add the Eigen library implementations of normal distributions computations
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nojhan
parent
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commit
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5 changed files with 301 additions and 106 deletions
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@ -34,8 +34,8 @@ Authors:
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#include "edoNormalMulti.h"
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#ifdef WITH_BOOST
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//! edoEstimatorNormalMulti< EOT >
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//! edoEstimatorNormalMulti< EOT >
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template < typename EOT >
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class edoEstimatorNormalMulti : public edoEstimator< edoNormalMulti< EOT > >
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{
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@ -43,95 +43,95 @@ public:
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class CovMatrix
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{
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public:
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typedef typename EOT::AtomType AtomType;
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typedef typename EOT::AtomType AtomType;
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CovMatrix( const eoPop< EOT >& pop )
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{
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//-------------------------------------------------------------
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// Some checks before starting to estimate covar
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//-------------------------------------------------------------
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CovMatrix( const eoPop< EOT >& pop )
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{
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//-------------------------------------------------------------
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// Some checks before starting to estimate covar
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//-------------------------------------------------------------
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unsigned int p_size = pop.size(); // population size
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assert(p_size > 0);
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unsigned int p_size = pop.size(); // population size
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assert(p_size > 0);
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unsigned int s_size = pop[0].size(); // solution size
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assert(s_size > 0);
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unsigned int s_size = pop[0].size(); // solution size
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assert(s_size > 0);
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//-------------------------------------------------------------
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//-------------------------------------------------------------
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//-------------------------------------------------------------
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// Copy the population to an ublas matrix
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//-------------------------------------------------------------
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//-------------------------------------------------------------
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// Copy the population to an ublas matrix
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//-------------------------------------------------------------
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ublas::matrix< AtomType > sample( p_size, s_size );
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ublas::matrix< AtomType > sample( p_size, s_size );
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for (unsigned int i = 0; i < p_size; ++i)
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{
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for (unsigned int j = 0; j < s_size; ++j)
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{
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sample(i, j) = pop[i][j];
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}
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}
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for (unsigned int i = 0; i < p_size; ++i)
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{
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for (unsigned int j = 0; j < s_size; ++j)
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{
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sample(i, j) = pop[i][j];
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}
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}
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//-------------------------------------------------------------
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//-------------------------------------------------------------
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_varcovar.resize(s_size);
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_varcovar.resize(s_size);
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//-------------------------------------------------------------
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// variance-covariance matrix are symmetric (and semi-definite
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// positive), thus a triangular storage is sufficient
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//
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// variance-covariance matrix computation : transpose(A) * A
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//-------------------------------------------------------------
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//-------------------------------------------------------------
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// variance-covariance matrix are symmetric (and semi-definite
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// positive), thus a triangular storage is sufficient
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//
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// variance-covariance matrix computation : transpose(A) * A
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//-------------------------------------------------------------
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ublas::symmetric_matrix< AtomType, ublas::lower > var = ublas::prod( ublas::trans( sample ), sample );
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ublas::symmetric_matrix< AtomType, ublas::lower > var = ublas::prod( ublas::trans( sample ), sample );
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// Be sure that the symmetric matrix got the good size
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// Be sure that the symmetric matrix got the good size
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assert(var.size1() == s_size);
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assert(var.size2() == s_size);
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assert(var.size1() == _varcovar.size1());
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assert(var.size2() == _varcovar.size2());
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assert(var.size1() == s_size);
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assert(var.size2() == s_size);
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assert(var.size1() == _varcovar.size1());
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assert(var.size2() == _varcovar.size2());
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//-------------------------------------------------------------
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//-------------------------------------------------------------
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// TODO: to remove the comment below
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// TODO: to remove the comment below
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// for (unsigned int i = 0; i < s_size; ++i)
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// {
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// // triangular LOWER matrix, thus j is not going further than i
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// for (unsigned int j = 0; j <= i; ++j)
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// {
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// // we want a reducted covariance matrix
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// _varcovar(i, j) = var(i, j) / p_size;
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// }
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// }
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// for (unsigned int i = 0; i < s_size; ++i)
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// {
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// // triangular LOWER matrix, thus j is not going further than i
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// for (unsigned int j = 0; j <= i; ++j)
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// {
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// // we want a reducted covariance matrix
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// _varcovar(i, j) = var(i, j) / p_size;
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// }
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// }
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_varcovar = var / p_size;
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_varcovar = var / p_size;
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_mean.resize(s_size); // FIXME: check if it is really used because of the assignation below
