C++ FVector::cbegin方法代码示例

void OutputFeatureScores( std::ostream& out
                          , const ScoreComponentCollection &features
                          , const FeatureFunction *ff
                          , std::string &lastName )
{
  const StaticData &staticData = StaticData::Instance();
  bool labeledOutput = staticData.IsLabeledNBestList();

  // regular features (not sparse)
  if (ff->GetNumScoreComponents() != 0) {
    if( labeledOutput && lastName != ff->GetScoreProducerDescription() ) {
      lastName = ff->GetScoreProducerDescription();
      out << " " << lastName << "=";
    }
    vector<float> scores = features.GetScoresForProducer( ff );
    for (size_t j = 0; j<scores.size(); ++j) {
      out << " " << scores[j];
    }
  }

  // sparse features
  const FVector scores = features.GetVectorForProducer( ff );
  for(FVector::FNVmap::const_iterator i = scores.cbegin(); i != scores.cend(); i++) {
    out << " " << i->first << "= " << i->second;
  }
}

void FVector::updateConfidenceCounts(const FVector& weightUpdate, bool signedCounts)
{
  for (size_t i = 0; i < weightUpdate.m_coreFeatures.size(); ++i) {
    if (signedCounts) {
      //int sign = weightUpdate.m_coreFeatures[i] >= 0 ? 1 : -1;
      //m_coreFeatures[i] += (weightUpdate.m_coreFeatures[i] * weightUpdate.m_coreFeatures[i]) * sign;
      m_coreFeatures[i] += weightUpdate.m_coreFeatures[i];
    } else
      //m_coreFeatures[i] += (weightUpdate.m_coreFeatures[i] * weightUpdate.m_coreFeatures[i]);
      m_coreFeatures[i] += abs(weightUpdate.m_coreFeatures[i]);
  }

  for (const_iterator i = weightUpdate.cbegin(); i != weightUpdate.cend(); ++i) {
    if (weightUpdate[i->first] == 0)
      continue;
    float value = get(i->first);
    if (signedCounts) {
      //int sign = weightUpdate[i->first] >= 0 ? 1 : -1;
      //value += (weightUpdate[i->first] * weightUpdate[i->first]) * sign;
      value += weightUpdate[i->first];
    } else
      //value += (weightUpdate[i->first] * weightUpdate[i->first]);
      value += abs(weightUpdate[i->first]);
    set(i->first, value);
  }
}

void IOWrapper::OutputSparseFeatureScores( std::ostream& out, const ChartTrellisPath &path, const FeatureFunction *ff, std::string &lastName )
{
  const StaticData &staticData = StaticData::Instance();
  bool labeledOutput = staticData.IsLabeledNBestList();
  const FVector scores = path.GetScoreBreakdown().GetVectorForProducer( ff );

  // report weighted aggregate
  if (! ff->GetSparseFeatureReporting()) {
  	const FVector &weights = staticData.GetAllWeights().GetScoresVector();
  	if (labeledOutput && !boost::contains(ff->GetScoreProducerDescription(), ":"))
  		out << " " << ff->GetScoreProducerWeightShortName() << ":";
    out << " " << scores.inner_product(weights);
  }

  // report each feature
  else {
  	for(FVector::FNVmap::const_iterator i = scores.cbegin(); i != scores.cend(); i++) {
  		if (i->second != 0) { // do not report zero-valued features
  			if (labeledOutput)
  				out << " " << i->first << ":";
        out << " " << i->second;
      }
    }
  }
}

// count non-zero occurrences for all sparse features
void FVector::setToBinaryOf(const FVector& rhs)
{
  for (const_iterator i = rhs.cbegin(); i != rhs.cend(); ++i)
    if (rhs.get(i->first) != 0)
      set(i->first, 1);
  for (size_t i = 0; i < rhs.m_coreFeatures.size(); ++i)
    m_coreFeatures[i] = 1;
}

void FVector::updateLearningRates(float decay_core, float decay_sparse, const FVector &confidenceCounts, float core_r0, float sparse_r0)
{
  for (size_t i = 0; i < confidenceCounts.m_coreFeatures.size(); ++i) {
    m_coreFeatures[i] = 1.0/(1.0/core_r0 + decay_core * abs(confidenceCounts.m_coreFeatures[i]));
  }

  for (const_iterator i = confidenceCounts.cbegin(); i != confidenceCounts.cend(); ++i) {
    float value = 1.0/(1.0/sparse_r0 + decay_sparse * abs(i->second));
    set(i->first, value);
  }
}

bool FVector::operator== (const FVector& rhs) const
{
  if (this == &rhs) {
    return true;
  }
  if (m_coreFeatures.size() != rhs.m_coreFeatures.size()) {
    return false;
  }
  for (size_t i = 0; i < m_coreFeatures.size(); ++i) {
    if (!equalsTolerance(m_coreFeatures[i], rhs.m_coreFeatures[i])) return false;
  }
  for (const_iterator i  = cbegin(); i != cend(); ++i) {
    if (!equalsTolerance(i->second,rhs.get(i->first))) return false;
  }
  for (const_iterator i = rhs.cbegin(); i != rhs.cend(); ++i) {
    if (!equalsTolerance(i->second, get(i->first))) return false;
  }
  return true;
}

void OutputFeatureScores( std::ostream& out, const TrellisPath &path, const FeatureFunction *ff, std::string &lastName )
{
  const StaticData &staticData = StaticData::Instance();
  bool labeledOutput = staticData.IsLabeledNBestList();

  // regular features (not sparse)
  if (ff->GetNumScoreComponents() != ScoreProducer::unlimited) {
    if( labeledOutput && lastName != ff->GetScoreProducerWeightShortName() ) {
      lastName = ff->GetScoreProducerWeightShortName();
      out << " " << lastName << ":";
    }
    vector<float> scores = path.GetScoreBreakdown().GetScoresForProducer( ff );
    for (size_t j = 0; j<scores.size(); ++j) {
      out << " " << scores[j];
    }
  }

  // sparse features
  else {
    const FVector scores = path.GetScoreBreakdown().GetVectorForProducer( ff );

    // report weighted aggregate
    if (! ff->GetSparseFeatureReporting()) {
      const FVector &weights = staticData.GetAllWeights().GetScoresVector();
      if (labeledOutput && !boost::contains(ff->GetScoreProducerDescription(), ":"))
        out << " " << ff->GetScoreProducerWeightShortName() << ":";
      out << " " << scores.inner_product(weights);
    }

    // report each feature
    else {
      for(FVector::FNVmap::const_iterator i = scores.cbegin(); i != scores.cend(); i++) 
	out << " " << i->first << ": " << i->second;
	/*        if (i->second != 0) { // do not report zero-valued features
	  float weight = staticData.GetSparseWeight(i->first);
          if (weight != 0)
	  out << " " << i->first << "=" << weight;
	  }*/
    }
  }
}

// add only sparse features
void FVector::sparsePlusEquals(const FVector& rhs)
{
  for (const_iterator i = rhs.cbegin(); i != rhs.cend(); ++i)
    set(i->first, get(i->first) + i->second);
}