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
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
|
#ifndef _CONDITIONAL_PSEG_H_
#define _CONDITIONAL_PSEG_H_
#include <vector>
#include <tr1/unordered_map>
#include <boost/functional/hash.hpp>
#include <iostream>
#include "m.h"
#include "prob.h"
#include "ccrp_nt.h"
#include "mfcr.h"
#include "trule.h"
#include "base_distributions.h"
#include "tdict.h"
template <typename ConditionalBaseMeasure>
struct MConditionalTranslationModel {
explicit MConditionalTranslationModel(ConditionalBaseMeasure& rcp0) :
rp0(rcp0), lambdas(1, prob_t::One()), p0s(1) {}
void Summary() const {
std::cerr << "Number of conditioning contexts: " << r.size() << std::endl;
for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
std::cerr << TD::GetString(it->first) << " \t(d=" << it->second.discount() << ",s=" << it->second.strength() << ") --------------------------" << std::endl;
for (MFCR<1,TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
std::cerr << " " << -1 << '\t' << i2->first << std::endl;
}
}
void ResampleHyperparameters(MT19937* rng) {
for (RuleModelHash::iterator it = r.begin(); it != r.end(); ++it)
it->second.resample_hyperparameters(rng);
}
int DecrementRule(const TRule& rule, MT19937* rng) {
RuleModelHash::iterator it = r.find(rule.f_);
assert(it != r.end());
const TableCount delta = it->second.decrement(rule, rng);
if (delta.count) {
if (it->second.num_customers() == 0) r.erase(it);
}
return delta.count;
}
int IncrementRule(const TRule& rule, MT19937* rng) {
RuleModelHash::iterator it = r.find(rule.f_);
if (it == r.end()) {
it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(1.0, 1.0, 1.0, 1.0, 1e-9, 4.0))).first;
}
p0s[0] = rp0(rule);
TableCount delta = it->second.increment(rule, p0s.begin(), lambdas.begin(), rng);
return delta.count;
}
prob_t RuleProbability(const TRule& rule) const {
prob_t p;
RuleModelHash::const_iterator it = r.find(rule.f_);
if (it == r.end()) {
p = rp0(rule);
} else {
p0s[0] = rp0(rule);
p = it->second.prob(rule, p0s.begin(), lambdas.begin());
}
return p;
}
prob_t Likelihood() const {
prob_t p = prob_t::One();
#if 0
for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
prob_t q; q.logeq(it->second.log_crp_prob());
p *= q;
for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
p *= rp0(i2->first);
}
#endif
return p;
}
const ConditionalBaseMeasure& rp0;
typedef std::tr1::unordered_map<std::vector<WordID>,
MFCR<1, TRule>,
boost::hash<std::vector<WordID> > > RuleModelHash;
RuleModelHash r;
std::vector<prob_t> lambdas;
mutable std::vector<prob_t> p0s;
};
template <typename ConditionalBaseMeasure>
struct ConditionalTranslationModel {
explicit ConditionalTranslationModel(ConditionalBaseMeasure& rcp0) :
rp0(rcp0) {}
void Summary() const {
std::cerr << "Number of conditioning contexts: " << r.size() << std::endl;
for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
std::cerr << TD::GetString(it->first) << " \t(\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
std::cerr << " " << i2->second << '\t' << i2->first << std::endl;
}
}
void ResampleHyperparameters(MT19937* rng) {
for (RuleModelHash::iterator it = r.begin(); it != r.end(); ++it)
it->second.resample_hyperparameters(rng);
}
int DecrementRule(const TRule& rule) {
RuleModelHash::iterator it = r.find(rule.f_);
assert(it != r.end());
int count = it->second.decrement(rule);
if (count) {
if (it->second.num_customers() == 0) r.erase(it);
}
return count;
}
int IncrementRule(const TRule& rule) {
RuleModelHash::iterator it = r.find(rule.f_);
if (it == r.end()) {
it = r.insert(make_pair(rule.f_, CCRP_NoTable<TRule>(1.0, 1.0, 8.0))).first;
}
int count = it->second.increment(rule);
return count;
}
void IncrementRules(const std::vector<TRulePtr>& rules) {
for (int i = 0; i < rules.size(); ++i)
IncrementRule(*rules[i]);
}
void DecrementRules(const std::vector<TRulePtr>& rules) {
for (int i = 0; i < rules.size(); ++i)
DecrementRule(*rules[i]);
}
prob_t RuleProbability(const TRule& rule) const {
prob_t p;
RuleModelHash::const_iterator it = r.find(rule.f_);
if (it == r.end()) {
p.logeq(log(rp0(rule)));
} else {
p.logeq(it->second.logprob(rule, log(rp0(rule))));
}
return p;
}
prob_t Likelihood() const {
prob_t p = prob_t::One();
for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
prob_t q; q.logeq(it->second.log_crp_prob());
p *= q;
for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
p *= rp0(i2->first);
}
return p;
}
const ConditionalBaseMeasure& rp0;
typedef std::tr1::unordered_map<std::vector<WordID>,
CCRP_NoTable<TRule>,
boost::hash<std::vector<WordID> > > RuleModelHash;
RuleModelHash r;
};
template <typename ConditionalBaseMeasure>
struct ConditionalParallelSegementationModel {
explicit ConditionalParallelSegementationModel(ConditionalBaseMeasure& rcp0) :
tmodel(rcp0), base(prob_t::One()), aligns(1,1) {}
ConditionalTranslationModel<ConditionalBaseMeasure> tmodel;
void DecrementRule(const TRule& rule) {
tmodel.DecrementRule(rule);
}
void IncrementRule(const TRule& rule) {
tmodel.IncrementRule(rule);
}
void IncrementRulesAndAlignments(const std::vector<TRulePtr>& rules) {
tmodel.IncrementRules(rules);
for (int i = 0; i < rules.size(); ++i) {
IncrementAlign(rules[i]->f_.size());
}
}
void DecrementRulesAndAlignments(const std::vector<TRulePtr>& rules) {
tmodel.DecrementRules(rules);
for (int i = 0; i < rules.size(); ++i) {
DecrementAlign(rules[i]->f_.size());
}
}
prob_t RuleProbability(const TRule& rule) const {
return tmodel.RuleProbability(rule);
}
void IncrementAlign(unsigned span) {
if (aligns.increment(span)) {
// TODO
}
}
void DecrementAlign(unsigned span) {
if (aligns.decrement(span)) {
// TODO
}
}
prob_t AlignProbability(unsigned span) const {
prob_t p;
p.logeq(aligns.logprob(span, Md::log_poisson(span, 1.0)));
return p;
}
prob_t Likelihood() const {
prob_t p; p.logeq(aligns.log_crp_prob());
p *= base;
p *= tmodel.Likelihood();
return p;
}
prob_t base;
CCRP_NoTable<unsigned> aligns;
};
#endif
|
