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authorChris Dyer <cdyer@cs.cmu.edu>2011-10-11 12:06:32 +0100
committerChris Dyer <cdyer@cs.cmu.edu>2011-10-11 12:06:32 +0100
commit0acc92a0eecf04a2c429f6f7685bfcaa68c7ec3a (patch)
treef85f238a7921541702112ae0835cf638c5c7abf2 /gi/pf/pfdist.cc
parentb77d23a3032f42be3705e88ae1734bae779fb9a3 (diff)
check in some experimental particle filtering code, some gitignore fixes
Diffstat (limited to 'gi/pf/pfdist.cc')
-rw-r--r--gi/pf/pfdist.cc621
1 files changed, 621 insertions, 0 deletions
diff --git a/gi/pf/pfdist.cc b/gi/pf/pfdist.cc
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+++ b/gi/pf/pfdist.cc
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+#include <iostream>
+#include <tr1/memory>
+#include <queue>
+
+#include <boost/functional.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "base_measures.h"
+#include "reachability.h"
+#include "viterbi.h"
+#include "hg.h"
+#include "trule.h"
+#include "tdict.h"
+#include "filelib.h"
+#include "dict.h"
+#include "sampler.h"
+#include "ccrp_nt.h"
+#include "ccrp_onetable.h"
+
+using namespace std;
+using namespace tr1;
+namespace po = boost::program_options;
+
+shared_ptr<MT19937> prng;
+
+size_t hash_value(const TRule& r) {
+ size_t h = boost::hash_value(r.e_);
+ boost::hash_combine(h, -r.lhs_);
+ boost::hash_combine(h, boost::hash_value(r.f_));
+ return h;
+}
+
+bool operator==(const TRule& a, const TRule& b) {
+ return (a.lhs_ == b.lhs_ && a.e_ == b.e_ && a.f_ == b.f_);
+}
+
+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")
+ ("particles,p",po::value<unsigned>()->default_value(30),"Number of particles")
+ ("filter_frequency,f",po::value<unsigned>()->default_value(5),"Number of time steps between filterings")
+ ("input,i",po::value<string>(),"Read parallel data from")
+ ("max_src_phrase",po::value<unsigned>()->default_value(5),"Maximum length of source language phrases")
+ ("max_trg_phrase",po::value<unsigned>()->default_value(5),"Maximum length of target language phrases")
+ ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)")
+ ("inverse_model1,M",po::value<string>(),"Inverse Model 1 parameters (used in backward estimate)")
+ ("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);
+ }
+}
+
+void ReadParallelCorpus(const string& filename,
+ vector<vector<WordID> >* f,
+ vector<vector<WordID> >* e,
+ set<WordID>* vocab_f,
+ set<WordID>* vocab_e) {
+ f->clear();
+ e->clear();
+ vocab_f->clear();
+ vocab_e->clear();
+ istream* in;
+ if (filename == "-")
+ in = &cin;
+ else
+ in = new ifstream(filename.c_str());
+ assert(*in);
+ string line;
+ const WordID kDIV = TD::Convert("|||");
+ vector<WordID> tmp;
+ while(*in) {
+ getline(*in, line);
+ if (line.empty() && !*in) break;
+ e->push_back(vector<int>());
+ f->push_back(vector<int>());
+ vector<int>& le = e->back();
+ vector<int>& lf = f->back();
+ tmp.clear();
+ TD::ConvertSentence(line, &tmp);
+ bool isf = true;
+ for (unsigned i = 0; i < tmp.size(); ++i) {
+ const int cur = tmp[i];
+ if (isf) {
+ if (kDIV == cur) { isf = false; } else {
+ lf.push_back(cur);
+ vocab_f->insert(cur);
+ }
+ } else {
+ assert(cur != kDIV);
