// O(1) if up to date, O(M^2) if epsilon new, O(M^3) if Sigma new
VectorXd MultivariateFNormalSufficient::evaluate_derivative_FM() const
{
// d(-log(p))/d(FM) = - N * P * epsilon
LOG( "MVN: evaluate_derivative_FM() = " << std::endl);
VectorXd tmp(-N_ * get_Peps()/SQUARE(factor_));
return tmp;
}
MatrixXd MultivariateFNormalSufficient::compute_PTP() const
{
LOG( "MVN: computing PTP" << std::endl);
VectorXd peps(get_Peps());
MatrixXd tmp(peps*peps.transpose());
return tmp;
}
MatrixXd MultivariateFNormalSufficient::evaluate_second_derivative_FM_Sigma(
unsigned k) const
{
CHECK(N_==1, "not implemented when N>1");
MatrixXd P(get_P());
VectorXd Peps(get_Peps());
MatrixXd ret(P.transpose().col(k)*Peps.transpose());
return ret/SQUARE(factor_);
}
MatrixXd MultivariateFNormalSufficient::compute_PTP() const
{
timer_.start(PTP);
IMP_LOG(TERSE, "MVN: computing PTP" << std::endl);
VectorXd peps(get_Peps());
MatrixXd tmp(peps*peps.transpose());
timer_.stop(PTP);
return tmp;
}
MatrixXd MultivariateFNormalSufficient::evaluate_second_derivative_FM_Sigma(
unsigned k) const
{
if (N_!=1) IMP_THROW("not implemented when N>1", ModelException);
MatrixXd P(get_P());
VectorXd Peps(get_Peps());
MatrixXd ret(P.transpose().col(k)*Peps.transpose());
return ret/IMP::square(factor_);
}
// O(1) if up to date, O(M^2) if epsilon new, O(M^3) if Sigma new
VectorXd MultivariateFNormalSufficient::evaluate_derivative_FM() const
{
timer_.start(DFM);
// d(-log(p))/d(FM) = - N * P * epsilon
IMP_LOG(TERSE, "MVN: evaluate_derivative_FM() = " << std::endl);
VectorXd tmp(-N_ * get_Peps()/IMP::square(factor_));
timer_.stop(DFM);
return tmp;
}
double MultivariateFNormalSufficient::get_mean_square_residuals() const
{
//std::cout << "P " << std::endl << P_ << std::endl;
//std::cout << "epsilon " << std::endl << get_epsilon() << std::endl;
VectorXd Peps(get_Peps());
VectorXd epsilon(get_epsilon());
double dist = epsilon.transpose()*Peps;
LOG( "MVN: get_mean_square_residuals = " << dist << std::endl);
return dist;
}
MatrixXd MultivariateFNormalSufficient::evaluate_second_derivative_Sigma_Sigma(
unsigned m, unsigned n) const
{
CHECK(N_==1, "not implemented when N>1");
MatrixXd P(get_P());
VectorXd Peps(get_Peps());
MatrixXd tmp(P.col(m)*Peps.transpose());
MatrixXd ret(0.5*(-P.col(n)*P.row(m)
+Peps(n)*(tmp+tmp.transpose())));
return ret/SQUARE(factor_);
}
MatrixXd MultivariateFNormalSufficient::evaluate_second_derivative_Sigma_Sigma(
unsigned m, unsigned n) const
{
if (N_!=1) IMP_THROW("not implemented when N>1", ModelException);
MatrixXd P(get_P());
VectorXd Peps(get_Peps());
MatrixXd tmp(P.col(m)*Peps.transpose());
MatrixXd ret(0.5*(-P.col(n)*P.row(m)
+Peps(n)*(tmp+tmp.transpose())));
return ret/IMP::square(factor_);
}