1 files changed, 225 insertions, 0 deletions
diff --git a/src/hg.h b/src/hg.h
new file mode 100644
index 00000000..7a2658b8
--- /dev/null
+++ b/src/hg.h
@@ -0,0 +1,225 @@
+#ifndef _HG_H_
+#define _HG_H_
+
+#include <string>
+#include <vector>
+
+#include "small_vector.h"
+#include "sparse_vector.h"
+#include "wordid.h"
+#include "trule.h"
+#include "prob.h"
+
+// class representing an acyclic hypergraph
+//  - edges have 1 head, 0..n tails
+class Hypergraph {
+ public:
+  Hypergraph() {}
+
+  // SmallVector is a fast, small vector<int> implementation for sizes <= 2
+  typedef SmallVector TailNodeVector;
+
+  // TODO get rid of state_ and cat_?
+  struct Node {
+    Node() : id_(), cat_() {}
+    int id_; // equal to this object's position in the nodes_ vector
+    WordID cat_;  // non-terminal category if <0, 0 if not set
+    std::vector<int> in_edges_;   // contents refer to positions in edges_
+    std::vector<int> out_edges_;  // contents refer to positions in edges_
+    std::string state_;           // opaque state
+  };
+
+  // TODO get rid of edge_prob_? (can be computed on the fly as the dot
+  // product of the weight vector and the feature values)
+  struct Edge {
+    Edge() : i_(-1), j_(-1), prev_i_(-1), prev_j_(-1) {}
+    inline int Arity() const { return tail_nodes_.size(); }
+    int head_node_;               // refers to a position in nodes_
+    TailNodeVector tail_nodes_;   // contents refer to positions in nodes_
+    TRulePtr rule_;
+    SparseVector<double> feature_values_;
+    prob_t edge_prob_;             // dot product of weights and feat_values
+    int id_;   // equal to this object's position in the edges_ vector
+
+    // span info. typically, i_ and j_ refer to indices in the source sentence
+    // if a synchronous parse has been executed i_ and j_ will refer to indices
+    // in the target sentence / lattice and prev_i_ prev_j_ will refer to
+    // positions in the source.  Note: it is up to the translator implementation
+    // to properly set these values.  For some models (like the Forest-input
+    // phrase based model) it may not be straightforward to do.  if these values
+    // are not properly set, most things will work but alignment and any features
+    // that depend on them will be broken.
+    short int i_;
+    short int j_;
+    short int prev_i_;
+    short int prev_j_;
+  };
+
+  void swap(Hypergraph& other) {
+    other.nodes_.swap(nodes_);
+    other.edges_.swap(edges_);
+  }
+
+  void ResizeNodes(int size) {
+    nodes_.resize(size);
+    for (int i = 0; i < size; ++i) nodes_[i].id_ = i;
+  }
+
+  // reserves space in the nodes vector to prevent memory locations
+  // from changing
+  void ReserveNodes(size_t n, size_t e = 0) {
+    nodes_.reserve(n);
+    if (e) edges_.reserve(e);
+  }
+
+  Edge* AddEdge(const TRulePtr& rule, const TailNodeVector& tail) {
+    edges_.push_back(Edge());
+    Edge* edge = &edges_.back();
+    edge->rule_ = rule;
+    edge->tail_nodes_ = tail;
+    edge->id_ = edges_.size() - 1;
+    for (int i = 0; i < edge->tail_nodes_.size(); ++i)
+      nodes_[edge->tail_nodes_[i]].out_edges_.push_back(edge->id_);
+    return edge;
+  }
+
+  Node* AddNode(const WordID& cat, const std::string& state = "") {
+    nodes_.push_back(Node());
+    nodes_.back().cat_ = cat;
+    nodes_.back().state_ = state;
+    nodes_.back().id_ = nodes_.size() - 1;
+    return &nodes_.back();
+  }
+
+  void ConnectEdgeToHeadNode(const int edge_id, const int head_id) {
+    edges_[edge_id].head_node_ = head_id;
+    nodes_[head_id].in_edges_.push_back(edge_id);
+  }
+
+  // TODO remove this - use the version that takes indices
+  void ConnectEdgeToHeadNode(Edge* edge, Node* head) {
+    edge->head_node_ = head->id_;
+    head->in_edges_.push_back(edge->id_);
+  }
+
+  // merge the goal node from other with this goal node
+  void Union(const Hypergraph& other);
+
+  void PrintGraphviz() const;
+
+  // compute the total number of paths in the forest
+  double NumberOfPaths() const;
+
+  // BEWARE. this assumes that the source and target language
+  // strings are identical and that there are no loops.
