1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
|
#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
template <class Base>
struct PYPWordModel {
explicit PYPWordModel(Base* b) :
base(*b),
r(1,1,1,1,0.66,50.0)
{}
void ResampleHyperparameters(MT19937* rng) {
r.resample_hyperparameters(rng);
std::cerr << " PYPWordModel(d=" << r.discount() << ",s=" << r.strength() << ")\n";
}
inline prob_t operator()(const std::vector<WordID>& s) const {
return r.prob(s, base(s));
}
inline void Increment(const std::vector<WordID>& s, MT19937* rng) {
if (r.increment(s, base(s), rng))
base.Increment(s, rng);
}
inline void Decrement(const std::vector<WordID>& s, MT19937 *rng) {
if (r.decrement(s, rng))
base.Decrement(s, rng);
}
inline prob_t Likelihood() const {
prob_t p; p.logeq(r.log_crp_prob());
p *= base.Likelihood();
return p;
}
void Summary() const {
std::cerr << "PYPWordModel: generations=" << r.num_customers()
<< " PYP(d=" << r.discount() << ",s=" << r.strength() << ')' << std::endl;
for (typename CCRP<std::vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it) {
std::cerr << " " << it->second.total_dish_count_
<< " (on " << it->second.table_counts_.size() << " tables) "
<< TD::GetString(it->first) << std::endl;
}
}
private:
Base& base; // keeps track of the draws from the base distribution
CCRP<std::vector<WordID> > r;
};
#endif
|
