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Diffstat (limited to 'gi/pf/condnaive.cc')
| -rw-r--r-- | gi/pf/condnaive.cc | 298 |
1 files changed, 0 insertions, 298 deletions
diff --git a/gi/pf/condnaive.cc b/gi/pf/condnaive.cc deleted file mode 100644 index 419731ac..00000000 --- a/gi/pf/condnaive.cc +++ /dev/null @@ -1,298 +0,0 @@ -#include <iostream> -#include <tr1/memory> -#include <queue> - -#include <boost/multi_array.hpp> -#include <boost/program_options.hpp> -#include <boost/program_options/variables_map.hpp> - -#include "base_distributions.h" -#include "monotonic_pseg.h" -#include "conditional_pseg.h" -#include "trule.h" -#include "tdict.h" -#include "filelib.h" -#include "dict.h" -#include "sampler.h" -#include "ccrp_nt.h" -#include "corpus.h" - -using namespace std; -using namespace std::tr1; -namespace po = boost::program_options; - -static unsigned kMAX_SRC_PHRASE; -static unsigned kMAX_TRG_PHRASE; - -void InitCommandLine(int argc, char** argv, po::variables_map* conf) { - po::options_description opts("Configuration options"); - opts.add_options() - ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples") - ("input,i",po::value<string>(),"Read parallel data from") - ("max_src_phrase",po::value<unsigned>()->default_value(4),"Maximum length of source language phrases") - ("max_trg_phrase",po::value<unsigned>()->default_value(4),"Maximum length of target language phrases") - ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)") - ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution") - ("random_seed,S",po::value<uint32_t>(), "Random seed"); - po::options_description clo("Command line options"); - clo.add_options() - ("config", po::value<string>(), "Configuration file") - ("help,h", "Print this help message and exit"); - po::options_description dconfig_options, dcmdline_options; - dconfig_options.add(opts); - dcmdline_options.add(opts).add(clo); - - po::store(parse_command_line(argc, argv, dcmdline_options), *conf); - if (conf->count("config")) { - ifstream config((*conf)["config"].as<string>().c_str()); - po::store(po::parse_config_file(config, dconfig_options), *conf); - } - po::notify(*conf); - - if (conf->count("help") || (conf->count("input") == 0)) { - cerr << dcmdline_options << endl; - exit(1); - } -} - -boost::shared_ptr<MT19937> prng; - -struct ModelAndData { - explicit ModelAndData(ConditionalParallelSegementationModel<PhraseConditionalBase>& m, const vector<vector<int> >& ce, const vector<vector<int> >& cf, const set<int>& ve, const set<int>& vf) : - model(m), - rng(&*prng), - corpuse(ce), - corpusf(cf), - vocabe(ve), - vocabf(vf), - mh_samples(), - mh_rejects(), - kX(-TD::Convert("X")), - derivations(corpuse.size()) {} - - void ResampleHyperparameters() { - } - - void InstantiateRule(const pair<short,short>& from, - const pair<short,short>& to, - const vector<int>& sentf, - const vector<int>& sente, - TRule* rule) const { - rule->f_.clear(); - rule->e_.clear(); - rule->lhs_ = kX; - for (short i = from.first; i < to.first; ++i) - rule->f_.push_back(sentf[i]); - for (short i = from.second; i < to.second; ++i) - rule->e_.push_back(sente[i]); - } - - void DecrementDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) { - if (d.size() < 2) return; - TRule x; - for (int i = 1; i < d.size(); ++i) { - InstantiateRule(d[i], d[i-1], sentf, sente, &x); - model.DecrementRule(x); - model.DecrementAlign(x.f_.size()); - } - } - - void PrintDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) { - if (d.size() < 2) return; - TRule x; - for (int i = 1; i < d.size(); ++i) { - InstantiateRule(d[i], d[i-1], sentf, sente, &x); - cerr << i << '/' << (d.size() - 1) << ": " << x << endl; - } - } - - void IncrementDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) { - if (d.size() < 2) return; - TRule x; - for (int i = 1; i < d.size(); ++i) { - InstantiateRule(d[i], d[i-1], sentf, sente, &x); - model.IncrementRule(x); - model.IncrementAlign(x.f_.size()); - } - } - - prob_t Likelihood() const { - return model.Likelihood(); - } - - prob_t DerivationProposalProbability(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) const { - prob_t p = prob_t::One(); - TRule x; - for (int i = 1; i < d.size(); ++i) { - InstantiateRule(d[i], d[i-1], sentf, sente, &x); - p *= model.RuleProbability(x); - p *= model.AlignProbability(x.f_.size()); - } - return p; - } - - void Sample(); - - ConditionalParallelSegementationModel<PhraseConditionalBase>& model; - MT19937* rng; - const vector<vector<int> >& corpuse, corpusf; - const set<int>& vocabe, vocabf; - unsigned mh_samples, mh_rejects; - const int kX; - vector<vector<pair<short, short> > > derivations; -}; - -void ModelAndData::Sample() { - unsigned MAXK = kMAX_SRC_PHRASE; - unsigned MAXL = kMAX_TRG_PHRASE; - TRule x; - x.lhs_ = -TD::Convert("X"); - - for (int samples = 0; samples < 1000; ++samples) { - if (samples % 1 == 0 && samples > 0) { - //ResampleHyperparameters(); - cerr << " [" << samples << " LLH=" << log(Likelihood()) << " MH=" << ((double)mh_rejects / mh_samples) << "]\n"; - for (int i = 0; i < 10; ++i) { - cerr << "SENTENCE: " << TD::GetString(corpusf[i]) << " ||| " << TD::GetString(corpuse[i]) << endl; - PrintDerivation(derivations[i], corpusf[i], corpuse[i]); - } - static TRule xx("[X] ||| w n ||| s h ||| X=0"); - const CCRP_NoTable<TRule>& dcrp = model.tmodel.r.find(xx.f_)->second; - for (CCRP_NoTable<TRule>::const_iterator it = dcrp.begin(); it != dcrp.end(); ++it) { - cerr << "\t" << it->second << "\t" << it->first << endl; - } - } - cerr << '.' << flush; - for (int s = 0; s < corpuse.size(); ++s) { - const vector<int>& sentf = corpusf[s]; - const vector<int>& sente = corpuse[s]; -// cerr << " CUSTOMERS: " << rules.num_customers() << endl; -// cerr << "SENTENCE: " << TD::GetString(sentf) << " ||| " << TD::GetString(sente) << endl; - - vector<pair<short, short> >& deriv = derivations[s]; - const prob_t p_cur = Likelihood(); - DecrementDerivation(deriv, sentf, sente); - - boost::multi_array<prob_t, 2> a(boost::extents[sentf.size() + 1][sente.size() + 1]); - boost::multi_array<prob_t, 4> trans(boost::extents[sentf.size() + 1][sente.size() + 1][MAXK][MAXL]); - a[0][0] = prob_t::One(); - for (int i = 0; i < sentf.size(); ++i) { - for (int j = 0; j < sente.size(); ++j) { - const prob_t src_a = a[i][j]; - x.f_.clear(); - for (int k = 1; k <= MAXK; ++k) { - if (i + k > sentf.size()) break; - x.f_.push_back(sentf[i + k - 1]); - x.e_.clear(); - const prob_t p_span = model.AlignProbability(k); // prob of consuming this much source - for (int l = 1; l <= MAXL; ++l) { - if (j + l > sente.size()) break; - x.e_.push_back(sente[j + l - 1]); - trans[i][j][k - 1][l - 1] = model.RuleProbability(x) * p_span; - a[i + k][j + l] += src_a * trans[i][j][k - 1][l - 1]; - } - } - } - } -// cerr << "Inside: " << log(a[sentf.size()][sente.size()]) << endl; - const prob_t q_cur = DerivationProposalProbability(deriv, sentf, sente); - - vector<pair<short,short> > newderiv; - int cur_i = sentf.size(); - int cur_j = sente.size(); - while(cur_i > 0 && cur_j > 0) { - newderiv.push_back(pair<short,short>(cur_i, cur_j)); -// cerr << "NODE: (" << cur_i << "," << cur_j << ")\n"; - SampleSet<prob_t> ss; - vector<pair<short,short> > nexts; - for (int k = 1; k <= MAXK; ++k) { - const int hyp_i = cur_i - k; - if (hyp_i < 0) break; - for (int l = 1; l <= MAXL; ++l) { - const int hyp_j = cur_j - l; - if (hyp_j < 0) break; - const prob_t& inside = a[hyp_i][hyp_j]; - if (inside == prob_t::Zero()) continue; - const prob_t& transp = trans[hyp_i][hyp_j][k - 1][l - 1]; - if (transp == prob_t::Zero()) continue; - const prob_t p = inside * transp; - ss.add(p); - nexts.push_back(pair<short,short>(hyp_i, hyp_j)); -// cerr << " (" << hyp_i << "," << hyp_j << ") <--- " << log(p) << endl; - } - } -// cerr << " sample set has " << nexts.size() << " elements.\n"; - const int selected = rng->SelectSample(ss); - cur_i = nexts[selected].first; - cur_j = nexts[selected].second; - } - newderiv.push_back(pair<short,short>(0,0)); - const prob_t q_new = DerivationProposalProbability(newderiv, sentf, sente); - IncrementDerivation(newderiv, sentf, sente); -// cerr << "SANITY: " << q_new << " " <<log(DerivationProposalProbability(newderiv, sentf, sente)) << endl; - if (deriv.empty()) { deriv = newderiv; continue; } - ++mh_samples; - - if (deriv != newderiv) { - const prob_t p_new = Likelihood(); -// cerr << "p_cur=" << log(p_cur) << "\t p_new=" << log(p_new) << endl; -// cerr << "q_cur=" << log(q_cur) << "\t q_new=" << log(q_new) << endl; - if (!rng->AcceptMetropolisHastings(p_new, p_cur, q_new, q_cur)) { - ++mh_rejects; - DecrementDerivation(newderiv, sentf, sente); - IncrementDerivation(deriv, sentf, sente); - } else { -// cerr << " ACCEPT\n"; - deriv = newderiv; - } - } - } - } -} - -int main(int argc, char** argv) { - po::variables_map conf; - InitCommandLine(argc, argv, &conf); - kMAX_TRG_PHRASE = conf["max_trg_phrase"].as<unsigned>(); - kMAX_SRC_PHRASE = conf["max_src_phrase"].as<unsigned>(); - - if (!conf.count("model1")) { - cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n"; - return 1; - } - if (conf.count("random_seed")) - prng.reset(new MT19937(conf["random_seed"].as<uint32_t>())); - else - prng.reset(new MT19937); -// MT19937& rng = *prng; - - vector<vector<int> > corpuse, corpusf; - set<int> vocabe, vocabf; - corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe); - cerr << "f-Corpus size: " << corpusf.size() << " sentences\n"; - cerr << "f-Vocabulary size: " << vocabf.size() << " types\n"; - cerr << "f-Corpus size: " << corpuse.size() << " sentences\n"; - cerr << "f-Vocabulary size: " << vocabe.size() << " types\n"; - assert(corpusf.size() == corpuse.size()); - - Model1 m1(conf["model1"].as<string>()); - - PhraseConditionalBase pcb0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size()); - ConditionalParallelSegementationModel<PhraseConditionalBase> x(pcb0); - - ModelAndData posterior(x, corpuse, corpusf, vocabe, vocabf); - posterior.Sample(); - - TRule r1("[X] ||| x ||| l e ||| X=0"); - TRule r2("[X] ||| A ||| a d ||| X=0"); - TRule r3("[X] ||| n ||| e r ||| X=0"); - TRule r4("[X] ||| x A n ||| b l a g ||| X=0"); - - PhraseConditionalUninformativeBase u0(vocabe.size()); - - cerr << (pcb0(r1)*pcb0(r2)*pcb0(r3)) << endl; - cerr << (u0(r4)) << endl; - - return 0; -} - |