diff options
author | Patrick Simianer <p@simianer.de> | 2012-04-23 21:44:02 +0200 |
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committer | Patrick Simianer <p@simianer.de> | 2012-04-23 21:44:02 +0200 |
commit | 1c733723583862a5fcee5352433022008a9dc4e0 (patch) | |
tree | d4f9a17498d6bc9210e0e3cebbcfc3635736b669 /rst_parser/arc_factored.cc | |
parent | d3aa71d1095a5c45c1d3ca3155259e5fe0b58df2 (diff) | |
parent | 44508c1ad1bf88b1568713317b4a1e0be78804f8 (diff) |
Merge remote-tracking branch 'upstream/master'
Conflicts:
Makefile.am
configure.ac
Diffstat (limited to 'rst_parser/arc_factored.cc')
-rw-r--r-- | rst_parser/arc_factored.cc | 128 |
1 files changed, 124 insertions, 4 deletions
diff --git a/rst_parser/arc_factored.cc b/rst_parser/arc_factored.cc index 1e75600b..74bf7516 100644 --- a/rst_parser/arc_factored.cc +++ b/rst_parser/arc_factored.cc @@ -1,31 +1,151 @@ #include "arc_factored.h" #include <set> +#include <tr1/unordered_set> #include <boost/pending/disjoint_sets.hpp> +#include <boost/functional/hash.hpp> + +#include "arc_ff.h" using namespace std; +using namespace std::tr1; using namespace boost; +void EdgeSubset::ExtractFeatures(const TaggedSentence& sentence, + const ArcFeatureFunctions& ffs, + SparseVector<double>* features) const { + SparseVector<weight_t> efmap; + for (int j = 0; j < h_m_pairs.size(); ++j) { + efmap.clear(); + ffs.EdgeFeatures(sentence, h_m_pairs[j].first, + h_m_pairs[j].second, + &efmap); + (*features) += efmap; + } + for (int j = 0; j < roots.size(); ++j) { + efmap.clear(); + ffs.EdgeFeatures(sentence, -1, roots[j], &efmap); + (*features) += efmap; + } +} + +void ArcFactoredForest::ExtractFeatures(const TaggedSentence& sentence, + const ArcFeatureFunctions& ffs) { + for (int m = 0; m < num_words_; ++m) { + for (int h = 0; h < num_words_; ++h) { + ffs.EdgeFeatures(sentence, h, m, &edges_(h,m).features); + } + ffs.EdgeFeatures(sentence, -1, m, &root_edges_[m].features); + } +} + +void ArcFactoredForest::PickBestParentForEachWord(EdgeSubset* st) const { + for (int m = 0; m < num_words_; ++m) { + int best_head = -2; + prob_t best_score; + for (int h = -1; h < num_words_; ++h) { + const Edge& edge = (*this)(h,m); + if (best_head < -1 || edge.edge_prob > best_score) { + best_score = edge.edge_prob; + best_head = h; + } + } + assert(best_head >= -1); + if (best_head >= 0) + st->h_m_pairs.push_back(make_pair<short,short>(best_head, m)); + else + st->roots.push_back(m); + } +} + +struct WeightedEdge { + WeightedEdge() : h(), m(), weight() {} + WeightedEdge(short hh, short mm, float w) : h(hh), m(mm), weight(w) {} + short h, m; + float weight; + inline bool operator==(const WeightedEdge& o) const { + return h == o.h && m == o.m && weight == o.weight; + } + inline bool operator!=(const WeightedEdge& o) const { + return h != o.h || m != o.m || weight != o.weight; + } +}; +inline bool operator<(const WeightedEdge& l, const WeightedEdge& o) { return l.weight < o.weight; } +inline size_t hash_value(const WeightedEdge& e) { return reinterpret_cast<const size_t&>(e); } + + +struct PriorityQueue { + void push(const WeightedEdge& e) {} + const WeightedEdge& top() const { + static WeightedEdge w(1,2,3); + return w; + } + void pop() {} + void increment_all(float p) {} +}; + // based on Trajan 1977 -void ArcFactoredForest::MaximumSpanningTree(SpanningTree* st) const { +void ArcFactoredForest::MaximumSpanningTree(EdgeSubset* st) const { typedef disjoint_sets_with_storage<identity_property_map, identity_property_map, find_with_full_path_compression> DisjointSet; DisjointSet strongly(num_words_ + 1); DisjointSet weakly(num_words_ + 1); - set<unsigned> roots, h, rset; - vector<pair<short, short> > enter(num_words_ + 1); + set<unsigned> roots, rset; + unordered_set<WeightedEdge, boost::hash<WeightedEdge> > h; + vector<PriorityQueue> qs(num_words_ + 1); + vector<WeightedEdge> enter(num_words_ + 1); + vector<unsigned> mins(num_words_ + 1); + const WeightedEdge kDUMMY(0,0,0.0f); for (unsigned i = 0; i <= num_words_; ++i) { + if (i > 0) { + // I(i) incidence on i -- all incoming edges + for (unsigned j = 0; j <= num_words_; ++j) { + qs[i].push(WeightedEdge(j, i, Weight(j,i))); + } + } strongly.make_set(i); weakly.make_set(i); roots.insert(i); + enter[i] = kDUMMY; + mins[i] = i; } while(!roots.empty()) { set<unsigned>::iterator it = roots.begin(); const unsigned k = *it; roots.erase(it); cerr << "k=" << k << endl; - pair<short,short> ij; // TODO = Max(k); + WeightedEdge ij = qs[k].top(); // MAX(k) + qs[k].pop(); + if (ij.weight <= 0) { + rset.insert(k); + } else { + if (strongly.find_set(ij.h) == k) { + roots.insert(k); + } else { + h.insert(ij); + if (weakly.find_set(ij.h) != weakly.find_set(ij.m)) { + weakly.union_set(ij.h, ij.m); + enter[k] = ij; + } else { + unsigned vertex = 0; + float val = 99999999999; + WeightedEdge xy = ij; + while(xy != kDUMMY) { + if (xy.weight < val) { + val = xy.weight; + vertex = strongly.find_set(xy.m); + } + xy = enter[strongly.find_set(xy.h)]; + } + qs[k].increment_all(val - ij.weight); + mins[k] = mins[vertex]; + xy = enter[strongly.find_set(ij.h)]; + while (xy != kDUMMY) { + } + } + } + } } } |