From 2e605eb2745e56619b16fdbcb8095e0a6543ab27 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/dcommon.h | 163 -------------------------------------------------------
 1 file changed, 163 deletions(-)
 delete mode 100644 dtrain/dcommon.h

(limited to 'dtrain/dcommon.h')

diff --git a/dtrain/dcommon.h b/dtrain/dcommon.h
deleted file mode 100644
index 6df841bb..00000000
--- a/dtrain/dcommon.h
+++ /dev/null
@@ -1,163 +0,0 @@
-#include <sstream>
-#include <iostream>
-#include <vector>
-#include <cassert>
-#include <cmath>
-
-#include "config.h"
-
-#include <boost/shared_ptr.hpp>
-#include <boost/algorithm/string.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "sentence_metadata.h"
-#include "scorer.h"
-#include "verbose.h"
-#include "viterbi.h"
-#include "hg.h"
-#include "prob.h"
-#include "kbest.h"
-#include "ff_register.h"
-#include "decoder.h"
-#include "filelib.h"
-#include "fdict.h"
-#include "weights.h"
-#include "sparse_vector.h"
-#include "sampler.h"
-
-using namespace std;
-namespace po = boost::program_options;
-
-
-
-
-struct ScorePair
-{
-  ScorePair(double modelscore, double score) : modelscore_(modelscore), score_(score) {} 
-  double modelscore_, score_;
-  double GetModelScore() { return modelscore_; }
-  double GetScore() { return score_; }
-};
-typedef vector<ScorePair> Scores;
-
-
-/*
- * KBestGetter
- *
- */
-struct KBestList {
-  vector<SparseVector<double> > feats;
-  vector<vector<WordID> > sents;
-  vector<double> scores;
-};
-struct KBestGetter : public DecoderObserver
-{
-  KBestGetter( const size_t k ) : k_(k) {}
-  const size_t k_;
-  KBestList kb;
-
-  virtual void
-  NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
-  {
-    GetKBest(smeta.GetSentenceID(), *hg);
-  }
-
-  KBestList* GetKBest() { return &kb; }
-
-  void
-  GetKBest(int sent_id, const Hypergraph& forest)
-  {
-    kb.scores.clear();
-    kb.sents.clear();
-    kb.feats.clear();
-    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest( forest, k_ );
-    for ( size_t i = 0; i < k_; ++i ) {
-      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
-        kbest.LazyKthBest( forest.nodes_.size() - 1, i );
-      if (!d) break;
-      kb.sents.push_back( d->yield);
-      kb.feats.push_back( d->feature_values );
-      kb.scores.push_back( d->score );
-    }
-  }
-};
-
-
-/*
- * NgramCounts
- *
- */
-struct NgramCounts
-{
-  NgramCounts( const size_t N ) : N_( N ) {
-    reset();
-  } 
-  size_t N_;
-  map<size_t, size_t> clipped;
-  map<size_t, size_t> sum;
-
-  void
-  operator+=( const NgramCounts& rhs )
-  {
-    assert( N_ == rhs.N_ );
-    for ( size_t i = 0; i < N_; i++ ) {
-      this->clipped[i] += rhs.clipped.find(i)->second;
-      this->sum[i] += rhs.sum.find(i)->second;
-    }
-  }
-
-  void
-  add( size_t count, size_t ref_count, size_t i )
-  {
-    assert( i < N_ );
-    if ( count > ref_count ) {
-      clipped[i] += ref_count;
-      sum[i] += count;
-    } else {
-      clipped[i] += count;
-      sum[i] += count;
-    }
-  }
-
-  void
-  reset()
-  {
-    size_t i;
-    for ( i = 0; i < N_; i++ ) {
-      clipped[i] = 0;
-      sum[i] = 0;
-    }
-  }
-
-  void
-  print()
-  {
-    for ( size_t i = 0; i < N_; i++ ) {
-      cout << i+1 << "grams (clipped):\t" << clipped[i] << endl;
-      cout << i+1 << "grams:\t\t\t" << sum[i] << endl;
-    }
-  }
-};
-
-
-
-
-typedef map<vector<WordID>, size_t> Ngrams;
-Ngrams make_ngrams( vector<WordID>& s, size_t N );
-NgramCounts make_ngram_counts( vector<WordID> hyp, vector<WordID> ref, size_t N );
-double brevity_penaly( const size_t hyp_len, const size_t ref_len );
-double bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, size_t N, vector<float> weights = vector<float>() );
-double stupid_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, size_t N, vector<float> weights = vector<float>() );
-double smooth_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, const size_t N, vector<float> weights = vector<float>() );
-double approx_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, const size_t N, vector<float> weights = vector<float>() );
-void register_and_convert(const vector<string>& strs, vector<WordID>& ids);
-void print_FD();
-void run_tests();
-void test_SetWeights();
-#include <boost/assign/std/vector.hpp>
-#include <iomanip>
-void test_metrics();
-double approx_equal( double x, double y );
-void test_ngrams();
-
-- 
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