//@{
double negativeBinomialDist::aic (const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu,
const ArrayXd& wt, double dev) const {
return 2. * (wt * (y + d_theta) * (mu + d_theta).log() -
y * mu.log() + (y + 1).unaryExpr(Lgamma<double>()) -
d_theta * std::log(d_theta) + lgamma(d_theta) -
(d_theta + y).unaryExpr(Lgamma<double>())).sum();
}
VectorXd probutils::logsumexp (const MatrixXd& X)
{
const VectorXd mx = X.rowwise().maxCoeff(); // Get max of each row
// Perform the sum(exp(x - mx)) part
ArrayXd se = ((X.colwise() - mx).array().exp()).rowwise().sum();
// return total log(sum(exp(x))) - hoping for return value optimisation
return (se.log()).matrix() + mx;
}
//@{
const ArrayXd logLink::linkFun(const ArrayXd& mu) const {return mu.log();}
//@{
double inverseGaussianDist::aic (const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu,
const ArrayXd& wt, double dev) const {
double wtsum(wt.sum());
return wtsum * (std::log(dev/wtsum * 2. * M_PI) + 1.) + 3. * (y.log() * wt).sum() + 2.;
}
//@{
double GaussianDist::aic (const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu,
const ArrayXd& wt, double dev) const {
double nn(mu.size());
return nn * (std::log(2. * M_PI * dev/nn) + 1.) + 2. - wt.log().sum();
}
