nojhan/paradiseo
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

working multi-normal sampler with eigen

Browse source 
Diagonal matrix are intermediate type, implicit conversion to matrix is needed.
This commit is contained in:
nojhan 2012-07-09 22:36:41 +02:00
commit 661ef08e44
1 changed files with 12 additions and 12 deletions
Download patch file Download diff file Expand all files Collapse all files

24
edo/src/edoSamplerNormalMulti.h
View file

@ -109,21 +109,19 @@ public:
unsigned int size = distrib.size();
assert(size > 0);
// L = cholesky decomposition of varcovar
// LsD = 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();
Matrix L = cholesky.matrixL();
Matrix D = cholesky.vectorD();
// now compute the final symetric matrix: this->_L = L D^1/2
// now compute the final symetric matrix: LsD = 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;
Matrix sqrtD = D.cwiseSqrt();
Matrix LsD = L * sqrtD;
// T = vector of size elements drawn in N(0,1)
Vector T( size );
@ -131,12 +129,14 @@ public:
T( i ) = rng.normal();
}
// LT = L * T
Vector LT = L * T;
// LDT = (L D^1/2) * T
Vector LDT = LsD * T;
// solution = means + LT
// solution = means + LDT
Vector mean = distrib.mean();
Vector typed_solution = mean + LT;
Vector typed_solution = mean + LDT;
// copy in the EOT structure (more probably a vector)
EOT solution( size );
for( unsigned int i = 0; i < mean.innerSize(); i++ ) {
solution.push_back( typed_solution(i) );