Move synutils under utils

1 files changed, 0 insertions, 402 deletions

diff --git a/utils/synutils/maxent-3.0/maxent.h b/utils/synutils/maxent-3.0/maxent.h
deleted file mode 100644
index 88a04e25..00000000
--- a/utils/synutils/maxent-3.0/maxent.h
+++ /dev/null
@@ -1,402 +0,0 @@
-/*
- * $Id: maxent.h,v 1.1.1.1 2007/05/15 08:30:35 kyoshida Exp $
- */
-
-#ifndef __MAXENT_H_
-#define __MAXENT_H_
-
-#include <string>
-#include <vector>
-#include <list>
-#include <map>
-#include <algorithm>
-#include <iostream>
-#include <string>
-#include <cassert>
-#include "mathvec.h"
-
-#define USE_HASH_MAP  // if you encounter errors with hash, try commenting out
-                      // this line. (the program will be a bit slower, though)
-#ifdef USE_HASH_MAP
-#include <ext/hash_map>
-#endif
-
-//
-// data format for each sample for training/testing
-//
-struct ME_Sample {
- public:
-  ME_Sample() : label("") {};
-  ME_Sample(const std::string& l) : label(l) {};
-  void set_label(const std::string& l) { label = l; }
-
-  // to add a binary feature
-  void add_feature(const std::string& f) { features.push_back(f); }
-
-  // to add a real-valued feature
-  void add_feature(const std::string& s, const double d) {
-    rvfeatures.push_back(std::pair<std::string, double>(s, d));
-  }
-
- public:
-  std::string label;
-  std::vector<std::string> features;
-  std::vector<std::pair<std::string, double> > rvfeatures;
-
-  // obsolete
-  void add_feature(const std::pair<std::string, double>& f) {
-    rvfeatures.push_back(f);  // real-valued features
-  }
-};
-
-//
-// for those who want to use load_from_array()
-//
-typedef struct ME_Model_Data {
-  char* label;
-  char* feature;
-  double weight;
-} ME_Model_Data;
-
-class ME_Model {
- public:
-  void add_training_sample(const ME_Sample& s);
-  int train();
-  std::vector<double> classify(ME_Sample& s) const;
-  bool load_from_file(const std::string& filename);
-  bool save_to_file(const std::string& filename, const double th = 0) const;
-  int num_classes() const { return _num_classes; }
-  std::string get_class_label(int i) const { return _label_bag.Str(i); }
-  int get_class_id(const std::string& s) const { return _label_bag.Id(s); }
-  void get_features(
-      std::list<std::pair<std::pair<std::string, std::string>, double> >& fl);
-  void set_heldout(const int h, const int n = 0) {
-    _nheldout = h;
-    _early_stopping_n = n;
-  };
-  void use_l1_regularizer(const double v) { _l1reg = v; }
-  void use_l2_regularizer(const double v) { _l2reg = v; }
-  void use_SGD(int iter = 30, double eta0 = 1, double alpha = 0.85) {
-    _optimization_method = SGD;
-    SGD_ITER = iter;
-    SGD_ETA0 = eta0;
-    SGD_ALPHA = alpha;
-  }
-  bool load_from_array(const ME_Model_Data data[]);
-  void set_reference_model(const ME_Model& ref_model) {
-    _ref_modelp = &ref_model;
-  };
-  void clear();
-
-  ME_Model() {
-    _l1reg = _l2reg = 0;
-    _nheldout = 0;
-    _early_stopping_n = 0;
-    _ref_modelp = NULL;
-    _optimization_method = LBFGS;
-  }
-
- public:
-  // obsolete. just for downward compatibility
-  int train(const std::vector<ME_Sample>& train);
-
- private:
-  enum OPTIMIZATION_METHOD {
-    LBFGS,
-    OWLQN,
-    SGD
-  } _optimization_method;
-  // OWLQN and SGD are available only for L1-regularization
-
-  int SGD_ITER;
-  double SGD_ETA0;
-  double SGD_ALPHA;
-
-  double _l1reg, _l2reg;
-
-  struct Sample {
-    int label;
-    std::vector<int> positive_features;
-    std::vector<std::pair<int, double> > rvfeatures;
-    std::vector<double> ref_pd;  // reference probability distribution
-    bool operator<(const Sample& x) const {
-      for (unsigned int i = 0; i < positive_features.size(); i++) {
-        if (i >= x.positive_features.size()) return false;
-        int v0 = positive_features[i];
-        int v1 = x.positive_features[i];
-        if (v0 < v1) return true;
-        if (v0 > v1) return false;
-      }
-      return false;
-    }
-  };
-
-  struct ME_Feature {
-    enum {
