/** * Adds the gradient of the expected log-likelihood of the specified * data point to the gradient table g. * * \param phead the distribution over a leading set of indices of f * \param tail a fixed assignment to the remaining indices of f * \param w the weight of the data point */ void add_gradient(const table<T>& phead, const uint_vector& tail, T w, table<T>& g) const { assert(phead.arity() + tail.size() == g.arity()); std::size_t index = g.offset().linear(tail, phead.arity()); for (std::size_t i = 0; i < phead.size(); ++i) { g[index + i] += phead[i] * w; } }
/** * Adds the diagonal of the Hessia of the expected log-likelihoood of * the specified data point to the Hessian diagonal h. */ void add_hessian_diag(const table<T>& phead, const uint_vector& tail, T w, table<T>& h) const { assert(phead.arity() + tail.size() == h.arity()); std::size_t index = h.offset().linear(tail, phead.arity()); for (std::size_t i = 0; i < phead.size(); ++i) { h[index + i] -= phead[i] * w / (f[index + i] * f[index + i]); } }