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rename everything from 'do' to 'edo'

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Johann Dreo 2011-01-27 11:23:23 +01:00
commit d618ab07df
94 changed files with 2936 additions and 1534 deletions
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129
src/doEstimatorNormalMulti.h
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@ -1,129 +0,0 @@
// (c) Thales group, 2010
/*
Authors:
Johann Dreo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _doEstimatorNormalMulti_h
#define _doEstimatorNormalMulti_h
#include "doEstimator.h"
#include "doNormalMulti.h"
template < typename EOT >
class doEstimatorNormalMulti : public doEstimator< doNormalMulti< EOT > >
{
public:
class CovMatrix
{
public:
typedef typename EOT::AtomType AtomType;
CovMatrix( const eoPop< EOT >& pop )
{
//-------------------------------------------------------------
// Some checks before starting to estimate covar
//-------------------------------------------------------------
unsigned int p_size = pop.size(); // population size
assert(p_size > 0);
unsigned int s_size = pop[0].size(); // solution size
assert(s_size > 0);
//-------------------------------------------------------------
//-------------------------------------------------------------
// Copy the population to an ublas matrix
//-------------------------------------------------------------
ublas::matrix< AtomType > sample( p_size, s_size );
for (unsigned int i = 0; i < p_size; ++i)
{
for (unsigned int j = 0; j < s_size; ++j)
{
sample(i, j) = pop[i][j];
}
}
//-------------------------------------------------------------
_varcovar.resize(s_size, s_size);
//-------------------------------------------------------------
// variance-covariance matrix are symmetric (and semi-definite
// positive), thus a triangular storage is sufficient
//
// variance-covariance matrix computation : transpose(A) * A
//-------------------------------------------------------------
ublas::symmetric_matrix< AtomType, ublas::lower > var = ublas::prod( ublas::trans( sample ), sample );
// Be sure that the symmetric matrix got the good size
assert(var.size1() == s_size);
assert(var.size2() == s_size);
assert(var.size1() == _varcovar.size1());
assert(var.size2() == _varcovar.size2());
//-------------------------------------------------------------
// TODO: to remove the comment below
// for (unsigned int i = 0; i < s_size; ++i)
// {
// // triangular LOWER matrix, thus j is not going further than i
// for (unsigned int j = 0; j <= i; ++j)
// {
// // we want a reducted covariance matrix
// _varcovar(i, j) = var(i, j) / p_size;
// }
// }
_varcovar = var / p_size;
_mean.resize(s_size); // FIXME: check if it is really used because of the assignation below
// unit vector
ublas::scalar_vector< AtomType > u( p_size, 1 );
// sum over columns
_mean = ublas::prod( ublas::trans( sample ), u );
// division by n
_mean /= p_size;
}
const ublas::symmetric_matrix< AtomType, ublas::lower >& get_varcovar() const {return _varcovar;}
const ublas::vector< AtomType >& get_mean() const {return _mean;}
private:
ublas::symmetric_matrix< AtomType, ublas::lower > _varcovar;
ublas::vector< AtomType > _mean;
};
public:
typedef typename EOT::AtomType AtomType;
doNormalMulti< EOT > operator()(eoPop<EOT>& pop)
{
unsigned int popsize = pop.size();
assert(popsize > 0);
unsigned int dimsize = pop[0].size();
assert(dimsize > 0);
CovMatrix cov( pop );
return doNormalMulti< EOT >( cov.get_mean(), cov.get_varcovar() );
}
};
#endif // !_doEstimatorNormalMulti_h