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-rw-r--r--gi/pf/Makefile.am10
-rw-r--r--gi/pf/align-lexonly-pyp.cc10
-rw-r--r--gi/pf/align-tl.cc2
-rw-r--r--gi/pf/bayes_lattice_score.cc309
-rw-r--r--gi/pf/brat.cc2
-rw-r--r--gi/pf/cfg_wfst_composer.cc3
-rw-r--r--gi/pf/condnaive.cc2
-rw-r--r--gi/pf/dpnaive.cc2
-rw-r--r--gi/pf/hpyp_tm.cc133
-rw-r--r--gi/pf/hpyp_tm.h38
-rw-r--r--gi/pf/itg.cc2
-rw-r--r--gi/pf/learn_cfg.cc2
-rw-r--r--gi/pf/mh_test.cc148
-rw-r--r--gi/pf/pf_test.cc148
-rw-r--r--gi/pf/pfbrat.cc2
-rw-r--r--gi/pf/pfdist.cc2
-rw-r--r--gi/pf/pfnaive.cc2
-rw-r--r--gi/pf/poisson_uniform_word_model.h50
-rw-r--r--gi/pf/pyp_lm.cc2
-rw-r--r--gi/pf/pyp_tm.cc11
-rw-r--r--gi/pf/pyp_tm.h7
-rw-r--r--gi/pf/pyp_word_model.cc20
-rw-r--r--gi/pf/pyp_word_model.h46
-rw-r--r--gi/pf/quasi_model2.h13
-rw-r--r--gi/pf/tied_resampler.h6
25 files changed, 899 insertions, 73 deletions
diff --git a/gi/pf/Makefile.am b/gi/pf/Makefile.am
index f9c979d0..86f8e07b 100644
--- a/gi/pf/Makefile.am
+++ b/gi/pf/Makefile.am
@@ -1,8 +1,14 @@
-bin_PROGRAMS = cbgi brat dpnaive pfbrat pfdist itg pfnaive condnaive align-lexonly-pyp learn_cfg pyp_lm nuisance_test align-tl
+bin_PROGRAMS = cbgi brat dpnaive pfbrat pfdist itg pfnaive condnaive align-lexonly-pyp learn_cfg pyp_lm nuisance_test align-tl pf_test bayes_lattice_score
noinst_LIBRARIES = libpf.a
-libpf_a_SOURCES = base_distributions.cc reachability.cc cfg_wfst_composer.cc corpus.cc unigrams.cc ngram_base.cc transliterations.cc backward.cc pyp_word_model.cc pyp_tm.cc
+libpf_a_SOURCES = base_distributions.cc reachability.cc cfg_wfst_composer.cc corpus.cc unigrams.cc ngram_base.cc transliterations.cc backward.cc hpyp_tm.cc pyp_tm.cc
+
+bayes_lattice_score_SOURCES = bayes_lattice_score.cc
+bayes_lattice_score_LDADD = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a -lz
+
+pf_test_SOURCES = pf_test.cc
+pf_test_LDADD = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a -lz
nuisance_test_SOURCES = nuisance_test.cc
nuisance_test_LDADD = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a -lz
diff --git a/gi/pf/align-lexonly-pyp.cc b/gi/pf/align-lexonly-pyp.cc
index 942dcf51..e7509f57 100644
--- a/gi/pf/align-lexonly-pyp.cc
+++ b/gi/pf/align-lexonly-pyp.cc
@@ -11,6 +11,7 @@
#include "sampler.h"
#include "corpus.h"
#include "pyp_tm.h"
+#include "hpyp_tm.h"
#include "quasi_model2.h"
using namespace std;
@@ -61,15 +62,17 @@ struct AlignedSentencePair {
Array2D<short> posterior;
};
+template <class LexicalTranslationModel>
struct Aligner {
Aligner(const vector<vector<WordID> >& lets,
+ int vocab_size,
int num_letters,
const po::variables_map& conf,
vector<AlignedSentencePair>* c) :
corpus(*c),
paj_model(conf["align_alpha"].as<double>(), conf["p_null"].as<double>()),
infer_paj(conf.count("infer_alignment_hyperparameters") > 0),
- model(lets, num_letters),
+ model(lets, vocab_size, num_letters),
kNULL(TD::Convert("NULL")) {
assert(lets[kNULL].size() == 0);
}
@@ -77,7 +80,7 @@ struct Aligner {
vector<AlignedSentencePair>& corpus;
QuasiModel2 paj_model;
const bool infer_paj;
- PYPLexicalTranslation model;
+ LexicalTranslationModel model;
const WordID kNULL;
void ResampleHyperparameters() {
@@ -217,7 +220,8 @@ int main(int argc, char** argv) {
ExtractLetters(vocabf, &letters, NULL);
letters[TD::Convert("NULL")].clear();
- Aligner aligner(letters, letset.size(), conf, &corpus);
+ //Aligner<PYPLexicalTranslation> aligner(letters, vocabe.size(), letset.size(), conf, &corpus);
+ Aligner<HPYPLexicalTranslation> aligner(letters, vocabe.size(), letset.size(), conf, &corpus);
aligner.InitializeRandom();
const unsigned samples = conf["samples"].as<unsigned>();
diff --git a/gi/pf/align-tl.cc b/gi/pf/align-tl.cc
index cbe8c6c8..f6608f1d 100644
--- a/gi/pf/align-tl.cc
+++ b/gi/pf/align-tl.cc