+  // It assumes a bunch of other things about where the
+  // epsilons will be.  It tries to assert failure if you
+  // break these assumptions, but it may not.
+  // TODO - make this work
+  void EpsilonRemove(WordID eps);
+
+  // multiple the weights vector by the edge feature vector
+  // (inner product) to set the edge probabilities
+  template <typename V>
+  void Reweight(const V& weights) {
+    for (int i = 0; i < edges_.size(); ++i) {
+      Edge& e = edges_[i];
+      e.edge_prob_.logeq(e.feature_values_.dot(weights));
+    }
+  }
+
+  // computes inside and outside scores for each
+  // edge in the hypergraph
+  // alpha->size = edges_.size = beta->size
+  // returns inside prob of goal node
+  prob_t ComputeEdgePosteriors(double scale,
+                               std::vector<prob_t>* posts) const;
+
+  // find the score of the very best path passing through each edge
+  prob_t ComputeBestPathThroughEdges(std::vector<prob_t>* posts) const;
+
+  // move weights as near to the source as possible, resulting in a
+  // stochastic automaton.  ONLY FUNCTIONAL FOR *LATTICES*.
+  // See M. Mohri and M. Riley. A Weight Pushing Algorithm for Large
+  //   Vocabulary Speech Recognition. 2001.
+  // the log semiring (NOT tropical) is used
+  void PushWeightsToSource(double scale = 1.0);
+  // same, except weights are pushed to the goal, works for HGs,
+  // not just lattices
+  void PushWeightsToGoal(double scale = 1.0);
+
+  void SortInEdgesByEdgeWeights();
+
+  void PruneUnreachable(int goal_node_id); // DEPRECATED
+
+  void RemoveNoncoaccessibleStates(int goal_node_id = -1);
+
+  // remove edges from the hypergraph if prune_edge[edge_id] is true
+  void PruneEdges(const std::vector<bool>& prune_edge);
+
+  // if you don't know, use_sum_prod_semiring should be false
+  void DensityPruneInsideOutside(const double scale, const bool use_sum_prod_semiring, const double density,
+                                 const std::vector<bool>* preserve_mask = NULL);
+
+  // prunes any edge whose score on the best path taking that edge is more than alpha away
+  // from the score of the global best past (or the highest edge posterior)
+  void BeamPruneInsideOutside(const double scale, const bool use_sum_prod_semiring, const double alpha,
+                              const std::vector<bool>* preserve_mask = NULL);
+
+  void clear() {
+    nodes_.clear();
+    edges_.clear();
+  }
+
+  inline size_t NumberOfEdges() const { return edges_.size(); }
+  inline size_t NumberOfNodes() const { return nodes_.size(); }
+  inline bool empty() const { return nodes_.empty(); }
+
+  // nodes_ is sorted in topological order
+  std::vector<Node> nodes_;
+  // edges_ is not guaranteed to be in any particular order
+  std::vector<Edge> edges_;
+
+  // reorder nodes_ so they are in topological order
+  // source nodes at 0 sink nodes at size-1
+  void TopologicallySortNodesAndEdges(int goal_idx,
+                                      const std::vector<bool>* prune_edges = NULL);
+ private:
+  // returns total nodes reachable
+  int MarkReachable(const Node& node,
+                    std::vector<bool>* rmap,
+                    const std::vector<bool>* prune_edges) const;
+
+  static TRulePtr kEPSRule;
+  static TRulePtr kUnaryRule;
+};
+
+// common WeightFunctions, map an edge -> WeightType
+// for generic Viterbi/Inside algorithms
+struct EdgeProb {
+  inline const prob_t& operator()(const Hypergraph::Edge& e) const { return e.edge_prob_; }
+};
+
+struct ScaledEdgeProb {
+  ScaledEdgeProb(const double& alpha) : alpha_(alpha) {}
+  inline prob_t operator()(const Hypergraph::Edge& e) const { return e.edge_prob_.pow(alpha_); }
+  const double alpha_;
+};
+
+struct EdgeFeaturesWeightFunction {
+  inline const SparseVector<double>& operator()(const Hypergraph::Edge& e) const { return e.feature_values_; }
+};
+
+struct TransitionEventWeightFunction {
+  inline SparseVector<prob_t> operator()(const Hypergraph::Edge& e) const {
+    SparseVector<prob_t> result;
+    result.set_value(e.id_, prob_t::One());
+    return result;
+  }
+};
+
+struct TransitionCountWeightFunction {
+  inline double operator()(const Hypergraph::Edge& e) const { (void)e; return 1.0; }
+};
+
+#endif