+ le.push_back(cur);
+ vocab_e->insert(cur);
+ }
+ }
+ assert(isf == false);
+ }
+ if (in != &cin) delete in;
+}
+
+#if 0
+struct MyConditionalModel {
+ MyConditionalModel(PhraseConditionalBase& rcp0) : rp0(&rcp0), base(prob_t::One()), src_phrases(1,1), src_jumps(200, CCRP_NoTable<int>(1,1)) {}
+
+ prob_t srcp0(const vector<WordID>& src) const {
+ prob_t p(1.0 / 3000.0);
+ p.poweq(src.size());
+ prob_t lenp; lenp.logeq(log_poisson(src.size(), 1.0));
+ p *= lenp;
+ return p;
+ }
+
+ void DecrementRule(const TRule& rule) {
+ const RuleCRPMap::iterator it = rules.find(rule.f_);
+ assert(it != rules.end());
+ if (it->second.decrement(rule)) {
+ base /= (*rp0)(rule);
+ if (it->second.num_customers() == 0)
+ rules.erase(it);
+ }
+ if (src_phrases.decrement(rule.f_))
+ base /= srcp0(rule.f_);
+ }
+
+ void IncrementRule(const TRule& rule) {
+ RuleCRPMap::iterator it = rules.find(rule.f_);
+ if (it == rules.end())
+ it = rules.insert(make_pair(rule.f_, CCRP_NoTable<TRule>(1,1))).first;
+ if (it->second.increment(rule)) {
+ base *= (*rp0)(rule);
+ }
+ if (src_phrases.increment(rule.f_))
+ base *= srcp0(rule.f_);
+ }
+
+ void IncrementRules(const vector<TRulePtr>& rules) {
+ for (int i = 0; i < rules.size(); ++i)
+ IncrementRule(*rules[i]);
+ }
+
+ void DecrementRules(const vector<TRulePtr>& rules) {
+ for (int i = 0; i < rules.size(); ++i)
+ DecrementRule(*rules[i]);
+ }
+
+ void IncrementJump(int dist, unsigned src_len) {
+ assert(src_len > 0);
+ if (src_jumps[src_len].increment(dist))
+ base *= jp0(dist, src_len);
+ }
+
+ void DecrementJump(int dist, unsigned src_len) {
+ assert(src_len > 0);
+ if (src_jumps[src_len].decrement(dist))
+ base /= jp0(dist, src_len);
+ }
+
+ void IncrementJumps(const vector<int>& js, unsigned src_len) {
+ for (unsigned i = 0; i < js.size(); ++i)
+ IncrementJump(js[i], src_len);
+ }
+
+ void DecrementJumps(const vector<int>& js, unsigned src_len) {
+ for (unsigned i = 0; i < js.size(); ++i)
+ DecrementJump(js[i], src_len);
+ }
+
+ // p(jump = dist | src_len , z)
+ prob_t JumpProbability(int dist, unsigned src_len) {
+ const prob_t p0 = jp0(dist, src_len);
+ const double lp = src_jumps[src_len].logprob(dist, log(p0));
+ prob_t q; q.logeq(lp);
+ return q;
+ }
+
+ // p(rule.f_ | z) * p(rule.e_ | rule.f_ , z)
+ prob_t RuleProbability(const TRule& rule) const {
+ const prob_t p0 = (*rp0)(rule);
+ prob_t srcp; srcp.logeq(src_phrases.logprob(rule.f_, log(srcp0(rule.f_))));
+ const RuleCRPMap::const_iterator it = rules.find(rule.f_);
+ if (it == rules.end()) return srcp * p0;
+ const double lp = it->second.logprob(rule, log(p0));
+ prob_t q; q.logeq(lp);
+ return q * srcp;
+ }
+
+ prob_t Likelihood() const {
+ prob_t p = base;
+ for (RuleCRPMap::const_iterator it = rules.begin();
+ it != rules.end(); ++it) {
+ prob_t cl; cl.logeq(it->second.log_crp_prob());
+ p *= cl;
+ }
+ for (unsigned l = 1; l < src_jumps.size(); ++l) {
+ if (src_jumps[l].num_customers() > 0) {
+ prob_t q;
+ q.logeq(src_jumps[l].log_crp_prob());
+ p *= q;
+ }
+ }
+ return p;
+ }
+
+ JumpBase jp0;
+ const PhraseConditionalBase* rp0;
+ prob_t base;
+ typedef unordered_map<vector<WordID>, CCRP_NoTable<TRule>, boost::hash<vector<WordID> > > RuleCRPMap;