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_mean.resize(s_size); // FIXME: check if it is really used because of the assignation below
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// unit vector
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ublas::scalar_vector< AtomType > u( p_size, 1 );
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// unit vector
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ublas::scalar_vector< AtomType > u( p_size, 1 );
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// sum over columns
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_mean = ublas::prod( ublas::trans( sample ), u );
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// sum over columns
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_mean = ublas::prod( ublas::trans( sample ), u );
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// division by n
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_mean /= p_size;
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}
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// division by n
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_mean /= p_size;
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}
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const ublas::symmetric_matrix< AtomType, ublas::lower >& get_varcovar() const {return _varcovar;}
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const ublas::symmetric_matrix< AtomType, ublas::lower >& get_varcovar() const {return _varcovar;}
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const ublas::vector< AtomType >& get_mean() const {return _mean;}
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const ublas::vector< AtomType >& get_mean() const {return _mean;}
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private:
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ublas::symmetric_matrix< AtomType, ublas::lower > _varcovar;
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ublas::vector< AtomType > _mean;
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ublas::symmetric_matrix< AtomType, ublas::lower > _varcovar;
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ublas::vector< AtomType > _mean;
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};
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public:
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@ -139,21 +139,109 @@ public:
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edoNormalMulti< EOT > operator()(eoPop<EOT>& pop)
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{
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unsigned int popsize = pop.size();
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assert(popsize > 0);
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unsigned int popsize = pop.size();
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assert(popsize > 0);
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unsigned int dimsize = pop[0].size();
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assert(dimsize > 0);
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unsigned int dimsize = pop[0].size();
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assert(dimsize > 0);
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CovMatrix cov( pop );
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CovMatrix cov( pop );
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return edoNormalMulti< EOT >( cov.get_mean(), cov.get_varcovar() );
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return edoNormalMulti< EOT >( cov.get_mean(), cov.get_varcovar() );
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}
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};
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#else
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#ifdef WITH_EIGEN
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//! edoEstimatorNormalMulti< EOT >
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template < typename EOT >
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class edoEstimatorNormalMulti : public edoEstimator< edoNormalMulti< EOT > >
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{
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public:
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class CovMatrix
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{
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public:
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typedef typename EOT::AtomType AtomType;
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typedef Eigen::Matrix< AtomType, Eigen::Dynamic, 1> Vector;
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typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> Matrix;
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CovMatrix( const eoPop< EOT >& pop )
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{
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// Some checks before starting to estimate covar
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unsigned int p_size = pop.size(); // population size
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assert(p_size > 0);
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unsigned int s_size = pop[0].size(); // solution size
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assert(s_size > 0);
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// Copy the population to an ublas matrix
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//ublas::matrix< AtomType > sample( p_size, s_size );
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Matrix sample( p_size, s_size );
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for (unsigned int i = 0; i < p_size; ++i) {
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for (unsigned int j = 0; j < s_size; ++j) {
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sample(i, j) = pop[i][j];
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}
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}
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// _varcovar.resize(s_size);
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// variance-covariance matrix are symmetric, thus a triangular storage is sufficient
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// variance-covariance matrix computation : transpose(A) * A
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//ublas::symmetric_matrix< AtomType, ublas::lower > var = ublas::prod( ublas::trans( sample ), sample );
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Matrix var = sample.transpose() * sample;
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// Be sure that the symmetric matrix got the good size
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assert(var.innerSize() == s_size);
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assert(var.outerSize() == s_size);
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assert(var.innerSize() == _varcovar.innerSize());
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assert(var.outerSize() == _varcovar.outerSize());
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_varcovar = var / p_size;
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// _mean.resize(s_size); // FIXME: check if it is really used because of the assignation below
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// unit vector
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// ublas::scalar_vector< AtomType > u( p_size, 1 );
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Vector u( p_size, 1);
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// sum over columns
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// _mean = ublas::prod( ublas::trans( sample ), u );
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_mean = sample.transpose() * u;
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// division by n
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_mean /= p_size;
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}
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// const ublas::symmetric_matrix< AtomType, ublas::lower >& get_varcovar() const {return _varcovar;}
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const Matrix& get_varcovar() const {return _varcovar;}
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// const ublas::vector< AtomType >& get_mean() const {return _mean;}
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const Vector& get_mean() const {return _mean;}
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private:
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// ublas::symmetric_matrix< AtomType, ublas::lower > _varcovar;
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Matrix _varcovar;
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// ublas::vector< AtomType > _mean;
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Vector _mean;
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};
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public:
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typedef typename EOT::AtomType AtomType;
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edoNormalMulti< EOT > operator()(eoPop<EOT>& pop)
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{
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unsigned int popsize = pop.size();
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assert(popsize > 0);
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unsigned int dimsize = pop[0].size();
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assert(dimsize > 0);
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CovMatrix cov( pop );
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return edoNormalMulti< EOT >( cov.get_mean(), cov.get_varcovar() );
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}
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};
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#endif // WITH_EIGEN
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#endif // WITH_BOOST
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