cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 566 deletions

diff --git a/gi/pyp-topics/src/pyp.hh b/gi/pyp-topics/src/pyp.hh
deleted file mode 100644
index b1cb62be..00000000
--- a/gi/pyp-topics/src/pyp.hh
+++ /dev/null
@@ -1,566 +0,0 @@
-#ifndef _pyp_hh
-#define _pyp_hh
-
-#include "slice-sampler.h"
-#include <math.h>
-#include <map>
-#include <tr1/unordered_map>
-//#include <google/sparse_hash_map>
-
-#include <boost/random/uniform_real.hpp>
-#include <boost/random/variate_generator.hpp>
-#include <boost/random/mersenne_twister.hpp>
-
-#include "log_add.h"
-#include "mt19937ar.h"
-
-//
-// Pitman-Yor process with customer and table tracking
-//
-
-template <typename Dish, typename Hash=std::tr1::hash<Dish> >
-class PYP : protected std::tr1::unordered_map<Dish, int, Hash>
-//class PYP : protected google::sparse_hash_map<Dish, int, Hash>
-{
-public:
-  using std::tr1::unordered_map<Dish,int>::const_iterator;
-  using std::tr1::unordered_map<Dish,int>::iterator;
-  using std::tr1::unordered_map<Dish,int>::begin;
-  using std::tr1::unordered_map<Dish,int>::end;
-//  using google::sparse_hash_map<Dish,int>::const_iterator;
-//  using google::sparse_hash_map<Dish,int>::iterator;
-//  using google::sparse_hash_map<Dish,int>::begin;
-//  using google::sparse_hash_map<Dish,int>::end;
-
-  PYP(double a, double b, unsigned long seed = 0, Hash hash=Hash());
-
-  virtual int increment(Dish d, double p0);
-  virtual int decrement(Dish d);
-
-  // lookup functions
-  int count(Dish d) const;
-  double prob(Dish dish, double p0) const;
-  double prob(Dish dish, double dcd, double dca, 
-              double dtd, double dta, double p0) const;
-  double unnormalised_prob(Dish dish, double p0) const;
-
-  int num_customers() const { return _total_customers; }
-  int num_types() const { return std::tr1::unordered_map<Dish,int>::size(); }
-  //int num_types() const { return google::sparse_hash_map<Dish,int>::size(); }
-  bool empty() const { return _total_customers == 0; }
-
-  double log_prob(Dish dish, double log_p0) const;
-  // nb. d* are NOT logs
-  double log_prob(Dish dish, double dcd, double dca, 
-                       double dtd, double dta, double log_p0) const;
-
-  int num_tables(Dish dish) const;
-  int num_tables() const;
-
-  double a() const { return _a; }
-  void set_a(double a) { _a = a; }
-
-  double b() const { return _b; }
-  void set_b(double b) { _b = b; }
-
-  virtual void clear();
-  std::ostream& debug_info(std::ostream& os) const;
-
-  double log_restaurant_prob() const;
-  double log_prior() const;
-  static double log_prior_a(double a, double beta_a, double beta_b);
-  static double log_prior_b(double b, double gamma_c, double gamma_s);
-
-  template <typename Uniform01>
-    void resample_prior(Uniform01& rnd);
-  template <typename Uniform01>
-    void resample_prior_a(Uniform01& rnd);
-  template <typename Uniform01>
-    void resample_prior_b(Uniform01& rnd);
-
-protected:
-  double _a, _b; // parameters of the Pitman-Yor distribution
