>
#include <numeric>
#include <cmath>
#include <stdexcept>
#include <iomanip>
#include <limits>
#include <mia/core/defines.hh>
#include <mia/core/errormacro.hh>
#include <boost/concept/requires.hpp>
#include <boost/concept_check.hpp>
NS_MIA_BEGIN
/// helper function called by kmeans - don't call it directly
template <typename InputIterator, typename OutputIterator>
bool kmeans_step(InputIterator ibegin, InputIterator iend, OutputIterator obegin,
std::vector<double>& classes, size_t l, int& biggest_class )
{
cvdebug()<< "kmeans enter: ";
for (size_t i = 0; i <= l; ++i )
cverb << std::setw(8) << classes[i]<< " ";
cverb << "\n";
biggest_class = -1;
const double convLimit = 0.005; // currently fixed
std::vector<double> sums(classes.size());
std::vector<size_t> count(classes.size());
bool conv = false;
int iter = 50;
while( iter-- && !conv) {
sort(classes.begin(), classes.end());
// assign closest cluster center
OutputIterator ob = obegin;
for (InputIterator b = ibegin; b != iend; ++b, ++ob) {
const double val = *b;
double dmin = std::numeric_limits<double>::max();
int c = 0;
for (size_t i = 0; i <= l; i++) {
double d = fabs (val - classes[i]);
if (d < dmin) {
dmin = d;
c = i;
};
};
*ob = c;
++count[c];
sums[c] += val;
};
// recompute cluster centers
conv = true;
size_t max_count = 0;
for (size_t i = 0; i <= l; i++) {
if (count[i]) {
double a = sums[i] / count[i];
if (a && fabs ((a - classes[i]) / a) > convLimit)
conv = false;
classes[i] = a;
if (max_count < count[i]) {
max_count = count[i];
biggest_class = i;
}
} else { // if a class is empty move it closer to neighbour
if (i == 0)
classes[i] = (classes[i] + classes[i + 1]) / 2.0;
else
classes[i] = (classes[i] + classes[i - 1]) / 2.0;
conv = false;
}
sums[i] = 0;
count[i] = 0;
};
};
cvinfo()<< "kmeans: " << l + 1 << " classes " << 50 - iter << " iterations";
for (size_t i = 0; i <= l; ++i )
cverb << std::setw(8) << classes[i]<< " ";
cverb << "\n";
return conv;
}
/**
\ingroup misc
Run a kmeans clustering on some input data and store the class centers and the
class membership.
\tparam InputIterator readable forward iterator,
\tparam OutputIterator writable forward iterator,
\param ibegin iterator indicating the start of the input data
\param iend iterator indicating the end of the input data, expect an STL-like handling,
i.e. iend points behind the last element to be accessed
\param obegin begin of the output range where the class membership will be stored
it is up to the caller to ensure that this range is at least as large as the input range
\param[in,out] classes at input the size of the vector indicates the number of clusters to be used
at output the vector elements contain the class centers in increasing order.
*/
template <typename InputIterator, typename OutputIterator>
BOOST_CONCEPT_REQUIRES( ((::boost::ForwardIterator<InputIterator>))
((::boost::Mutable_ForwardIterator<OutputIterator>)),
(void)
)
kmeans(InputIterator ibegin, InputIterator iend, OutputIterator obegin,
std::vector<double>& classes)
{
if (classes.size() < 2)
throw create_exception<std::invalid_argument>("kmeans: requested ", classes.size(),
"class(es), required are at least two");
const size_t nclusters = classes.size();
const double size = std::distance(ibegin, iend);
if ( size < nclusters )
throw create_exception<std::invalid_argument>("kmeans: insufficient input: want ", nclusters ,
" classes, but git only ", size, " input elements");
double sum = std::accumulate(ibegin, iend, 0.0);
// simple initialization splitting at the mean
classes[0] = sum / (size - 1);
classes[1] = sum / (size + 1);
// first run calles directly
int biggest_class = 0;
kmeans_step(ibegin, iend, obegin, classes, 1, biggest_class);
// further clustering always splits biggest class
for (size_t l = 2; l < nclusters; l++) {
const size_t pos = biggest_class > 0 ? biggest_class - 1 : biggest_class + 1;
classes[l] = 0.5 * (classes[biggest_class] + classes[pos]);
kmeans_step(ibegin, iend, obegin, classes, l, biggest_class);
};
// some post iteration until centers no longer change
for (size_t l = 1; l < 3; l++) {
if (kmeans_step(ibegin, iend, obegin, classes, nclusters - 1, biggest_class))
break;
}
}
NS_MIA_END
#endif
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