summaryrefslogtreecommitdiff
path: root/gi/pf/condnaive.cc
diff options
context:
space:
mode:
Diffstat (limited to 'gi/pf/condnaive.cc')
-rw-r--r--gi/pf/condnaive.cc298
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;
-}
-