-  double _a_beta_a, _a_beta_b; // parameters of Beta prior on a
-  double _b_gamma_s, _b_gamma_c; // parameters of Gamma prior on b
-
-  struct TableCounter {
-    TableCounter() : tables(0) {};
-    int tables;
-    std::map<int, int> table_histogram; // num customers at table -> number tables
-  };
-  typedef std::tr1::unordered_map<Dish, TableCounter, Hash> DishTableType;
-  //typedef google::sparse_hash_map<Dish, TableCounter, Hash> DishTableType;
-  DishTableType _dish_tables;
-  int _total_customers, _total_tables;
-
-  typedef boost::mt19937 base_generator_type;
-  typedef boost::uniform_real<> uni_dist_type;
-  typedef boost::variate_generator<base_generator_type&, uni_dist_type> gen_type;
-
-//  uni_dist_type uni_dist;
-//  base_generator_type rng; //this gets the seed
-//  gen_type rnd; //instantiate: rnd(rng, uni_dist)
-                //call: rnd() generates uniform on [0,1)
-
-  // Function objects for calculating the parts of the log_prob for 
-  // the parameters a and b
-  struct resample_a_type {
-    int n, m; double b, a_beta_a, a_beta_b;
-    const DishTableType& dish_tables;
-    resample_a_type(int n, int m, double b, double a_beta_a, 
-                    double a_beta_b, const DishTableType& dish_tables)
-      : n(n), m(m), b(b), a_beta_a(a_beta_a), a_beta_b(a_beta_b), dish_tables(dish_tables) {}
-
-    double operator() (double proposed_a) const {
-      double log_prior = log_prior_a(proposed_a, a_beta_a, a_beta_b);
-      double log_prob = 0.0;
-      double lgamma1a = lgamma(1.0 - proposed_a);
-      for (typename DishTableType::const_iterator dish_it=dish_tables.begin(); dish_it != dish_tables.end(); ++dish_it) 
-        for (std::map<int, int>::const_iterator table_it=dish_it->second.table_histogram.begin(); 
-             table_it !=dish_it->second.table_histogram.end(); ++table_it) 
-          log_prob += (table_it->second * (lgamma(table_it->first - proposed_a) - lgamma1a));
-
-      log_prob += (proposed_a == 0.0 ? (m-1.0)*log(b) 
-                   : ((m-1.0)*log(proposed_a) + lgamma((m-1.0) + b/proposed_a) - lgamma(b/proposed_a)));
-      assert(std::isfinite(log_prob));
-      return log_prob + log_prior;
-    }
-  };
-
-  struct resample_b_type {
-    int n, m; double a, b_gamma_c, b_gamma_s;
-    resample_b_type(int n, int m, double a, double b_gamma_c, double b_gamma_s)
-      : n(n), m(m), a(a), b_gamma_c(b_gamma_c), b_gamma_s(b_gamma_s) {}
-
-    double operator() (double proposed_b) const {
-      double log_prior = log_prior_b(proposed_b, b_gamma_c, b_gamma_s);
-      double log_prob = 0.0;
-      log_prob += (a == 0.0  ? (m-1.0)*log(proposed_b) 
-                  : ((m-1.0)*log(a) + lgamma((m-1.0) + proposed_b/a) - lgamma(proposed_b/a)));
-      log_prob += (lgamma(1.0+proposed_b) - lgamma(n+proposed_b));
-      return log_prob + log_prior;
-    }
-  };
-   
-  /* lbetadist() returns the log probability density of x under a Beta(alpha,beta)
-   * distribution. - copied from Mark Johnson's gammadist.c
-   */
-  static long double lbetadist(long double x, long double alpha, long double beta);
