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-#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
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-21 19:49:37 +0000