beautify

1 files changed, 36 insertions, 35 deletions

diff --git a/mert/kbmira.cpp b/mert/kbmira.cpp
index f0d1624e6..a2665ac13 100644
--- a/mert/kbmira.cpp
+++ b/mert/kbmira.cpp
@@ -2,7 +2,7 @@
 // vim:tabstop=2
 /***********************************************************************
 K-best Batch MIRA for Moses
-Copyright (C) 2012, National Research Council Canada / Conseil national 
+Copyright (C) 2012, National Research Council Canada / Conseil national
 de recherches du Canada
 ***********************************************************************/
 
@@ -49,13 +49,14 @@ using namespace MosesTuning;
 
 namespace po = boost::program_options;
 
-ValType evaluate(HypPackEnumerator* train, const AvgWeightVector& wv) {
+ValType evaluate(HypPackEnumerator* train, const AvgWeightVector& wv)
+{
   vector<ValType> stats(kBleuNgramOrder*2+1,0);
   for(train->reset(); !train->finished(); train->next()) {
     // Find max model
     size_t max_index=0;
     ValType max_score=0;
-    for(size_t i=0;i<train->cur_size();i++) {
+    for(size_t i=0; i<train->cur_size(); i++) {
       MiraFeatureVector vec(train->featuresAt(i));
       ValType score = wv.score(vec);
       if(i==0 || score > max_score) {
@@ -64,8 +65,8 @@ ValType evaluate(HypPackEnumerator* train, const AvgWeightVector& wv) {
       }
     }
     // Update stats
-    const vector<float>& sent = train->scoresAt(max_index);    
-    for(size_t i=0;i<sent.size();i++) {
+    const vector<float>& sent = train->scoresAt(max_index);
+    for(size_t i=0; i<sent.size(); i++) {
       stats[i]+=sent[i];
     }
   }
@@ -90,26 +91,26 @@ int main(int argc, char** argv)
   bool model_bg = false; // Use model for background corpus
   bool verbose = false; // Verbose updates
   bool safe_hope = false; // Model score cannot have more than BLEU_RATIO times more influence than BLEU
-  
+
   // Command-line processing follows pro.cpp
   po::options_description desc("Allowed options");
   desc.add_options()
-      ("help,h", po::value(&help)->zero_tokens()->default_value(false), "Print this help message and exit")
-      ("scfile,S", po::value<vector<string> >(&scoreFiles), "Scorer data files")
-      ("ffile,F", po::value<vector<string> > (&featureFiles), "Feature data files")
-      ("random-seed,r", po::value<int>(&seed), "Seed for random number generation")
-      ("output-file,o", po::value<string>(&outputFile), "Output file")
-      ("cparam,C", po::value<float>(&c), "MIRA C-parameter, lower for more regularization (default 0.01)")
-      ("decay,D", po::value<float>(&decay), "BLEU background corpus decay rate (default 0.999)")
-      ("iters,J", po::value<int>(&n_iters), "Number of MIRA iterations to run (default 60)")
-      ("dense-init,d", po::value<string>(&denseInitFile), "Weight file for dense features")
-      ("sparse-init,s", po::value<string>(&sparseInitFile), "Weight file for sparse features")
-      ("streaming", po::value(&streaming)->zero_tokens()->default_value(false), "Stream n-best lists to save memory, implies --no-shuffle")
-      ("no-shuffle", po::value(&no_shuffle)->zero_tokens()->default_value(false), "Don't shuffle hypotheses before each epoch")
-      ("model-bg", po::value(&model_bg)->zero_tokens()->default_value(false), "Use model instead of hope for BLEU background")
-      ("verbose", po::value(&verbose)->zero_tokens()->default_value(false), "Verbose updates")
-      ("safe-hope", po::value(&safe_hope)->zero_tokens()->default_value(false), "Mode score's influence on hope decoding is limited")
-      ;
+  ("help,h", po::value(&help)->zero_tokens()->default_value(false), "Print this help message and exit")
+  ("scfile,S", po::value<vector<string> >(&scoreFiles), "Scorer data files")
+  ("ffile,F", po::value<vector<string> > (&featureFiles), "Feature data files")
+  ("random-seed,r", po::value<int>(&seed), "Seed for random number generation")
+  ("output-file,o", po::value<string>(&outputFile), "Output file")
+  ("cparam,C", po::value<float>(&c), "MIRA C-parameter, lower for more regularization (default 0.01)")
+  ("decay,D", po::value<float>(&decay), "BLEU background corpus decay rate (default 0.999)")
+  ("iters,J", po::value<int>(&n_iters), "Number of MIRA iterations to run (default 60)")
+  ("dense-init,d", po::value<string>(&denseInitFile), "Weight file for dense features")
+  ("sparse-init,s", po::value<string>(&sparseInitFile), "Weight file for sparse features")
+  ("streaming", po::value(&streaming)->zero_tokens()->default_value(false), "Stream n-best lists to save memory, implies --no-shuffle")
+  ("no-shuffle", po::value(&no_shuffle)->zero_tokens()->default_value(false), "Don't shuffle hypotheses before each epoch")
+  ("model-bg", po::value(&model_bg)->zero_tokens()->default_value(false), "Use model instead of hope for BLEU background")
+  ("verbose", po::value(&verbose)->zero_tokens()->default_value(false), "Verbose updates")
+  ("safe-hope", po::value(&safe_hope)->zero_tokens()->default_value(false), "Mode score's influence on hope decoding is limited")
+  ;
 