-
-  /* lgammadist() returns the log probability density of x under a Gamma(alpha,beta)
-   * distribution - copied from Mark Johnson's gammadist.c
-   */
-  static long double lgammadist(long double x, long double alpha, long double beta);
-
-};
-
-template <typename Dish, typename Hash>
-PYP<Dish,Hash>::PYP(double a, double b, unsigned long seed, Hash)
-: std::tr1::unordered_map<Dish, int, Hash>(10), _a(a), _b(b), 
-//: google::sparse_hash_map<Dish, int, Hash>(10), _a(a), _b(b), 
-  _a_beta_a(1), _a_beta_b(1), _b_gamma_s(1), _b_gamma_c(1),
-  //_a_beta_a(1), _a_beta_b(1), _b_gamma_s(10), _b_gamma_c(0.1),
-  _total_customers(0), _total_tables(0)//,
-  //uni_dist(0,1), rng(seed == 0 ? (unsigned long)this : seed), rnd(rng, uni_dist)
-{
-//  std::cerr << "\t##PYP<Dish,Hash>::PYP(a=" << _a << ",b=" << _b << ")" << std::endl;
-  //set_deleted_key(-std::numeric_limits<Dish>::max());
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::prob(Dish dish, double p0) const
-{
-  int c = count(dish), t = num_tables(dish);
-  double r = num_tables() * _a + _b;
-  //std::cerr << "\t\t\t\tPYP<Dish,Hash>::prob(" << dish << "," << p0 << ") c=" << c << " r=" << r << std::endl;
-  if (c > 0)
-    return (c - _a * t + r * p0) / (num_customers() + _b);
-  else
-    return r * p0 / (num_customers() + _b);
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::unnormalised_prob(Dish dish, double p0) const
-{
-  int c = count(dish), t = num_tables(dish);
-  double r = num_tables() * _a + _b;
-  if (c > 0) return (c - _a * t + r * p0);
-  else       return r * p0;
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::prob(Dish dish, double dcd, double dca, 
-                     double dtd, double dta, double p0)
-const
-{
-  int c = count(dish) + dcd, t = num_tables(dish) + dtd;
-  double r = (num_tables() + dta) * _a + _b;
-  if (c > 0)
-    return (c - _a * t + r * p0) / (num_customers() + dca + _b);
-  else
-    return r * p0 / (num_customers() + dca + _b);
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_prob(Dish dish, double log_p0) const
-{
-  using std::log;
-  int c = count(dish), t = num_tables(dish);
-  double r = log(num_tables() * _a + b);
-  if (c > 0)
-    return Log<double>::add(log(c - _a * t), r + log_p0)
-      - log(num_customers() + _b);
-  else
-    return r + log_p0 - log(num_customers() + b);
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_prob(Dish dish, double dcd, double dca, 
-                         double dtd, double dta, double log_p0)
-const
-{
-  using std::log;
-  int c = count(dish) + dcd, t = num_tables(dish) + dtd;
-  double r = log((num_tables() + dta) * _a + b);
-  if (c > 0)
-    return Log<double>::add(log(c - _a * t), r + log_p0)
-      - log(num_customers() + dca + _b);
-  else
-    return r + log_p0 - log(num_customers() + dca + b);
-}
-
-template <typename Dish, typename Hash>
-int 
-PYP<Dish,Hash>::increment(Dish dish, double p0) {
-  int delta = 0;
-  TableCounter &tc = _dish_tables[dish];
-
-  // seated on a new or existing table?