+ RuleCRPMap rules;
+ CCRP_NoTable<vector<WordID> > src_phrases;
+ vector<CCRP_NoTable<int> > src_jumps;
+};
+
+#endif
+
+struct MyJointModel {
+ MyJointModel(PhraseJointBase& rcp0) :
+ rp0(rcp0), base(prob_t::One()), rules(1,1), src_jumps(200, CCRP_NoTable<int>(1,1)) {}
+
+ void DecrementRule(const TRule& rule) {
+ if (rules.decrement(rule))
+ base /= rp0(rule);
+ }
+
+ void IncrementRule(const TRule& rule) {
+ if (rules.increment(rule))
+ base *= rp0(rule);
+ }
+
+ void IncrementRules(const vector<TRulePtr>& rules) {
+ for (int i = 0; i < rules.size(); ++i)
+ IncrementRule(*rules[i]);
+ }
+
+ void DecrementRules(const vector<TRulePtr>& rules) {
+ for (int i = 0; i < rules.size(); ++i)
+ DecrementRule(*rules[i]);
+ }
+
+ void IncrementJump(int dist, unsigned src_len) {
+ assert(src_len > 0);
+ if (src_jumps[src_len].increment(dist))
+ base *= jp0(dist, src_len);
+ }
+
+ void DecrementJump(int dist, unsigned src_len) {
+ assert(src_len > 0);
+ if (src_jumps[src_len].decrement(dist))
+ base /= jp0(dist, src_len);
+ }
+
+ void IncrementJumps(const vector<int>& js, unsigned src_len) {
+ for (unsigned i = 0; i < js.size(); ++i)
+ IncrementJump(js[i], src_len);
+ }
+
+ void DecrementJumps(const vector<int>& js, unsigned src_len) {
+ for (unsigned i = 0; i < js.size(); ++i)
+ DecrementJump(js[i], src_len);
+ }
+
+ // p(jump = dist | src_len , z)
+ prob_t JumpProbability(int dist, unsigned src_len) {
+ const prob_t p0 = jp0(dist, src_len);
+ const double lp = src_jumps[src_len].logprob(dist, log(p0));
+ prob_t q; q.logeq(lp);
+ return q;
+ }
+
+ // p(rule.f_ | z) * p(rule.e_ | rule.f_ , z)
+ prob_t RuleProbability(const TRule& rule) const {
+ prob_t p; p.logeq(rules.logprob(rule, log(rp0(rule))));
+ return p;
+ }
+
+ prob_t Likelihood() const {
+ prob_t p = base;
+ prob_t q; q.logeq(rules.log_crp_prob());
+ p *= q;
+ for (unsigned l = 1; l < src_jumps.size(); ++l) {
+ if (src_jumps[l].num_customers() > 0) {
+ prob_t q;
+ q.logeq(src_jumps[l].log_crp_prob());
+ p *= q;
+ }
+ }
+ return p;
+ }
+
+ JumpBase jp0;
+ const PhraseJointBase& rp0;
+ prob_t base;
+ CCRP_NoTable<TRule> rules;
+ vector<CCRP_NoTable<int> > src_jumps;
+};
+
+struct BackwardEstimate {
+ BackwardEstimate(const Model1& m1, const vector<WordID>& src, const vector<WordID>& trg) :
+ model1_(m1), src_(src), trg_(trg) {
+ }
+ const prob_t& operator()(const vector<bool>& src_cov, unsigned trg_cov) const {
+ assert(src_.size() == src_cov.size());
+ assert(trg_cov <= trg_.size());
+ prob_t& e = cache_[src_cov][trg_cov];
+ if (e.is_0()) {
+ if (trg_cov == trg_.size()) { e = prob_t::One(); return e; }
+ vector<WordID> r(src_.size() + 1); r.clear();
+ r.push_back(0); // NULL word
+ for (int i = 0; i < src_cov.size(); ++i)
+ if (!src_cov[i]) r.push_back(src_[i]);
+ const prob_t uniform_alignment(1.0 / r.size());
+ e.logeq(log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
+ for (unsigned j = trg_cov; j < trg_.size(); ++j) {
+ prob_t p;
+ for (unsigned i = 0; i < r.size(); ++i)
+ p += model1_(r[i], trg_[j]);
+ if (p.is_0()) {
+ cerr << "ERROR: p(" << TD::Convert(trg_[j]) << " | " << TD::GetString(r) << ") = 0!\n";
