diff options
Diffstat (limited to 'decoder/ff.cc')
| -rw-r--r-- | decoder/ff.cc | 197 |
1 files changed, 8 insertions, 189 deletions
diff --git a/decoder/ff.cc b/decoder/ff.cc index 557e0b5f..a6a035b5 100644 --- a/decoder/ff.cc +++ b/decoder/ff.cc @@ -1,9 +1,3 @@ -//TODO: non-sparse vector for all feature functions? modelset applymodels keeps track of who has what features? it's nice having FF that could generate a handful out of 10000 possible feats, though. - -//TODO: actually score rule_feature()==true features once only, hash keyed on rule or modify TRule directly? need to keep clear in forest which features come from models vs. rules; then rescoring could drop all the old models features at once - -#include "fast_lexical_cast.hpp" -#include <stdexcept> #include "ff.h" #include "tdict.h" @@ -16,8 +10,7 @@ FeatureFunction::~FeatureFunction() {} void FeatureFunction::PrepareForInput(const SentenceMetadata&) {} void FeatureFunction::FinalTraversalFeatures(const void* /* ant_state */, - SparseVector<double>* /* features */) const { -} + SparseVector<double>* /* features */) const {} string FeatureFunction::usage_helper(std::string const& name,std::string const& params,std::string const& details,bool sp,bool sd) { string r=name; @@ -32,188 +25,14 @@ string FeatureFunction::usage_helper(std::string const& name,std::string const& return r; } -Features FeatureFunction::single_feature(WordID feat) { - return Features(1,feat); -} - -Features ModelSet::all_features(std::ostream *warn,bool warn0) { - //return ::all_features(models_,weights_,warn,warn0); -} - -void show_features(Features const& ffs,DenseWeightVector const& weights_,std::ostream &out,std::ostream &warn,bool warn_zero_wt) { - out << "Weight Feature\n"; - for (unsigned i=0;i<ffs.size();++i) { - WordID fid=ffs[i]; - string const& fname=FD::Convert(fid); - double wt=weights_[fid]; - if (warn_zero_wt && wt==0) - warn<<"WARNING: "<<fname<<" has 0 weight."<<endl; - out << wt << " " << fname<<endl; - } -} - -void ModelSet::show_features(std::ostream &out,std::ostream &warn,bool warn_zero_wt) -{ -// ::show_features(all_features(),weights_,out,warn,warn_zero_wt); - //show_all_features(models_,weights_,out,warn,warn_zero_wt,warn_zero_wt); -} - -// Hiero and Joshua use log_10(e) as the value, so I do to -WordPenalty::WordPenalty(const string& param) : - fid_(FD::Convert("WordPenalty")), - value_(-1.0 / log(10)) { - if (!param.empty()) { - cerr << "Warning WordPenalty ignoring parameter: " << param << endl; - } -} - -void FeatureFunction::TraversalFeaturesImpl(const SentenceMetadata& smeta, - const Hypergraph::Edge& edge, - const std::vector<const void*>& ant_states, - SparseVector<double>* features, - SparseVector<double>* estimated_features, - void* state) const { - throw std::runtime_error("TraversalFeaturesImpl not implemented - override it or TraversalFeaturesLog.\n"); +void FeatureFunction::TraversalFeaturesImpl(const SentenceMetadata&, + const Hypergraph::Edge&, + const std::vector<const void*>&, + SparseVector<double>*, + SparseVector<double>*, + void*) const { + cerr << "TraversalFeaturesImpl not implemented - override it or TraversalFeaturesLog\n"; abort(); } -void WordPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta, - const Hypergraph::Edge& edge, - const std::vector<const void*>& ant_states, - SparseVector<double>* features, - SparseVector<double>* estimated_features, - void* state) const { - (void) smeta; - (void) ant_states; - (void) state; - (void) estimated_features; - features->set_value(fid_, edge.rule_->EWords() * value_); -} - -SourceWordPenalty::SourceWordPenalty(const string& param) : - fid_(FD::Convert("SourceWordPenalty")), - value_(-1.0 / log(10)) { - if (!param.empty()) { - cerr << "Warning SourceWordPenalty ignoring parameter: " << param << endl; - } -} - -Features