@@ -58,7 +58,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
}
-shared_ptr<MT19937> prng;
+boost::shared_ptr<MT19937> prng;
struct LexicalAlignment {
unsigned char src_index;
diff --git a/gi/pf/bayes_lattice_score.cc b/gi/pf/bayes_lattice_score.cc
new file mode 100644
index 00000000..70cb8dc2
--- /dev/null
+++ b/gi/pf/bayes_lattice_score.cc
@@ -0,0 +1,309 @@
+#include <iostream>
+#include <queue>
+
+#include <boost/functional.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "inside_outside.h"
+#include "hg.h"
+#include "hg_io.h"
+#include "bottom_up_parser.h"
+#include "fdict.h"
+#include "grammar.h"
+#include "m.h"
+#include "trule.h"
+#include "tdict.h"
+#include "filelib.h"
+#include "dict.h"
+#include "sampler.h"
+#include "ccrp.h"
+#include "ccrp_onetable.h"
+
+using namespace std;
+using namespace tr1;
+namespace po = boost::program_options;
+
+boost::shared_ptr<MT19937> prng;
+
+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")
+ ("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", "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);
+ }
+}
+
+unsigned ReadCorpus(const string& filename,
+ vector<Lattice>* e,
+ set<WordID>* vocab_e) {
+ e->clear();
+ vocab_e->clear();
+ ReadFile rf(filename);
+ istream* in = rf.stream();
+ assert(*in);
+ string line;
+ unsigned toks = 0;
+ while(*in) {
+ getline(*in, line);
+ if (line.empty() && !*in) break;
+ e->push_back(Lattice());
+ Lattice& le = e->back();
+ LatticeTools::ConvertTextOrPLF(line, & le);
+ for (unsigned i = 0; i < le.size(); ++i)
+ for (unsigned j = 0; j < le[i].size(); ++j)
+ vocab_e->insert(le[i][j].label);
+ toks += le.size();
+ }
+ return toks;
+}
+
+struct BaseModel {
+ explicit BaseModel(unsigned tc) :
+ unif(1.0 / tc), p(prob_t::One()) {}
+ prob_t prob(const TRule& r) const {
+ return unif;
+ }
+ void increment(const TRule& r, MT19937* rng) {
+ p *= prob(r);
+ }
+ void decrement(const TRule& r, MT19937* rng) {
+ p /= prob(r);
+ }
+ prob_t Likelihood() const {
+ return p;
+ }
+ const prob_t unif;
+ prob_t p;
+};
+
+struct UnigramModel {
+ explicit UnigramModel(unsigned tc) : base(tc), crp(1,1,1,1), glue(1,1,1,1) {}
+ BaseModel base;
+ CCRP<TRule> crp;
+ CCRP<TRule> glue;
+
+ prob_t Prob(const TRule& r) const {
+ if (r.Arity() != 0) {
+ return glue.prob(r, prob_t(0.5));
+ }
+ return crp.prob(r, base.prob(r));
+ }
+
+ int Increment(const TRule& r, MT19937* rng) {
+ if (r.Arity() != 0) {
+ glue.increment(r, 0.5, rng);
+ return 0;
+ } else {
+ if (crp.increment(r, base.prob(r), rng)) {
+ base.increment(r, rng);
+ return 1;
+ }
+ return 0;
+ }
+ }
+
+ int Decrement(const TRule& r, MT19937* rng) {
+ if (r.Arity() != 0) {
+ glue.decrement(r, rng);
+ return 0;
+ } else {
+ if (crp.decrement(r, rng)) {
+ base.decrement(r, rng);
+ return -1;
+ }
+ return 0;
+ }
+ }
+
+ prob_t Likelihood() const {
+ prob_t p;
+ p.logeq(crp.log_crp_prob() + glue.log_crp_prob());
+ p *= base.Likelihood();
+ return p;
+ }
+
+ void ResampleHyperparameters(MT19937* rng) {
+ crp.resample_hyperparameters(rng);
+ glue.resample_hyperparameters(rng);
+ cerr << " d=" << crp.discount() << ", s=" << crp.strength() << "\t STOP d=" << glue.discount() << ", s=" << glue.strength() << endl;
+ }
+};
+
+UnigramModel* plm;
+
+void SampleDerivation(const Hypergraph& hg, MT19937* rng, vector<unsigned>* sampled_deriv) {
+ vector<prob_t> node_probs;
+ Inside<prob_t, EdgeProb>(hg, &node_probs);
+ queue<unsigned> q;
+ q.push(hg.nodes_.size() - 2);
+ while(!q.empty()) {
+ unsigned cur_node_id = q.front();
+// cerr << "NODE=" << cur_node_id << endl;
+ q.pop();
+ const Hypergraph::Node& node = hg.nodes_[cur_node_id];
+ const unsigned num_in_edges = node.in_edges_.size();
+ unsigned sampled_edge = 0;
+ if (num_in_edges == 1) {
+ sampled_edge = node.in_edges_[0];
