unit tests for PYP sampler

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@620 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 3 insertions, 35 deletions

diff --git a/gi/clda/src/clda.cc b/gi/clda/src/clda.cc
index 0232331b..10056bc9 100644
--- a/gi/clda/src/clda.cc
+++ b/gi/clda/src/clda.cc
@@ -25,39 +25,7 @@ void ShowTopWordsForTopic(const map<WordID, int>& counts) {
   cerr << endl;
 }
 
-void tc() {
-  MT19937 rng;
-  CCRP<string> crp(0.1, 5);
-  double un = 0.25;
-  int tt = 0;
-  tt += crp.increment("hi", un, &rng);
-  tt += crp.increment("foo", un, &rng);
-  tt += crp.increment("bar", un, &rng);
-  tt += crp.increment("bar", un, &rng);
-  tt += crp.increment("bar", un, &rng);
-  tt += crp.increment("bar", un, &rng);
-  tt += crp.increment("bar", un, &rng);
-  tt += crp.increment("bar", un, &rng);
-  tt += crp.increment("bar", un, &rng);
-  cout << "tt=" << tt << endl;
-  cout << crp << endl;
-  cout << "  P(bar)=" << crp.prob("bar", un) << endl;
-  cout << "  P(hi)=" << crp.prob("hi", un) << endl;
-  cout << "  P(baz)=" << crp.prob("baz", un) << endl;
-  cout << "  P(foo)=" << crp.prob("foo", un) << endl;
-  double x = crp.prob("bar", un) + crp.prob("hi", un) + crp.prob("baz", un) + crp.prob("foo", un);
-  cout << "    tot=" << x << endl;
-  tt += crp.decrement("hi", &rng);
-  tt += crp.decrement("bar", &rng);
-  cout << crp << endl;
-  tt += crp.decrement("bar", &rng);
-  cout << crp << endl;
-  cout << "tt=" << tt << endl;
-  cout << crp.log_crp_prob() << endl;
-}
-
 int main(int argc, char** argv) {
-  tc();
   if (argc != 3) {
     cerr << "Usage: " << argv[0] << " num-classes num-samples\n";
     return 1;
@@ -88,11 +56,11 @@ int main(int argc, char** argv) {
   MT19937 rng;
   cerr << "INITIALIZING RANDOM TOPIC ASSIGNMENTS\n";
   zji.resize(wji.size());
-  double disc = 0.05;
+  double disc = 0.1;
   double beta = 10.0;
   double alpha = 50.0;
-  double uniform_topic = 1.0 / num_classes;
-  double uniform_word = 1.0 / TD::NumWords();
+  const double uniform_topic = 1.0 / num_classes;
+  const double uniform_word = 1.0 / TD::NumWords();
   vector<CCRP<int> > dr(zji.size(), CCRP<int>(disc, beta)); // dr[i] describes the probability of using a topic in document i
   vector<CCRP<int> > wr(num_classes, CCRP<int>(disc, alpha)); // wr[k] describes the probability of generating a word in topic k
   for (int j = 0; j < zji.size(); ++j) {