///
/// Build both the covariance and the correlation matrix
/// (members covMatrix and corMatrix)
/// from the stat and syst correlation matrics and the
/// respective errors.
///
void PDF_Abs::buildCov()
{
// add diagonals, symmetrize
buildCorMatrix(corStatMatrix);
buildCorMatrix(corSystMatrix);
// make total cov matrix
TMatrixDSym *covStat = buildCovMatrix(corStatMatrix, StatErr);
TMatrixDSym *covSyst = buildCovMatrix(corSystMatrix, SystErr);
covMatrix = *covStat + *covSyst;
// check if total cov matrix is invertible
if ( covMatrix.Determinant()==0 ) {
cout << "PDF_Abs::buildCov() : ERROR : Total covariance matrix is not invertable (det(COV)=0)." << endl;
cout << "PDF_Abs::buildCov() : ERROR : Check inputs! Ordering correct? Nobs correct?" << endl;
cout << "PDF_Abs::buildCov() : PDF: " << name << endl;
cout << "PDF_Abs::buildCov() : stat cov: " << endl;
covStat->Print("v");
cout << "PDF_Abs::buildCov() : syst cov: " << endl;
covSyst->Print("v");
cout << "PDF_Abs::buildCov() : full cov: " << endl;
covMatrix.Print("v");
//exit(1);
throw TString("need help");
}
// make total cor matrix
for ( int i=0; i<covMatrix.GetNcols(); i++ )
for ( int j=0; j<covMatrix.GetNcols(); j++ )
{
corMatrix[i][j] = covMatrix[i][j]/sqrt(covMatrix[i][i])/sqrt(covMatrix[j][j]);
}
// check if total cor matrix is positive definite
if ( ! isPosDef(&corMatrix) ) {
cout << "PDF_Abs::buildCov() : ERROR : Total correlation matrix is not positive definite." << endl;
cout << "PDF_Abs::buildCov() : ERROR : Check inputs! Ordering correct?" << endl;
cout << "PDF_Abs::buildCov() : Sometimes this happens when for very large correlations" << endl;
cout << "PDF_Abs::buildCov() : the given precision is not enough (e.g. rho=0.98 rather than 0.978)." << endl;
cout << "PDF_Abs::buildCov() : PDF: " << name << endl;
cout << "PDF_Abs::buildCov() : stat cor: " << endl;
corStatMatrix.Print("v");
cout << "PDF_Abs::buildCov() : syst cor: " << endl;
corSystMatrix.Print("v");
//exit(1);
throw TString("need help");
}
delete covStat;
delete covSyst;
// this is needed for the pull computation and the PDF_Abs::print() function:
storeErrorsInObs();
}
double operator() (double *x, double *p) {
// 4 parameters
int dim = X.GetNrows();
int k = 0;
for (int i = 0; i<dim; ++i) { X[i] = x[i] - p[k]; k++; }
for (int i = 0; i<dim; ++i) {
CovMat(i,i) = p[k]*p[k];
k++;
}
for (int i = 0; i<dim; ++i) {
for (int j = i+1; j<dim; ++j) {
// p now are the correlations N(N-1)/2
CovMat(i,j) = p[k]*sqrt(CovMat(i,i)*CovMat(j,j));
CovMat(j,i) = CovMat(i,j);
k++;
}
}
if (debug) {
X.Print();
CovMat.Print();
}
double det = CovMat.Determinant();
if (det <= 0) {
Fatal("GausND","Determinant is <= 0 det = %f",det);
CovMat.Print();
return 0;
}
double norm = std::pow( 2. * TMath::Pi(), dim/2) * sqrt(det);
// compute the gaussians
CovMat.Invert();
double fval = std::exp( - 0.5 * CovMat.Similarity(X) )/ norm;
if (debug) {
std::cout << "det " << det << std::endl;
std::cout << "norm " << norm << std::endl;
std::cout << "fval " << fval << std::endl;
}
return fval;
}
