/usr/include/fflas-ffpack/fflas/fflas_sparse/utils.h is in fflas-ffpack-common 2.2.2-5.
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// vim:sts=8:sw=8:ts=8:noet:sr:cino=>s,f0,{0,g0,(0,\:0,t0,+0,=s
/*
* Copyright (C) 2014 the FFLAS-FFPACK group
*
* Written by Bastien Vialla <bastien.vialla@lirmm.fr>
*
*
* ========LICENCE========
* This file is part of the library FFLAS-FFPACK.
*
* FFLAS-FFPACK is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* This library 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
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
* ========LICENCE========
*.
*/
/** @file fflas/fflas_sparse.h
*/
#ifndef __FFLASFFPACK_fflas_fflas_sparse_utils_H
#define __FFLASFFPACK_fflas_fflas_sparse_utils_H
#include <algorithm>
#include <numeric>
#include <vector>
namespace FFLAS{
struct StatsMatrix {
uint64_t rowdim = 0;
uint64_t coldim = 0;
uint64_t nOnes = 0;
uint64_t nMOnes = 0;
uint64_t nOthers = 0;
uint64_t nnz = 0;
uint64_t maxRow = 0;
uint64_t minRow = 0;
uint64_t averageRow = 0;
uint64_t deviationRow = 0;
uint64_t maxCol = 0;
uint64_t minCol = 0;
uint64_t averageCol = 0;
uint64_t deviationCol = 0;
uint64_t minColDifference = 0;
uint64_t maxColDifference = 0;
uint64_t averageColDifference = 0;
uint64_t deviationColDifference = 0;
uint64_t minRowDifference = 0;
uint64_t maxRowDifference = 0;
uint64_t averageRowDifference = 0;
uint64_t deviationRowDifference = 0;
uint64_t nDenseRows = 0;
uint64_t nDenseCols = 0;
uint64_t nEmptyRows = 0;
uint64_t nEmptyCols = 0;
uint64_t nEmptyColsEnd = 0;
std::vector<uint64_t> denseRows;
std::vector<uint64_t> denseCols;
};
template <class It> double computeDeviation(It begin, It end) {
using T = typename std::decay<decltype(*begin)>::type;
T average = 0;
average = std::accumulate(begin, end, 0) / (end - begin);
T sum = 0;
for (It i = begin; i != end; ++i) {
sum += ((*(i)) - average) * ((*(i)) - average);
}
return std::sqrt(sum / (end - begin));
}
template <class Field>
StatsMatrix getStat(const Field &F, const index_t *row, const index_t *col, typename Field::ConstElement_ptr val,
uint64_t rowdim, uint64_t coldim, uint64_t nnz) {
StatsMatrix stats;
stats.nnz = nnz;
stats.rowdim = rowdim;
stats.coldim = coldim;
std::vector<int64_t> rows(rowdim+1);
std::vector<int64_t> cols(coldim);
std::fill(rows.begin(), rows.end(), 0);
std::fill(cols.begin(), cols.end(), 0);
for (uint64_t i = 0; i < nnz; ++i) {
cols[col[i]]++;
if (F.isOne(val[i])) {
stats.nOnes++;
} else if (F.isMOne(val[i])) {
stats.nMOnes++;
} else {
stats.nOthers++;
}
}
rows[0] = row[0];
for(size_t i = 1 ; i < rowdim+1 ; ++i){
rows[i] = row[i] - row[i-1];
}
stats.nEmptyRows = std::count(rows.begin(), rows.end(), 0);
stats.nEmptyCols = std::count(cols.begin(), cols.end(), 0);
auto rowMinMax = std::minmax_element(rows.begin(), rows.end());
auto colMinMax = std::minmax_element(cols.begin(), cols.end());
stats.minRow = (*(rowMinMax.first));
stats.maxRow = (*(rowMinMax.second));
stats.minCol = (*(colMinMax.first));
stats.maxCol = (*(colMinMax.second));
stats.averageRow = std::accumulate(rows.begin(), rows.end(), 0) / rowdim;
stats.averageCol = std::accumulate(cols.begin(), cols.end(), 0) / coldim;
stats.deviationRow = (uint64_t)computeDeviation(rows.begin(), rows.end());
stats.deviationCol = (uint64_t)computeDeviation(cols.begin(), cols.end());
stats.nDenseRows = std::count_if(rows.begin(), rows.begin(),
[rowdim](uint64_t &x) { return x >= DENSE_THRESHOLD * rowdim; });
stats.nDenseCols = std::count_if(cols.begin(), cols.begin(),
[coldim](uint64_t &x) { return x >= DENSE_THRESHOLD * coldim; });
return stats;
}
}
#endif
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