cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 69 deletions

diff --git a/gi/pf/unigrams.h b/gi/pf/unigrams.h
deleted file mode 100644
index 1660d1ed..00000000
--- a/gi/pf/unigrams.h
+++ /dev/null
@@ -1,69 +0,0 @@
-#ifndef _UNIGRAMS_H_
-#define _UNIGRAMS_H_
-
-#include <vector>
-#include <string>
-#include <tr1/unordered_map>
-#include <boost/functional.hpp>
-
-#include "wordid.h"
-#include "prob.h"
-#include "tdict.h"
-
-struct UnigramModel {
-  explicit UnigramModel(const std::string& fname, unsigned vocab_size) :
-      use_uniform_(fname.size() == 0),
-      uniform_(1.0 / vocab_size),
-      probs_() {
-    if (fname.size() > 0) {
-      probs_.resize(TD::NumWords() + 1);
-      LoadUnigrams(fname);
-    }
-  }
-
-  const prob_t& operator()(const WordID& w) const {
-    assert(w);
-    if (use_uniform_) return uniform_;
-    return probs_[w];
-  }
-
- private:
-  void LoadUnigrams(const std::string& fname);
-
-  const bool use_uniform_;
-  const prob_t uniform_;
-  std::vector<prob_t> probs_;
-};
-
-
-// reads an ARPA unigram file and converts words like 'cat' into a string 'c a t'
-struct UnigramWordModel {
-  explicit UnigramWordModel(const std::string& fname) :
-      use_uniform_(false),
-      uniform_(1.0),
-      probs_() {
-    LoadUnigrams(fname);
-  }
-
-  explicit UnigramWordModel(const unsigned vocab_size) :
-      use_uniform_(true),
-      uniform_(1.0 / vocab_size),
-      probs_() {}
-
-  const prob_t& operator()(const std::vector<WordID>& s) const {
-    if (use_uniform_) return uniform_;
-    const VectorProbHash::const_iterator it = probs_.find(s);
-    assert(it != probs_.end());
-    return it->second;
-  }
-
- private:
-  void LoadUnigrams(const std::string& fname);
-
-  const bool use_uniform_;
-  const prob_t uniform_;
-  typedef std::tr1::unordered_map<std::vector<WordID>, prob_t, boost::hash<std::vector<WordID> > > VectorProbHash;
-  VectorProbHash probs_;
-};
-
-#endif