-      MAX_LABEL_TYPES = 255
-    };
-
-    //    ME_Feature(const int l, const int f) : _body((l << 24) + f) {
-    //      assert(l >= 0 && l < 256);
-    //      assert(f >= 0 && f <= 0xffffff);
-    //    };
-    //    int label() const { return _body >> 24; }
-    //    int feature() const { return _body & 0xffffff; }
-    ME_Feature(const int l, const int f) : _body((f << 8) + l) {
-      assert(l >= 0 && l <= MAX_LABEL_TYPES);
-      assert(f >= 0 && f <= 0xffffff);
-    };
-    int label() const { return _body & 0xff; }
-    int feature() const { return _body >> 8; }
-    unsigned int body() const { return _body; }
-
-   private:
-    unsigned int _body;
-  };
-
-  struct ME_FeatureBag {
-#ifdef USE_HASH_MAP
-    typedef __gnu_cxx::hash_map<unsigned int, int> map_type;
-#else
-    typedef std::map<unsigned int, int> map_type;
-#endif
-    map_type mef2id;
-    std::vector<ME_Feature> id2mef;
-    int Put(const ME_Feature& i) {
-      map_type::const_iterator j = mef2id.find(i.body());
-      if (j == mef2id.end()) {
-        int id = id2mef.size();
-        id2mef.push_back(i);
-        mef2id[i.body()] = id;
-        return id;
-      }
-      return j->second;
-    }
-    int Id(const ME_Feature& i) const {
-      map_type::const_iterator j = mef2id.find(i.body());
-      if (j == mef2id.end()) {
-        return -1;
-      }
-      return j->second;
-    }
-    ME_Feature Feature(int id) const {
-      assert(id >= 0 && id < (int)id2mef.size());
-      return id2mef[id];
-    }
-    int Size() const { return id2mef.size(); }
-    void Clear() {
-      mef2id.clear();
-      id2mef.clear();
-    }
-  };
-
-  struct hashfun_str {
-    size_t operator()(const std::string& s) const {
-      assert(sizeof(int) == 4 && sizeof(char) == 1);
-      const int* p = reinterpret_cast<const int*>(s.c_str());
-      size_t v = 0;
-      int n = s.size() / 4;
-      for (int i = 0; i < n; i++, p++) {
-        //      v ^= *p;
-        v ^= *p << (4 * (i % 2));  // note) 0 <= char < 128
-      }
-      int m = s.size() % 4;
-      for (int i = 0; i < m; i++) {
-        v ^= s[4 * n + i] << (i * 8);
-      }
-      return v;
-    }
-  };
-
-  struct MiniStringBag {
-#ifdef USE_HASH_MAP
-    typedef __gnu_cxx::hash_map<std::string, int, hashfun_str> map_type;
-#else
-    typedef std::map<std::string, int> map_type;
-#endif
-    int _size;
-    map_type str2id;
-    MiniStringBag() : _size(0) {}
-    int Put(const std::string& i) {
-      map_type::const_iterator j = str2id.find(i);
-      if (j == str2id.end()) {
-        int id = _size;
-        _size++;
-        str2id[i] = id;
-        return id;
-      }
-      return j->second;
-    }
-    int Id(const std::string& i) const {
-      map_type::const_iterator j = str2id.find(i);
-      if (j == str2id.end()) return -1;
-      return j->second;
-    }
-    int Size() const { return _size; }
-    void Clear() {
-      str2id.clear();
-      _size = 0;
-    }
-    map_type::const_iterator begin() const { return str2id.begin(); }
-    map_type::const_iterator end() const { return str2id.end(); }
-  };
-
-  struct StringBag : public MiniStringBag {
-    std::vector<std::string> id2str;
-    int Put(const std::string& i) {
-      map_type::const_iterator j = str2id.find(i);
-      if (j == str2id.end()) {
-        int id = id2str.size();
-        id2str.push_back(i);
-        str2id[i] = id;
-        return id;
-      }
-      return j->second;
-    }
-    std::string Str(const int id) const {
-      assert(id >= 0 && id < (int)id2str.size());
-      return id2str[id];
-    }
-    int Size() const { return id2str.size(); }
-    void Clear() {
-      str2id.clear();
-      id2str.clear();
-    }
-  };
-
-  std::vector<Sample> _vs;  // vector of training_samples
-  StringBag _label_bag;
-  MiniStringBag _featurename_bag;
-  std::vector<double> _vl;  // vector of lambda
-  ME_FeatureBag _fb;
-  int _num_classes;
-  std::vector<double> _vee;  // empirical expectation
-  std::vector<double> _vme;  // empirical expectation