+ abort();
+ }
+ p *= uniform_alignment;
+ e *= p;
+ }
+ }
+ return e;
+ }
+ const Model1& model1_;
+ const vector<WordID>& src_;
+ const vector<WordID>& trg_;
+ mutable unordered_map<vector<bool>, map<unsigned, prob_t>, boost::hash<vector<bool> > > cache_;
+};
+
+struct BackwardEstimateSym {
+ BackwardEstimateSym(const Model1& m1,
+ const Model1& invm1, const vector<WordID>& src, const vector<WordID>& trg) :
+ model1_(m1), invmodel1_(invm1), src_(src), trg_(trg) {
+ }
+ const prob_t& operator()(const vector<bool>& src_cov, unsigned trg_cov) const {
+ assert(src_.size() == src_cov.size());
+ assert(trg_cov <= trg_.size());
+ prob_t& e = cache_[src_cov][trg_cov];
+ if (e.is_0()) {
+ if (trg_cov == trg_.size()) { e = prob_t::One(); return e; }
+ vector<WordID> r(src_.size() + 1); r.clear();
+ for (int i = 0; i < src_cov.size(); ++i)
+ if (!src_cov[i]) r.push_back(src_[i]);
+ r.push_back(0); // NULL word
+ const prob_t uniform_alignment(1.0 / r.size());
+ e.logeq(log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
+ for (unsigned j = trg_cov; j < trg_.size(); ++j) {
+ prob_t p;
+ for (unsigned i = 0; i < r.size(); ++i)
+ p += model1_(r[i], trg_[j]);
+ if (p.is_0()) {
+ cerr << "ERROR: p(" << TD::Convert(trg_[j]) << " | " << TD::GetString(r) << ") = 0!\n";
+ abort();
+ }
+ p *= uniform_alignment;
+ e *= p;
+ }
+ r.pop_back();
+ const prob_t inv_uniform(1.0 / (trg_.size() - trg_cov + 1.0));
+ prob_t inv;
+ inv.logeq(log_poisson(r.size(), trg_.size() - trg_cov));
+ for (unsigned i = 0; i < r.size(); ++i) {
+ prob_t p;
+ for (unsigned j = trg_cov - 1; j < trg_.size(); ++j)
+ p += invmodel1_(j < trg_cov ? 0 : trg_[j], r[i]);
+ if (p.is_0()) {
+ cerr << "ERROR: p_inv(" << TD::Convert(r[i]) << " | " << TD::GetString(trg_) << ") = 0!\n";
+ abort();
+ }
+ p *= inv_uniform;
+ inv *= p;
+ }
+ prob_t x = pow(e * inv, 0.5);
+ e = x;
+ //cerr << "Forward: " << log(e) << "\tBackward: " << log(inv) << "\t prop: " << log(x) << endl;
+ }
+ return e;
+ }
+ const Model1& model1_;
+ const Model1& invmodel1_;
+ const vector<WordID>& src_;
+ const vector<WordID>& trg_;
+ mutable unordered_map<vector<bool>, map<unsigned, prob_t>, boost::hash<vector<bool> > > cache_;
+};
+
+struct Particle {
+ Particle() : weight(prob_t::One()), src_cov(), trg_cov(), prev_pos(-1) {}
+ prob_t weight;
+ prob_t gamma_last;
+ vector<int> src_jumps;
+ vector<TRulePtr> rules;
+ vector<bool> src_cv;
+ int src_cov;
+ int trg_cov;
+ int prev_pos;
+};
+
+ostream& operator<<(ostream& o, const vector<bool>& v) {
+ for (int i = 0; i < v.size(); ++i)
+ o << (v[i] ? '1' : '0');
+ return o;
+}
+ostream& operator<<(ostream& o, const Particle& p) {
+ o << "[cv=" << p.src_cv << " src_cov=" << p.src_cov << " trg_cov=" << p.trg_cov << " last_pos=" << p.prev_pos << " num_rules=" << p.rules.size() << " w=" << log(p.weight) << ']';
+ return o;
+}
+
+void FilterCrapParticlesAndReweight(vector<Particle>* pps) {
+ vector<Particle>& ps = *pps;
+ SampleSet<prob_t> ss;
+ for (int i = 0; i < ps.size(); ++i)
+ ss.add(ps[i].weight);
+ vector<Particle> nps; nps.reserve(ps.size());
+ const prob_t uniform_weight(1.0 / ps.size());
