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#ifndef _HG_KBEST_H_
#define _HG_KBEST_H_
#include <vector>
#include <utility>
#include <tr1/unordered_set>
#include <boost/shared_ptr.hpp>
#include "wordid.h"
#include "hg.h"
namespace KBest {
// default, don't filter any derivations from the k-best list
struct NoFilter {
bool operator()(const std::vector<WordID>& yield) {
(void) yield;
return false;
}
};
// optional, filter unique yield strings
struct FilterUnique {
std::tr1::unordered_set<std::vector<WordID>, boost::hash<std::vector<WordID> > > unique;
bool operator()(const std::vector<WordID>& yield) {
return !unique.insert(yield).second;
}
};
// utility class to lazily create the k-best derivations from a forest, uses
// the lazy k-best algorithm (Algorithm 3) from Huang and Chiang (IWPT 2005)
template<typename T, // yield type (returned by Traversal)
typename Traversal,
typename DerivationFilter = NoFilter,
typename WeightType = prob_t,
typename WeightFunction = EdgeProb>
struct KBestDerivations {
KBestDerivations(const Hypergraph& hg,
const size_t k,
const Traversal& tf = Traversal(),
const WeightFunction& wf = WeightFunction()) :
traverse(tf), w(wf), g(hg), nds(g.nodes_.size()), k_prime(k) {}
~KBestDerivations() {
for (int i = 0; i < freelist.size(); ++i)
delete freelist[i];
}
struct Derivation {
Derivation(const Hypergraph::Edge& e,
const SmallVector& jv,
const WeightType& w,
const SparseVector<double>& f) :
edge(&e),
j(jv),
score(w),
feature_values(f) {}
// dummy constructor, just for query
Derivation(const Hypergraph::Edge& e,
const SmallVector& jv) : edge(&e), j(jv) {}
T yield;
const Hypergraph::Edge* const edge;
const SmallVector j;
const WeightType score;
const SparseVector<double> feature_values;
};
struct HeapCompare {
bool operator()(const Derivation* a, const Derivation* b) const {
return a->score < b->score;
}
};
struct DerivationCompare {
bool operator()(const Derivation* a, const Derivation* b) const {
return a->score > b->score;
}
};
struct DerivationUniquenessHash {
size_t operator()(const Derivation* d) const {
size_t x = 5381;
x = ((x << 5) + x) ^ d->edge->id_;
for (int i = 0; i < d->j.size(); ++i)
x = ((x << 5) + x) ^ d->j[i];
return x;
}
};
struct DerivationUniquenessEquals {
bool operator()(const Derivation* a, const Derivation* b) const {
return (a->edge == b->edge) && (a->j == b->j);
}
};
typedef std::vector<Derivation*> CandidateHeap;
typedef std::vector<Derivation*> DerivationList;
typedef std::tr1::unordered_set<
const Derivation*, DerivationUniquenessHash, DerivationUniquenessEquals> UniqueDerivationSet;
struct NodeDerivationState {
CandidateHeap cand;
DerivationList D;
DerivationFilter filter;
UniqueDerivationSet ds;
explicit NodeDerivationState(const DerivationFilter& f = DerivationFilter()) : filter(f) {}
};
Derivation* LazyKthBest(int v, int k) {
NodeDerivationState& s = GetCandidates(v);
CandidateHeap& cand = s.cand;
DerivationList& D = s.D;
DerivationFilter& filter = s.filter;
bool add_next = true;
while (D.size() <= k) {
if (add_next && D.size() > 0) {
const Derivation* d = D.back();
LazyNext(d, &cand, &s.ds);
}
add_next = false;
if (cand.size() > 0) {
std::pop_heap(cand.begin(), cand.end(), HeapCompare());
Derivation* d = cand.back();
cand.pop_back();
std::vector<const T*> ants(d->edge->Arity());
for (int j = 0; j < ants.size(); ++j)
ants[j] = &LazyKthBest(d->edge->tail_nodes_[j], d->j[j])->yield;
traverse(*d->edge, ants, &d->yield);
if (!filter(d->yield)) {
D.push_back(d);
add_next = true;
}
} else {
break;
}
}
if (k < D.size()) return D[k]; else return NULL;
}
private:
// creates a derivation object with all fields set but the yield
// the yield is computed in LazyKthBest before the derivation is added to D
// returns NULL if j refers to derivation numbers larger than the
// antecedent structure define
Derivation* CreateDerivation(const Hypergraph::Edge& e, const SmallVector& j) {
WeightType score = w(e);
SparseVector<double> feats = e.feature_values_;
for (int i = 0; i < e.Arity(); ++i) {
const Derivation* ant = LazyKthBest(e.tail_nodes_[i], j[i]);
if (!ant) { return NULL; }
score *= ant->score;
feats += ant->feature_values;
}
freelist.push_back(new Derivation(e, j, score, feats));
return freelist.back();
}
NodeDerivationState& GetCandidates(int v) {
NodeDerivationState& s = nds[v];
if (!s.D.empty() || !s.cand.empty()) return s;
const Hypergraph::Node& node = g.nodes_[v];
for (int i = 0; i < node.in_edges_.size(); ++i) {
const Hypergraph::Edge& edge = g.edges_[node.in_edges_[i]];
SmallVector jv(edge.Arity(), 0);
Derivation* d = CreateDerivation(edge, jv);
assert(d);
s.cand.push_back(d);
}
const int effective_k = std::min(k_prime, s.cand.size());
const typename CandidateHeap::iterator kth = s.cand.begin() + effective_k;
std::nth_element(s.cand.begin(), kth, s.cand.end(), DerivationCompare());
s.cand.resize(effective_k);
std::make_heap(s.cand.begin(), s.cand.end(), HeapCompare());
return s;
}
void LazyNext(const Derivation* d, CandidateHeap* cand, UniqueDerivationSet* ds) {
for (int i = 0; i < d->j.size(); ++i) {
SmallVector j = d->j;
++j[i];
const Derivation* ant = LazyKthBest(d->edge->tail_nodes_[i], j[i]);
if (ant) {
Derivation query_unique(*d->edge, j);
if (ds->count(&query_unique) == 0) {
Derivation* new_d = CreateDerivation(*d->edge, j);
if (new_d) {
cand->push_back(new_d);
std::push_heap(cand->begin(), cand->end(), HeapCompare());
bool inserted = ds->insert(new_d).second; // insert into uniqueness set
assert(inserted);
}
}
}
}
}
const Traversal traverse;
const WeightFunction w;
const Hypergraph& g;
std::vector<NodeDerivationState> nds;
std::vector<Derivation*> freelist;
const size_t k_prime;
};
}
#endif
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