merge with upstream

1 files changed, 275 insertions, 0 deletions

diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
new file mode 100644
index 00000000..81ddb206
--- /dev/null
+++ b/gi/pf/conditional_pseg.h
@@ -0,0 +1,275 @@
+#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
+