test binary now computes average errors of the differents cholesky factorization methods over random covariance matrices
This commit is contained in:
nojhan
parent
17ce8a90e3
commit
06100a6b57
2 changed files with 130 additions and 44 deletions
|
|
@ -58,7 +58,11 @@ public:
|
|||
class Cholesky
|
||||
{
|
||||
public:
|
||||
//! The covariance-matrix is symetric
|
||||
typedef ublas::symmetric_matrix< AtomType, ublas::lower > CovarMat;
|
||||
|
||||
//! The factorization matrix is triangular
|
||||
// FIXME check if triangular types behaviour is like having 0
|
||||
typedef ublas::matrix< AtomType > FactorMat;
|
||||
|
||||
enum Method {
|
||||
|
|
|
|||
|
|
@ -25,12 +25,13 @@ Authors:
|
|||
Johann Dréo <johann.dreo@thalesgroup.com>
|
||||
*/
|
||||
|
||||
#include <vector>
|
||||
//#include <vector>
|
||||
#include <cstdlib>
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
#include <limits>
|
||||
#include <iomanip>
|
||||
#include <ctime>
|
||||
|
||||
#include <eo>
|
||||
#include <es.h>
|
||||
|
|
@ -97,66 +98,147 @@ bool equal( const MT& M1, const MT& M2, double prec /* = 1/std::numeric_limits<d
|
|||
}
|
||||
|
||||
|
||||
template<typename MT >
|
||||
MT error( const MT& M1, const MT& M2 )
|
||||
{
|
||||
assert( M1.size1() == M2.size1() && M1.size1() == M2.size2() );
|
||||
|
||||
MT Err = ublas::zero_matrix<double>(M1.size1(),M1.size2());
|
||||
|
||||
for( unsigned int i=0; i<M1.size1(); ++i ) {
|
||||
for( unsigned int j=0; j<M1.size2(); ++j ) {
|
||||
Err(i,j) = M1(i,j) - M2(i,j);
|
||||
}
|
||||
}
|
||||
|
||||
return Err;
|
||||
}
|
||||
|
||||
|
||||
template<typename MT >
|
||||
double trigsum( const MT& M )
|
||||
{
|
||||
double sum;
|
||||
for( unsigned int i=0; i<M.size1(); ++i ) {
|
||||
for( unsigned int j=i; j<M.size2(); ++j ) { // triangular browsing
|
||||
sum += fabs( M(i,j) ); // absolute deviation
|
||||
}
|
||||
}
|
||||
return sum;
|
||||
}
|
||||
|
||||
|
||||
template<typename T>
|
||||
double sum( const T& c )
|
||||
{
|
||||
return std::accumulate(c.begin(), c.end(), 0);
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
srand(time(0));
|
||||
|
||||
unsigned int N = 4;
|
||||
double precision = 1e-15;
|
||||
unsigned int N = 4; // size of matrix
|
||||
unsigned int R = 1000; // nb of repetitions
|
||||
|
||||
if( argc >= 2 ) {
|
||||
N = std::atof(argv[1]);
|
||||
N = std::atoi(argv[1]);
|
||||
}
|
||||
if( argc >= 3 ) {
|
||||
precision = std::atof(argv[2]);
|
||||
R = std::atoi(argv[2]);
|
||||
}
|
||||
|
||||
std::cout << "Usage: t-cholesky [matrix size] [repetitions]" << std::endl;
|
||||
std::cout << "matrix size = " << N << std::endl;
|
||||
std::cout << "repetitions = " << R << std::endl;
|
||||
|
||||
typedef edoSamplerNormalMulti<EOT,EOD>::Cholesky::CovarMat CovarMat;
|
||||
typedef edoSamplerNormalMulti<EOT,EOD>::Cholesky::FactorMat FactorMat;
|
||||
|
||||
// a variance-covariance matrix of size N*N
|
||||
CovarMat V(N,N);
|
||||
edoSamplerNormalMulti<EOT,EOD>::Cholesky LLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::standard );
