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#ifndef _ARC_FACTORED_H_
#define _ARC_FACTORED_H_
#include <iostream>
#include <cassert>
#include <vector>
#include <utility>
#include <boost/shared_ptr.hpp>
#include "array2d.h"
#include "sparse_vector.h"
#include "prob.h"
#include "weights.h"
#include "wordid.h"
struct TaggedSentence {
std::vector<WordID> words;
std::vector<WordID> pos;
};
struct ArcFeatureFunction;
struct EdgeSubset {
EdgeSubset() {}
std::vector<short> roots; // unless multiroot trees are supported, this
// will have a single member
std::vector<std::pair<short, short> > h_m_pairs; // h,m start at 0
void ExtractFeatures(const TaggedSentence& sentence,
const std::vector<boost::shared_ptr<ArcFeatureFunction> >& ffs,
SparseVector<double>* features) const;
};
class ArcFactoredForest {
public:
ArcFactoredForest() : num_words_() {}
explicit ArcFactoredForest(short num_words) : num_words_(num_words) {
resize(num_words);
}
unsigned size() const { return num_words_; }
void resize(unsigned num_words) {
num_words_ = num_words;
root_edges_.clear();
edges_.clear();
root_edges_.resize(num_words);
edges_.resize(num_words, num_words);
for (int h = 0; h < num_words; ++h) {
for (int m = 0; m < num_words; ++m) {
edges_(h, m).h = h;
edges_(h, m).m = m;
}
root_edges_[h].h = -1;
root_edges_[h].m = h;
}
}
// compute the maximum spanning tree based on the current weighting
// using the O(n^2) CLE algorithm
void MaximumSpanningTree(EdgeSubset* st) const;
// Reweight edges so that edge_prob is the edge's marginals
// optionally returns log partition
void EdgeMarginals(prob_t* p_log_z = NULL);
// This may not return a tree
void PickBestParentForEachWord(EdgeSubset* st) const;
struct Edge {
Edge() : h(), m(), features(), edge_prob(prob_t::Zero()) {}
short h;
short m;
SparseVector<weight_t> features;
prob_t edge_prob;
};
// set eges_[*].features
void ExtractFeatures(const TaggedSentence& sentence,
const std::vector<boost::shared_ptr<ArcFeatureFunction> >& ffs);
const Edge& operator()(short h, short m) const {
return h >= 0 ? edges_(h, m) : root_edges_[m];
}
Edge& operator()(short h, short m) {
return h >= 0 ? edges_(h, m) : root_edges_[m];
}
float Weight(short h, short m) const {
return log((*this)(h,m).edge_prob);
}
template <class V>
void Reweight(const V& weights) {
for (int m = 0; m < num_words_; ++m) {
for (int h = 0; h < num_words_; ++h) {
if (h != m) {
Edge& e = edges_(h, m);
e.edge_prob.logeq(e.features.dot(weights));
}
}
Edge& e = root_edges_[m];
e.edge_prob.logeq(e.features.dot(weights));
}
}
private:
int num_words_;
std::vector<Edge> root_edges_;
Array2D<Edge> edges_;
};
inline std::ostream& operator<<(std::ostream& os, const ArcFactoredForest::Edge& edge) {
return os << "(" << edge.h << " < " << edge.m << ")";
}
inline std::ostream& operator<<(std::ostream& os, const EdgeSubset& ss) {
for (unsigned i = 0; i < ss.roots.size(); ++i)
os << "ROOT < " << ss.roots[i] << std::endl;
for (unsigned i = 0; i < ss.h_m_pairs.size(); ++i)
os << ss.h_m_pairs[i].first << " < " << ss.h_m_pairs[i].second << std::endl;
return os;
}
#endif
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