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#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, 1.0), 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.d() << ",\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
for (MFCR<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<TRule>(1, 1.0, 1.0, 1.0, 1.0, 1e-9, 4.0))).first;
}
p0s[0] = rp0(rule).as_float();
TableCount delta = it->second.increment(rule, p0s, lambdas, 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.logeq(log(rp0(rule)));
} else {
p0s[0] = rp0(rule).as_float();
p = prob_t(it->second.prob(rule, p0s, lambdas));
}
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<TRule>,
boost::hash<std::vector<WordID> > > RuleModelHash;
RuleModelHash r;
std::vector<double> lambdas;
mutable std::vector<double> 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.concentration() << ") --------------------------" << 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
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