SourceWordPenalty::features() const { - return single_feature(fid_); -} - -Features WordPenalty::features() const { - return single_feature(fid_); -} - - -void SourceWordPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta, - const Hypergraph::Edge& edge, - const std::vector<const void*>& ant_states, - SparseVector<double>* features, - SparseVector<double>* estimated_features, - void* state) const { - (void) smeta; - (void) ant_states; - (void) state; - (void) estimated_features; - features->set_value(fid_, edge.rule_->FWords() * value_); -} - -ArityPenalty::ArityPenalty(const std::string& param) : - value_(-1.0 / log(10)) { - string fname = "Arity_"; - unsigned MAX=DEFAULT_MAX_ARITY; - using namespace boost; - if (!param.empty()) - MAX=lexical_cast<unsigned>(param); - for (unsigned i = 0; i <= MAX; ++i) { - WordID fid=FD::Convert(fname+lexical_cast<string>(i)); - fids_.push_back(fid); - } - while (!fids_.empty() && fids_.back()==0) fids_.pop_back(); // pretty up features vector in case FD was frozen. doesn't change anything -} - -Features ArityPenalty::features() const { - return Features(fids_.begin(),fids_.end()); -} - -void ArityPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta, - const Hypergraph::Edge& edge, - const std::vector<const void*>& ant_states, - SparseVector<double>* features, - SparseVector<double>* estimated_features, - void* state) const { - (void) smeta; - (void) ant_states; - (void) state; - (void) estimated_features; - unsigned a=edge.Arity(); - features->set_value(a<fids_.size()?fids_[a]:0, value_); -} - -ModelSet::ModelSet(const vector<double>& w, const vector<const FeatureFunction*>& models) : - models_(models), - weights_(w), - state_size_(0), - model_state_pos_(models.size()) { - for (int i = 0; i < models_.size(); ++i) { - model_state_pos_[i] = state_size_; - state_size_ += models_[i]->NumBytesContext(); - } -} - -void ModelSet::PrepareForInput(const SentenceMetadata& smeta) { - for (int i = 0; i < models_.size(); ++i) - const_cast<FeatureFunction*>(models_[i])->PrepareForInput(smeta); -} - -void ModelSet::AddFeaturesToEdge(const SentenceMetadata& smeta, - const Hypergraph& /* hg */, - const FFStates& node_states, - Hypergraph::Edge* edge, - FFState* context, - prob_t* combination_cost_estimate) const { - edge->reset_info(); - context->resize(state_size_); - if (state_size_ > 0) { - memset(&(*context)[0], 0, state_size_); - } - SparseVector<double> est_vals; // only computed if combination_cost_estimate is non-NULL - if (combination_cost_estimate) *combination_cost_estimate = prob_t::One(); - for (int i = 0; i < models_.size(); ++i) { - const FeatureFunction& ff = *models_[i]; - void* cur_ff_context = NULL; - vector<const void*> ants(edge->tail_nodes_.size()); - bool has_context = ff.NumBytesContext() > 0; - if (has_context) { - int spos = model_state_pos_[i]; - cur_ff_context = &(*context)[spos]; - for (int i = 0; i < ants.size(); ++i) { - ants[i] = &node_states[edge->tail_nodes_[i]][spos]; - } - } - ff.TraversalFeatures(smeta, *edge, ants, &edge->feature_values_, &est_vals, cur_ff_context); - } - if (combination_cost_estimate) - combination_cost_estimate->logeq(est_vals.dot(weights_)); - edge->edge_prob_.logeq(edge->feature_values_.dot(weights_)); -} - -void ModelSet::AddFinalFeatures(const FFState& state, Hypergraph::Edge* edge,SentenceMetadata const& smeta) const { - assert(1 == edge->rule_->Arity()); - edge->reset_info(); - for (int i = 0; i < models_.size(); ++i) { - const FeatureFunction& ff = *models_[i]; - const void* ant_state = NULL; - bool has_context = ff.NumBytesContext() > 0; - if (has_context) { - int spos = model_state_pos_[i]; - ant_state = &state[spos]; - } - ff.FinalTraversalFeatures(smeta, *edge, ant_state, &edge->feature_values_); - } - edge->edge_prob_.logeq(edge->feature_values_.dot(weights_)); -} - |