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+#ifndef _MFCR_H_
+#define _MFCR_H_
+
+#include <algorithm>
+#include <numeric>
+#include <cassert>
+#include <cmath>
+#include <list>
+#include <iostream>
+#include <vector>
+#include <iterator>
+#include <tr1/unordered_map>
+#include <boost/functional/hash.hpp>
+#include "sampler.h"
+#include "slice_sampler.h"
+#include "m.h"
+
+struct TableCount {
+ TableCount() : count(), floor() {}
+ TableCount(int c, int f) : count(c), floor(f) {
+ assert(f >= 0);
+ }
+ int count; // count or delta (may be 0, <0, or >0)
+ unsigned char floor; // from which floor?
+};
+
+std::ostream& operator<<(std::ostream& o, const TableCount& tc) {
+ return o << "[c=" << tc.count << " floor=" << static_cast<unsigned int>(tc.floor) << ']';
+}
+
+// Multi-Floor Chinese Restaurant as proposed by Wood & Teh (AISTATS, 2009) to simulate
+// graphical Pitman-Yor processes.
+// http://jmlr.csail.mit.edu/proceedings/papers/v5/wood09a/wood09a.pdf
+//
+// Implementation is based on Blunsom, Cohn, Goldwater, & Johnson (ACL 2009) and code
+// referenced therein.
+// http://www.aclweb.org/anthology/P/P09/P09-2085.pdf
+//
+template <unsigned Floors, typename Dish, typename DishHash = boost::hash<Dish> >
+class MFCR {
+ public:
+
+ MFCR(double d, double strength) :
+ num_tables_(),
+ num_customers_(),
+ discount_(d),
+ 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(); }
+
+ MFCR(double discount_strength, double discount_beta, double strength_shape, double strength_rate, double d = 0.9, double strength = 10.0) :
+ num_tables_(),
+ num_customers_(),
+ discount_(d),
+ strength_(strength),
+ discount_prior_strength_(discount_strength),
+ discount_prior_beta_(discount_beta),
+ strength_prior_shape_(strength_shape),
+ strength_prior_rate_(strength_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_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();
+ }
+
+ // this is not terribly efficient but it should not typically be necessary to execute this query
+ unsigned num_tables(const Dish& dish, const unsigned floor) const {
+ const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
+ if (it == dish_locs_.end()) return 0;
+ unsigned c = 0;
+ for (typename std::list<TableCount>::const_iterator i = it->second.table_counts_.begin();
+ i != it->second.table_counts_.end(); ++i) {
+ if (i->floor == floor) ++c;
+ }
+ return c;
+ }
+
+ 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 (delta, floor) indicating whether a new table (delta) was opened and on which floor
+ template <class InputIterator, class InputIterator2>
+ TableCount increment(const Dish& dish, InputIterator p0s, InputIterator2 lambdas, MT19937* rng) {
+ DishLocations& loc = dish_locs_[dish];
+ // marg_p0 = marginal probability of opening a new table on any floor with label dish
+ typedef typename std::iterator_traits<InputIterator>::value_type F;
+ const F marg_p0 = std::inner_product(p0s, p0s + Floors, lambdas, F(0.0));
+ assert(marg_p0 <= F(1.0001));
+ int floor = -1;
+ bool share_table = false;
+ if (loc.total_dish_count_) {
+ const F p_empty = F(strength_ + num_tables_ * discount_) * marg_p0;
+ const F p_share = F(loc.total_dish_count_ - loc.table_counts_.size() * discount_);
+ share_table = rng->SelectSample(p_empty, p_share);
+ }
+ if (share_table) {
+ // this can be done with doubles since P0 (which may be tiny) is not involved
+ double r = rng->next() * (loc.total_dish_count_ - loc.table_counts_.size() * discount_);
+ for (typename std::list<TableCount>::iterator ti = loc.table_counts_.begin();
+ ti != loc.table_counts_.end(); ++ti) {
+ r -= ti->count - discount_;
+ if (r <= 0.0) {
+ ++ti->count;
+ floor = ti->floor;
+ break;
+ }
+ }
+ if (r > 0.0) {
+ std::cerr << "Serious error: r=" << r << std::endl;