   po::options_description cmdline_options;
   cmdline_options.add(desc);
@@ -118,9 +119,9 @@ int main(int argc, char** argv)
             options(cmdline_options).run(), vm);
   po::notify(vm);
   if (help) {
-      cout << "Usage: " + string(argv[0]) +  " [options]" << endl;
-      cout << desc << endl;
-      exit(0);
+    cout << "Usage: " + string(argv[0]) +  " [options]" << endl;
+    cout << desc << endl;
+    exit(0);
   }
 
   cerr << "kbmira with c=" << c << " decay=" << decay << " no_shuffle=" << no_shuffle << endl;
@@ -165,7 +166,8 @@ int main(int argc, char** argv)
       exit(3);
     }
     int sparseCount=0;
-    parameter_t val; std::string name;
+    parameter_t val;
+    std::string name;
     while(opt >> name >> val) {
       size_t id = SparseVector::encode(name) + initDenseSize;
       while(initParams.size()<=id) initParams.push_back(0.0);
@@ -175,17 +177,17 @@ int main(int argc, char** argv)
     cerr << "Found " << sparseCount << " initial sparse features" << endl;
     opt.close();
   }
-  
+
   MiraWeightVector wv(initParams);
 
   // Initialize background corpus
   vector<ValType> bg;
-  for(int j=0;j<kBleuNgramOrder;j++){
+  for(int j=0; j<kBleuNgramOrder; j++) {
     bg.push_back(kBleuNgramOrder-j);
     bg.push_back(kBleuNgramOrder-j);
   }
   bg.push_back(kBleuNgramOrder);
-  
+
   // Training loop
   boost::scoped_ptr<HypPackEnumerator> train;
   if(streaming)
@@ -194,8 +196,7 @@ int main(int argc, char** argv)
     train.reset(new RandomAccessHypPackEnumerator(featureFiles, scoreFiles, no_shuffle));
   cerr << "Initial BLEU = " << evaluate(train.get(), wv.avg()) << endl;
   ValType bestBleu = 0;
-  for(int j=0;j<n_iters;j++)
-  {
+  for(int j=0; j<n_iters; j++) {
     // MIRA train for one epoch
     int iNumHyps = 0;
     int iNumExamples = 0;
@@ -273,7 +274,7 @@ int main(int argc, char** argv)
         }
         // Update BLEU statistics
         const vector<float>& model_stats = train->scoresAt(model_index);
-        for(size_t k=0;k<bg.size();k++) {
+        for(size_t k=0; k<bg.size(); k++) {
           bg[k]*=decay;
           if(model_bg)
             bg[k]+=model_stats[k];
@@ -286,7 +287,7 @@ int main(int argc, char** argv)
     // Training Epoch summary
     cerr << iNumUpdates << "/" << iNumExamples << " updates"
          << ", avg loss = " << (totalLoss / iNumExamples);
-         
+
 
     // Evaluate current average weights
     AvgWeightVector avg = wv.avg();
@@ -312,11 +313,11 @@ int main(int argc, char** argv)
       } else {
         out = &cout;
       }
-      for(size_t i=0;i<avg.size();i++) {
+      for(size_t i=0; i<avg.size(); i++) {
         if(i<num_dense)
           *out << "F" << i << " " << avg.weight(i) << endl;
         else {
-          if(abs(avg.weight(i))>1e-8) 
+          if(abs(avg.weight(i))>1e-8)
             *out << SparseVector::decode(i-num_dense) << " " << avg.weight(i) << endl;
         }
       }