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| -rw-r--r-- | gi/pf/conditional_pseg.h | 155 |
1 files changed, 155 insertions, 0 deletions
diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h new file mode 100644 index 00000000..edcdc813 --- /dev/null +++ b/gi/pf/conditional_pseg.h @@ -0,0 +1,155 @@ +#ifndef _CONDITIONAL_PSEG_H_ +#define _CONDITIONAL_PSEG_H_ + +#include <vector> +#include <tr1/unordered_map> +#include <boost/functional/hash.hpp> +#include <iostream> + +#include "prob.h" +#include "ccrp_nt.h" +#include "trule.h" +#include "base_measures.h" +#include "tdict.h" + +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, 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 + |