/* energy (score) functions, aka -log(p).
* O(M) if Sigma is up to date
* O(M^3) if Sigma has changed (Cholesky decomp.) */
double MultivariateFNormalSufficient::evaluate() const
{
double e = get_norms()[1] + get_minus_log_jacobian();
if (N_==1)
{
e += 0.5*get_mean_square_residuals()/SQUARE(factor_) ;
/*std::cout << "mvn"
<< " " << e
<< " " << get_norms()[0]
<< " " << get_norms()[1]
<< " " << get_minus_log_jacobian()
<< " " << get_mean_square_residuals()/SQUARE(factor_)
<<std::endl; */
} else {
e += 0.5*( trace_WP()
+ double(N_)*get_mean_square_residuals())/SQUARE(factor_) ;
}
//if log(norm) is -inf, make the score bad, e.g. +inf.
//should check if epsilon==0 in which case we should return -inf
//to mimic the Dirac distribution, but this case has undefined derivatives
if (std::abs(e) > std::numeric_limits<double>::max())
e=std::numeric_limits<double>::infinity();
LOG( "MVN: evaluate() = " << e << std::endl);
return e;
}
/* energy (score) functions, aka -log(p).
* O(M) if Sigma is up to date
* O(M^3) if Sigma has changed (Cholesky decomp.) */
double MultivariateFNormalSufficient::evaluate() const
{
timer_.start(EVAL);
double e;
if (N_==1)
{
e = get_norms()[1] + get_minus_log_jacobian()
+ 0.5*get_mean_square_residuals()/IMP::square(factor_) ;
/*std::cout << "mvn"
<< " " << e
<< " " << get_norms()[0]
<< " " << get_norms()[1]
<< " " << get_minus_log_jacobian()
<< " " << get_mean_square_residuals()/IMP::square(factor_)
<<std::endl; */
} else {
e = get_norms()[1] + get_minus_log_jacobian()
+ 0.5*( trace_WP()
+ double(N_)*get_mean_square_residuals())/IMP::square(factor_) ;
}
IMP_LOG(TERSE, "MVN: evaluate() = " << e << std::endl);
timer_.stop(EVAL);
return e;
}
double MultivariateFNormalSufficient::get_minus_log_normalization() const
{
double e = get_norms()[1] + get_minus_log_jacobian();
return e;
}
