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#include "pyp-topics.hh"
//#include "mt19937ar.h"
void PYPTopics::sample(const Corpus& corpus, int samples) {
if (!m_backoff.get()) {
m_word_pyps.clear();
m_word_pyps.push_back(PYPs());
}
std::cerr << " Training with " << m_word_pyps.size()-1 << " backoff level"
<< (m_word_pyps.size()==2 ? ":" : "s:") << std::endl;
for (int i=0; i<(int)m_word_pyps.size(); ++i)
m_word_pyps.at(i).resize(m_num_topics, PYP<int>(0.5, 1.0));
std::cerr << std::endl;
m_document_pyps.resize(corpus.num_documents(), PYP<int>(0.5, 1.0));
m_topic_p0 = 1.0/m_num_topics;
m_term_p0 = 1.0/corpus.num_types();
m_backoff_p0 = 1.0/corpus.num_documents();
std::cerr << " Documents: " << corpus.num_documents() << " Terms: "
<< corpus.num_types() << std::endl;
// Initialisation pass
int document_id=0, topic_counter=0;
for (Corpus::const_iterator corpusIt=corpus.begin();
corpusIt != corpus.end(); ++corpusIt, ++document_id) {
m_corpus_topics.push_back(DocumentTopics(corpusIt->size(), 0));
int term_index=0;
for (Document::const_iterator docIt=corpusIt->begin();
docIt != corpusIt->end(); ++docIt, ++term_index) {
topic_counter++;
Term term = *docIt;
// sample a new_topic
//int new_topic = (topic_counter % m_num_topics);
int new_topic = (document_id % m_num_topics);
// add the new topic to the PYPs
m_corpus_topics[document_id][term_index] = new_topic;
increment(term, new_topic);
m_document_pyps[document_id].increment(new_topic, m_topic_p0);
}
}
int* randomDocIndices = new int[corpus.num_documents()];
for (int i = 0; i < corpus.num_documents(); ++i)
randomDocIndices[i] = i;
// Sampling phase
for (int curr_sample=0; curr_sample < samples; ++curr_sample) {
std::cerr << "\n -- Sample " << curr_sample << " "; std::cerr.flush();
// Randomize the corpus indexing array
int tmp;
for (int i = corpus.num_documents()-1; i > 0; --i)
{
int j = (int)(mt_genrand_real1() * i);
tmp = randomDocIndices[i];
randomDocIndices[i] = randomDocIndices[j];
randomDocIndices[j] = tmp;
}
// for each document in the corpus
int document_id;
for (int i=0; i<corpus.num_documents(); ++i)
{
document_id = randomDocIndices[i];
// for each term in the document
int term_index=0;
Document::const_iterator docEnd = corpus.at(document_id).end();
for (Document::const_iterator docIt=corpus.at(document_id).begin();
docIt != docEnd; ++docIt, ++term_index) {
Term term = *docIt;
// remove the prevous topic from the PYPs
int current_topic = m_corpus_topics[document_id][term_index];
decrement(term, current_topic);
m_document_pyps[document_id].decrement(current_topic);
// sample a new_topic
int new_topic = sample(document_id, term);
// add the new topic to the PYPs
m_corpus_topics[document_id][term_index] = new_topic;
increment(term, new_topic);
m_document_pyps[document_id].increment(new_topic, m_topic_p0);
}
if (document_id && document_id % 10000 == 0) {
std::cerr << "."; std::cerr.flush();
}
}
if (curr_sample != 0 && curr_sample % 10 == 0) {
std::cerr << " ||| Resampling hyperparameters "; std::cerr.flush();
// resample the hyperparamters
F log_p=0.0; int resample_counter=0;
for (std::vector<PYPs>::iterator levelIt=m_word_pyps.begin();
levelIt != m_word_pyps.end(); ++levelIt) {
for (PYPs::iterator pypIt=levelIt->begin();
pypIt != levelIt->end(); ++pypIt) {
pypIt->resample_prior();
log_p += pypIt->log_restaurant_prob();
if (resample_counter++ % 100 == 0) {
std::cerr << "."; std::cerr.flush();
}
}
}
for (PYPs::iterator pypIt=m_document_pyps.begin();
pypIt != m_document_pyps.end(); ++pypIt) {
pypIt->resample_prior();
log_p += pypIt->log_restaurant_prob();
}
std::cerr << " ||| LLH=" << log_p << std::endl;
}
}
delete [] randomDocIndices;
}
void PYPTopics::decrement(const Term& term, int topic, int level) {
