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add the Eigen library implementations of normal distributions computations

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nojhan 2012-07-09 18:47:35 +02:00
commit f3e1562a14
5 changed files with 301 additions and 106 deletions
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67
edo/src/edoSamplerNormalMulti.h
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@ -33,12 +33,6 @@ Authors:
#include <edoSampler.h>
#ifdef WITH_BOOST
#include <utils/edoCholesky.h>
#include <boost/numeric/ublas/lu.hpp>
#include <boost/numeric/ublas/symmetric.hpp>
/** Sample points in a multi-normal law defined by a mean vector and a covariance matrix.
*
* Given M the mean vector and V the covariance matrix, of order n:
@ -46,6 +40,13 @@ Authors:
* - compute the Cholesky decomposition L of V (i.e. such as V=LL*)
* - return X = M + LT
*/
#ifdef WITH_BOOST
#include <utils/edoCholesky.h>
#include <boost/numeric/ublas/lu.hpp>
#include <boost/numeric/ublas/symmetric.hpp>
template< class EOT, typename EOD = edoNormalMulti< EOT > >
class edoSamplerNormalMulti : public edoSampler< EOD >
{
@ -90,6 +91,60 @@ protected:
#else
#ifdef WITH_EIGEN
template< class EOT, typename EOD = edoNormalMulti< EOT > >
class edoSamplerNormalMulti : public edoSampler< EOD >
{
public:
typedef typename EOT::AtomType AtomType;
typedef Eigen::Matrix< AtomType, Eigen::Dynamic, 1> Vector;
typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> Matrix;
edoSamplerNormalMulti( edoRepairer<EOT> & repairer )
: edoSampler< EOD >( repairer)
{}
EOT sample( EOD& distrib )
{
unsigned int size = distrib.size();
assert(size > 0);
// L = cholesky decomposition of varcovar
// Computes L and D such as V = L D L^T
Eigen::LDLT<Matrix> cholesky( distrib.varcovar() );
Matrix L0 = cholesky.matrixL();
Eigen::Diagonal<const Matrix> D = cholesky.vectorD();
// now compute the final symetric matrix: this->_L = L D^1/2
// remember that V = ( L D^1/2) ( L D^1/2)^T
// fortunately, the square root of a diagonal matrix is the square
// root of all its elements
Eigen::Diagonal<const Matrix> sqrtD = D.cwiseSqrt();
Matrix L = L0 * D;
// T = vector of size elements drawn in N(0,1)
Vector T( size );
for ( unsigned int i = 0; i < size; ++i ) {
T( i ) = rng.normal();
}
// LT = L * T
Vector LT = L * T;
// solution = means + LT
Vector mean = distrib.mean();
Vector typed_solution = mean + LT;
EOT solution( size );
for( unsigned int i = 0; i < mean.innerSize(); i++ ) {
solution.push_back( typed_solution(i) );
}
return solution;
}
};
#endif // WITH_EIGEN
#endif // WITH_BOOST