size_t -> unsigned

1 files changed, 31 insertions, 30 deletions

diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 0481cf96..44090242 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -6,23 +6,24 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
 {
   po::options_description ini("Configuration File Options");
   ini.add_options()
-    ("input",          po::value<string>()->default_value("-"),                          "input file")
-    ("output",         po::value<string>()->default_value("-"),       "output weights file (or VOID)")
-    ("input_weights",  po::value<string>(),       "input weights file (e.g. from previous iteration)")
-    ("decoder_config", po::value<string>(),                             "configuration file for cdec")
-    ("k",              po::value<size_t>()->default_value(100), "size of kbest or sample from forest")
-    ("sample_from",    po::value<string>()->default_value("kbest"),  "where to get translations from")
-    ("filter",         po::value<string>()->default_value("unique"),              "filter kbest list")
-    ("pair_sampling",  po::value<string>()->default_value("all"),    "how to sample pairs: all, rand")
-    ("N",              po::value<size_t>()->default_value(3),                          "N for Ngrams")
-    ("epochs",         po::value<size_t>()->default_value(2),                     "# of iterations T") 
-    ("scorer",         po::value<string>()->default_value("stupid_bleu"),            "scoring metric")
-    ("stop_after",     po::value<size_t>()->default_value(0),          "stop after X input sentences")
-    ("print_weights",  po::value<string>(),                      "weights to print on each iteration")
-    ("hstreaming",     po::value<bool>()->zero_tokens(),               "run in hadoop streaming mode")
-    ("learning_rate",  po::value<double>()->default_value(0.0005),                    "learning rate")
-    ("gamma",          po::value<double>()->default_value(0.),     "gamma for SVM (0 for perceptron)")
-    ("noup",           po::value<bool>()->zero_tokens(),                      "do not update weights");
+    ("input",          po::value<string>()->default_value("-"),                            "input file")
+    ("output",         po::value<string>()->default_value("-"),         "output weights file (or VOID)")
+    ("input_weights",  po::value<string>(),         "input weights file (e.g. from previous iteration)")
+    ("decoder_config", po::value<string>(),                               "configuration file for cdec")
+    ("k",              po::value<unsigned>()->default_value(100), "size of kbest or sample from forest")
+    ("sample_from",    po::value<string>()->default_value("kbest"),    "where to get translations from")
+    ("filter",         po::value<string>()->default_value("unique"),                "filter kbest list")
+    ("pair_sampling",  po::value<string>()->default_value("all"),      "how to sample pairs: all, rand")
+    ("N",              po::value<unsigned>()->default_value(3),                          "N for Ngrams")
+    ("epochs",         po::value<unsigned>()->default_value(2),                     "# of iterations T") 
+    ("scorer",         po::value<string>()->default_value("stupid_bleu"),              "scoring metric")
+    ("stop_after",     po::value<unsigned>()->default_value(0),          "stop after X input sentences")
+    ("print_weights",  po::value<string>(),                        "weights to print on each iteration")
+    ("hstreaming",     po::value<bool>()->zero_tokens(),                 "run in hadoop streaming mode")
+    ("learning_rate",  po::value<double>()->default_value(0.0005),                      "learning rate")
+    ("gamma",          po::value<double>()->default_value(0.),       "gamma for SVM (0 for perceptron)")
+    ("tmp",            po::value<string>()->default_value("/tmp"),                    "temp dir to use") // FIXME
+    ("noup",           po::value<bool>()->zero_tokens(),                        "do not update weights");
   po::options_description cl("Command Line Options");
   cl.add_options()
     ("config,c",         po::value<string>(),              "dtrain config file")
@@ -75,10 +76,10 @@ main(int argc, char** argv)
     hstreaming = true;
     quiet = true;
   }
-  const size_t k = cfg["k"].as<size_t>();
-  const size_t N = cfg["N"].as<size_t>(); 
-  const size_t T = cfg["epochs"].as<size_t>();
-  const size_t stop_after = cfg["stop_after"].as<size_t>();
+  const unsigned k = cfg["k"].as<unsigned>();
+  const unsigned N = cfg["N"].as<unsigned>(); 
+  const unsigned T = cfg["epochs"].as<unsigned>();
+  const unsigned stop_after = cfg["stop_after"].as<unsigned>();
   const string filter_type = cfg["filter"].as<string>();
   const string sample_from = cfg["sample_from"].as<string>();
   const string pair_sampling = cfg["pair_sampling"].as<string>();
@@ -105,7 +106,7 @@ main(int argc, char** argv)
 
   // scoring metric/scorer
   string scorer_str = cfg["scorer"].as<string>();
-  score_t (*scorer)(NgramCounts&, const size_t, const size_t, size_t, vector<score_t>);
+  score_t (*scorer)(NgramCounts&, const unsigned, const unsigned, unsigned, vector<score_t>);
   if (scorer_str == "bleu") {
     scorer = &bleu;
   } else if (scorer_str == "stupid_bleu") {
@@ -119,8 +120,8 @@ main(int argc, char** argv)
     exit(1);
   }
   NgramCounts global_counts(N); // counts for 1 best translations
-  size_t global_hyp_len = 0;    // sum hypothesis lengths
-  size_t global_ref_len = 0;    // sum reference lengths
+  unsigned global_hyp_len = 0;    // sum hypothesis lengths
+  unsigned global_ref_len = 0;    // sum reference lengths
   // ^^^ global_* for approx_bleu
   vector<score_t> bleu_weights;   // we leave this empty -> 1/N 
   if (!quiet) cerr << setw(26) << "scorer '" << scorer_str << "'" << endl << endl;
@@ -149,10 +150,10 @@ main(int argc, char** argv)
   ogzstream grammar_buf_out;
   grammar_buf_out.open(grammar_buf_fn);
   
-  size_t in_sz = 999999999; // input index, input size
+  unsigned in_sz = 999999999; // input index, input size
   vector<pair<score_t,score_t> > all_scores;
   score_t max_score = 0.;
-  size_t best_it = 0;
+  unsigned best_it = 0;
   float overall_time = 0.;
 
   // output cfg
@@ -178,7 +179,7 @@ main(int argc, char** argv)
   }
 
 
-  for (size_t t = 0; t < T; t++) // T epochs
+  for (unsigned t = 0; t < T; t++) // T epochs
   {
 
   time_t start, end;  
@@ -186,7 +187,7 @@ main(int argc, char** argv)
   igzstream grammar_buf_in;
   if (t > 0) grammar_buf_in.open(grammar_buf_fn);
   score_t score_sum = 0., model_sum = 0.;
-  size_t ii = 0;
+  unsigned ii = 0;
   if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
   
   while(true)
@@ -279,10 +280,10 @@ main(int argc, char** argv)
     // (local) scoring
     if (t > 0) ref_ids = ref_ids_buf[ii];
     score_t score = 0.;
-    for (size_t i = 0; i < samples->size(); i++) {
+    for (unsigned i = 0; i < samples->size(); i++) {
       NgramCounts counts = make_ngram_counts(ref_ids, (*samples)[i].w, N);
       if (scorer_str == "approx_bleu") {
-        size_t hyp_len = 0;
+        unsigned hyp_len = 0;
         if (i == 0) { // 'context of 1best translations'
           global_counts  += counts;
           global_hyp_len += (*samples)[i].w.size();