#include #include #include #include "timer.h" #include "crp.h" #include "sampler.h" #include "tdict.h" Dict TD::dict_; std::string TD::empty = ""; std::string TD::space = " "; using namespace std; void ShowTopWords(const map& counts) { multimap ms; for (map::const_iterator it = counts.begin(); it != counts.end(); ++it) ms.insert(make_pair(it->second, it->first)); } int main(int argc, char** argv) { if (argc != 2) { cerr << "Usage: " << argv[0] << " num-classes\n"; return 1; } const int num_classes = atoi(argv[1]); if (num_classes < 2) { cerr << "Must request more than 1 class\n"; return 1; } cerr << "CLASSES: " << num_classes << endl; char* buf = new char[800000]; vector > wji; // w[j][i] - observed word i of doc j vector > zji; // z[j][i] - topic assignment for word i of doc j cerr << "READING DOCUMENTS\n"; while(cin) { cin.getline(buf, 800000); if (buf[0] == 0) continue; wji.push_back(vector()); TD::ConvertSentence(buf, &wji.back()); } cerr << "READ " << wji.size() << " DOCUMENTS\n"; MT19937 rng; cerr << "INITIALIZING RANDOM TOPIC ASSIGNMENTS\n"; zji.resize(wji.size()); double beta = 0.01; double alpha = 0.001; vector > dr(zji.size(), CRP(beta)); // dr[i] describes the probability of using a topic in document i vector > wr(num_classes, CRP(alpha)); // wr[k] describes the probability of generating a word in topic k int random_topic = rng.next() * num_classes; for (int j = 0; j < zji.size(); ++j) { const size_t num_words = wji[j].size(); vector& zj = zji[j]; const vector& wj = wji[j]; zj.resize(num_words); for (int i = 0; i < num_words; ++i) { if (random_topic == num_classes) { --random_topic; } zj[i] = random_topic; const int word = wj[i]; dr[j].increment(random_topic); wr[random_topic].increment(word); } } cerr << "SAMPLING\n"; vector > t2w(num_classes); const int num_iterations = 1000; const int burnin_size = 800; bool needline = false; Timer timer; SampleSet ss; ss.resize(num_classes); double total_time = 0; for (int iter = 0; iter < num_iterations; ++iter) { if (iter && iter % 10 == 0) { total_time += timer.Elapsed(); timer.Reset(); cerr << '.'; needline=true; prob_t lh = prob_t::One(); for (int j = 0; j < zji.size(); ++j) { const size_t num_words = wji[j].size(); vector& zj = zji[j]; const vector& wj = wji[j]; for (int i = 0; i < num_words; ++i) { const int word = wj[i]; const int cur_topic = zj[i]; lh *= dr[j].prob(cur_topic); lh *= wr[cur_topic].prob(word); if (iter > burnin_size) { ++t2w[cur_topic][word]; } } } if (iter && iter % 200 == 0) { cerr << " [ITER=" << iter << " SEC/SAMPLE=" << (total_time / 200) << " LLH=" << log(lh) << "]\n"; needline=false; total_time=0; } //cerr << "ITERATION " << iter << " LOG LIKELIHOOD: " << log(lh) << endl; } for (int j = 0; j < zji.size(); ++j) { const size_t num_words = wji[j].size(); vector& zj = zji[j]; const vector& wj = wji[j]; for (int i = 0; i < num_words; ++i) { const int word = wj[i]; const int cur_topic = zj[i]; dr[j].decrement(cur_topic); wr[cur_topic].decrement(word); for (int k = 0; k < num_classes; ++k) { ss[k]= dr[j].prob(k) * wr[k].prob(word); } const int new_topic = rng.SelectSample(ss); dr[j].increment(new_topic); wr[new_topic].increment(word); zj[i] = new_topic; } } } if (needline) cerr << endl; for (int j = 0; j < zji.size(); ++j) { const size_t num_words = wji[j].size(); vector& zj = zji[j]; const vector& wj = wji[j]; zj.resize(num_words); for (int i = 0; i < num_words; ++i) { cout << TD::Convert(wj[i]) << '(' << zj[i] << ") "; } cout << endl; } for (int i = 0; i < num_classes; ++i) { ShowTopWords(t2w[i]); } for (map::iterator it = t2w[0].begin(); it != t2w[0].end(); ++it) cerr << TD::Convert(it->first) << " " << it->second << endl; cerr << "---------------------------------\n"; for (map::iterator it = t2w[1].begin(); it != t2w[1].end(); ++it) cerr << TD::Convert(it->first) << " " << it->second << endl; cerr << "---------------------------------\n"; for (map::iterator it = t2w[2].begin(); it != t2w[2].end(); ++it) cerr << TD::Convert(it->first) << " " << it->second << endl; return 0; }