From d0c482c1d69a5c26f7d1bc27cf5b3a252716cb2e Mon Sep 17 00:00:00 2001
From: Patrick Simianer <p@simianer.de>
Date: Wed, 3 Aug 2011 01:29:52 +0200
Subject: refactoring, cleaning up

---
 dtrain/score.cc | 166 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 166 insertions(+)
 create mode 100644 dtrain/score.cc

(limited to 'dtrain/score.cc')

diff --git a/dtrain/score.cc b/dtrain/score.cc
new file mode 100644
index 00000000..72e6db71
--- /dev/null
+++ b/dtrain/score.cc
@@ -0,0 +1,166 @@
+#include "score.h"
+
+
+namespace dtrain
+{
+
+
+/******************************************************************************
+ * NGRAMS
+ *
+ *
+ * make_ngrams
+ *
+ */
+typedef map<vector<WordID>, size_t> Ngrams;
+Ngrams
+make_ngrams( vector<WordID>& s, size_t N )
+{
+  Ngrams ngrams;
+  vector<WordID> ng;
+  for ( size_t i = 0; i < s.size(); i++ ) {
+    ng.clear();
+    for ( size_t j = i; j < min( i+N, s.size() ); j++ ) {
+      ng.push_back( s[j] );
+      ngrams[ng]++;
+    }
+  }
+  return ngrams;
+}
+
+
+/*
+ * ngram_matches
+ *
+ */
+NgramCounts
+make_ngram_counts( vector<WordID> hyp, vector<WordID> ref, size_t N )
+{
+  Ngrams hyp_ngrams = make_ngrams( hyp, N );
+  Ngrams ref_ngrams = make_ngrams( ref, N );
+  NgramCounts counts( N );
+  Ngrams::iterator it;
+  Ngrams::iterator ti;
+  for ( it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++ ) {
+    ti = ref_ngrams.find( it->first );
+    if ( ti != ref_ngrams.end() ) {
+      counts.add( it->second, ti->second, it->first.size() - 1 );
+    } else {
+      counts.add( it->second, 0, it->first.size() - 1 );
+    }
+  }
+  return counts;
+}
+
+
+/******************************************************************************
+ * SCORES
+ *
+ *
+ * brevity_penaly
+ *
+ */
+double
+brevity_penaly( const size_t hyp_len, const size_t ref_len )
+{
+  if ( hyp_len > ref_len ) return 1;
+  return exp( 1 - (double)ref_len/(double)hyp_len );
+}
+
+
+/*
+ * bleu
+ * as in "BLEU: a Method for Automatic Evaluation of Machine Translation" (Papineni et al. '02)
+ * page TODO
+ * 0 if for N one of the counts = 0
+ */
+double
+bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
+      size_t N, vector<float> weights  )
+{
+  if ( hyp_len == 0 || ref_len == 0 ) return 0;
+  if ( ref_len < N ) N = ref_len;
+  float N_ = (float)N;
+  if ( weights.empty() )
+  {
+    for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ );
+  }
+  double sum = 0;
+  for ( size_t i = 0; i < N; i++ ) {
+    if ( counts.clipped[i] == 0 || counts.sum[i] == 0 ) return 0;
+    sum += weights[i] * log( (double)counts.clipped[i] / (double)counts.sum[i] );
+  }
+  return brevity_penaly( hyp_len, ref_len ) * exp( sum );
+}
+
+
+/*
+ * stupid_bleu
+ * as in "ORANGE: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation (Lin & Och '04)
+ * page TODO
+ * 0 iff no 1gram match
+ */
+double
+stupid_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
+             size_t N, vector<float> weights  )
+{
+  if ( hyp_len == 0 || ref_len == 0 ) return 0;
+  if ( ref_len < N ) N = ref_len;
+  float N_ = (float)N;
+  if ( weights.empty() )
+  {
+    for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ );
+  }
+  double sum = 0;
+  float add = 0;
+  for ( size_t i = 0; i < N; i++ ) {
+    if ( i == 1 ) add = 1;
+    sum += weights[i] * log( ((double)counts.clipped[i] + add) / ((double)counts.sum[i] + add) );
+  }
+  return brevity_penaly( hyp_len, ref_len ) * exp( sum );
+}
+
+
+/*
+ * smooth_bleu
+ * as in "An End-to-End Discriminative Approach to Machine Translation" (Liang et al. '06)
+ * page TODO
+ * max. 0.9375
+ */
+double
+smooth_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
+             const size_t N, vector<float> weights  )
+{
+  if ( hyp_len == 0 || ref_len == 0 ) return 0;
+  float N_ = (float)N;
+  if ( weights.empty() )
+  {
+    for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ );
+  }
+  double sum = 0;
+  float j = 1;
+  for ( size_t i = 0; i < N; i++ ) {
+    if ( counts.clipped[i] == 0 || counts.sum[i] == 0) continue;
+    sum += exp((weights[i] * log((double)counts.clipped[i]/(double)counts.sum[i]))) / pow( 2, N_-j+1 );
+    j++;
+  }
+  return brevity_penaly( hyp_len, ref_len ) * sum;
+}
+
+
+/*
+ * approx_bleu
+ * as in "Online Large-Margin Training for Statistical Machine Translation" (Watanabe et al. '07)
+ * page TODO
+ *
+ */
+double
+approx_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
+     const size_t N, vector<float> weights )
+{
+  return bleu( counts, hyp_len, ref_len, N, weights );
+}
+
+
+} // namespace
+
-- 
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