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authorPatrick Simianer <p@simianer.de>2011-10-20 02:31:25 +0200
committerPatrick Simianer <p@simianer.de>2011-10-20 02:31:25 +0200
commita5a92ebe23c5819ed104313426012011e32539da (patch)
tree3416818c758d5ece4e71fe522c571e75ea04f100 /utils/ccrp_onetable.h
parentb88332caac2cbe737c99b8098813f868ca876d8b (diff)
parent78baccbb4231bb84a456702d4f574f8e601a8182 (diff)
finalized merge
Diffstat (limited to 'utils/ccrp_onetable.h')
-rw-r--r--utils/ccrp_onetable.h241
1 files changed, 241 insertions, 0 deletions
diff --git a/utils/ccrp_onetable.h b/utils/ccrp_onetable.h
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+++ b/utils/ccrp_onetable.h
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+#ifndef _CCRP_ONETABLE_H_
+#define _CCRP_ONETABLE_H_
+
+#include <numeric>
+#include <cassert>
+#include <cmath>
+#include <list>
+#include <iostream>
+#include <tr1/unordered_map>
+#include <boost/functional/hash.hpp>
+#include "sampler.h"
+#include "slice_sampler.h"
+
+// Chinese restaurant process (Pitman-Yor parameters) with one table approximation
+
+template <typename Dish, typename DishHash = boost::hash<Dish> >
+class CCRP_OneTable {
+ typedef std::tr1::unordered_map<Dish, unsigned, DishHash> DishMapType;
+ public:
+ CCRP_OneTable(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_OneTable(double d_alpha, double d_beta, double c_shape, double c_rate, double d = 0.9, double c = 1.0) :
+ 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_; }
+ void set_concentration(double c) { concentration_ = c; }
+ void set_discount(double d) { discount_ = d; }
+
+ 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_counts_.clear();
+ }
+
+ unsigned num_tables() const {
+ return num_tables_;
+ }
+
+ unsigned num_tables(const Dish& dish) const {
+ const typename DishMapType::const_iterator it = dish_counts_.find(dish);
+ if (it == dish_counts_.end()) return 0;
+ return 1;
+ }
+
+ unsigned num_customers() const {
+ return num_customers_;
+ }
+
+ unsigned num_customers(const Dish& dish) const {
+ const typename DishMapType::const_iterator it = dish_counts_.find(dish);
+ if (it == dish_counts_.end()) return 0;
+ return it->second;
+ }
+
+ // returns +1 or 0 indicating whether a new table was opened
+ int increment(const Dish& dish) {
+ unsigned& dc = dish_counts_[dish];
+ ++dc;
+ ++num_customers_;
+ if (dc == 1) {
+ ++num_tables_;
+ return 1;
+ } else {
+ return 0;
+ }
+ }
+
+ // returns -1 or 0, indicating whether a table was closed
+ int decrement(const Dish& dish) {
+ unsigned& dc = dish_counts_[dish];
+ assert(dc > 0);
+ if (dc == 1) {
+ dish_counts_.erase(dish);
+ --num_tables_;
+ --num_customers_;
+ return -1;
+ } else {
+ assert(dc > 1);
+ --dc;
+ --num_customers_;
+ return 0;
+ }
+ }
+
+ double prob(const Dish& dish, const double& p0) const {
+ const typename DishMapType::const_iterator it = dish_counts_.find(dish);
+ const double r = num_tables_ * discount_ + concentration_;
+ if (it == dish_counts_.end()) {
+ return r * p0 / (num_customers_ + concentration_);
+ } else {
+ return (it->second - discount_ + 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 DishMapType::const_iterator it = dish_counts_.begin();
+ it != dish_counts_.end(); ++it) {
+ const unsigned& cur = it->second;
+ lp += lgamma(cur - 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_OneTable& crp) : crp_(crp) {}
+ const CCRP_OneTable& crp_;
+ double operator()(const double& proposed_discount) const {
+ return crp_.log_crp_prob(proposed_discount, crp_.concentration_);
+ }
+ };
+
+ struct ConcentrationResampler {
+ ConcentrationResampler(const CCRP_OneTable& crp) : crp_(crp) {}
+ const CCRP_OneTable& crp_;
+ double operator()(const double& proposed_concentration) const {
+ return crp_.log_crp_prob(crp_.discount_, proposed_concentration);
+ }
+ };
+
+ void Print(std::ostream* out) const {
+ (*out) << "PYP(d=" << discount_ << ",c=" << concentration_ << ") customers=" << num_customers_ << std::endl;
+ for (typename DishMapType::const_iterator it = dish_counts_.begin(); it != dish_counts_.end(); ++it) {
+ (*out) << " " << it->first << " = " << it->second << std::endl;
+ }
+ }
+
+ typedef typename DishMapType::const_iterator const_iterator;
+ const_iterator begin() const {
+ return dish_counts_.begin();
+ }
+ const_iterator end() const {
+ return dish_counts_.end();
+ }
+
+ unsigned num_tables_;
+ unsigned num_customers_;
+ DishMapType dish_counts_;
+
+ 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_OneTable<T,H>& c) {
+ c.Print(&o);
+ return o;
+}
+
+#endif