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_);
}
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::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_);
}
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;
}
