diff options
Diffstat (limited to 'gi/pyp-topics/src/pyp.hh')
| -rw-r--r-- | gi/pyp-topics/src/pyp.hh | 32 |
1 files changed, 23 insertions, 9 deletions
diff --git a/gi/pyp-topics/src/pyp.hh b/gi/pyp-topics/src/pyp.hh index 80c79fe1..64fb5b58 100644 --- a/gi/pyp-topics/src/pyp.hh +++ b/gi/pyp-topics/src/pyp.hh @@ -5,10 +5,13 @@ #include <map> #include <tr1/unordered_map> +#include <boost/random/uniform_real.hpp> +#include <boost/random/variate_generator.hpp> +#include <boost/random/mersenne_twister.hpp> + #include "log_add.h" #include "gammadist.h" #include "slice-sampler.h" -#include "mt19937ar.h" // // Pitman-Yor process with customer and table tracking @@ -23,7 +26,7 @@ public: using std::tr1::unordered_map<Dish,int>::begin; using std::tr1::unordered_map<Dish,int>::end; - PYP(double a, double b, Hash hash=Hash()); + PYP(double a, double b, unsigned long seed = 0, Hash hash=Hash()); int increment(Dish d, double p0); int decrement(Dish d); @@ -80,6 +83,16 @@ private: DishTableType _dish_tables; int _total_customers, _total_tables; + typedef boost::mt19937 base_generator_type; + 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) + //call: rnd() generates uniform on [0,1) + + // Function objects for calculating the parts of the log_prob for // the parameters a and b struct resample_a_type { @@ -122,11 +135,12 @@ private: }; template <typename Dish, typename Hash> -PYP<Dish,Hash>::PYP(double a, double b, Hash) +PYP<Dish,Hash>::PYP(double a, double b, unsigned long seed, Hash) : std::tr1::unordered_map<Dish, int, Hash>(), _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) + _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; } @@ -211,7 +225,7 @@ PYP<Dish,Hash>::increment(Dish dish, double p0) { assert (pshare >= 0.0); //assert (pnew > 0.0); - if (mt_genrand_res53() < pnew / (pshare + pnew)) { + if (rnd() < pnew / (pshare + pnew)) { // assign to a new table tc.tables += 1; tc.table_histogram[1] += 1; @@ -221,7 +235,7 @@ PYP<Dish,Hash>::increment(Dish dish, double p0) { else { // randomly assign to an existing table // remove constant denominator from inner loop - double r = mt_genrand_res53() * (c - _a*t); + double r = rnd() * (c - _a*t); for (std::map<int,int>::iterator hit = tc.table_histogram.begin(); hit != tc.table_histogram.end(); ++hit) { @@ -283,7 +297,7 @@ PYP<Dish,Hash>::decrement(Dish dish) //std::cerr << "count: " << count(dish) << " "; //std::cerr << "tables: " << tc.tables << "\n"; - double r = mt_genrand_res53() * count(dish); + double r = rnd() * count(dish); for (std::map<int,int>::iterator hit = tc.table_histogram.begin(); hit != tc.table_histogram.end(); ++hit) { @@ -467,7 +481,7 @@ 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, mt_genrand_res53, (double) 0.0, std::numeric_limits<double>::infinity(), + _b = slice_sampler1d(b_log_prob, _b, rnd, (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; } @@ -481,7 +495,7 @@ 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, mt_genrand_res53, std::numeric_limits<double>::min(), + _a = slice_sampler1d(a_log_prob, _a, rnd, std::numeric_limits<double>::min(), (double) 1.0, (double) 0.0, niterations, 100*niterations); } |