コード例 #1
0
bool operator==(const ScoreStats& s1, const ScoreStats& s2)
{
  size_t size = s1.size();

  if (size != s2.size())
    return false;

  for (size_t k=0; k < size; k++) {
    if (s1.get(k) != s2.get(k))
      return false;
  }

  return true;
}
コード例 #2
0
void ScoreStats::Copy(const ScoreStats &stats)
{
  m_available_size = stats.available();
  m_entries = stats.size();
  m_array = new ScoreStatsType[m_available_size];
  memcpy(m_array, stats.getArray(), GetArraySizeWithBytes());
}
コード例 #3
0
int main(int argc, char **argv)
{
  if (argc == 1) {
    cerr << "Usage: ./sentence-bleu ref1 [ref2 ...] < candidate > bleu-scores" << endl;
    return 1;
  }

  vector<string> refFiles(argv + 1, argv + argc);

  // TODO all of these are empty for now
  string config;
  string factors;
  string filter;

  BleuScorer scorer(config);
  scorer.setFactors(factors);
  scorer.setFilter(filter);

  // initialize reference streams
  vector<boost::shared_ptr<ifstream> > refStreams;
  for (vector<string>::const_iterator refFile=refFiles.begin(); refFile!=refFiles.end(); ++refFile) {
    TRACE_ERR("Loading reference from " << *refFile << endl);
    boost::shared_ptr<ifstream> ifs(new ifstream(refFile->c_str()));
    UTIL_THROW_IF2(!ifs, "Cannot open " << *refFile);
    refStreams.push_back(ifs);
  }

  // load sentences, preparing statistics, score
  string hypothesisLine;
  size_t sid = 0;
  while (getline(std::cin, hypothesisLine)) {
    Reference ref;
    if (!scorer.GetNextReferenceFromStreams(refStreams, ref)) {
      UTIL_THROW2("Missing references");
    }
    ScoreStats scoreStats;
    scorer.CalcBleuStats(ref, hypothesisLine, scoreStats);
    vector<float> stats(scoreStats.getArray(), scoreStats.getArray() + scoreStats.size());
    std::cout << smoothedSentenceBleu(stats) << std::endl;
    ++sid;
  }

  return 0;
}
コード例 #4
0
ファイル: Scorer.cpp プロジェクト: lolobaro/mosesdecoder
void  StatisticsBasedScorer::score(const candidates_t& candidates, const diffs_t& diffs,
                                   statscores_t& scores) const
{
  if (!m_score_data) {
    throw runtime_error("Score data not loaded");
  }
  // calculate the score for the candidates
  if (m_score_data->size() == 0) {
    throw runtime_error("Score data is empty");
  }
  if (candidates.size() == 0) {
    throw runtime_error("No candidates supplied");
  }
  int numCounts = m_score_data->get(0,candidates[0]).size();
  vector<int> totals(numCounts);
  for (size_t i = 0; i < candidates.size(); ++i) {
    ScoreStats stats = m_score_data->get(i,candidates[i]);
    if (stats.size() != totals.size()) {
      stringstream msg;
      msg << "Statistics for (" << "," << candidates[i] << ") have incorrect "
          << "number of fields. Found: " << stats.size() << " Expected: "
          << totals.size();
      throw runtime_error(msg.str());
    }
    for (size_t k = 0; k < totals.size(); ++k) {
      totals[k] += stats.get(k);
    }
  }
  scores.push_back(calculateScore(totals));

  candidates_t last_candidates(candidates);
  // apply each of the diffs, and get new scores
  for (size_t i = 0; i < diffs.size(); ++i) {
    for (size_t j = 0; j < diffs[i].size(); ++j) {
      size_t sid = diffs[i][j].first;
      size_t nid = diffs[i][j].second;
      size_t last_nid = last_candidates[sid];
      for (size_t k  = 0; k < totals.size(); ++k) {
        int diff = m_score_data->get(sid,nid).get(k)
                   - m_score_data->get(sid,last_nid).get(k);
        totals[k] += diff;
      }
      last_candidates[sid] = nid;
    }
    scores.push_back(calculateScore(totals));
  }

  // Regularisation. This can either be none, or the min or average as described in
  // Cer, Jurafsky and Manning at WMT08.
  if (m_regularization_type == NONE || m_regularization_window <= 0) {
    // no regularisation
    return;
  }

  // window size specifies the +/- in each direction
  statscores_t raw_scores(scores);      // copy scores
  for (size_t i = 0; i < scores.size(); ++i) {
    size_t start = 0;
    if (i >= m_regularization_window) {
      start = i - m_regularization_window;
    }
    const size_t end = min(scores.size(), i + m_regularization_window + 1);
    if (m_regularization_type == AVERAGE) {
      scores[i] = score_average(raw_scores,start,end);
    } else {
      scores[i] = score_min(raw_scores,start,end);
    }
  }
}