From d3ccf26cf501cb15ed300bc0ad17596a4e59fbeb Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cs.cmu.edu>
Date: Sat, 18 Feb 2012 15:16:17 -0500
Subject: fix diagonal model

---
 configure.ac       |  2 +-
 training/model1.cc | 29 +++++++++++++++++------------
 2 files changed, 18 insertions(+), 13 deletions(-)

diff --git a/configure.ac b/configure.ac
index aa79027f..026dad01 100644
--- a/configure.ac
+++ b/configure.ac
@@ -51,7 +51,7 @@ then
   SAVE_CPPFLAGS="$CPPFLAGS"
   CPPFLAGS="$CPPFLAGS -I${with_eigen}"
 
-  AC_CHECK_HEADER(Eigen,
+  AC_CHECK_HEADER(Eigen/Dense,
                  [AC_DEFINE([HAVE_EIGEN], [1], [flag for Eigen linear algebra library])],
                  [AC_MSG_ERROR([Cannot find Eigen!])])
 
diff --git a/training/model1.cc b/training/model1.cc
index a87d388f..73104304 100644
--- a/training/model1.cc
+++ b/training/model1.cc
@@ -4,6 +4,7 @@
 #include <boost/program_options.hpp>
 #include <boost/program_options/variables_map.hpp>
 
+#include "m.h"
 #include "lattice.h"
 #include "stringlib.h"
 #include "filelib.h"
@@ -13,11 +14,6 @@
 namespace po = boost::program_options;
 using namespace std;
 
-inline double log_poisson(unsigned x, const double& lambda) {
-  assert(lambda > 0.0);
-  return log(lambda) * x - lgamma(x + 1) - lambda;
-}
-
 bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   po::options_description opts("Configuration options");
   opts.add_options()
@@ -82,6 +78,7 @@ int main(int argc, char** argv) {
   TTable::Word2Word2Double was_viterbi;
   double tot_len_ratio = 0;
   double mean_srclen_multiplier = 0;
+  vector<double> unnormed_a_i;
   for (int iter = 0; iter < ITERATIONS; ++iter) {
     const bool final_iteration = (iter == (ITERATIONS - 1));
     cerr << "ITERATION " << (iter + 1) << (final_iteration ? " (FINAL)" : "") << endl;
@@ -108,6 +105,8 @@ int main(int argc, char** argv) {
         assert(src.size() > 0);
         assert(trg.size() > 0);
       }
+      if (src.size() > unnormed_a_i.size())
+        unnormed_a_i.resize(src.size());
       if (iter == 0)
         tot_len_ratio += static_cast<double>(trg.size()) / static_cast<double>(src.size());
       denom += trg.size();
@@ -125,13 +124,15 @@ int main(int argc, char** argv) {
         }
         double az = 0;
         if (favor_diagonal) {
-          for (int ta = 0; ta < src.size(); ++ta)
-            az += exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension);
+          for (int ta = 0; ta < src.size(); ++ta) {
+            unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension);
+            az += unnormed_a_i[ta];
+          }
           az /= prob_align_not_null;
         }
         for (int i = 1; i <= src.size(); ++i) {
           if (favor_diagonal)
-            prob_a_i = exp(-fabs(double(i) / src.size() - j_over_ts) * diagonal_tension) / az;
+            prob_a_i = unnormed_a_i[i-1] / az;
           probs[i] = tt.prob(src[i-1][0].label, f_j) * prob_a_i;
           sum += probs[i];
         }
@@ -202,7 +203,9 @@ int main(int argc, char** argv) {
       Lattice src, trg;
       LatticeTools::ConvertTextToLattice(ssrc, &src);
       LatticeTools::ConvertTextToLattice(strg, &trg);
-      double log_prob = log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier);
+      double log_prob = Md::log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier);
+      if (src.size() > unnormed_a_i.size())
+        unnormed_a_i.resize(src.size());
 
       // compute likelihood
       for (int j = 0; j < trg.size(); ++j) {
@@ -216,13 +219,15 @@ int main(int argc, char** argv) {
         }
         double az = 0;
         if (favor_diagonal) {
-          for (int ta = 0; ta < src.size(); ++ta)
-            az += exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension);
+          for (int ta = 0; ta < src.size(); ++ta) {
+            unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension);
+            az += unnormed_a_i[ta];
+          }
           az /= prob_align_not_null;
         }
         for (int i = 1; i <= src.size(); ++i) {
           if (favor_diagonal)
-            prob_a_i = exp(-fabs(double(i) / src.size() - j_over_ts) * diagonal_tension) / az;
+            prob_a_i = unnormed_a_i[i-1] / az;
           sum += tt.prob(src[i-1][0].label, f_j) * prob_a_i;
         }
         log_prob += log(sum);
-- 
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