+ for (int i = 0; i < ps.size(); ++i) {
+ nps.push_back(ps[prng->SelectSample(ss)]);
+ nps[i].weight = uniform_weight;
+ }
+ nps.swap(ps);
+}
+
+int main(int argc, char** argv) {
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+ const unsigned kMAX_TRG_PHRASE = conf["max_trg_phrase"].as<unsigned>();
+ const unsigned kMAX_SRC_PHRASE = conf["max_src_phrase"].as<unsigned>();
+ const unsigned particles = conf["particles"].as<unsigned>();
+ const unsigned samples = conf["samples"].as<unsigned>();
+ const unsigned rejuv_freq = conf["filter_frequency"].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<WordID> > corpuse, corpusf;
+ set<WordID> vocabe, vocabf;
+ cerr << "Reading corpus...\n";
+ ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
+ cerr << "F-corpus size: " << corpusf.size() << " sentences\t (" << vocabf.size() << " word types)\n";
+ cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
+ assert(corpusf.size() == corpuse.size());
+
+ const int kLHS = -TD::Convert("X");
+ Model1 m1(conf["model1"].as<string>());
+ Model1 invm1(conf["inverse_model1"].as<string>());
+
+#if 0
+ PhraseConditionalBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size());
+ MyConditionalModel m(lp0);
+#else
+ PhraseJointBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
+ MyJointModel m(lp0);
+#endif
+
+ cerr << "Initializing reachability limits...\n";
+ vector<Particle> ps(corpusf.size());
+ vector<Reachability> reaches; reaches.reserve(corpusf.size());
+ for (int ci = 0; ci < corpusf.size(); ++ci)
+ reaches.push_back(Reachability(corpusf[ci].size(),
+ corpuse[ci].size(),
+ kMAX_SRC_PHRASE,
+ kMAX_TRG_PHRASE));
+ cerr << "Sampling...\n";
+ vector<Particle> tmp_p(10000); // work space
+ SampleSet<prob_t> pfss;
+ for (int SS=0; SS < samples; ++SS) {
+ for (int ci = 0; ci < corpusf.size(); ++ci) {
+ vector<int>& src = corpusf[ci];
+ vector<int>& trg = corpuse[ci];
+ m.DecrementRules(ps[ci].rules);
+ m.DecrementJumps(ps[ci].src_jumps, src.size());
+
+ //BackwardEstimate be(m1, src, trg);
+ BackwardEstimateSym be(m1, invm1, src, trg);
+ const Reachability& r = reaches[ci];
+ vector<Particle> lps(particles);
+
+ for (int pi = 0; pi < particles; ++pi) {
+ Particle& p = lps[pi];
+ p.src_cv.resize(src.size(), false);
+ }
+
+ bool all_complete = false;
+ while(!all_complete) {
+ SampleSet<prob_t> ss;
+
+ // all particles have now been extended a bit, we will reweight them now
+ if (lps[0].trg_cov > 0)
+ FilterCrapParticlesAndReweight(&lps);
+
+ // loop over all particles and extend them
+ bool done_nothing = true;
+ for (int pi = 0; pi < particles; ++pi) {
+ Particle& p = lps[pi];
+ int tic = 0;
+ while(p.trg_cov < trg.size() && tic < rejuv_freq) {
+ ++tic;
+ done_nothing = false;
+ ss.clear();
+ TRule x; x.lhs_ = kLHS;
+ prob_t z;
+ int first_uncovered = src.size();
+ int last_uncovered = -1;
+ for (int i = 0; i < src.size(); ++i) {
+ const bool is_uncovered = !p.src_cv[i];
+ if (i < first_uncovered && is_uncovered) first_uncovered = i;
+ if (is_uncovered && i > last_uncovered) last_uncovered = i;
+ }
+ assert(last_uncovered > -1);
+ assert(first_uncovered < src.size());
+
+ for (int trg_len = 1; trg_len <= kMAX_TRG_PHRASE; ++trg_len) {
