factor_type gather(icontext_type& context, const vertex_type& vertex,
edge_type& edge) const {
vertex_type other_vertex = get_other_vertex(edge, vertex);
// VIOLATING THE ABSTRACTION!
vertex_data& vdata = graph_type::vertex_type(vertex).data();
// VIOLATING THE ABSTRACTION!
vertex_data& other_vdata = other_vertex.data();
factor_type& doc_topic_count =
is_doc(vertex) ? vdata.factor : other_vdata.factor;
factor_type& word_topic_count =
is_word(vertex) ? vdata.factor : other_vdata.factor;
ASSERT_EQ(doc_topic_count.size(), NTOPICS);
ASSERT_EQ(word_topic_count.size(), NTOPICS);
// run the actual gibbs sampling
factor_type& belief = edge.data().belief;
const uint32_t count = edge.data().count;
// Resample the topics
double sum = 0, old_sum = 0;
for(size_t t = 0; t < NTOPICS; ++t) {
old_sum += belief[t];
doc_topic_count[t] -= belief[t];
word_topic_count[t] -= belief[t];
GLOBAL_TOPIC_COUNT[t] -= belief[t];
const double n_dt =
std::max(count_type(doc_topic_count[t]), count_type(0));
ASSERT_GE(n_dt, 0);
const double n_wt =
std::max(count_type(word_topic_count[t]), count_type(0));
ASSERT_GE(n_wt, 0);
const double n_t =
std::max(count_type(GLOBAL_TOPIC_COUNT[t]), count_type(0));
ASSERT_GE(n_t, 0);
belief[t] = (ALPHA + n_dt) * (BETA + n_wt) / (BETA * NWORDS + n_t);
sum += belief[t];
} // End of loop over each token
ASSERT_GT(sum, 0);
if(old_sum == 0) {
size_t asg = graphlab::random::multinomial(belief);
for(size_t i = 0; i < NTOPICS; ++i) belief[i] = 0;
belief[asg] = count;
return belief;
}
for(size_t t = 0; t < NTOPICS; ++t) {
belief[t] = count * (belief[t]/sum);
doc_topic_count[t] += belief[t];
word_topic_count[t] += belief[t];
GLOBAL_TOPIC_COUNT[t] += belief[t];
}
return belief;
} // end of gather
void apply(icontext_type& context, vertex_type& vertex,
const gather_type& total)
{
vertex_data v=vertex.parse<vertex_data>();
if(is_doc(vertex))
{
for (int k = 0; k < env_inst.NTOPICS; k++)
{
env_inst.theta[k2id(v.id)][k] += (v.n[k] + env_inst.ALPHA) / (env_inst.ndsum[k2id(v.id)] + env_inst.NTOPICS * env_inst.ALPHA);
}
}
else
{
for (int k = 0; k < env_inst.NTOPICS; k++)
env_inst.phi[k][v.id] += (v.n[k] + env_inst.BETA) / (env_inst.nwsum[k] + env_inst.NWORDS * env_inst.BETA);
}
}