//std::cerr << "PYPTopics::decrement(" << term << "," << topic << "," << level << ")" << std::endl;
m_word_pyps.at(level).at(topic).decrement(term);
if (m_backoff.get()) {
Term backoff_term = (*m_backoff)[term];
if (!m_backoff->is_null(backoff_term))
decrement(backoff_term, topic, level+1);
}
}
void PYPTopics::increment(const Term& term, int topic, int level) {
//std::cerr << "PYPTopics::increment(" << term << "," << topic << "," << level << ")" << std::endl;
m_word_pyps.at(level).at(topic).increment(term, word_pyps_p0(term, topic, level));
if (m_backoff.get()) {
Term backoff_term = (*m_backoff)[term];
if (!m_backoff->is_null(backoff_term))
increment(backoff_term, topic, level+1);
}
}
int PYPTopics::sample(const DocumentId& doc, const Term& term) {
// First pass: collect probs
F sum=0.0;
std::vector<F> sums;
for (int k=0; k<m_num_topics; ++k) {
F p_w_k = prob(term, k);
F p_k_d = m_document_pyps[doc].prob(k, m_topic_p0);
sum += (p_w_k*p_k_d);
sums.push_back(sum);
}
// Second pass: sample a topic
F cutoff = mt_genrand_res53() * sum;
for (int k=0; k<m_num_topics; ++k) {
if (cutoff <= sums[k])
return k;
}
assert(false);
}
PYPTopics::F PYPTopics::word_pyps_p0(const Term& term, int topic, int level) const {
//for (int i=0; i<level+1; ++i) std::cerr << " ";
//std::cerr << "PYPTopics::word_pyps_p0(" << term << "," << topic << "," << level << ")" << std::endl;
F p0 = m_term_p0;
if (m_backoff.get()) {
//static F fudge=m_backoff_p0; // TODO
Term backoff_term = (*m_backoff)[term];
if (!m_backoff->is_null(backoff_term)) {
assert (level < m_backoff->order());
p0 = (1.0/(double)m_backoff->terms_at_level(level))*prob(backoff_term, topic, level+1);
}
else
p0 = m_term_p0;
}
//for (int i=0; i<level+1; ++i) std::cerr << " ";
//std::cerr << "PYPTopics::word_pyps_p0(" << term << "," << topic << "," << level << ") = " << p0 << std::endl;
return p0;
}
PYPTopics::F PYPTopics::prob(const Term& term, int topic, int level) const {
//for (int i=0; i<level+1; ++i) std::cerr << " ";
//std::cerr << "PYPTopics::prob(" << term << "," << topic << "," << level << " " << factor << ")" << std::endl;
F p0 = word_pyps_p0(term, topic, level);
F p_w_k = m_word_pyps.at(level).at(topic).prob(term, p0);
//for (int i=0; i<level+1; ++i) std::cerr << " ";
//std::cerr << "PYPTopics::prob(" << term << "," << topic << "," << level << ") = " << p_w_k << std::endl;
return p_w_k;
}
int PYPTopics::max(const DocumentId& doc, const Term& term) {
//std::cerr << "PYPTopics::max(" << doc << "," << term << ")" << std::endl;
// collect probs
F current_max=0.0;
int current_topic=-1;
for (int k=0; k<m_num_topics; ++k) {
F p_w_k = prob(term, k);
F p_k_d = m_document_pyps[doc].prob(k, m_topic_p0);
F prob = (p_w_k*p_k_d);
if (prob > current_max) {
current_max = prob;
current_topic = k;
}
}
assert(current_topic >= 0);
return current_topic;
}
std::ostream& PYPTopics::print_document_topics(std::ostream& out) const {
for (CorpusTopics::const_iterator corpusIt=m_corpus_topics.begin();
corpusIt != m_corpus_topics.end(); ++corpusIt) {
int term_index=0;
for (DocumentTopics::const_iterator docIt=corpusIt->begin();
docIt != corpusIt->end(); ++docIt, ++term_index) {
if (term_index) out << " ";
out << *docIt;
}
out << std::endl;
}
return out;
}
std::ostream& PYPTopics::print_topic_terms(std::ostream& out) const {
for (PYPs::const_iterator pypsIt=m_word_pyps.front().begin();
pypsIt != m_word_pyps.front().end(); ++pypsIt) {
int term_index=0;
for (PYP<int>::const_iterator termIt=pypsIt->begin();
termIt != pypsIt->end(); ++termIt, ++term_index) {
if (term_index) out << " ";
out << termIt->first << ":" << termIt->second;
}
out << std::endl;
}
return out;
}
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