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//TODO: 0 size state != rule-local feature, i.e. still may depend on source span loc/context. identify truly rule-local features so if we want they can be added to grammar rules (minor speedup)
#include "ff.h"
#include "tdict.h"
#include "hg.h"
using namespace std;
FeatureFunction::~FeatureFunction() {}
void FeatureFunction::FinalTraversalFeatures(const void* ant_state,
SparseVector<double>* features) const {
(void) ant_state;
(void) features;
}
// 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 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;
}
}
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_X";
for (int i = 0; i < 10; ++i) {
fname[6]=i + '0';
fids_[i] = FD::Convert(fname);
}
}
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;
features->set_value(fids_[edge.Arity()], 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::AddFeaturesToEdge(const SentenceMetadata& smeta,
const Hypergraph& /* hg */,
const vector<string>& node_states,
Hypergraph::Edge* edge,
string* context,
prob_t* combination_cost_estimate) const {
context->resize(state_size_);
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 std::string& state, Hypergraph::Edge* edge) const {
assert(1 == edge->rule_->Arity());
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(ant_state, &edge->feature_values_);
}
edge->edge_prob_.logeq(edge->feature_values_.dot(weights_));
}
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