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-rw-r--r--gi/clda/src/ccrp.h119
1 files changed, 112 insertions, 7 deletions
diff --git a/gi/clda/src/ccrp.h b/gi/clda/src/ccrp.h
index eeccce1a..74d5be29 100644
--- a/gi/clda/src/ccrp.h
+++ b/gi/clda/src/ccrp.h
@@ -1,6 +1,7 @@
#ifndef _CCRP_H_
#define _CCRP_H_
+#include <numeric>
#include <cassert>
#include <cmath>
#include <list>
@@ -9,6 +10,7 @@
#include <tr1/unordered_map>
#include <boost/functional/hash.hpp>
#include "sampler.h"
+#include "slice_sampler.h"
// Chinese restaurant process (Pitman-Yor parameters) with explicit table
// tracking.
@@ -16,7 +18,36 @@
template <typename Dish, typename DishHash = boost::hash<Dish> >
class CCRP {
public:
- CCRP(double disc, double conc) : num_tables_(), num_customers_(), discount_(disc), concentration_(conc) {}
+ 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;
@@ -115,24 +146,90 @@ class CCRP {
}
}
+ 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 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_) {
- const double r = lgamma(1.0 - discount_);
- lp = lgamma(concentration_) - lgamma(concentration_ + num_customers_)
- + num_tables_ * discount_ + lgamma(concentration_ / discount_ + num_tables_)
- - lgamma(concentration_ / discount_);
+ const double r = lgamma(1.0 - discount);
+ lp += lgamma(concentration) - lgamma(concentration + num_customers_)
+ + num_tables_ * 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;
- lp += lgamma(cur.total_dish_count_ - discount_) - r;
+ for (std::list<unsigned>::const_iterator ti = cur.table_counts_.begin(); ti != cur.table_counts_.end(); ++ti) {
+ lp += lgamma(*ti - discount) - r;
+ }
}
}
+ assert(std::isfinite(lp));
return lp;
}
+ void resample_hyperparameters(MT19937* rng) {
+ assert(has_discount_prior() || has_concentration_prior());
+ DiscountResampler dr(*this);
+ ConcentrationResampler cr(*this);
+ const int niterations = 10;
+ double gamma_upper = std::numeric_limits<double>::infinity();
+ for (int iter = 0; iter < 5; ++iter) {
+ if (has_concentration_prior()) {
+ concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
+ gamma_upper, 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,
+ gamma_upper, 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
@@ -166,6 +263,14 @@ class CCRP {
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>