From b7568a8dad2720d5ea0418171e9b85229adbbcc5 Mon Sep 17 00:00:00 2001
From: Patrick Simianer <p@simianer.de>
Date: Sun, 31 Jul 2011 19:24:02 +0200
Subject: bugfixing, begin refactoring

---
 dtrain/dcommon.h | 71 ++------------------------------------------------------
 1 file changed, 2 insertions(+), 69 deletions(-)

(limited to 'dtrain/dcommon.h')

diff --git a/dtrain/dcommon.h b/dtrain/dcommon.h
index ff796642..6df841bb 100644
--- a/dtrain/dcommon.h
+++ b/dtrain/dcommon.h
@@ -30,6 +30,8 @@ using namespace std;
 namespace po = boost::program_options;
 
 
+
+
 struct ScorePair
 {
   ScorePair(double modelscore, double score) : modelscore_(modelscore), score_(score) {} 
@@ -139,72 +141,7 @@ struct NgramCounts
 };
 
 
-/*class Learnerx
-{
-  public:
-    virtual void Init(const vector<SparseVector<double> >& kbest, const Scores& scores) {};
-    virtual void Update(SparseVector<double>& lambdas);
-};*/
-
-class SofiaLearner //: public Learnerx FIXME
-{
-  // TODO bool invert_score
-  public:
-  void
-  Init( const size_t sid, const vector<SparseVector<double> >& kbest, /*const*/ Scores& scores )
-  {
-    assert( kbest.size() == scores.size() );
-    ofstream o;
-    unlink( "/tmo/sofia_ml_training" );
-    o.open( "/tmp/sofia_ml_training", ios::trunc ); // TODO randomize, filename exists
-    int fid = 0;
-    map<int,int>::iterator ff;
-    for ( size_t k = 0; k < kbest.size(); ++k ) {
-      SparseVector<double>::const_iterator it = kbest[k].begin();
-      o << scores[k].GetScore();
-      for ( ; it != kbest[k].end(); ++it) {
-        ff = fmap.find( it->first );
-        if ( ff == fmap.end() ) {
-          fmap.insert( pair<int,int>(it->first, fid) );
-          fmap1.insert( pair<int,int>(fid, it->first) );
-          fid++;
-        }
-        o << " "<< fmap[it->first] << ":" << it->second;
-      }
-      o << endl;
-    }
-    o.close();
-  }
-
-  void
-  Update(SparseVector<double>& lambdas)
-  {
-    string call = "./sofia-ml --training_file /tmp/sofia_ml_training --model_out /tmp/sofia_ml_model --loop_type stochastic --lambda 100 --dimensionality ";
-    std::stringstream out;
-    out << fmap.size();
-    call += out.str();
-    call += " &>/dev/null";
-    system ( call.c_str() );
-    ifstream i;
-    unlink( "/tmo/sofia_ml_model" );
-    i.open( "/tmp/sofia_ml_model", ios::in );
-    string model;
-    getline( i, model );
-    //cout << model << endl;
-    vector<string> strs;
-    boost::split( strs, model, boost::is_any_of(" ") );
-    int j = 0;
-    for ( vector<string>::iterator it = strs.begin(); it != strs.end(); ++it ) {
-      lambdas.set_value(fmap1[j], atof( it->c_str() ) );
-      j++;
-    }
-
-  }
 
-  private:
-    map<int,int> fmap;
-    map<int,int> fmap1;
-};
 
 typedef map<vector<WordID>, size_t> Ngrams;
 Ngrams make_ngrams( vector<WordID>& s, size_t N );
@@ -215,10 +152,6 @@ double stupid_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_
 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();
-- 
cgit v1.2.3