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#ifndef _PYP_WORD_MODEL_H_
#define _PYP_WORD_MODEL_H_
#include <iostream>
#include <cmath>
#include <vector>
#include "prob.h"
#include "ccrp.h"
#include "m.h"
#include "tdict.h"
#include "os_phrase.h"
// PYP(d,s,poisson-uniform) represented as a CRP
struct PYPWordModel {
explicit PYPWordModel(const unsigned vocab_e_size, const double mean_len = 5) :
base(prob_t::One()), r(1,1,1,1,0.66,50.0), u0(-std::log(vocab_e_size)), mean_length(mean_len) {}
void ResampleHyperparameters(MT19937* rng);
inline prob_t operator()(const std::vector<WordID>& s) const {
return r.prob(s, p0(s));
}
inline void Increment(const std::vector<WordID>& s, MT19937* rng) {
if (r.increment(s, p0(s), rng))
base *= p0(s);
}
inline void Decrement(const std::vector<WordID>& s, MT19937 *rng) {
if (r.decrement(s, rng))
base /= p0(s);
}
inline prob_t Likelihood() const {
prob_t p; p.logeq(r.log_crp_prob());
p *= base;
return p;
}
void Summary() const;
private:
inline double logp0(const std::vector<WordID>& s) const {
return Md::log_poisson(s.size(), mean_length) + s.size() * u0;
}
inline prob_t p0(const std::vector<WordID>& s) const {
prob_t p; p.logeq(logp0(s));
return p;
}
prob_t base; // keeps track of the draws from the base distribution
CCRP<std::vector<WordID> > r;
const double u0; // uniform log prob of generating a letter
const double mean_length; // mean length of a word in the base distribution
};
#endif
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