+ } else {
+ //prob_t z;
+ assert(num_in_edges > 1);
+ SampleSet<prob_t> ss;
+ for (unsigned j = 0; j < num_in_edges; ++j) {
+ const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
+ prob_t p = edge.edge_prob_;
+ for (unsigned k = 0; k < edge.tail_nodes_.size(); ++k)
+ p *= node_probs[edge.tail_nodes_[k]];
+ ss.add(p);
+// cerr << log(ss[j]) << " ||| " << edge.rule_->AsString() << endl;
+ //z += p;
+ }
+// for (unsigned j = 0; j < num_in_edges; ++j) {
+// const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
+// cerr << exp(log(ss[j] / z)) << " ||| " << edge.rule_->AsString() << endl;
+// }
+// cerr << " --- \n";
+ sampled_edge = node.in_edges_[rng->SelectSample(ss)];
+ }
+ sampled_deriv->push_back(sampled_edge);
+ const Hypergraph::Edge& edge = hg.edges_[sampled_edge];
+ for (unsigned j = 0; j < edge.tail_nodes_.size(); ++j) {
+ q.push(edge.tail_nodes_[j]);
+ }
+ }
+// for (unsigned i = 0; i < sampled_deriv->size(); ++i) {
+// cerr << *hg.edges_[(*sampled_deriv)[i]].rule_ << endl;
+// }
+}
+
+void IncrementDerivation(const Hypergraph& hg, const vector<unsigned>& d, UnigramModel* plm, MT19937* rng) {
+ for (unsigned i = 0; i < d.size(); ++i)
+ plm->Increment(*hg.edges_[d[i]].rule_, rng);
+}
+
+void DecrementDerivation(const Hypergraph& hg, const vector<unsigned>& d, UnigramModel* plm, MT19937* rng) {
+ for (unsigned i = 0; i < d.size(); ++i)
+ plm->Decrement(*hg.edges_[d[i]].rule_, rng);
+}
+
+prob_t TotalProb(const Hypergraph& hg) {
+ return Inside<prob_t, EdgeProb>(hg);
+}
+
+void IncrementLatticePath(const Hypergraph& hg, const vector<unsigned>& d, Lattice* pl) {
+ Lattice& lat = *pl;
+ for (int i = 0; i < d.size(); ++i) {
+ const Hypergraph::Edge& edge = hg.edges_[d[i]];
+ if (edge.rule_->Arity() != 0) continue;
+ WordID sym = edge.rule_->e_[0];
+ vector<LatticeArc>& las = lat[edge.i_];
+ int dist = edge.j_ - edge.i_;
+ assert(dist > 0);
+ for (int j = 0; j < las.size(); ++j) {
+ if (las[j].dist2next == dist &&
+ las[j].label == sym) {
+ las[j].cost += 1;
+ }
+ }
+ }
+}
+
+int main(int argc, char** argv) {
+ po::variables_map conf;
+
+ InitCommandLine(argc, argv, &conf);
+ vector<GrammarPtr> grammars(2);
+ grammars[0].reset(new GlueGrammar("S","X"));
+ const unsigned samples = conf["samples"].as<unsigned>();
+
+ if (conf.count("random_seed"))
+ prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+ else
+ prng.reset(new MT19937);
+ MT19937& rng = *prng;
+ vector<Lattice> corpuse;
+ set<WordID> vocabe;
+ cerr << "Reading corpus...\n";
+ const unsigned toks = ReadCorpus(conf["input"].as<string>(), &corpuse, &vocabe);
+ cerr << "E-corpus size: " << corpuse.size() << " lattices\t (" << vocabe.size() << " word types)\n";
+ UnigramModel lm(vocabe.size());
+ vector<Hypergraph> hgs(corpuse.size());
+ vector<vector<unsigned> > derivs(corpuse.size());
+ for (int i = 0; i < corpuse.size(); ++i) {
+ grammars[1].reset(new PassThroughGrammar(corpuse[i], "X"));
+ ExhaustiveBottomUpParser parser("S", grammars);
+ bool res = parser.Parse(corpuse[i], &hgs[i]); // exhaustive parse
+ assert(res);
+ }
+
+ double csamples = 0;
+ for (int SS=0; SS < samples; ++SS) {
+ const bool is_last = ((samples - 1) == SS);
+ prob_t dlh = prob_t::One();
+ bool record_sample = (SS > (samples * 1 / 3) && (SS % 5 == 3));
+ if (record_sample) csamples++;
+ for (int ci = 0; ci < corpuse.size(); ++ci) {
+ Lattice& lat = corpuse[ci];
+ Hypergraph& hg = hgs[ci];
+ vector<unsigned>& d = derivs[ci];
+ if (!is_last) DecrementDerivation(hg, d, &lm, &rng);
+ for (unsigned i = 0; i < hg.edges_.size(); ++i) {
+ TRule& r = *hg.edges_[i].rule_;
+ if (r.Arity() != 0)
+ hg.edges_[i].edge_prob_ = prob_t::One();
+ else
+ hg.edges_[i].edge_prob_ = lm.Prob(r);
+ }
+ if (!is_last) {
+ d.clear();
+ SampleDerivation(hg, &rng, &d);