+ x.e_.push_back(trg[trg_len - 1 + p.trg_cov]);
+ for (int src_len = 1; src_len <= kMAX_SRC_PHRASE; ++src_len) {
+ if (!r.edges[p.src_cov][p.trg_cov][src_len][trg_len]) continue;
+
+ const int last_possible_start = last_uncovered - src_len + 1;
+ assert(last_possible_start >= 0);
+ //cerr << src_len << "," << trg_len << " is allowed. E=" << TD::GetString(x.e_) << endl;
+ //cerr << " first_uncovered=" << first_uncovered << " last_possible_start=" << last_possible_start << endl;
+ for (int i = first_uncovered; i <= last_possible_start; ++i) {
+ if (p.src_cv[i]) continue;
+ assert(ss.size() < tmp_p.size()); // if fails increase tmp_p size
+ Particle& np = tmp_p[ss.size()];
+ np = p;
+ x.f_.clear();
+ int gap_add = 0;
+ bool bad = false;
+ prob_t jp = prob_t::One();
+ int prev_pos = p.prev_pos;
+ for (int j = 0; j < src_len; ++j) {
+ if ((j + i + gap_add) == src.size()) { bad = true; break; }
+ while ((i+j+gap_add) < src.size() && p.src_cv[i + j + gap_add]) { ++gap_add; }
+ if ((j + i + gap_add) == src.size()) { bad = true; break; }
+ np.src_cv[i + j + gap_add] = true;
+ x.f_.push_back(src[i + j + gap_add]);
+ jp *= m.JumpProbability(i + j + gap_add - prev_pos, src.size());
+ int jump = i + j + gap_add - prev_pos;
+ assert(jump != 0);
+ np.src_jumps.push_back(jump);
+ prev_pos = i + j + gap_add;
+ }
+ if (bad) continue;
+ np.prev_pos = prev_pos;
+ np.src_cov += x.f_.size();
+ np.trg_cov += x.e_.size();
+ if (x.f_.size() != src_len) continue;
+ prob_t rp = m.RuleProbability(x);
+ np.gamma_last = rp * jp;
+ const prob_t u = pow(np.gamma_last * be(np.src_cv, np.trg_cov), 0.2);
+ //cerr << "**rule=" << x << endl;
+ //cerr << " u=" << log(u) << " rule=" << rp << " jump=" << jp << endl;
+ ss.add(u);
+ np.rules.push_back(TRulePtr(new TRule(x)));
+ z += u;
+
+ const bool completed = (p.trg_cov == trg.size());
+ if (completed) {
+ int last_jump = src.size() - p.prev_pos;
+ assert(last_jump > 0);
+ p.src_jumps.push_back(last_jump);
+ p.weight *= m.JumpProbability(last_jump, src.size());
+ }
+ }
+ }
+ }
+ cerr << "number of edges to consider: " << ss.size() << endl;
+ const int sampled = rng.SelectSample(ss);
+ prob_t q_n = ss[sampled] / z;
+ p = tmp_p[sampled];
+ //m.IncrementRule(*p.rules.back());
+ p.weight *= p.gamma_last / q_n;
+ cerr << "[w=" << log(p.weight) << "]\tsampled rule: " << p.rules.back()->AsString() << endl;
+ cerr << p << endl;
+ }
+ } // loop over particles (pi = 0 .. particles)
+ if (done_nothing) all_complete = true;
+ }
+ pfss.clear();
+ for (int i = 0; i < lps.size(); ++i)
+ pfss.add(lps[i].weight);
+ const int sampled = rng.SelectSample(pfss);
+ ps[ci] = lps[sampled];
+ m.IncrementRules(lps[sampled].rules);
+ m.IncrementJumps(lps[sampled].src_jumps, src.size());
+ for (int i = 0; i < lps[sampled].rules.size(); ++i) { cerr << "S:\t" << lps[sampled].rules[i]->AsString() << "\n"; }
+ cerr << "tmp-LLH: " << log(m.Likelihood()) << endl;
+ }
+ cerr << "LLH: " << log(m.Likelihood()) << endl;
+ for (int sni = 0; sni < 5; ++sni) {
+ for (int i = 0; i < ps[sni].rules.size(); ++i) { cerr << "\t" << ps[sni].rules[i]->AsString() << endl; }
+ }
+ }
+ return 0;
+}
+