-  int c = count(dish), t = num_tables(dish), T = num_tables();
-  double pshare = (c > 0) ? (c - _a*t) : 0.0;
-  double pnew = (_b + _a*T) * p0;
-  assert (pshare >= 0.0);
-  //assert (pnew > 0.0);
-
-  //if (rnd() < pnew / (pshare + pnew)) {
-  if (mt_genrand_res53() < pnew / (pshare + pnew)) {
-    // assign to a new table
-    tc.tables += 1;
-    tc.table_histogram[1] += 1;
-    _total_tables += 1;
-    delta = 1;
-  }
-  else {
-    // randomly assign to an existing table
-    // remove constant denominator from inner loop
-    //double r = rnd() * (c - _a*t);
-    double r = mt_genrand_res53() * (c - _a*t);
-    for (std::map<int,int>::iterator
-         hit = tc.table_histogram.begin();
-         hit != tc.table_histogram.end(); ++hit) {
-      r -= ((hit->first - _a) * hit->second);
-      if (r <= 0) {
-        tc.table_histogram[hit->first+1] += 1;
-        hit->second -= 1;
-        if (hit->second == 0)
-          tc.table_histogram.erase(hit);
-        break;
-      }
-    }
-    if (r > 0) {
-      std::cerr << r << " " << c << " " << _a << " " << t << std::endl;
-      assert(false);
-    }
-    delta = 0;
-  }
-
-  std::tr1::unordered_map<Dish,int,Hash>::operator[](dish) += 1;
-  //google::sparse_hash_map<Dish,int,Hash>::operator[](dish) += 1;
-  _total_customers += 1;
-
-  return delta;
-}
-
-template <typename Dish, typename Hash>
-int 
-PYP<Dish,Hash>::count(Dish dish) const
-{
-  typename std::tr1::unordered_map<Dish, int>::const_iterator 
-  //typename google::sparse_hash_map<Dish, int>::const_iterator 
-    dcit = find(dish);
-  if (dcit != end())
-    return dcit->second;
-  else
-    return 0;
-}
-
-template <typename Dish, typename Hash>
-int 
-PYP<Dish,Hash>::decrement(Dish dish)
-{
-  typename std::tr1::unordered_map<Dish, int>::iterator dcit = find(dish);
-  //typename google::sparse_hash_map<Dish, int>::iterator dcit = find(dish);
-  if (dcit == end()) {
-    std::cerr << dish << std::endl;
-    assert(false);
-  } 
-
-  int delta = 0;
-
-  typename std::tr1::unordered_map<Dish, TableCounter>::iterator dtit = _dish_tables.find(dish);
-  //typename google::sparse_hash_map<Dish, TableCounter>::iterator dtit = _dish_tables.find(dish);
-  if (dtit == _dish_tables.end()) {
-    std::cerr << dish << std::endl;
-    assert(false);
-  } 
-  TableCounter &tc = dtit->second;
-
-  //std::cerr << "\tdecrement for " << dish << "\n";
-  //std::cerr << "\tBEFORE histogram: " << tc.table_histogram << " ";
-  //std::cerr << "count: " << count(dish) << " ";
-  //std::cerr << "tables: " << tc.tables << "\n";
-
-  //double r = rnd() * count(dish);
-  double r = mt_genrand_res53() * count(dish);
-  for (std::map<int,int>::iterator hit = tc.table_histogram.begin();
-       hit != tc.table_histogram.end(); ++hit)
-  {
-    //r -= (hit->first - _a) * hit->second;
-    r -= (hit->first) * hit->second;
-    if (r <= 0)
-    {
-      if (hit->first > 1)
-        tc.table_histogram[hit->first-1] += 1;
-      else
-      {
-        delta = -1;
-        tc.tables -= 1;
-        _total_tables -= 1;
-      }
-
-      hit->second -= 1;
-      if (hit->second == 0) tc.table_histogram.erase(hit);
-      break;
-    }
-  }
-  if (r > 0) {
-    std::cerr << r << " " << count(dish) << " " << _a << " " << num_tables(dish) << std::endl;
-    assert(false);
-  }
-
-  // remove the customer
-  dcit->second -= 1;
-  _total_customers -= 1;
-  assert(dcit->second >= 0);
-  if (dcit->second == 0) {
-    erase(dcit);
-    _dish_tables.erase(dtit);
-    //std::cerr << "\tAFTER histogram: Empty\n";
-  }
-  else {
