1 files changed, 167 insertions, 0 deletions
diff --git a/decoder/incremental.cc b/decoder/incremental.cc
new file mode 100644
index 00000000..46615b0b
--- /dev/null
+++ b/decoder/incremental.cc
@@ -0,0 +1,167 @@
+#include "incremental.h"
+
+#include "hg.h"
+#include "fdict.h"
+#include "tdict.h"
+
+#include "lm/enumerate_vocab.hh"
+#include "lm/model.hh"
+#include "search/config.hh"
+#include "search/context.hh"
+#include "search/edge.hh"
+#include "search/edge_generator.hh"
+#include "search/rule.hh"
+#include "search/vertex.hh"
+#include "search/vertex_generator.hh"
+#include "util/exception.hh"
+
+#include <boost/scoped_ptr.hpp>
+#include <boost/scoped_array.hpp>
+
+#include <iostream>
+#include <vector>
+
+namespace {
+
+struct MapVocab : public lm::EnumerateVocab {
+  public:
+    MapVocab() {}
+
+    // Do not call after Lookup.  
+    void Add(lm::WordIndex index, const StringPiece &str) {
+      const WordID cdec_id = TD::Convert(str.as_string());
+      if (cdec_id >= out_.size()) out_.resize(cdec_id + 1);
+      out_[cdec_id] = index;
+    }
+
+    // Assumes Add has been called and will never be called again.  
+    lm::WordIndex FromCDec(WordID id) const {
+      return out_[out_.size() > id ? id : 0];
+    }
+
+  private:
+    std::vector<lm::WordIndex> out_;
+};
+
+template <class Model> class Incremental : public IncrementalBase {
+  public:
+    Incremental(const char *model_file, const std::vector<weight_t> &weights) :
+      IncrementalBase(weights), 
+      m_(model_file, GetConfig()),
+      weights_(
+          weights[FD::Convert("KLanguageModel")],
+          weights[FD::Convert("KLanguageModel_OOV")],
+          weights[FD::Convert("WordPenalty")]) {
+      std::cerr << "Weights KLanguageModel " << weights_.LM() << " KLanguageModel_OOV " << weights_.OOV() << " WordPenalty " << weights_.WordPenalty() << std::endl;
+    }
+    void Search(unsigned int pop_limit, const Hypergraph &hg) const;
+
+  private:
+    void ConvertEdge(const search::Context<Model> &context, bool final, search::Vertex *vertices, const Hypergraph::Edge &in, search::EdgeGenerator &gen) const;
+
+    lm::ngram::Config GetConfig() {
+      lm::ngram::Config ret;
+      ret.enumerate_vocab = &vocab_;
+      return ret;
+    }
+
+    MapVocab vocab_;
+
+    const Model m_;
+
+    const search::Weights weights_;
+};
+
+void PrintFinal(const Hypergraph &hg, const search::Final final) {
+  const std::vector<WordID> &words = static_cast<const Hypergraph::Edge*>(final.GetNote().vp)->rule_->e();
+  const search::Final *child(final.Children());
+  for (std::vector<WordID>::const_iterator i = words.begin(); i != words.end(); ++i) {
+    if (*i > 0) {
+      std::cout << TD::Convert(*i) << ' ';
+    } else {
+      PrintFinal(hg, *child++);
+    }
+  }
+}
+
+template <class Model> void Incremental<Model>::Search(unsigned int pop_limit, const Hypergraph &hg) const {
+  boost::scoped_array<search::Vertex> out_vertices(new search::Vertex[hg.nodes_.size()]);
+  search::Config config(weights_, pop_limit);
+  search::Context<Model> context(config, m_);
+
+  for (unsigned int i = 0; i < hg.nodes_.size() - 1; ++i) {
+    search::EdgeGenerator gen;
+    const Hypergraph::EdgesVector &down_edges = hg.nodes_[i].in_edges_;
+    for (unsigned int j = 0; j < down_edges.size(); ++j) {
+      unsigned int edge_index = down_edges[j];
+      ConvertEdge(context, i == hg.nodes_.size() - 2, out_vertices.get(), hg.edges_[edge_index], gen);
+    }
+    search::VertexGenerator vertex_gen(context, out_vertices[i]);
+    gen.Search(context, vertex_gen);
+  }
+  const search::Final top = out_vertices[hg.nodes_.size() - 2].BestChild();
+  if (!top.Valid()) {
+    std::cout << "NO PATH FOUND" << std::endl;
+  } else {
+    PrintFinal(hg, top);
+    std::cout << "||| " << top.GetScore() << std::endl;
+  }
+}
+
+template <class Model> void Incremental<Model>::ConvertEdge(const search::Context<Model> &context, bool final, search::Vertex *vertices, const Hypergraph::Edge &in, search::EdgeGenerator &gen) const {
+  const std::vector<WordID> &e = in.rule_->e();
+  std::vector<lm::WordIndex> words;
+  words.reserve(e.size());
+  std::vector<search::PartialVertex> nts;
+  unsigned int terminals = 0;
+  float score = 0.0;
+  for (std::vector<WordID>::const_iterator word = e.begin(); word != e.end(); ++word) {
+    if (*word <= 0) {
+      nts.push_back(vertices[in.tail_nodes_[-*word]].RootPartial());
+      if (nts.back().Empty()) return;
+      score += nts.back().Bound();
+      words.push_back(lm::kMaxWordIndex);
+    } else {
+      ++terminals;
+      words.push_back(vocab_.FromCDec(*word));
+    }
+  }
+
+  if (final) {
+    words.push_back(m_.GetVocabulary().EndSentence());
+  }
+
+  search::PartialEdge out(gen.AllocateEdge(nts.size()));
+
+  memcpy(out.NT(), &nts[0], sizeof(search::PartialVertex) * nts.size());
+
+  search::Note note;
+  note.vp = &in;
+  out.SetNote(note);
+
+  score += in.rule_->GetFeatureValues().dot(cdec_weights_);
+  score -= static_cast<float>(terminals) * context.GetWeights().WordPenalty() / M_LN10;
+  score += search::ScoreRule(context, words, final, out.Between());
+  out.SetScore(score);
+
+  gen.AddEdge(out);
+}
+
+} // namespace
+
+IncrementalBase *IncrementalBase::Load(const char *model_file, const std::vector<weight_t> &weights) {
+  lm::ngram::ModelType model_type;
+  if (!lm::ngram::RecognizeBinary(model_file, model_type)) model_type = lm::ngram::PROBING;
+  switch (model_type) {
+    case lm::ngram::PROBING:
+      return new Incremental<lm::ngram::ProbingModel>(model_file, weights);
+    case lm::ngram::REST_PROBING:
+      return new Incremental<lm::ngram::RestProbingModel>(model_file, weights);
+    default:
+      UTIL_THROW(util::Exception, "Sorry this lm type isn't supported yet.");
+  }
+}
+
+IncrementalBase::~IncrementalBase() {}
+
+IncrementalBase::IncrementalBase(const std::vector<weight_t> &weights) : cdec_weights_(weights) {}