+ IncrementDerivation(hg, derivs[ci], &lm, &rng);
+ } else {
+ prob_t p = TotalProb(hg);
+ dlh *= p;
+ cerr << " p(sentence) = " << log(p) << "\t" << log(dlh) << endl;
+ }
+ if (record_sample) IncrementLatticePath(hg, derivs[ci], &lat);
+ }
+ double llh = log(lm.Likelihood());
+ cerr << "LLH=" << llh << "\tENTROPY=" << (-llh / log(2) / toks) << "\tPPL=" << pow(2, -llh / log(2) / toks) << endl;
+ if (SS % 10 == 9) lm.ResampleHyperparameters(&rng);
+ if (is_last) {
+ double z = log(dlh);
+ cerr << "TOTAL_PROB=" << z << "\tENTROPY=" << (-z / log(2) / toks) << "\tPPL=" << pow(2, -z / log(2) / toks) << endl;
+ }
+ }
+ cerr << lm.crp << endl;
+ cerr << lm.glue << endl;
+ for (int i = 0; i < corpuse.size(); ++i) {
+ for (int j = 0; j < corpuse[i].size(); ++j)
+ for (int k = 0; k < corpuse[i][j].size(); ++k) {
+ corpuse[i][j][k].cost /= csamples;
+ corpuse[i][j][k].cost += 1e-3;
+ corpuse[i][j][k].cost = log(corpuse[i][j][k].cost);
+ }
+ cout << HypergraphIO::AsPLF(corpuse[i]) << endl;
+ }
+ return 0;
+}
+
diff --git a/gi/pf/brat.cc b/gi/pf/brat.cc
index c2c52760..832f22cf 100644
--- a/gi/pf/brat.cc
+++ b/gi/pf/brat.cc
@@ -489,7 +489,7 @@ int main(int argc, char** argv) {
cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
return 1;
}
- shared_ptr<MT19937> prng;
+ boost::shared_ptr<MT19937> prng;
if (conf.count("random_seed"))
prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
else
diff --git a/gi/pf/cfg_wfst_composer.cc b/gi/pf/cfg_wfst_composer.cc
index a31b5be8..20520c81 100644
--- a/gi/pf/cfg_wfst_composer.cc
+++ b/gi/pf/cfg_wfst_composer.cc
@@ -16,7 +16,6 @@
#include "tdict.h"
#include "hg.h"
-using boost::shared_ptr;
namespace po = boost::program_options;
using namespace std;
using namespace std::tr1;
@@ -114,7 +113,7 @@ struct Edge {
const Edge* const active_parent; // back pointer, NULL for PREDICT items
const Edge* const passive_parent; // back pointer, NULL for SCAN and PREDICT items
TRulePtr tps; // translations
- shared_ptr<SparseVector<double> > features; // features from CFG rule
+ boost::shared_ptr<SparseVector<double> > features; // features from CFG rule
bool IsPassive() const {
// when a rule is completed, this value will be set
diff --git a/gi/pf/condnaive.cc b/gi/pf/condnaive.cc
index 3ea88016..419731ac 100644
--- a/gi/pf/condnaive.cc
+++ b/gi/pf/condnaive.cc
@@ -55,7 +55,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
}
-shared_ptr<MT19937> prng;
+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) :
diff --git a/gi/pf/dpnaive.cc b/gi/pf/dpnaive.cc
index 469dff5c..75ccad72 100644
--- a/gi/pf/dpnaive.cc
+++ b/gi/pf/dpnaive.cc
@@ -55,7 +55,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
}
-shared_ptr<MT19937> prng;
+boost::shared_ptr<MT19937> prng;
template <typename Base>
struct ModelAndData {
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);
+}
+
diff --git a/gi/pf/hpyp_tm.h b/gi/pf/hpyp_tm.h
new file mode 100644
index 00000000..af3215ba
--- /dev/null
+++ b/gi/pf/hpyp_tm.h
@@ -0,0 +1,38 @@
+#ifndef HPYP_LEX_TRANS
+#define HPYP_LEX_TRANS
+
+#include <vector>
+#include "wordid.h"
+#include "prob.h"
+#include "sampler.h"
+#include "freqdict.h"
+#include "poisson_uniform_word_model.h"
+
+struct FreqBinner;
+template <class B> struct PYPWordModel;
+template <typename T, class B> struct ConditionalPYPWordModel;
+
+struct HPYPLexicalTranslation {
+ explicit HPYPLexicalTranslation(const std::vector<std::vector<WordID> >& lets,
+ const unsigned vocab_size,
+ const unsigned num_letters);
+
+ prob_t Likelihood() const;
+
+ void ResampleHyperparameters(MT19937* rng);
+ prob_t Prob(WordID src, WordID trg) const; // return p(trg | src)
+ void Summary() const;
+ void Increment(WordID src, WordID trg, MT19937* rng);
+ void Decrement(WordID src, WordID trg, MT19937* rng);
