1 files changed, 0 insertions, 148 deletions
diff --git a/gi/clda/src/clda.cc b/gi/clda/src/clda.cc
deleted file mode 100644
index f548997f..00000000
--- a/gi/clda/src/clda.cc
+++ /dev/null
@@ -1,148 +0,0 @@
-#include <iostream>
-#include <vector>
-#include <map>
-#include <string>
-
-#include "timer.h"
-#include "crp.h"
-#include "ccrp.h"
-#include "sampler.h"
-#include "tdict.h"
-const size_t MAX_DOC_LEN_CHARS = 10000000;
-
-using namespace std;
-
-void ShowTopWordsForTopic(const map<WordID, int>& counts) {
-  multimap<int, WordID> ms;
-  for (map<WordID,int>::const_iterator it = counts.begin(); it != counts.end(); ++it)
-    ms.insert(make_pair(it->second, it->first));
-  int cc = 0;
-  for (multimap<int, WordID>::reverse_iterator it = ms.rbegin(); it != ms.rend(); ++it) {
-    cerr << it->first << ':' << TD::Convert(it->second) << " ";
-    ++cc;
-    if (cc==20) break;
-  }
-  cerr << endl;
-}
-
-int main(int argc, char** argv) {
-  if (argc != 3) {
-    cerr << "Usage: " << argv[0] << " num-classes num-samples\n";
-    return 1;
-  }
-  const int num_classes = atoi(argv[1]);
-  const int num_iterations = atoi(argv[2]);
-  const int burnin_size = num_iterations * 0.9;
-  if (num_classes < 2) {
-    cerr << "Must request more than 1 class\n";
-    return 1;
-  }
-  if (num_iterations < 5) {
-    cerr << "Must request more than 5 iterations\n";
-    return 1;
-  }
-  cerr << "CLASSES: " << num_classes << endl;
-  char* buf = new char[MAX_DOC_LEN_CHARS];
-  vector<vector<int> > wji;   // w[j][i] - observed word i of doc j
-  vector<vector<int> > zji;   // z[j][i] - topic assignment for word i of doc j
-  cerr << "READING DOCUMENTS\n";
-  while(cin) {
-    cin.getline(buf, MAX_DOC_LEN_CHARS);
-    if (buf[0] == 0) continue;
-    wji.push_back(vector<WordID>());
-    TD::ConvertSentence(buf, &wji.back());
-  }
-  cerr << "READ " << wji.size() << " DOCUMENTS\n";
-  MT19937 rng;
-  cerr << "INITIALIZING RANDOM TOPIC ASSIGNMENTS\n";
-  zji.resize(wji.size());
-  double disc = 0.1;
-  double beta = 10.0;
-  double alpha = 50.0;
-  const double uniform_topic = 1.0 / num_classes;
-  const double uniform_word = 1.0 / TD::NumWords();
-  vector<CCRP<int> > dr(zji.size(), CCRP<int>(1,1,1,1,disc, beta)); // dr[i] describes the probability of using a topic in document i
-  vector<CCRP<int> > wr(num_classes, CCRP<int>(1,1,1,1,disc, alpha)); // wr[k] describes the probability of generating a word in topic k
-  for (int j = 0; j < zji.size(); ++j) {
-    const size_t num_words = wji[j].size();
-    vector<int>& zj = zji[j];
-    const vector<int>& wj = wji[j];
-    zj.resize(num_words);
-    for (int i = 0; i < num_words; ++i) {
-      int random_topic = rng.next() * num_classes;
-      if (random_topic == num_classes) { --random_topic; }
-      zj[i] = random_topic;
-      const int word = wj[i];
-      dr[j].increment(random_topic, uniform_topic, &rng);
-      wr[random_topic].increment(word, uniform_word, &rng);
-    }
-  }
-  cerr << "SAMPLING\n";
-  vector<map<WordID, int> > t2w(num_classes);
-  Timer timer;
-  SampleSet<double> ss;
-  ss.resize(num_classes);
-  double total_time = 0;
-  for (int iter = 0; iter < num_iterations; ++iter) {
-    cerr << '.';
-    if (iter && iter % 10 == 0) {
-      total_time += timer.Elapsed();
-      timer.Reset();
-      double llh = 0;
-#if 1
-      for (int j = 0; j < dr.size(); ++j)
-        dr[j].resample_hyperparameters(&rng);
-      for (int j = 0; j < wr.size(); ++j)
-        wr[j].resample_hyperparameters(&rng);
-#endif
-
-      for (int j = 0; j < dr.size(); ++j)
-        llh += dr[j].log_crp_prob();
-      for (int j = 0; j < wr.size(); ++j)
-        llh += wr[j].log_crp_prob();
-      cerr << " [LLH=" << llh << " I=" << iter << "]\n";
-    }
-    for (int j = 0; j < zji.size(); ++j) {
-      const size_t num_words = wji[j].size();
-      vector<int>& zj = zji[j];
-      const vector<int>& 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, &rng);
-        wr[cur_topic].decrement(word, &rng);
- 
-        for (int k = 0; k < num_classes; ++k) {
-          ss[k]= dr[j].prob(k, uniform_topic) * wr[k].prob(word, uniform_word);
-        }
-        const int new_topic = rng.SelectSample(ss);
-        dr[j].increment(new_topic, uniform_topic, &rng);
-        wr[new_topic].increment(word, uniform_word, &rng);
-        zj[i] = new_topic;
-        if (iter > burnin_size) {
-          ++t2w[cur_topic][word];
-        }
-      }
-    }
-  }
-  for (int i = 0; i < num_classes; ++i) {
-    cerr << "---------------------------------\n";
-    cerr << " final PYP(" << wr[i].discount() << "," << wr[i].concentration() << ")\n";
-    ShowTopWordsForTopic(t2w[i]);
-  }
-  cerr << "-------------\n";
-#if 0
-  for (int j = 0; j < zji.size(); ++j) {
-    const size_t num_words = wji[j].size();
-    vector<int>& zj = zji[j];
-    const vector<int>& wj = wji[j];
-    zj.resize(num_words);
-    for (int i = 0; i < num_words; ++i) {
-      cerr << TD::Convert(wji[j][i]) << '(' << zj[i] << ") ";
-    }
-    cerr << endl;
-  }
-#endif
-  return 0;
-}
-