|
||||
edoSamplerNormalMulti<EOT,EOD>::Cholesky LLTa( edoSamplerNormalMulti<EOT,EOD>::Cholesky::absolute );
|
||||
edoSamplerNormalMulti<EOT,EOD>::Cholesky LDLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::robust );
|
||||
|
||||
// random covariance matrix
|
||||
for( unsigned int i=0; i<N; ++i) {
|
||||
V(i,i) = 1 + std::pow(rand(),2); // variance should be > 0
|
||||
for( unsigned int j=i+1; j<N; ++j) {
|
||||
V(i,j) = rand();
|
||||
std::vector<double> s0,s1,s2;
|
||||
for( unsigned int n=0; n<R; ++n ) {
|
||||
|
||||
// a variance-covariance matrix of size N*N
|
||||
CovarMat V(N,N);
|
||||
|
||||
// random covariance matrix
|
||||
for( unsigned int i=0; i<N; ++i) {
|
||||
V(i,i) = std::pow(rand(),2); // variance should be >= 0
|
||||
for( unsigned int j=i+1; j<N; ++j) {
|
||||
V(i,j) = rand();
|
||||
}
|
||||
}
|
||||
|
||||
FactorMat L0 = LLT(V);
|
||||
CovarMat V0 = ublas::prod( L0, ublas::trans(L0) );
|
||||
s0.push_back( trigsum(error(V,V0)) );
|
||||
|
||||
FactorMat L1 = LLTa(V);
|
||||
CovarMat V1 = ublas::prod( L1, ublas::trans(L1) );
|
||||
s1.push_back( trigsum(error(V,V1)) );
|
||||
|
||||
FactorMat L2 = LDLT(V);
|
||||
CovarMat V2 = ublas::prod( L2, ublas::trans(L2) );
|
||||
s2.push_back( trigsum(error(V,V2)) );
|
||||
}
|
||||
|
||||
std::cout << "usage: t-cholesky [N] [precision]" << std::endl;
|
||||
std::cout << "N = " << N << std::endl;
|
||||
std::cout << "precision = " << precision << std::endl;
|
||||
std::string linesep = "--------------------------------------------------------------------------------------------";
|
||||
std::cout << linesep << std::endl;
|
||||
std::cout << "Average error:" << std::endl;
|
||||
std::cout << "\tLLT: " << sum(s0)/R << std::endl;
|
||||
std::cout << "\tLLTa: " << sum(s1)/R << std::endl;
|
||||
std::cout << "\tLDLT: " << sum(s2)/R << std::endl;
|
||||
|
||||
setformat(std::cout);
|
||||
|
||||
std::cout << "Covariance matrix" << std::endl << format(V) << std::endl;
|
||||
std::cout << linesep << std::endl;
|
||||
|
||||
edoSamplerNormalMulti<EOT,EOD>::Cholesky LLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::standard );
|
||||
FactorMat L0 = LLT(V);
|
||||
std::cout << "LLT" << std::endl << format(L0) << std::endl;
|
||||
CovarMat V0 = ublas::prod( L0, ublas::trans(L0) );
|
||||
std::cout << "LLT covar" << std::endl << format(V0) << std::endl;
|
||||
assert( equal(V0,V,precision) );
|
||||
std::cout << linesep << std::endl;
|
||||
|
||||
edoSamplerNormalMulti<EOT,EOD>::Cholesky LLTa( edoSamplerNormalMulti<EOT,EOD>::Cholesky::absolute );
|
||||
FactorMat L1 = LLTa(V);
|
||||
std::cout << "LLT abs" << std::endl << format(L1) << std::endl;
|
||||
CovarMat V1 = ublas::prod( L1, ublas::trans(L1) );
|
||||
std::cout << "LLT covar" << std::endl << format(V1) << std::endl;
|
||||
assert( equal(V1,V,precision) );
|
||||
std::cout << linesep << std::endl;
|
||||
|
||||
edoSamplerNormalMulti<EOT,EOD>::Cholesky LDLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::robust );
