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authorChris Dyer <cdyer@cs.cmu.edu>2012-02-25 21:22:27 -0500
committerChris Dyer <cdyer@cs.cmu.edu>2012-02-25 21:22:27 -0500
commit9007216a43c5572c2c343a1700ac79fb35b7d82f (patch)
tree10d9ccd68e9bb1ace905fe0ca5eff6f2ef87b5a7 /phrasinator/ccrp.h
parent2faca3e7b3b8e4eba6c036c635a5b23883e72337 (diff)
really slow hiero lm
Diffstat (limited to 'phrasinator/ccrp.h')
-rw-r--r--phrasinator/ccrp.h294
1 files changed, 0 insertions, 294 deletions
diff --git a/phrasinator/ccrp.h b/phrasinator/ccrp.h
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--- a/phrasinator/ccrp.h
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@@ -1,294 +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"
-
-// Chinese restaurant process (Pitman-Yor parameters) with table tracking.
-
-template <typename Dish, typename DishHash = boost::hash<Dish> >
-class CCRP {
- public:
- CCRP(double disc, double conc) :
- num_tables_(),
- num_customers_(),
- discount_(disc),
- concentration_(conc),
- discount_prior_alpha_(std::numeric_limits<double>::quiet_NaN()),
- discount_prior_beta_(std::numeric_limits<double>::quiet_NaN()),
- concentration_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
- concentration_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
-
- CCRP(double d_alpha, double d_beta, double c_shape, double c_rate, double d = 0.1, double c = 10.0) :
- num_tables_(),
- num_customers_(),
- discount_(d),
- concentration_(c),
- discount_prior_alpha_(d_alpha),
- discount_prior_beta_(d_beta),
- concentration_prior_shape_(c_shape),
- concentration_prior_rate_(c_rate) {}
-
- double discount() const { return discount_; }
- double concentration() const { return concentration_; }
-
- bool has_discount_prior() const {
- return !std::isnan(discount_prior_alpha_);
- }
-
- bool has_concentration_prior() const {
- return !std::isnan(concentration_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
- int increment(const Dish& dish, const double& p0, MT19937* rng) {
- DishLocations& loc = dish_locs_[dish];
- bool share_table = false;
- if (loc.total_dish_count_) {
- const double p_empty = (concentration_ + num_tables_ * discount_) * p0;
- const double p_share = (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) {
- ++(*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);
- ++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;
- }
- }
-
- double prob(const Dish& dish, const double& p0) const {
- const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
- const double r = num_tables_ * discount_ + concentration_;
- if (it == dish_locs_.end()) {
- return r * p0 / (num_customers_ + concentration_);
- } else {
- return (it->second.total_dish_count_ - discount_ * it->second.table_counts_.size() + r * p0) /
- (num_customers_ + concentration_);
- }
- }
-
- double log_crp_prob() const {
- return log_crp_prob(discount_, concentration_);
- }
-
- static double log_beta_density(const double& x, const double& alpha, const double& beta) {
- assert(x > 0.0);
- assert(x < 1.0);
- assert(alpha > 0.0);
- assert(beta > 0.0);
- const double lp = (alpha-1)*log(x)+(beta-1)*log(1-x)+lgamma(alpha+beta)-lgamma(alpha)-lgamma(beta);
- return lp;
- }
-
- static double log_gamma_density(const double& x, const double& shape, const double& rate) {
- assert(x >= 0.0);
- assert(shape > 0.0);
- assert(rate > 0.0);
- const double lp = (shape-1)*log(x) - shape*log(rate) - x/rate - lgamma(shape);
- return lp;
- }
-
- // 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& concentration) const {
- double lp = 0.0;
- if (has_discount_prior())
- lp = log_beta_density(discount, discount_prior_alpha_, discount_prior_beta_);
- if (has_concentration_prior())
- lp += log_gamma_density(concentration, concentration_prior_shape_, concentration_prior_rate_);
- assert(lp <= 0.0);
- if (num_customers_) {
- if (discount > 0.0) {
- const double r = lgamma(1.0 - discount);
- lp += lgamma(concentration) - lgamma(concentration + num_customers_)
- + num_tables_ * log(discount) + lgamma(concentration / discount + num_tables_)
- - lgamma(concentration / discount);
- 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 {
- assert(!"not implemented yet");
- }
- }
- assert(std::isfinite(lp));
- return lp;
- }
-
- void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
- assert(has_discount_prior() || has_concentration_prior());
- DiscountResampler dr(*this);
- ConcentrationResampler cr(*this);
- for (int iter = 0; iter < nloop; ++iter) {
- if (has_concentration_prior()) {
- concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
- std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
- }
- if (has_discount_prior()) {
- discount_ = slice_sampler1d(dr, discount_, *rng, std::numeric_limits<double>::min(),
- 1.0, 0.0, niterations, 100*niterations);
- }
- }
- concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
- 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_.concentration_);
- }
- };
-
- struct ConcentrationResampler {
- ConcentrationResampler(const CCRP& crp) : crp_(crp) {}
- const CCRP& crp_;
- double operator()(const double& proposed_concentration) const {
- return crp_.log_crp_prob(crp_.discount_, proposed_concentration);
- }
- };
-
- 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=" << concentration_ << ") 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 concentration_;
-
- // optional beta prior on discount_ (NaN if no prior)
- double discount_prior_alpha_;
- double discount_prior_beta_;
-
- // optional gamma prior on concentration_ (NaN if no prior)
- double concentration_prior_shape_;
- double concentration_prior_rate_;
-};
-
-template <typename T,typename H>
-std::ostream& operator<<(std::ostream& o, const CCRP<T,H>& c) {
- c.Print(&o);
- return o;
-}
-
-#endif