double operator()(double nu)const{
nu_[which_] = nu;
double ans = pri_->logp(nu);
if(!std::isfinite(ans)) return ans;
ans += dirichlet_loglike(nu_, 0, 0, sumlog_, nobs_);
return ans;
}
double PDM::loglike(const Vector &Nu_columns) const {
Matrix Nu(dim(), dim(), Nu_columns.data());
const Matrix &sumlog(suf()->sumlog());
double n = suf()->n();
double ans = 0;
for (uint i = 0; i < nrow(Nu); ++i)
ans += dirichlet_loglike(Nu.row(i), 0, 0, sumlog.row(i), n);
return ans;
}
//======================================================================
double DirichletModel::Loglike(const Vector &nu, Vector &g, Matrix &h,
uint nd) const {
/* returns log likelihood for the parameters of a Dirichlet
distribution with sufficient statistic sumlogpi(lo..hi). If
pi(1)(lo..hi)..pi(nobs)(lo..hi) are probability vectors, then
sumlogpi(j) = sum_i log(pi(i,j))
if(nd>0) then the g(lo..hi) is filled with the gradient (with
respect to nu). If nd>1 then hess(lo..hi)(lo..hi) is filled
with the hessian (wrt nu). Otherwise the algorithm can be called
with either g or hess = 0.
*/
const Vector &sumlogpi(suf()->sumlog());
double nobs = suf()->n();
Vector *G(nd > 0 ? &g : nullptr);
Matrix *H(nd > 1 ? &h : nullptr);
return dirichlet_loglike(nu, G, H, sumlogpi, nobs);
}
double PDM::dloglike(const Vector &Nu_columns, Vector &g) const {
Matrix Nu(dim(), dim(), Nu_columns.data());
const Matrix &sumlog(suf()->sumlog());
double n = suf()->n();
uint nr = nrow(Nu);
Matrix G(nr, nr);
Vector g_row(nr);
double ans = 0;
for (uint i = 0; i < nrow(Nu); ++i) {
ans += dirichlet_loglike(Nu.row(i), &g_row, 0, sumlog.row(i), n);
G.row(i) = g_row;
}
G = G.transpose();
g.assign(G.begin(), G.end());
// need to check that g is vectorized in the right way.. virtual
// functions might expect columns instead of rows.
return ans;
}