+ unsigned UniqueConditioningContexts() const;
+
+ private:
+ const std::vector<std::vector<WordID> >& letters; // spelling dictionary
+ PoissonUniformWordModel base; // "generator" of English types
+ PYPWordModel<PoissonUniformWordModel>* up0; // model English lexicon
+ ConditionalPYPWordModel<PYPWordModel<PoissonUniformWordModel>, FreqBinner>* tmodel; // translation distributions
+ // (model English word | French word)
+ const WordID kX;
+};
+
+#endif
diff --git a/gi/pf/itg.cc b/gi/pf/itg.cc
index a38fe672..29ec3860 100644
--- a/gi/pf/itg.cc
+++ b/gi/pf/itg.cc
@@ -231,7 +231,7 @@ int main(int argc, char** argv) {
cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
return 1;
}
- shared_ptr<MT19937> prng;
+ boost::shared_ptr<MT19937> prng;
if (conf.count("random_seed"))
prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
else
diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
index ed1772bf..44eaa162 100644
--- a/gi/pf/learn_cfg.cc
+++ b/gi/pf/learn_cfg.cc
@@ -24,7 +24,7 @@ using namespace std;
using namespace tr1;
namespace po = boost::program_options;
-shared_ptr<MT19937> prng;
+boost::shared_ptr<MT19937> prng;
vector<int> nt_vocab;
vector<int> nt_id_to_index;
static unsigned kMAX_RULE_SIZE = 0;
diff --git a/gi/pf/mh_test.cc b/gi/pf/mh_test.cc
new file mode 100644
index 00000000..296e7285
--- /dev/null
+++ b/gi/pf/mh_test.cc
@@ -0,0 +1,148 @@
+#include "ccrp.h"
+
+#include <vector>
+#include <iostream>
+
+#include "tdict.h"
+#include "transliterations.h"
+
+using namespace std;
+
+MT19937 rng;
+
+static bool verbose = false;
+
+struct Model {
+
+ Model() : bp(), base(0.2, 0.6) , ccrps(5, CCRP<int>(0.8, 0.5)) {}
+
+ double p0(int x) const {
+ assert(x > 0);
+ assert(x < 5);
+ return 1.0/4.0;
+ }
+
+ double llh() const {
+ double lh = bp + base.log_crp_prob();
+ for (int ctx = 1; ctx < 5; ++ctx)
+ lh += ccrps[ctx].log_crp_prob();
+ return lh;
+ }
+
+ double prob(int ctx, int x) const {
+ assert(ctx > 0 && ctx < 5);
+ return ccrps[ctx].prob(x, base.prob(x, p0(x)));
+ }
+
+ void increment(int ctx, int x) {
+ assert(ctx > 0 && ctx < 5);
+ if (ccrps[ctx].increment(x, base.prob(x, p0(x)), &rng)) {
+ if (base.increment(x, p0(x), &rng)) {
+ bp += log(1.0 / 4.0);
+ }
+ }
+ }
+
+ // this is just a biased estimate
+ double est_base_prob(int x) {
+ return (x + 1) * x / 40.0;
+ }
+
+ void increment_is(int ctx, int x) {
+ assert(ctx > 0 && ctx < 5);
+ SampleSet<double> ss;
+ const int PARTICLES = 25;
+ vector<CCRP<int> > s1s(PARTICLES, CCRP<int>(0.5,0.5));
+ vector<CCRP<int> > sbs(PARTICLES, CCRP<int>(0.5,0.5));
+ vector<double> sp0s(PARTICLES);
+
+ CCRP<int> s1 = ccrps[ctx];
+ CCRP<int> sb = base;
+ double sp0 = bp;
+ for (int pp = 0; pp < PARTICLES; ++pp) {
+ if (pp > 0) {
+ ccrps[ctx] = s1;
+ base = sb;
+ bp = sp0;
+ }
+
+ double q = 1;
+ double gamma = 1;
+ double est_p = est_base_prob(x);
+ //base.prob(x, p0(x)) + rng.next() * 0.1;
+ if (ccrps[ctx].increment(x, est_p, &rng, &q)) {
+ gamma = q * base.prob(x, p0(x));
+ q *= est_p;
+ if (verbose) cerr << "(DP-base draw) ";
+ double qq = -1;
+ if (base.increment(x, p0(x), &rng, &qq)) {
+ if (verbose) cerr << "(G0 draw) ";
+ bp += log(p0(x));
+ qq *= p0(x);
+ }
+ } else { gamma = q; }
+ double w = gamma / q;
+ if (verbose)
+ cerr << "gamma=" << gamma << " q=" << q << "\tw=" << w << endl;
+ ss.add(w);
+ s1s[pp] = ccrps[ctx];
+ sbs[pp] = base;
+ sp0s[pp] = bp;
+ }
+ int ps = rng.SelectSample(ss);
+ ccrps[ctx] = s1s[ps];
+ base = sbs[ps];
+ bp = sp0s[ps];
+ if (verbose) {
+ cerr << "SELECTED: " << ps << endl;
+ static int cc = 0; cc++; if (cc ==10) exit(1);
+ }
+ }
+
+ void decrement(int ctx, int x) {
+ assert(ctx > 0 && ctx < 5);
+ if (ccrps[ctx].decrement(x, &rng)) {
+ if (base.decrement(x, &rng)) {
+ bp -= log(p0(x));
+ }
+ }
+ }
+
+ double bp;
