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author | Patrick Simianer <simianer@cl.uni-heidelberg.de> | 2012-04-07 16:58:55 +0200 |
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committer | Patrick Simianer <simianer@cl.uni-heidelberg.de> | 2012-04-07 16:58:55 +0200 |
commit | 715245dc7042ac0dca4fea94031d7c6de8058033 (patch) | |
tree | 3a7ff0b88f2e113a08aef663d2487edec0b5f67f /gi/pf/hpyp_tm.cc | |
parent | 89211ab30937672d84a54fac8fa435805499e38d (diff) | |
parent | 6001b81eba37985d2e7dea6e6ebb488b787789a6 (diff) |
Merge remote-tracking branch 'upstream/master'
Diffstat (limited to 'gi/pf/hpyp_tm.cc')
-rw-r--r-- | gi/pf/hpyp_tm.cc | 133 |
1 files changed, 133 insertions, 0 deletions
diff --git a/gi/pf/hpyp_tm.cc b/gi/pf/hpyp_tm.cc new file mode 100644 index 00000000..784f9958 --- /dev/null +++ b/gi/pf/hpyp_tm.cc @@ -0,0 +1,133 @@ +#include "hpyp_tm.h" + +#include <tr1/unordered_map> +#include <iostream> +#include <queue> + +#include "tdict.h" +#include "ccrp.h" +#include "pyp_word_model.h" +#include "tied_resampler.h" + +using namespace std; +using namespace std::tr1; + +struct FreqBinner { + FreqBinner(const std::string& fname) { fd_.Load(fname); } + unsigned NumberOfBins() const { return fd_.Max() + 1; } + unsigned Bin(const WordID& w) const { return fd_.LookUp(w); } + FreqDict<unsigned> fd_; +}; + +template <typename Base, class Binner = FreqBinner> +struct ConditionalPYPWordModel { + ConditionalPYPWordModel(Base* b, const Binner* bnr = NULL) : + base(*b), + binner(bnr), + btr(binner ? binner->NumberOfBins() + 1u : 2u) {} + + void Summary() const { + cerr << "Number of conditioning contexts: " << r.size() << endl; + for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) { + cerr << TD::Convert(it->first) << " \tPYP(d=" << it->second.discount() << ",s=" << it->second.strength() << ") --------------------------" << endl; + for (CCRP<vector<WordID> >::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2) + cerr << " " << i2->second.total_dish_count_ << '\t' << TD::GetString(i2->first) << endl; + } + } + + void ResampleHyperparameters(MT19937* rng) { + btr.ResampleHyperparameters(rng); + } + + prob_t Prob(const WordID src, const vector<WordID>& trglets) const { + RuleModelHash::const_iterator it = r.find(src); + if (it == r.end()) { + return base(trglets); + } else { + return it->second.prob(trglets, base(trglets)); + } + } + + void Increment(const WordID src, const vector<WordID>& trglets, MT19937* rng) { + RuleModelHash::iterator it = r.find(src); + if (it == r.end()) { + it = r.insert(make_pair(src, CCRP<vector<WordID> >(0.5,1.0))).first; + static const WordID kNULL = TD::Convert("NULL"); + unsigned bin = (src == kNULL ? 0 : 1); + if (binner && bin) { bin = binner->Bin(src) + 1; } + btr.Add(bin, &it->second); + } + if (it->second.increment(trglets, base(trglets), rng)) + base.Increment(trglets, rng); + } + + void Decrement(const WordID src, const vector<WordID>& trglets, MT19937* rng) { + RuleModelHash::iterator it = r.find(src); + assert(it != r.end()); + if (it->second.decrement(trglets, rng)) { + base.Decrement(trglets, rng); + } + } + + 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; + } + return p; + } + + unsigned UniqueConditioningContexts() const { + return r.size(); + } + + // TODO tie PYP hyperparameters based on source word frequency bins + Base& base; + const Binner* binner; + BinTiedResampler<CCRP<vector<WordID> > > btr; + typedef unordered_map<WordID, CCRP<vector<WordID> > > RuleModelHash; + RuleModelHash r; +}; + +HPYPLexicalTranslation::HPYPLexicalTranslation(const vector<vector<WordID> >& lets, + const unsigned vocab_size, + const unsigned num_letters) : + letters(lets), + base(vocab_size, num_letters, 5), + up0(new PYPWordModel<PoissonUniformWordModel>(&base)), + tmodel(new ConditionalPYPWordModel<PYPWordModel<PoissonUniformWordModel> >(up0, new FreqBinner("10k.freq"))), + kX(-TD::Convert("X")) {} + +void HPYPLexicalTranslation::Summary() const { + tmodel->Summary(); + up0->Summary(); +} + +prob_t HPYPLexicalTranslation::Likelihood() const { + prob_t p = up0->Likelihood(); + p *= tmodel->Likelihood(); + return p; +} + +void HPYPLexicalTranslation::ResampleHyperparameters(MT19937* rng) { + tmodel->ResampleHyperparameters(rng); + up0->ResampleHyperparameters(rng); +} + +unsigned HPYPLexicalTranslation::UniqueConditioningContexts() const { + return tmodel->UniqueConditioningContexts(); +} + +prob_t HPYPLexicalTranslation::Prob(WordID src, WordID trg) const { + return tmodel->Prob(src, letters[trg]); +} + +void HPYPLexicalTranslation::Increment(WordID src, WordID trg, MT19937* rng) { + tmodel->Increment(src, letters[trg], rng); +} + +void HPYPLexicalTranslation::Decrement(WordID src, WordID trg, MT19937* rng) { + tmodel->Decrement(src, letters[trg], rng); +} + |