+ Print(&std::cerr);
+ assert(r <= 0.0);
+ }
+ } else { // sit at currently empty table -- must sample what floor
+ if (Floors == 1) {
+ floor = 0;
+ } else {
+ F r = F(rng->next()) * marg_p0;
+ for (unsigned i = 0; i < Floors; ++i) {
+ r -= (*p0s) * (*lambdas);
+ ++p0s;
+ ++lambdas;
+ if (r <= F(0.0)) {
+ floor = i;
+ break;
+ }
+ }
+ }
+ assert(floor >= 0);
+ loc.table_counts_.push_back(TableCount(1, floor));
+ ++num_tables_;
+ }
+ ++loc.total_dish_count_;
+ ++num_customers_;
+ return (share_table ? TableCount(0, floor) : TableCount(1, floor));
+ }
+
+ // returns first = -1 or 0, indicating whether a table was closed, and on what floor (second)
+ TableCount decrement(const Dish& dish, MT19937* rng) {
+ DishLocations& loc = dish_locs_[dish];
+ assert(loc.total_dish_count_);
+ int floor = -1;
+ int delta = 0;
+ if (loc.total_dish_count_ == 1) {
+ floor = loc.table_counts_.front().floor;
+ dish_locs_.erase(dish);
+ --num_tables_;
+ --num_customers_;
+ delta = -1;
+ } else {
+ // sample customer to remove UNIFORMLY. that is, do NOT use the d
+ // here. if you do, it will introduce (unwanted) bias!
+ double r = rng->next() * loc.total_dish_count_;
+ --loc.total_dish_count_;
+ --num_customers_;
+ for (typename std::list<TableCount>::iterator ti = loc.table_counts_.begin();
+ ti != loc.table_counts_.end(); ++ti) {
+ r -= ti->count;
+ if (r <= 0.0) {
+ floor = ti->floor;
+ if ((--ti->count) == 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);
+ }
+ }
+ return TableCount(delta, floor);
+ }
+
+ template <class InputIterator, class InputIterator2>
+ typename std::iterator_traits<InputIterator>::value_type prob(const Dish& dish, InputIterator p0s, InputIterator2 lambdas) const {
+ typedef typename std::iterator_traits<InputIterator>::value_type F;
+ const F marg_p0 = std::inner_product(p0s, p0s + Floors, lambdas, F(0.0));
+ assert(marg_p0 <= F(1.0001));
+ const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
+ const F r = F(num_tables_ * discount_ + strength_);
+ if (it == dish_locs_.end()) {
+ return r * marg_p0 / F(num_customers_ + strength_);
+ } else {
+ return (F(it->second.total_dish_count_ - discount_ * it->second.table_counts_.size()) + F(r * marg_p0)) /
+ F(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 draws from G_w'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<TableCount>::const_iterator ti = cur.table_counts_.begin(); ti != cur.table_counts_.end(); ++ti) {
+ lp += lgamma(ti->count - 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());
+ DiscountResampler dr(*this);
+ StrengthResampler sr(*this);
+ for (int iter = 0; iter < nloop; ++iter) {
+ if (has_strength_prior()) {
+ strength_ = slice_sampler1d(sr, strength_, *rng, -discount_,
+ 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 MFCR& crp) : crp_(crp) {}
+ const MFCR& crp_;
+ double operator()(const double& proposed_d) const {
+ return crp_.log_crp_prob(proposed_d, crp_.strength_);
+ }
+ };
+
+ struct StrengthResampler {
+ StrengthResampler(const MFCR& crp) : crp_(crp) {}
+ const MFCR& crp_;
+ double operator()(const double& proposediscount_strength) const {
+ return crp_.log_crp_prob(crp_.discount_, proposediscount_strength);
+ }
+ };
+
+ struct DishLocations {
+ DishLocations() : total_dish_count_() {}
+ unsigned total_dish_count_; // customers at all tables with this dish
+ std::list<TableCount> 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 {
+ (*out) << "MFCR<" << Floors << ">(d=" << discount_ << ",strength=" << 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<TableCount>::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 <unsigned N,typename T,typename H>
+std::ostream& operator<<(std::ostream& o, const MFCR<N,T,H>& c) {
+ c.Print(&o);
+ return o;
+}
+
+#endif