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-rw-r--r--gi/pf/conditional_pseg.h275
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diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
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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), d(0.5), strength(1.0), 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 << " " << i2->second.total_dish_count_ << '\t' << i2->first << std::endl;
+ }
+ }
+
+ double log_likelihood(const double& dd, const double& aa) const {
+ if (aa <= -dd) return -std::numeric_limits<double>::infinity();
+ //double llh = Md::log_beta_density(dd, 10, 3) + Md::log_gamma_density(aa, 1, 1);
+ double llh = Md::log_beta_density(dd, 1, 1) +
+ Md::log_gamma_density(dd + aa, 1, 1);
+ typename std::tr1::unordered_map<std::vector<WordID>, MFCR<1,TRule>, boost::hash<std::vector<WordID> > >::const_iterator it;
+ for (it = r.begin(); it != r.end(); ++it)
+ llh += it->second.log_crp_prob(dd, aa);
+ return llh;
+ }
+
+ struct DiscountResampler {
+ DiscountResampler(const MConditionalTranslationModel& m) : m_(m) {}
+ const MConditionalTranslationModel& m_;
+ double operator()(const double& proposed_discount) const {
+ return m_.log_likelihood(proposed_discount, m_.strength);
+ }
+ };
+
+ struct AlphaResampler {
+ AlphaResampler(const MConditionalTranslationModel& m) : m_(m) {}
+ const MConditionalTranslationModel& m_;
+ double operator()(const double& proposed_strength) const {
+ return m_.log_likelihood(m_.d, proposed_strength);
+ }
+ };
+
+ void ResampleHyperparameters(MT19937* rng) {
+ typename std::tr1::unordered_map<std::vector<WordID>, MFCR<1,TRule>, boost::hash<std::vector<WordID> > >::iterator it;
+#if 1
+ for (it = r.begin(); it != r.end(); ++it) {
+ it->second.resample_hyperparameters(rng);
+ }
+#else
+ const unsigned nloop = 5;
+ const unsigned niterations = 10;
+ DiscountResampler dr(*this);
+ AlphaResampler ar(*this);
+ for (int iter = 0; iter < nloop; ++iter) {
+ strength = slice_sampler1d(ar, strength, *rng, -d + std::numeric_limits<double>::min(),
+ std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
+ double min_discount = std::numeric_limits<double>::min();
+ if (strength < 0.0) min_discount -= strength;
+ d = slice_sampler1d(dr, d, *rng, min_discount,
+ 1.0, 0.0, niterations, 100*niterations);
+ }
+ strength = slice_sampler1d(ar, strength, *rng, -d,
+ std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
+ std::cerr << "MConditionalTranslationModel(d=" << d << ",s=" << strength << ") = " << log_likelihood(d, strength) << std::endl;
+ for (it = r.begin(); it != r.end(); ++it) {
+ it->second.set_discount(d);
+ it->second.set_strength(strength);
+ }
+#endif
+ }
+
+ 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>(d, strength))).first;
+ it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(1,1,1,1,0.6, -0.12))).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; p.logeq(log_likelihood(d, strength));
+ return p;
+ }
+
+ const ConditionalBaseMeasure& rp0;
+ typedef std::tr1::unordered_map<std::vector<WordID>,
+ MFCR<1, TRule>,
+ boost::hash<std::vector<WordID> > > RuleModelHash;
+ RuleModelHash r;
+ double d, strength;
+ 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
+
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