void LDA::InitVarParamter(const Corpus &cor, int d, const LdaModel &m, VReal* digamma,VReal* ga, VVReal* phi) const {
ga->resize(m.num_topics);
digamma->resize(m.num_topics);
phi->resize(cor.ULen(d));
for (int k = 0; k < m.num_topics; k++) {
(*ga)[k] = m.alpha[k] + (cor.docs[d].total / ((double) m.num_topics));
(*digamma)[k] = DiGamma((*ga)[k]);
}
for (VReal::size_type n = 0; n < phi->size(); n++) {
phi->at(n).resize(m.num_topics);
for (int k = 0; k < m.num_topics; k++) {
(*phi)[n][k] = 1.0 / m.num_topics;
}
}
}
double LDA::Likelihood(const Corpus &cor, int d, const LdaModel &m, VRealC &gamma,VVRealC &phi) const {
double alpha_sum = double_array_sum(m.alpha,m.num_topics);
double gamma_sum = std::accumulate(gamma.begin(), gamma.end(), 0.0);
double digsum = DiGamma(gamma_sum);
const int &num = m.num_topics;
VReal expect(num);
for (int k = 0; k < num; k++) {
expect.at(k) = DiGamma(gamma.at(k)) - digsum;
}
double l = lgamma(alpha_sum) - lgamma(gamma_sum);
for (int k = 0; k < num; k++) {
l += ((m.alpha[k] - gamma.at(k)) * expect[k] + lgamma(gamma.at(k)) - lgamma(m.alpha[k]));
for (size_t n = 0; n < cor.ULen(d); n++) {
if (phi[n][k] > 0) {
l += cor.Count(d, n) * phi[n][k] * (expect[k] - log(phi[n][k]) + m.log_prob_w[k][cor.Word(d, n)]);
}
}
}
return l;
}