+ CCRP<int> base;
+ vector<CCRP<int> > ccrps;
+
+};
+
+int main(int argc, char** argv) {
+ if (argc > 1) { verbose = true; }
+ vector<int> counts(15, 0);
+ vector<int> tcounts(15, 0);
+ int points[] = {1,2, 2,2, 3,2, 4,1, 3, 4, 3, 3, 2, 3, 4, 1, 4, 1, 3, 2, 1, 3, 1, 4, 0, 0};
+ double tlh = 0;
+ double tt = 0;
+ for (int n = 0; n < 1000; ++n) {
+ if (n % 10 == 0) cerr << '.';
+ if ((n+1) % 400 == 0) cerr << " [" << (n+1) << "]\n";
+ Model m;
+ for (int *x = points; *x; x += 2)
+ m.increment(x[0], x[1]);
+
+ for (int j = 0; j < 24; ++j) {
+ for (int *x = points; *x; x += 2) {
+ if (rng.next() < 0.8) {
+ m.decrement(x[0], x[1]);
+ m.increment_is(x[0], x[1]);
+ }
+ }
+ }
+ counts[m.base.num_customers()]++;
+ tcounts[m.base.num_tables()]++;
+ tlh += m.llh();
+ tt += 1.0;
+ }
+ cerr << "mean LLH = " << (tlh / tt) << endl;
+ for (int i = 0; i < 15; ++i)
+ cerr << i << ": " << (counts[i] / tt) << "\t" << (tcounts[i] / tt) << endl;
+}
+
diff --git a/gi/pf/pf_test.cc b/gi/pf/pf_test.cc
new file mode 100644
index 00000000..296e7285
--- /dev/null
+++ b/gi/pf/pf_test.cc
@@ -0,0 +1,148 @@
+#include "ccrp.h"
+
+#include <vector>
+#include <iostream>
+
+#include "tdict.h"
+#include "transliterations.h"
+
+using namespace std;
+
+MT19937 rng;
+
+static bool verbose = false;
+
+struct Model {
+
+ Model() : bp(), base(0.2, 0.6) , ccrps(5, CCRP<int>(0.8, 0.5)) {}
+
+ double p0(int x) const {
+ assert(x > 0);
+ assert(x < 5);
+ return 1.0/4.0;
+ }
+
+ double llh() const {
+ double lh = bp + base.log_crp_prob();
+ for (int ctx = 1; ctx < 5; ++ctx)
+ lh += ccrps[ctx].log_crp_prob();
+ return lh;
+ }
+
+ double prob(int ctx, int x) const {
+ assert(ctx > 0 && ctx < 5);
+ return ccrps[ctx].prob(x, base.prob(x, p0(x)));
+ }
+
+ void increment(int ctx, int x) {
+ assert(ctx > 0 && ctx < 5);
+ if (ccrps[ctx].increment(x, base.prob(x, p0(x)), &rng)) {
+ if (base.increment(x, p0(x), &rng)) {
+ bp += log(1.0 / 4.0);
+ }
+ }
+ }
+
+ // this is just a biased estimate
+ double est_base_prob(int x) {
+ return (x + 1) * x / 40.0;
+ }
+
+ void increment_is(int ctx, int x) {
+ assert(ctx > 0 && ctx < 5);
+ SampleSet<double> ss;
+ const int PARTICLES = 25;
+ vector<CCRP<int> > s1s(PARTICLES, CCRP<int>(0.5,0.5));
+ vector<CCRP<int> > sbs(PARTICLES, CCRP<int>(0.5,0.5));
+ vector<double> sp0s(PARTICLES);
+
+ CCRP<int> s1 = ccrps[ctx];
+ CCRP<int> sb = base;
+ double sp0 = bp;
+ for (int pp = 0; pp < PARTICLES; ++pp) {
+ if (pp > 0) {
+ ccrps[ctx] = s1;
+ base = sb;
+ bp = sp0;
+ }
+
+ double q = 1;
+ double gamma = 1;
+ double est_p = est_base_prob(x);
+ //base.prob(x, p0(x)) + rng.next() * 0.1;
+ if (ccrps[ctx].increment(x, est_p, &rng, &q)) {
+ gamma = q * base.prob(x, p0(x));
+ q *= est_p;
+ if (verbose) cerr << "(DP-base draw) ";
+ double qq = -1;
+ if (base.increment(x, p0(x), &rng, &qq)) {
+ if (verbose) cerr << "(G0 draw) ";
+ bp += log(p0(x));
+ qq *= p0(x);
+ }
+ } else { gamma = q; }
+ double w = gamma / q;
+ if (verbose)
+ cerr << "gamma=" << gamma << " q=" << q << "\tw=" << w << endl;
+ ss.add(w);
+ s1s[pp] = ccrps[ctx];
+ sbs[pp] = base;
+ sp0s[pp] = bp;
+ }
+ int ps = rng.SelectSample(ss);
+ ccrps[ctx] = s1s[ps];
+ base = sbs[ps];
+ bp = sp0s[ps];
+ if (verbose) {
+ cerr << "SELECTED: " << ps << endl;
+ static int cc = 0; cc++; if (cc ==10) exit(1);
+ }
+ }
+
+ void decrement(int ctx, int x) {
+ assert(ctx > 0 && ctx < 5);
+ if (ccrps[ctx].decrement(x, &rng)) {
+ if (base.decrement(x, &rng)) {
+ bp -= log(p0(x));
+ }
+ }
+ }
+
+ double bp;
+ CCRP<int> base;
+ vector<CCRP<int> > ccrps;
+
+};
+
+int main(int argc, char** argv) {
+ if (argc > 1) { verbose = true; }
+ vector<int> counts(15, 0);
+ vector<int> tcounts(15, 0);
+ int points[] = {1,2, 2,2, 3,2, 4,1, 3, 4, 3, 3, 2, 3, 4, 1, 4, 1, 3, 2, 1, 3, 1, 4, 0, 0};
+ double tlh = 0;
