1 files changed, 130 insertions, 0 deletions
diff --git a/training/test_ngram.cc b/training/test_ngram.cc
new file mode 100644
index 00000000..c481b564
--- /dev/null
+++ b/training/test_ngram.cc
@@ -0,0 +1,130 @@
+#include <fstream>
+#include <iostream>
+#include <vector>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "lm/model.hh"
+#include "lm/enumerate_vocab.hh"
+
+namespace po = boost::program_options;
+using namespace std;
+
+lm::ngram::ProbingModel* ngram;
+struct GetVocab : public lm::ngram::EnumerateVocab {
+  GetVocab(vector<lm::WordIndex>* out) : out_(out) { }
+  void Add(lm::WordIndex index, const StringPiece &str) {
+    out_->push_back(index);
+  }
+  vector<lm::WordIndex>* out_;
+};
+
+bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("model,m",po::value<string>(),"n-gram language model file (KLM)");
+  po::options_description clo("Command line options");
+  clo.add_options()
+        ("config", po::value<string>(), "Configuration file")
+        ("help,h", "Print this help message and exit");
+  po::options_description dconfig_options, dcmdline_options;
+  po::positional_options_description p;
+  p.add("grammar", -1);
+  
+  dconfig_options.add(opts);
+  dcmdline_options.add(opts).add(clo);
+
+  po::store(po::command_line_parser(argc, argv).options(dcmdline_options).positional(p).run(), *conf);
+  if (conf->count("config")) {
+    ifstream config((*conf)["config"].as<string>().c_str());
+    po::store(po::parse_config_file(config, dconfig_options), *conf);
+  }
+  po::notify(*conf);
+
+  if (conf->count("help")) {
+    cerr << "Usage " << argv[0] << " [OPTIONS]\n";
+    cerr << dcmdline_options << endl;
+    return false;
+  }
+  return true;
+}
+
+template <class Model> double BlanketProb(const vector<lm::WordIndex>& sentence, const lm::WordIndex word, const int subst_pos, const Model &model) {
+  typename Model::State state, out;
+  lm::FullScoreReturn ret;
+  double total = 0;
+  state = model.NullContextState();
+
+  const int begin = max(subst_pos - model.Order() + 1, 0);
+  const int end = min(subst_pos + model.Order(), (int)sentence.size());
+  int lookups = 0;
+  bool have_full_context = false;
+  for (int i = begin; i < end; ++i) {
+    if (i == 0) {
+      state = model.BeginSentenceState();
+      have_full_context = true;
+    } else {
+      lookups++;
+      if (lookups == model.Order()) { have_full_context = true; }
+      ret = model.FullScore(state, (subst_pos == i ? word : sentence[i]), out);
+      if (have_full_context) { total += ret.prob; }
+      state = out;
+    }
+  }
+  return total;
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf)) return 1;
+  lm::ngram::Config kconf;
+  vector<lm::WordIndex> vocab;
+  GetVocab gv(&vocab);
+  kconf.enumerate_vocab = &gv; 
+  ngram = new lm::ngram::ProbingModel(conf["model"].as<string>().c_str(), kconf);
+  cerr << "Loaded " << (int)ngram->Order() << "-gram KenLM (vocab size=" << vocab.size() << ")\n";
+  vector<int> exclude(vocab.size(), 0);
+  exclude[0] = 1; // exclude OOVs
+
+  double prob_sum = 0;
+  int counter = 0;
+  int rank_error = 0;
+  string line;
+  while (getline(cin, line)) {
+    stringstream line_stream(line);
+    vector<string> tokens;
+    tokens.push_back("<s>");
+    string token;
+    while (line_stream >> token)
+      tokens.push_back(token);
+    tokens.push_back("</s>");
+
+    vector<lm::WordIndex> sentence(tokens.size());
+    for (int i = 0; i < tokens.size(); ++i)
+      sentence[i] = ngram->GetVocabulary().Index(tokens[i]);
+    exclude[sentence[0]] = 1;
+    exclude[sentence.back()] = 1;
+    for (int i = 1; i < tokens.size()-1; ++i) {
+      cerr << tokens[i] << endl;
+      ++counter;
+      lm::WordIndex gold = sentence[i];
+      double blanket_prob = BlanketProb<lm::ngram::ProbingModel>(sentence, gold, i, *ngram);
+      double z = 0;
+      for (int v = 0; v < vocab.size(); ++v) {
+        if (exclude[v]) continue;
+        double lp = BlanketProb<lm::ngram::ProbingModel>(sentence, v, i, *ngram);
+        if (lp > blanket_prob) ++rank_error;
+        z += pow(10.0, lp);
+      }
+      double post_prob = blanket_prob - log10(z);
+      cerr << "  " << post_prob << endl;
+      prob_sum -= post_prob;
+    }
+  }
+  cerr << "perplexity=" << pow(10,prob_sum/(double)counter) << endl;
+  cerr << "Rank error=" << rank_error/(double)counter << endl;
+
+  return 0;
+}
+