From fd4259da347b371a1a399b9130f62938e3db462b Mon Sep 17 00:00:00 2001
From: redpony <redpony@ec762483-ff6d-05da-a07a-a48fb63a330f>
Date: Wed, 1 Dec 2010 00:03:35 +0000
Subject: optional variational bayes

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@734 ec762483-ff6d-05da-a07a-a48fb63a330f
---
 training/model1.cc | 15 ++++++++++++++-
 training/ttables.h | 14 ++++++++++++++
 2 files changed, 28 insertions(+), 1 deletion(-)

(limited to 'training')

diff --git a/training/model1.cc b/training/model1.cc
index eacf4b32..4023735c 100644
--- a/training/model1.cc
+++ b/training/model1.cc
@@ -20,6 +20,8 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
         ("iterations,i",po::value<unsigned>()->default_value(5),"Number of iterations of EM training")
         ("beam_threshold,t",po::value<double>()->default_value(-4),"log_10 of beam threshold (-10000 to include everything, 0 max)")
         ("no_null_word,N","Do not generate from the null token")
+        ("variational_bayes,v","Add a symmetric Dirichlet prior and infer VB estimate of weights")
+        ("alpha,a", po::value<double>()->default_value(0.01), "Hyperparameter for optional Dirichlet prior")
         ("no_add_viterbi,V","Do not add Viterbi alignment points (may generate a grammar where some training sentence pairs are unreachable)");
   po::options_description clo("Command line options");
   clo.add_options()
@@ -53,6 +55,12 @@ int main(int argc, char** argv) {
   const bool use_null = (conf.count("no_null_word") == 0);
   const WordID kNULL = TD::Convert("<eps>");
   const bool add_viterbi = (conf.count("no_add_viterbi") == 0);
+  const bool variational_bayes = (conf.count("variational_bayes") > 0);
+  const double alpha = conf["alpha"].as<double>();
+  if (variational_bayes && alpha <= 0.0) {
+    cerr << "--alpha must be > 0\n";
+    return 1;
+  }
 
   TTable tt;
   TTable::Word2Word2Double was_viterbi;
@@ -125,7 +133,12 @@ int main(int argc, char** argv) {
     cerr << "  log likelihood: " << likelihood << endl;
     cerr << "   cross entropy: " << (-likelihood / denom) << endl;
     cerr << "      perplexity: " << pow(2.0, -likelihood / denom) << endl;
-    if (!final_iteration) tt.Normalize();
+    if (!final_iteration) {
+      if (variational_bayes)
+        tt.NormalizeVB(alpha);
+      else
+        tt.Normalize();
+    }
   }
   for (TTable::Word2Word2Double::iterator ei = tt.ttable.begin(); ei != tt.ttable.end(); ++ei) {
     const TTable::Word2Double& cpd = ei->second;
diff --git a/training/ttables.h b/training/ttables.h
index 53f5f2ab..50d85a68 100644
--- a/training/ttables.h
+++ b/training/ttables.h
@@ -6,6 +6,7 @@
 
 #include "wordid.h"
 #include "tdict.h"
+#include "em_utils.h"
 
 class TTable {
  public:
@@ -29,6 +30,19 @@ class TTable {
   inline void Increment(const int& e, const int& f, double x) {
     counts[e][f] += x;
   }
+  void NormalizeVB(const double alpha) {
+    ttable.swap(counts);
+    for (Word2Word2Double::iterator cit = ttable.begin();
+         cit != ttable.end(); ++cit) {
+      double tot = 0;
+      Word2Double& cpd = cit->second;
+      for (Word2Double::iterator it = cpd.begin(); it != cpd.end(); ++it)
+        tot += it->second + alpha;
+      for (Word2Double::iterator it = cpd.begin(); it != cpd.end(); ++it)
+        it->second = exp(digamma(it->second + alpha) - digamma(tot));
+    }
+    counts.clear();
+  }
   void Normalize() {
     ttable.swap(counts);
     for (Word2Word2Double::iterator cit = ttable.begin();
-- 
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