#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_);
}

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_));
}