cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 238 deletions

diff --git a/gi/pf/base_distributions.h b/gi/pf/base_distributions.h
deleted file mode 100644
index 41b513f8..00000000
--- a/gi/pf/base_distributions.h
+++ /dev/null
@@ -1,238 +0,0 @@
-#ifndef _BASE_MEASURES_H_
-#define _BASE_MEASURES_H_
-
-#include <vector>
-#include <map>
-#include <string>
-#include <cmath>
-#include <iostream>
-#include <cassert>
-
-#include "unigrams.h"
-#include "trule.h"
-#include "prob.h"
-#include "tdict.h"
-#include "sampler.h"
-#include "m.h"
-#include "os_phrase.h"
-
-struct Model1 {
-  explicit Model1(const std::string& fname) :
-      kNULL(TD::Convert("<eps>")),
-      kZERO() {
-    LoadModel1(fname);
-  }
-
-  void LoadModel1(const std::string& fname);
-
-  // returns prob 0 if src or trg is not found
-  const prob_t& operator()(WordID src, WordID trg) const {
-    if (src == 0) src = kNULL;
-    if (src < ttable.size()) {
-      const std::map<WordID, prob_t>& cpd = ttable[src];
-      const std::map<WordID, prob_t>::const_iterator it = cpd.find(trg);
-      if (it != cpd.end())
-        return it->second;
-    }
-    return kZERO;
-  }
-
-  const WordID kNULL;
-  const prob_t kZERO;
-  std::vector<std::map<WordID, prob_t> > ttable;
-};
-
-struct PoissonUniformUninformativeBase {
-  explicit PoissonUniformUninformativeBase(const unsigned ves) : kUNIFORM(1.0 / ves) {}
-  prob_t operator()(const TRule& r) const {
-    prob_t p; p.logeq(Md::log_poisson(r.e_.size(), 1.0));
-    prob_t q = kUNIFORM; q.poweq(r.e_.size());
-    p *= q;
-    return p;
-  }
-  void Summary() const {}
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  const prob_t kUNIFORM;
-};
-
-struct CompletelyUniformBase {
-  explicit CompletelyUniformBase(const unsigned ves) : kUNIFORM(1.0 / ves) {}
-  prob_t operator()(const TRule&) const {
-    return kUNIFORM;
-  }
-  void Summary() const {}
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  const prob_t kUNIFORM;
-};
-
-struct UnigramWordBase {
-  explicit UnigramWordBase(const std::string& fname) : un(fname) {}
-  prob_t operator()(const TRule& r) const {
-    return un(r.e_);
-  }
-  const UnigramWordModel un;
-};
-
-struct RuleHasher {
-  size_t operator()(const TRule& r) const {
-    return hash_value(r);
-  }
-};
-
-struct TableLookupBase {
-  TableLookupBase(const std::string& fname);
-
-  prob_t operator()(const TRule& rule) const {
-    const std::tr1::unordered_map<TRule,prob_t,RuleHasher>::const_iterator it = table.find(rule);
-    if (it == table.end()) {
-      std::cerr << rule << " not found\n";
-      abort();
-    }
-    return it->second;
-  }
-
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  void Summary() const {}
-
-  std::tr1::unordered_map<TRule,prob_t,RuleHasher> table;
-};
-
-struct PhraseConditionalUninformativeBase {
-  explicit PhraseConditionalUninformativeBase(const unsigned vocab_e_size) :
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  void Summary() const {}
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  const prob_t kUNIFORM_TARGET;
-};
-
-struct PhraseConditionalUninformativeUnigramBase {
-  explicit PhraseConditionalUninformativeUnigramBase(const std::string& file, const unsigned vocab_e_size) : u(file, vocab_e_size) {}
-
-  // return p0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const UnigramModel u;
-};
-
-struct PhraseConditionalBase {
-  explicit PhraseConditionalBase(const Model1& m1, const double m1mixture, const unsigned vocab_e_size) :
-      model1(m1),
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const Model1& model1;
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_TARGET;
-};
-
-struct PhraseJointBase {
-  explicit PhraseJointBase(const Model1& m1, const double m1mixture, const unsigned vocab_e_size, const unsigned vocab_f_size) :
-      model1(m1),
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_SOURCE(1.0 / vocab_f_size),
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ , rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const Model1& model1;
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_SOURCE;
-  const prob_t kUNIFORM_TARGET;
-};
-
-struct PhraseJointBase_BiDir {
-  explicit PhraseJointBase_BiDir(const Model1& m1,
-                                 const Model1& im1,
-                                 const double m1mixture,
-                                 const unsigned vocab_e_size,
-                                 const unsigned vocab_f_size) :
-      model1(m1),
-      invmodel1(im1),
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_SOURCE(1.0 / vocab_f_size),
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ , rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const Model1& model1;
-  const Model1& invmodel1;
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_SOURCE;
-  const prob_t kUNIFORM_TARGET;
-};
-
-// base distribution for jump size multinomials
-// basically p(0) = 0 and then, p(1) is max, and then
-// you drop as you move to the max jump distance
-struct JumpBase {
-  JumpBase();
-
-  const prob_t& operator()(int jump, unsigned src_len) const {
-    assert(jump != 0);
-    const std::map<int, prob_t>::const_iterator it = p[src_len].find(jump);
-    assert(it != p[src_len].end());
-    return it->second;
-  }
-  std::vector<std::map<int, prob_t> > p;
-};
-
-
-#endif