+ double tt = 0;
+ for (int n = 0; n < 1000; ++n) {
+ if (n % 10 == 0) cerr << '.';
+ if ((n+1) % 400 == 0) cerr << " [" << (n+1) << "]\n";
+ Model m;
+ for (int *x = points; *x; x += 2)
+ m.increment(x[0], x[1]);
+
+ for (int j = 0; j < 24; ++j) {
+ for (int *x = points; *x; x += 2) {
+ if (rng.next() < 0.8) {
+ m.decrement(x[0], x[1]);
+ m.increment_is(x[0], x[1]);
+ }
+ }
+ }
+ counts[m.base.num_customers()]++;
+ tcounts[m.base.num_tables()]++;
+ tlh += m.llh();
+ tt += 1.0;
+ }
+ cerr << "mean LLH = " << (tlh / tt) << endl;
+ for (int i = 0; i < 15; ++i)
+ cerr << i << ": " << (counts[i] / tt) << "\t" << (tcounts[i] / tt) << endl;
+}
+
diff --git a/gi/pf/pfbrat.cc b/gi/pf/pfbrat.cc
index c2c52760..832f22cf 100644
--- a/gi/pf/pfbrat.cc
+++ b/gi/pf/pfbrat.cc
@@ -489,7 +489,7 @@ int main(int argc, char** argv) {
cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
return 1;
}
- shared_ptr<MT19937> prng;
+ boost::shared_ptr<MT19937> prng;
if (conf.count("random_seed"))
prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
else
diff --git a/gi/pf/pfdist.cc b/gi/pf/pfdist.cc
index 3d578db2..a3e46064 100644
--- a/gi/pf/pfdist.cc
+++ b/gi/pf/pfdist.cc
@@ -23,7 +23,7 @@ using namespace std;
using namespace tr1;
namespace po = boost::program_options;
-shared_ptr<MT19937> prng;
+boost::shared_ptr<MT19937> prng;
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
diff --git a/gi/pf/pfnaive.cc b/gi/pf/pfnaive.cc
index e1a53f5c..958ec4e2 100644
--- a/gi/pf/pfnaive.cc
+++ b/gi/pf/pfnaive.cc
@@ -25,7 +25,7 @@ using namespace std;
using namespace tr1;
namespace po = boost::program_options;
-shared_ptr<MT19937> prng;
+boost::shared_ptr<MT19937> prng;
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
diff --git a/gi/pf/poisson_uniform_word_model.h b/gi/pf/poisson_uniform_word_model.h
new file mode 100644
index 00000000..76204a0e
--- /dev/null
+++ b/gi/pf/poisson_uniform_word_model.h
@@ -0,0 +1,50 @@
+#ifndef _POISSON_UNIFORM_WORD_MODEL_H_
+#define _POISSON_UNIFORM_WORD_MODEL_H_
+
+#include <cmath>
+#include <vector>
+#include "prob.h"
+#include "m.h"
+
+// len ~ Poisson(lambda)
+// for (1..len)
+// e_i ~ Uniform({Vocabulary})
+struct PoissonUniformWordModel {
+ explicit PoissonUniformWordModel(const unsigned vocab_size,
+ const unsigned alphabet_size,
+ const double mean_len = 5) :
+ lh(prob_t::One()),
+ v0(-std::log(vocab_size)),
+ u0(-std::log(alphabet_size)),
+ mean_length(mean_len) {}
+
+ void ResampleHyperparameters(MT19937*) {}
+
+ inline prob_t operator()(const std::vector<WordID>& s) const {
+ prob_t p;
+ p.logeq(Md::log_poisson(s.size(), mean_length) + s.size() * u0);
+ //p.logeq(v0);
+ return p;
+ }
+
+ inline void Increment(const std::vector<WordID>& w, MT19937*) {
+ lh *= (*this)(w);
+ }
+
+ inline void Decrement(const std::vector<WordID>& w, MT19937 *) {
+ lh /= (*this)(w);
+ }
+
+ inline prob_t Likelihood() const { return lh; }
+
+ void Summary() const {}
+
+ private:
+
+ prob_t lh; // keeps track of the draws from the base distribution
+ const double v0; // uniform log prob of generating a word
+ const double u0; // uniform log prob of generating a letter
+ const double mean_length; // mean length of a word in the base distribution
+};
+
+#endif
diff --git a/gi/pf/pyp_lm.cc b/gi/pf/pyp_lm.cc
index 91029688..e2b67e17 100644
--- a/gi/pf/pyp_lm.cc
+++ b/gi/pf/pyp_lm.cc
@@ -25,7 +25,7 @@ using namespace std;
using namespace tr1;
namespace po = boost::program_options;
-shared_ptr<MT19937> prng;
+boost::shared_ptr<MT19937> prng;
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
diff --git a/gi/pf/pyp_tm.cc b/gi/pf/pyp_tm.cc
index e21f0267..6bc8a5bf 100644
--- a/gi/pf/pyp_tm.cc
+++ b/gi/pf/pyp_tm.cc
@@ -91,26 +91,23 @@ struct ConditionalPYPWordModel {
};