|
||||
FactorMat L2 = LDLT(V);
|
||||
std::cout << "LDLT" << std::endl << format(L2) << std::endl;
|
||||
CovarMat V2 = ublas::prod( L2, ublas::trans(L2) );
|
||||
std::cout << "LDLT covar" << std::endl << format(V2) << std::endl;
|
||||
assert( equal(V2,V,precision) );
|
||||
std::cout << linesep << std::endl;
|
||||
// double precision = 1e-15;
|
||||
// if( argc >= 4 ) {
|
||||
// precision = std::atof(argv[3]);
|
||||
// }
|
||||
// std::cout << "precision = " << precision << std::endl;
|
||||
// std::cout << "usage: t-cholesky [N] [precision]" << std::endl;
|
||||
// std::cout << "N = " << N << std::endl;
|
||||
// std::cout << "precision = " << precision << std::endl;
|
||||
// std::string linesep = "--------------------------------------------------------------------------------------------";
|
||||
// std::cout << linesep << std::endl;
|
||||
//
|
||||
// setformat(std::cout);
|
||||
//
|
||||
// std::cout << "Covariance matrix" << std::endl << format(V) << std::endl;
|
||||
// std::cout << linesep << std::endl;
|
||||
//
|
||||
// edoSamplerNormalMulti<EOT,EOD>::Cholesky LLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::standard );
|
||||
// FactorMat L0 = LLT(V);
|
||||
// CovarMat V0 = ublas::prod( L0, ublas::trans(L0) );
|
||||
// CovarMat E0 = error(V,V0);
|
||||
// std::cout << "LLT" << std::endl << format(E0) << std::endl;
|
||||
// std::cout << trigsum(E0) << std::endl;
|
||||
// std::cout << "LLT" << std::endl << format(L0) << std::endl;
|
||||
// std::cout << "LLT covar" << std::endl << format(V0) << std::endl;
|
||||
// assert( equal(V0,V,precision) );
|
||||
// std::cout << linesep << std::endl;
|
||||
//
|
||||
// edoSamplerNormalMulti<EOT,EOD>::Cholesky LLTa( edoSamplerNormalMulti<EOT,EOD>::Cholesky::absolute );
|
||||
// FactorMat L1 = LLTa(V);
|
||||
// CovarMat V1 = ublas::prod( L1, ublas::trans(L1) );
|
||||
// CovarMat E1 = error(V,V1);
|
||||
// std::cout << "LLT abs" << std::endl << format(E1) << std::endl;
|
||||
// std::cout << trigsum(E1) << std::endl;
|
||||
// std::cout << "LLT abs" << std::endl << format(L1) << std::endl;
|
||||
// std::cout << "LLT covar" << std::endl << format(V1) << std::endl;
|
||||
// assert( equal(V1,V,precision) );
|
||||
// std::cout << linesep << std::endl;
|
||||
//
|
||||
// edoSamplerNormalMulti<EOT,EOD>::Cholesky LDLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::robust );
|
||||
// FactorMat L2 = LDLT(V);
|
||||
// CovarMat V2 = ublas::prod( L2, ublas::trans(L2) );
|
||||
// CovarMat E2 = error(V,V2);
|
||||
// std::cout << "LDLT" << std::endl << format(E2) << std::endl;
|
||||
// std::cout << trigsum(E2) << std::endl;
|
||||
// std::cout << "LDLT" << std::endl << format(L2) << std::endl;
|
||||
// std::cout << "LDLT covar" << std::endl << format(V2) << std::endl;
|
||||
// assert( equal(V2,V,precision) );
|
||||
// std::cout << linesep << std::endl;
|
||||
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue