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Diffstat (limited to 'utils/ccrp.h')
| -rw-r--r-- | utils/ccrp.h | 317 |
1 files changed, 0 insertions, 317 deletions
diff --git a/utils/ccrp.h b/utils/ccrp.h deleted file mode 100644 index 1d41a3ef..00000000 --- a/utils/ccrp.h +++ /dev/null @@ -1,317 +0,0 @@ -#ifndef _CCRP_H_ -#define _CCRP_H_ - -#include <numeric> -#include <cassert> -#include <cmath> -#include <list> -#include <iostream> -#include <vector> -#include <tr1/unordered_map> -#include <boost/functional/hash.hpp> -#include "sampler.h" -#include "slice_sampler.h" -#include "m.h" - -// Chinese restaurant process (Pitman-Yor parameters) with table tracking. - -template <typename Dish, typename DishHash = boost::hash<Dish> > -class CCRP { - public: - CCRP(double disc, double strength) : - num_tables_(), - num_customers_(), - discount_(disc), - strength_(strength), - discount_prior_strength_(std::numeric_limits<double>::quiet_NaN()), - discount_prior_beta_(std::numeric_limits<double>::quiet_NaN()), - strength_prior_shape_(std::numeric_limits<double>::quiet_NaN()), - strength_prior_rate_(std::numeric_limits<double>::quiet_NaN()) { - check_hyperparameters(); - } - - CCRP(double d_strength, double d_beta, double c_shape, double c_rate, double d = 0.9, double c = 1.0) : - num_tables_(), - num_customers_(), - discount_(d), - strength_(c), - discount_prior_strength_(d_strength), - discount_prior_beta_(d_beta), - strength_prior_shape_(c_shape), - strength_prior_rate_(c_rate) { - check_hyperparameters(); - } - - void check_hyperparameters() { - if (discount_ < 0.0 || discount_ >= 1.0) { - std::cerr << "Bad discount: " << discount_ << std::endl; - abort(); - } - if (strength_ <= -discount_) { - std::cerr << "Bad strength: " << strength_ << " (discount=" << discount_ << ")" << std::endl; - abort(); - } - } - - double discount() const { return discount_; } - double strength() const { return strength_; } - void set_hyperparameters(double d, double s) { - discount_ = d; strength_ = s; - check_hyperparameters(); - } - void set_discount(double d) { discount_ = d; check_hyperparameters(); } - void set_strength(double a) { strength_ = a; check_hyperparameters(); } - - bool has_discount_prior() const { - return !std::isnan(discount_prior_strength_); - } - - bool has_strength_prior() const { - return !std::isnan(strength_prior_shape_); - } - - void clear() { - num_tables_ = 0; - num_customers_ = 0; - dish_locs_.clear(); - } - - unsigned num_tables() const { - return num_tables_; - } - - unsigned num_tables(const Dish& dish) const { - const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish); - if (it == dish_locs_.end()) return 0; - return it->second.table_counts_.size(); - } - - unsigned num_customers() const { - return num_customers_; - } - - unsigned num_customers(const Dish& dish) const { - const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish); - if (it == dish_locs_.end()) return 0; - return it->total_dish_count_; - } - - // returns +1 or 0 indicating whether a new table was opened - // p = probability with which the particular table was selected - // excluding p0 - template <typename T> - int increment(const Dish& dish, const T& p0, MT19937* rng, T* p = NULL) { - DishLocations& loc = dish_locs_[dish]; - bool share_table = false; - if (loc.total_dish_count_) { - const T p_empty = T(strength_ + num_tables_ * discount_) * p0; - const T p_share = T(loc.total_dish_count_ - loc.table_counts_.size() * discount_); - share_table = rng->SelectSample(p_empty, p_share); - } - if (share_table) { - double r = rng->next() * (loc.total_dish_count_ - loc.table_counts_.size() * discount_); - for (typename std::list<unsigned>::iterator ti = loc.table_counts_.begin(); - ti != loc.table_counts_.end(); ++ti) { - r -= (*ti - discount_); - if (r <= 0.0) { - if (p) { *p = T(*ti - discount_) / T(strength_ + num_customers_); } - ++(*ti); - break; - } - } - if (r > 0.0) { - std::cerr << "Serious error: r=" << r << std::endl; - Print(&std::cerr); - assert(r <= 0.0); - } - } else { - loc.table_counts_.push_back(1u); - if (p) { *p = T(strength_ + discount_ * num_tables_) / T(strength_ + num_customers_); } - ++num_tables_; - } - ++loc.total_dish_count_; - ++num_customers_; - return (share_table ? 0 : 1); - } - - // returns -1 or 0, indicating whether a table was closed - int decrement(const Dish& dish, MT19937* rng) { - DishLocations& loc = dish_locs_[dish]; - assert(loc.total_dish_count_); - if (loc.total_dish_count_ == 1) { - dish_locs_.erase(dish); - --num_tables_; - --num_customers_; - return -1; - } else { - int delta = 0; - // sample customer to remove UNIFORMLY. that is, do NOT use the discount - // here. if you do, it will introduce (unwanted) bias! - double r = rng->next() * loc.total_dish_count_; - --loc.total_dish_count_; - for (typename std::list<unsigned>::iterator ti = loc.table_counts_.begin(); - ti != loc.table_counts_.end(); ++ti) { - r -= *ti; - if (r <= 0.0) { - if ((--(*ti)) == 0) { - --num_tables_; - delta = -1; - loc.table_counts_.erase(ti); - } - break; - } - } - if (r > 0.0) { - std::cerr << "Serious error: r=" << r << std::endl; - Print(&std::cerr); - assert(r <= 0.0); - } - --num_customers_; - return delta; - } - } - - template <typename T> - T prob(const Dish& dish, const T& p0) const { - const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish); - const T r = T(num_tables_ * discount_ + strength_); - if (it == dish_locs_.end()) { - return r * p0 / T(num_customers_ + strength_); - } else { - return (T(it->second.total_dish_count_ - discount_ * it->second.table_counts_.size()) + r * p0) / - T(num_customers_ + strength_); - } - } - - double log_crp_prob() const { - return log_crp_prob(discount_, strength_); - } - - // taken from http://en.wikipedia.org/wiki/Chinese_restaurant_process - // does not include P_0's - double log_crp_prob(const double& discount, const double& strength) const { - double lp = 0.0; - if (has_discount_prior()) - lp = Md::log_beta_density(discount, discount_prior_strength_, discount_prior_beta_); - if (has_strength_prior()) - lp += Md::log_gamma_density(strength + discount, strength_prior_shape_, strength_prior_rate_); - assert(lp <= 0.0); - if (num_customers_) { - if (discount > 0.0) { - const double r = lgamma(1.0 - discount); - if (strength) - lp += lgamma(strength) - lgamma(strength / discount); - lp += - lgamma(strength + num_customers_) - + num_tables_ * log(discount) + lgamma(strength / discount + num_tables_); - assert(std::isfinite(lp)); - for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin(); - it != dish_locs_.end(); ++it) { - const DishLocations& cur = it->second; - for (std::list<unsigned>::const_iterator ti = cur.table_counts_.begin(); ti != cur.table_counts_.end(); ++ti) { - lp += lgamma(*ti - discount) - r; - } - } - } else if (!discount) { // discount == 0.0 - lp += lgamma(strength) + num_tables_ * log(strength) - lgamma(strength + num_tables_); - assert(std::isfinite(lp)); - for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin(); - it != dish_locs_.end(); ++it) { - const DishLocations& cur = it->second; - lp += lgamma(cur.table_counts_.size()); - } - } else { - assert(!"discount less than 0 detected!"); - } - } - assert(std::isfinite(lp)); - return lp; - } - - void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) { - assert(has_discount_prior() || has_strength_prior()); - if (num_customers() == 0) return; - DiscountResampler dr(*this); - StrengthResampler sr(*this); - for (unsigned iter = 0; iter < nloop; ++iter) { - if (has_strength_prior()) { - strength_ = slice_sampler1d(sr, strength_, *rng, -discount_ + std::numeric_limits<double>::min(), - std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations); - } - if (has_discount_prior()) { - double min_discount = std::numeric_limits<double>::min(); - if (strength_ < 0.0) min_discount -= strength_; - discount_ = slice_sampler1d(dr, discount_, *rng, min_discount, - 1.0, 0.0, niterations, 100*niterations); - } - } - strength_ = slice_sampler1d(sr, strength_, *rng, -discount_, - std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations); - } - - struct DiscountResampler { - DiscountResampler(const CCRP& crp) : crp_(crp) {} - const CCRP& crp_; - double operator()(const double& proposed_discount) const { - return crp_.log_crp_prob(proposed_discount, crp_.strength_); - } - }; - - struct StrengthResampler { - StrengthResampler(const CCRP& crp) : crp_(crp) {} - const CCRP& crp_; - double operator()(const double& proposed_strength) const { - return crp_.log_crp_prob(crp_.discount_, proposed_strength); - } - }; - - struct DishLocations { - DishLocations() : total_dish_count_() {} - unsigned total_dish_count_; // customers at all tables with this dish - std::list<unsigned> table_counts_; // list<> gives O(1) deletion and insertion, which we want - // .size() is the number of tables for this dish - }; - - void Print(std::ostream* out) const { - std::cerr << "PYP(d=" << discount_ << ",c=" << strength_ << ") customers=" << num_customers_ << std::endl; - for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin(); - it != dish_locs_.end(); ++it) { - (*out) << it->first << " (" << it->second.total_dish_count_ << " on " << it->second.table_counts_.size() << " tables): "; - for (typename std::list<unsigned>::const_iterator i = it->second.table_counts_.begin(); - i != it->second.table_counts_.end(); ++i) { - (*out) << " " << *i; - } - (*out) << std::endl; - } - } - - typedef typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator const_iterator; - const_iterator begin() const { - return dish_locs_.begin(); - } - const_iterator end() const { - return dish_locs_.end(); - } - - unsigned num_tables_; - unsigned num_customers_; - std::tr1::unordered_map<Dish, DishLocations, DishHash> dish_locs_; - - double discount_; - double strength_; - - // optional beta prior on discount_ (NaN if no prior) - double discount_prior_strength_; - double discount_prior_beta_; - - // optional gamma prior on strength_ (NaN if no prior) - double strength_prior_shape_; - double strength_prior_rate_; -}; - -template <typename T,typename H> -std::ostream& operator<<(std::ostream& o, const CCRP<T,H>& c) { - c.Print(&o); - return o; -} - -#endif |