MatrixXd MultivariateFNormalSufficient::evaluate_derivative_Sigma() const
{
//d(-log(p))/dSigma = 1/2 (N P - N P epsilon transpose(epsilon) P - PWP)
LOG( "MVN: evaluate_derivative_Sigma() = " << std::endl);
MatrixXd R;
if (N_==1) // O(M) if up to date, O(M^3) if Sigma new
{
R = 0.5*(get_P()-compute_PTP()/SQUARE(factor_));
} else { // O(M^2) if up to date, O(M^3) if Sigma new
double f2=SQUARE(factor_);
R = 0.5*(N_*(get_P()-compute_PTP()/f2)-compute_PWP()/f2);
}
return R;
}
MatrixXd MultivariateFNormalSufficient::evaluate_derivative_Sigma() const
{
timer_.start(DSIGMA);
//d(-log(p))/dSigma = 1/2 (N P - N P epsilon transpose(epsilon) P - PWP)
IMP_LOG(TERSE, "MVN: evaluate_derivative_Sigma() = " << std::endl);
MatrixXd R;
if (N_==1) // O(M) if up to date, O(M^3) if Sigma new
{
R = 0.5*(get_P()-compute_PTP()/IMP::square(factor_));
} else { // O(M^2) if up to date, O(M^3) if Sigma new
double f2=IMP::square(factor_);
R = 0.5*(N_*(get_P()-compute_PTP()/f2)-compute_PWP()/f2);
}
timer_.stop(DSIGMA);
return R;
}
cholmod_sparse *
MultivariateFNormalSufficientSparse::evaluate_derivative_Sigma() const
{
//d(-log(p))/dSigma = 1/2 (N P - N P epsilon transpose(epsilon) P - P W P)
IMP_LOG(TERSE, "MVNsparse: evaluate_derivative_Sigma() = " << std::endl);
cholmod_sparse *ptp(compute_PTP());
cholmod_sparse *pwp(compute_PWP());
//std::cout << " ptp " << std::endl << ptp << std::endl << std::endl;
//std::cout << " pwp " << std::endl << pwp << std::endl << std::endl;
static double one[2]={1,0};
static double minusone[2]={-1,0};
cholmod_sparse *tmp =
cholmod_add(P_, ptp, one, minusone, true, false, c_);
double enn[2]={0.5*N_,0};
static double ptfive[2]={-0.5,0};
cholmod_sparse *R = cholmod_add(tmp, pwp, enn, ptfive, true, false, c_);
cholmod_free_sparse(&ptp, c_);
cholmod_free_sparse(&pwp, c_);
cholmod_free_sparse(&tmp, c_);
return R;
}