-  std::vector<std::vector<int> > _feature2mef;
-  std::vector<Sample> _heldout;
-  double _train_error;    // current error rate on the training data
-  double _heldout_error;  // current error rate on the heldout data
-  int _nheldout;
-  int _early_stopping_n;
-  std::vector<double> _vhlogl;
-  const ME_Model* _ref_modelp;
-
-  double heldout_likelihood();
-  int conditional_probability(const Sample& nbs,
-                              std::vector<double>& membp) const;
-  int make_feature_bag(const int cutoff);
-  int classify(const Sample& nbs, std::vector<double>& membp) const;
-  double update_model_expectation();
-  int perform_QUASI_NEWTON();
-  int perform_SGD();
-  int perform_GIS(int C);
-  std::vector<double> perform_LBFGS(const std::vector<double>& x0);
-  std::vector<double> perform_OWLQN(const std::vector<double>& x0,
-                                    const double C);
-  double backtracking_line_search(const Vec& x0, const Vec& grad0,
-                                  const double f0, const Vec& dx, Vec& x,
-                                  Vec& grad1);
-  double regularized_func_grad(const double C, const Vec& x, Vec& grad);
-  double constrained_line_search(double C, const Vec& x0, const Vec& grad0,
-                                 const double f0, const Vec& dx, Vec& x,
-                                 Vec& grad1);
-
-  void set_ref_dist(Sample& s) const;
-  void init_feature2mef();
-
-  double FunctionGradient(const std::vector<double>& x,
-                          std::vector<double>& grad);
-  static double FunctionGradientWrapper(const std::vector<double>& x,
-                                        std::vector<double>& grad);
-};
-
-#endif
-
-/*
- * $Log: maxent.h,v $
- * Revision 1.1.1.1  2007/05/15 08:30:35  kyoshida
- * stepp tagger, by Okanohara and Tsuruoka
- *
- * Revision 1.24  2006/08/21 17:30:38  tsuruoka
- * use MAX_LABEL_TYPES
- *
- * Revision 1.23  2006/07/25 13:19:53  tsuruoka
- * sort _vs[]
- *
- * Revision 1.22  2006/07/18 11:13:15  tsuruoka
- * modify comments
- *
- * Revision 1.21  2006/07/18 10:02:15  tsuruoka
- * remove sample2feature[]
- * speed up conditional_probability()
- *
- * Revision 1.20  2006/07/18 05:10:51  tsuruoka
- * add ref_dist
- *
- * Revision 1.19  2005/12/23 10:33:02  tsuruoka
- * support real-valued features
- *
- * Revision 1.18  2005/12/23 09:15:29  tsuruoka
- * modify _train to reduce memory consumption
- *
- * Revision 1.17  2005/10/28 13:02:34  tsuruoka
- * set_heldout(): add default value
- * Feature()
- *
- * Revision 1.16  2005/09/12 13:51:16  tsuruoka
- * Sample: list -> vector
- *
- * Revision 1.15  2005/09/12 13:27:10  tsuruoka
- * add add_training_sample()
- *
- * Revision 1.14  2005/04/27 11:22:27  tsuruoka
- * bugfix
- * ME_Sample: list -> vector
- *
- * Revision 1.13  2005/04/27 10:20:19  tsuruoka
- * MiniStringBag -> StringBag
- *
- * Revision 1.12  2005/04/27 10:00:42  tsuruoka
- * remove tmpfb
- *
- * Revision 1.11  2005/04/26 14:25:53  tsuruoka
- * add MiniStringBag, USE_HASH_MAP
- *
- * Revision 1.10  2004/10/04 05:50:25  tsuruoka
- * add Clear()
- *
- * Revision 1.9  2004/08/09 12:27:21  tsuruoka
- * change messages
- *
- * Revision 1.8  2004/08/04 13:55:19  tsuruoka
- * modify _sample2feature
- *
- * Revision 1.7  2004/07/29 05:51:13  tsuruoka
- * remove modeldata.h
- *
- * Revision 1.6  2004/07/28 13:42:58  tsuruoka
- * add AGIS
- *
- * Revision 1.5  2004/07/28 05:54:14  tsuruoka
- * get_class_name() -> get_class_label()
- * ME_Feature: bugfix
- *
- * Revision 1.4  2004/07/27 16:58:47  tsuruoka
- * modify the interface of classify()
- *
- * Revision 1.3  2004/07/26 17:23:46  tsuruoka
- * _sample2feature: list -> vector
- *
- * Revision 1.2  2004/07/26 15:49:23  tsuruoka
- * modify ME_Feature
- *
- * Revision 1.1  2004/07/26 13:10:55  tsuruoka
- * add files
- *
- * Revision 1.18  2004/07/22 08:34:45  tsuruoka
- * modify _sample2feature[]
- *
- * Revision 1.17  2004/07/21 16:33:01  tsuruoka
- * remove some comments
- *
- */