-    //std::cerr << "\tAFTER histogram: " << _dish_tables[dish].table_histogram << " ";
-    //std::cerr << "count: " << count(dish) << " ";
-    //std::cerr << "tables: " << _dish_tables[dish].tables << "\n";
-  }
-
-  return delta;
-}
-
-template <typename Dish, typename Hash>
-int 
-PYP<Dish,Hash>::num_tables(Dish dish) const
-{
-  typename std::tr1::unordered_map<Dish, TableCounter, Hash>::const_iterator 
-  //typename google::sparse_hash_map<Dish, TableCounter, Hash>::const_iterator 
-    dtit = _dish_tables.find(dish);
-
-  //assert(dtit != _dish_tables.end());
-  if (dtit == _dish_tables.end())
-    return 0;
-
-  return dtit->second.tables;
-}
-
-template <typename Dish, typename Hash>
-int 
-PYP<Dish,Hash>::num_tables() const
-{
-  return _total_tables;
-}
-
-template <typename Dish, typename Hash>
-std::ostream&
-PYP<Dish,Hash>::debug_info(std::ostream& os) const
-{
-  int hists = 0, tables = 0;
-  for (typename std::tr1::unordered_map<Dish, TableCounter, Hash>::const_iterator 
-  //for (typename google::sparse_hash_map<Dish, TableCounter, Hash>::const_iterator 
-       dtit = _dish_tables.begin(); dtit != _dish_tables.end(); ++dtit)
-  {
-    hists += dtit->second.table_histogram.size();
-    tables += dtit->second.tables;
-
-//    if (dtit->second.tables <= 0)
-//      std::cerr << dtit->first << " " << count(dtit->first) << std::endl;
-    assert(dtit->second.tables > 0);
-    assert(!dtit->second.table_histogram.empty());
-
-//    os << "Dish " << dtit->first << " has " << count(dtit->first) << " customers, and is sitting at " << dtit->second.tables << " tables.\n"; 
-    for (std::map<int,int>::const_iterator 
-         hit = dtit->second.table_histogram.begin();
-         hit != dtit->second.table_histogram.end(); ++hit) {
-//      os << "    " << hit->second << " tables with " << hit->first << " customers." << std::endl; 
-      assert(hit->second > 0);
-    }
-  }
-
-  os << "restaurant has " 
-    << _total_customers << " customers; "
-    << _total_tables << " tables; " 
-    << tables << " tables'; " 
-    << num_types() << " dishes; "
-    << _dish_tables.size() << " dishes'; and "
-    << hists << " histogram entries\n";
-
-  return os;
-}
-
-template <typename Dish, typename Hash>
-void 
-PYP<Dish,Hash>::clear()
-{
-  this->std::tr1::unordered_map<Dish,int,Hash>::clear();
-  //this->google::sparse_hash_map<Dish,int,Hash>::clear();
-  _dish_tables.clear();
-  _total_tables = _total_customers = 0;
-}
-
-// log_restaurant_prob returns the log probability of the PYP table configuration.
-// Excludes Hierarchical P0 term which must be calculated separately.
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_restaurant_prob() const {
-  if (_total_customers < 1)
-    return (double)0.0;
-
-  double log_prob = 0.0;
-  double lgamma1a = lgamma(1.0-_a);
-
-  //std::cerr << "-------------------\n" << std::endl;
-  for (typename DishTableType::const_iterator dish_it=_dish_tables.begin(); 
-       dish_it != _dish_tables.end(); ++dish_it) {
-    for (std::map<int, int>::const_iterator table_it=dish_it->second.table_histogram.begin(); 
-         table_it !=dish_it->second.table_histogram.end(); ++table_it) {
-      log_prob += (table_it->second * (lgamma(table_it->first - _a) - lgamma1a));
-      //std::cerr << "|" << dish_it->first->parent << " --> " << dish_it->first->rhs << " " << table_it->first << " " << table_it->second << " " << log_prob;
-    }
-  }
-  //std::cerr << std::endl;
-
-  log_prob += (_a == (double)0.0 ? (_total_tables-1.0)*log(_b) : (_total_tables-1.0)*log(_a) + lgamma((_total_tables-1.0) + _b/_a) - lgamma(_b/_a));
