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-rw-r--r--gi/pyp-topics/src/pyp.hh52
1 files changed, 37 insertions, 15 deletions
diff --git a/gi/pyp-topics/src/pyp.hh b/gi/pyp-topics/src/pyp.hh
index 7a520d6a..dc47244b 100644
--- a/gi/pyp-topics/src/pyp.hh
+++ b/gi/pyp-topics/src/pyp.hh
@@ -4,6 +4,7 @@
#include <math.h>
#include <map>
#include <tr1/unordered_map>
+//#include <google/sparse_hash_map>
#include <boost/random/uniform_real.hpp>
#include <boost/random/variate_generator.hpp>
@@ -11,6 +12,7 @@
#include "log_add.h"
#include "slice-sampler.h"
+#include "mt19937ar.h"
//
// Pitman-Yor process with customer and table tracking
@@ -18,12 +20,17 @@
template <typename Dish, typename Hash=std::tr1::hash<Dish> >
class PYP : protected std::tr1::unordered_map<Dish, int, Hash>
+//class PYP : protected google::sparse_hash_map<Dish, int, Hash>
{
public:
using std::tr1::unordered_map<Dish,int>::const_iterator;
using std::tr1::unordered_map<Dish,int>::iterator;
using std::tr1::unordered_map<Dish,int>::begin;
using std::tr1::unordered_map<Dish,int>::end;
+// using google::sparse_hash_map<Dish,int>::const_iterator;
+// using google::sparse_hash_map<Dish,int>::iterator;
+// using google::sparse_hash_map<Dish,int>::begin;
+// using google::sparse_hash_map<Dish,int>::end;
PYP(double a, double b, unsigned long seed = 0, Hash hash=Hash());
@@ -39,6 +46,7 @@ public:
int num_customers() const { return _total_customers; }
int num_types() const { return std::tr1::unordered_map<Dish,int>::size(); }
+ //int num_types() const { return google::sparse_hash_map<Dish,int>::size(); }
bool empty() const { return _total_customers == 0; }
double log_prob(Dish dish, double log_p0) const;
@@ -79,6 +87,7 @@ private:
std::map<int, int> table_histogram; // num customers at table -> number tables
};
typedef std::tr1::unordered_map<Dish, TableCounter, Hash> DishTableType;
+ //typedef google::sparse_hash_map<Dish, TableCounter, Hash> DishTableType;
DishTableType _dish_tables;
int _total_customers, _total_tables;
@@ -86,11 +95,10 @@ private:
typedef boost::uniform_real<> uni_dist_type;
typedef boost::variate_generator<base_generator_type&, uni_dist_type> gen_type;
- uni_dist_type uni_dist;
- base_generator_type rng; //this gets the seed
- gen_type rnd; //instantiate: rnd(rng, uni_dist)
+// uni_dist_type uni_dist;
+// base_generator_type rng; //this gets the seed
+// gen_type rnd; //instantiate: rnd(rng, uni_dist)
//call: rnd() generates uniform on [0,1)
-
// Function objects for calculating the parts of the log_prob for
// the parameters a and b
@@ -132,12 +140,12 @@ private:
}
};
- /* lbetadist() returns the log probability density of x under a Beta(alpha,beta)
+ /* lbetadist() returns the log probability density of x under a Beta(alpha,beta)
* distribution. - copied from Mark Johnson's gammadist.c
*/
- static long double lbetadist(long double x, long double alpha, long double beta);
+ static long double lbetadist(long double x, long double alpha, long double beta);
- /* lgammadist() returns the log probability density of x under a Gamma(alpha,beta)
+ /* lgammadist() returns the log probability density of x under a Gamma(alpha,beta)
* distribution - copied from Mark Johnson's gammadist.c
*/
static long double lgammadist(long double x, long double alpha, long double beta);
@@ -146,13 +154,15 @@ private:
template <typename Dish, typename Hash>
PYP<Dish,Hash>::PYP(double a, double b, unsigned long seed, Hash)
-: std::tr1::unordered_map<Dish, int, Hash>(), _a(a), _b(b),
+: std::tr1::unordered_map<Dish, int, Hash>(10), _a(a), _b(b),
+//: google::sparse_hash_map<Dish, int, Hash>(10), _a(a), _b(b),
_a_beta_a(1), _a_beta_b(1), _b_gamma_s(1), _b_gamma_c(1),
//_a_beta_a(1), _a_beta_b(1), _b_gamma_s(10), _b_gamma_c(0.1),
- _total_customers(0), _total_tables(0),
- uni_dist(0,1), rng(seed == 0 ? (unsigned long)this : seed), rnd(rng, uni_dist)
+ _total_customers(0), _total_tables(0)//,
+ //uni_dist(0,1), rng(seed == 0 ? (unsigned long)this : seed), rnd(rng, uni_dist)
{
// std::cerr << "\t##PYP<Dish,Hash>::PYP(a=" << _a << ",b=" << _b << ")" << std::endl;
+ //set_deleted_key(-std::numeric_limits<Dish>::max());
}
template <typename Dish, typename Hash>
@@ -235,7 +245,8 @@ PYP<Dish,Hash>::increment(Dish dish, double p0) {
assert (pshare >= 0.0);
//assert (pnew > 0.0);
- if (rnd() < pnew / (pshare + pnew)) {
