initial check in

1 files changed, 93 insertions, 0 deletions

diff --git a/src/ff.cc b/src/ff.cc
new file mode 100644
index 00000000..488e6468
--- /dev/null
+++ b/src/ff.cc
@@ -0,0 +1,93 @@
+#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_);
+}
+
+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,
+                                 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] = &hg.nodes_[edge->tail_nodes_[i]].state_[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_));
+}
+