-  //std::cerr << "\t\t" << log_prob << std::endl;
-  log_prob += (lgamma(1.0 + _b) - lgamma(_total_customers + _b));
-
-  //std::cerr << _total_customers << " " << _total_tables << " " << log_prob << " " << log_prior() << std::endl;
-  //std::cerr << _a << " " << _b << std::endl;
-  if (!std::isfinite(log_prob)) {
-    assert(false);
-  }
-  //return log_prob;
-  if (log_prob > 0.0)
-    std::cerr << log_prob << std::endl;
-  return log_prob;// + log_prior();
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_prior() const {
-  double prior = 0.0;
-  if (_a_beta_a > 0.0 && _a_beta_b > 0.0 && _a > 0.0)
-    prior += log_prior_a(_a, _a_beta_a, _a_beta_b);
-  if (_b_gamma_s > 0.0 && _b_gamma_c > 0.0)
-    prior += log_prior_b(_b, _b_gamma_c, _b_gamma_s);
-
-  return prior;
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_prior_a(double a, double beta_a, double beta_b) {
-  return lbetadist(a, beta_a, beta_b); 
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_prior_b(double b, double gamma_c, double gamma_s) {
-  return lgammadist(b, gamma_c, gamma_s); 
-}
-
-template <typename Dish, typename Hash>
-long double PYP<Dish,Hash>::lbetadist(long double x, long double alpha, long double beta) {
-  assert(x > 0);
-  assert(x < 1);
-  assert(alpha > 0);
-  assert(beta > 0);
-  return (alpha-1)*log(x)+(beta-1)*log(1-x)+lgamma(alpha+beta)-lgamma(alpha)-lgamma(beta);
-//boost::math::lgamma
-}
-
-template <typename Dish, typename Hash>
-long double PYP<Dish,Hash>::lgammadist(long double x, long double alpha, long double beta) {
-  assert(alpha > 0);
-  assert(beta > 0);
-  return (alpha-1)*log(x) - alpha*log(beta) - x/beta - lgamma(alpha);
-}
-
-
-template <typename Dish, typename Hash>
-  template <typename Uniform01>
-void 
-PYP<Dish,Hash>::resample_prior(Uniform01& rnd) {
-  for (int num_its=5; num_its >= 0; --num_its) {
-    resample_prior_b(rnd);
-    resample_prior_a(rnd);
-  }
-  resample_prior_b(rnd);
-}
-
-template <typename Dish, typename Hash>
-  template <typename Uniform01>
-void 
-PYP<Dish,Hash>::resample_prior_b(Uniform01& rnd) {
-  if (_total_tables == 0) 
-    return;
-
-  //int niterations = 10;   // number of resampling iterations
-  int niterations = 5;   // number of resampling iterations
-  //std::cerr << "\n## resample_prior_b(), initial a = " << _a << ", b = " << _b << std::endl;
-  resample_b_type b_log_prob(_total_customers, _total_tables, _a, _b_gamma_c, _b_gamma_s);
-  _b = slice_sampler1d(b_log_prob, _b, rnd, (double) 0.0, std::numeric_limits<double>::infinity(), 
-  //_b = slice_sampler1d(b_log_prob, _b, mt_genrand_res53, (double) 0.0, std::numeric_limits<double>::infinity(), 
-                       (double) 0.0, niterations, 100*niterations);
-  //std::cerr << "\n## resample_prior_b(), final a = " << _a << ", b = " << _b << std::endl;
-}
-
-template <typename Dish, typename Hash>
-  template <typename Uniform01>
-void 
-PYP<Dish,Hash>::resample_prior_a(Uniform01& rnd) {
-  if (_total_tables == 0) 
-    return;
-
-  //int niterations = 10;
-  int niterations = 5;
-  //std::cerr << "\n## Initial a = " << _a << ", b = " << _b << std::endl;
-  resample_a_type a_log_prob(_total_customers, _total_tables, _b, _a_beta_a, _a_beta_b, _dish_tables);
-  _a = slice_sampler1d(a_log_prob, _a, rnd, std::numeric_limits<double>::min(), 
-  //_a = slice_sampler1d(a_log_prob, _a, mt_genrand_res53, std::numeric_limits<double>::min(), 
-                       (double) 1.0, (double) 0.0, niterations, 100*niterations);
-}
-
-#endif