+ //if (rnd() < pnew / (pshare + pnew)) {
+ if (mt_genrand_res53() < pnew / (pshare + pnew)) {
// assign to a new table
tc.tables += 1;
tc.table_histogram[1] += 1;
@@ -245,7 +256,8 @@ PYP<Dish,Hash>::increment(Dish dish, double p0) {
else {
// randomly assign to an existing table
// remove constant denominator from inner loop
- double r = rnd() * (c - _a*t);
+ //double r = rnd() * (c - _a*t);
+ double r = mt_genrand_res53() * (c - _a*t);
for (std::map<int,int>::iterator
hit = tc.table_histogram.begin();
hit != tc.table_histogram.end(); ++hit) {
@@ -266,6 +278,7 @@ PYP<Dish,Hash>::increment(Dish dish, double p0) {
}
std::tr1::unordered_map<Dish,int,Hash>::operator[](dish) += 1;
+ //google::sparse_hash_map<Dish,int,Hash>::operator[](dish) += 1;
_total_customers += 1;
return delta;
@@ -276,6 +289,7 @@ int
PYP<Dish,Hash>::count(Dish dish) const
{
typename std::tr1::unordered_map<Dish, int>::const_iterator
+ //typename google::sparse_hash_map<Dish, int>::const_iterator
dcit = find(dish);
if (dcit != end())
return dcit->second;
@@ -288,6 +302,7 @@ int
PYP<Dish,Hash>::decrement(Dish dish)
{
typename std::tr1::unordered_map<Dish, int>::iterator dcit = find(dish);
+ //typename google::sparse_hash_map<Dish, int>::iterator dcit = find(dish);
if (dcit == end()) {
std::cerr << dish << std::endl;
assert(false);
@@ -296,6 +311,7 @@ PYP<Dish,Hash>::decrement(Dish dish)
int delta = 0;
typename std::tr1::unordered_map<Dish, TableCounter>::iterator dtit = _dish_tables.find(dish);
+ //typename google::sparse_hash_map<Dish, TableCounter>::iterator dtit = _dish_tables.find(dish);
if (dtit == _dish_tables.end()) {
std::cerr << dish << std::endl;
assert(false);
@@ -307,7 +323,8 @@ PYP<Dish,Hash>::decrement(Dish dish)
//std::cerr << "count: " << count(dish) << " ";
//std::cerr << "tables: " << tc.tables << "\n";
- double r = rnd() * count(dish);
+ //double r = rnd() * count(dish);
+ double r = mt_genrand_res53() * count(dish);
for (std::map<int,int>::iterator hit = tc.table_histogram.begin();
hit != tc.table_histogram.end(); ++hit)
{
@@ -357,6 +374,7 @@ int
PYP<Dish,Hash>::num_tables(Dish dish) const
{
typename std::tr1::unordered_map<Dish, TableCounter, Hash>::const_iterator
+ //typename google::sparse_hash_map<Dish, TableCounter, Hash>::const_iterator
dtit = _dish_tables.find(dish);
//assert(dtit != _dish_tables.end());
@@ -379,6 +397,7 @@ PYP<Dish,Hash>::debug_info(std::ostream& os) const
{
int hists = 0, tables = 0;
for (typename std::tr1::unordered_map<Dish, TableCounter, Hash>::const_iterator
+ //for (typename google::sparse_hash_map<Dish, TableCounter, Hash>::const_iterator
dtit = _dish_tables.begin(); dtit != _dish_tables.end(); ++dtit)
{
hists += dtit->second.table_histogram.size();
@@ -409,6 +428,7 @@ void
PYP<Dish,Hash>::clear()
{
this->std::tr1::unordered_map<Dish,int,Hash>::clear();
+ //this->google::sparse_hash_map<Dish,int,Hash>::clear();
_dish_tables.clear();
_total_tables = _total_customers = 0;
}
@@ -509,7 +529,8 @@ PYP<Dish,Hash>::resample_prior_b() {
int niterations = 10; // number of resampling iterations
//std::cerr << "\n## resample_prior_b(), initial a = " << _a << ", b = " << _b << std::endl;
resample_b_type b_log_prob(_total_customers, _total_tables, _a, _b_gamma_c, _b_gamma_s);
- _b = slice_sampler1d(b_log_prob, _b, rnd, (double) 0.0, std::numeric_limits<double>::infinity(),
+ //_b = slice_sampler1d(b_log_prob, _b, rnd, (double) 0.0, std::numeric_limits<double>::infinity(),
+ _b = slice_sampler1d(b_log_prob, _b, random, (double) 0.0, std::numeric_limits<double>::infinity(),
(double) 0.0, niterations, 100*niterations);
//std::cerr << "\n## resample_prior_b(), final a = " << _a << ", b = " << _b << std::endl;
}
@@ -523,7 +544,8 @@ PYP<Dish,Hash>::resample_prior_a() {
int niterations = 10;
//std::cerr << "\n## Initial a = " << _a << ", b = " << _b << std::endl;
resample_a_type a_log_prob(_total_customers, _total_tables, _b, _a_beta_a, _a_beta_b, _dish_tables);
- _a = slice_sampler1d(a_log_prob, _a, rnd, std::numeric_limits<double>::min(),
+ //_a = slice_sampler1d(a_log_prob, _a, rnd, std::numeric_limits<double>::min(),
+ _a = slice_sampler1d(a_log_prob, _a, random, std::numeric_limits<double>::min(),
(double) 1.0, (double) 0.0, niterations, 100*niterations);
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 09:28:44 +0000