PYPLexicalTranslation::PYPLexicalTranslation(const vector<vector<WordID> >& lets,
+ const unsigned vocab_size,
const unsigned num_letters) :
letters(lets),
- up0(new PYPWordModel(num_letters)),
- tmodel(new ConditionalPYPWordModel<PYPWordModel>(up0, new FreqBinner("10k.freq"))),
+ base(vocab_size, num_letters, 5),
+ tmodel(new ConditionalPYPWordModel<PoissonUniformWordModel>(&base, new FreqBinner("10k.freq"))),
kX(-TD::Convert("X")) {}
void PYPLexicalTranslation::Summary() const {
tmodel->Summary();
- up0->Summary();
}
prob_t PYPLexicalTranslation::Likelihood() const {
- prob_t p = up0->Likelihood();
- p *= tmodel->Likelihood();
- return p;
+ return tmodel->Likelihood() * base.Likelihood();
}
void PYPLexicalTranslation::ResampleHyperparameters(MT19937* rng) {
tmodel->ResampleHyperparameters(rng);
- up0->ResampleHyperparameters(rng);
}
unsigned PYPLexicalTranslation::UniqueConditioningContexts() const {
diff --git a/gi/pf/pyp_tm.h b/gi/pf/pyp_tm.h
index 63e7c96d..2b076a25 100644
--- a/gi/pf/pyp_tm.h
+++ b/gi/pf/pyp_tm.h
@@ -6,13 +6,14 @@
#include "prob.h"
#include "sampler.h"
#include "freqdict.h"
+#include "poisson_uniform_word_model.h"
struct FreqBinner;
-struct PYPWordModel;
template <typename T, class B> struct ConditionalPYPWordModel;
struct PYPLexicalTranslation {
explicit PYPLexicalTranslation(const std::vector<std::vector<WordID> >& lets,
+ const unsigned vocab_size,
const unsigned num_letters);
prob_t Likelihood() const;
@@ -26,8 +27,8 @@ struct PYPLexicalTranslation {
private:
const std::vector<std::vector<WordID> >& letters; // spelling dictionary
- PYPWordModel* up0; // base distribuction (model English word)
- ConditionalPYPWordModel<PYPWordModel, FreqBinner>* tmodel; // translation distributions
+ PoissonUniformWordModel base; // "generator" of English types
+ ConditionalPYPWordModel<PoissonUniformWordModel, FreqBinner>* tmodel; // translation distributions
// (model English word | French word)
const WordID kX;
};
diff --git a/gi/pf/pyp_word_model.cc b/gi/pf/pyp_word_model.cc
deleted file mode 100644
index 12df4abf..00000000
--- a/gi/pf/pyp_word_model.cc
+++ /dev/null
@@ -1,20 +0,0 @@
-#include "pyp_word_model.h"
-
-#include <iostream>
-
-using namespace std;
-
-void PYPWordModel::ResampleHyperparameters(MT19937* rng) {
- r.resample_hyperparameters(rng);
- cerr << " PYPWordModel(d=" << r.discount() << ",s=" << r.strength() << ")\n";
-}
-
-void PYPWordModel::Summary() const {
- cerr << "PYPWordModel: generations=" << r.num_customers()
- << " PYP(d=" << r.discount() << ",s=" << r.strength() << ')' << endl;
- for (CCRP<vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it)
- cerr << " " << it->second.total_dish_count_
- << " (on " << it->second.table_counts_.size() << " tables) "
- << TD::GetString(it->first) << endl;
-}
-
diff --git a/gi/pf/pyp_word_model.h b/gi/pf/pyp_word_model.h
index ff366865..224a9034 100644
--- a/gi/pf/pyp_word_model.h
+++ b/gi/pf/pyp_word_model.h
@@ -11,48 +11,52 @@
#include "os_phrase.h"
// PYP(d,s,poisson-uniform) represented as a CRP
+template <class Base>
struct PYPWordModel {
- explicit PYPWordModel(const unsigned vocab_e_size, const double mean_len = 5) :
- base(prob_t::One()), r(1,1,1,1,0.66,50.0), u0(-std::log(vocab_e_size)), mean_length(mean_len) {}
-
- void ResampleHyperparameters(MT19937* rng);
+ explicit PYPWordModel(Base* b) :
+ base(*b),
+ r(1,1,1,1,0.66,50.0)
+ {}
+
+ void ResampleHyperparameters(MT19937* rng) {
+ r.resample_hyperparameters(rng);
+ std::cerr << " PYPWordModel(d=" << r.discount() << ",s=" << r.strength() << ")\n";
+ }
inline prob_t operator()(const std::vector<WordID>& s) const {
- return r.prob(s, p0(s));
+ return r.prob(s, base(s));
}
inline void Increment(const std::vector<WordID>& s, MT19937* rng) {
- if (r.increment(s, p0(s), rng))
- base *= p0(s);
+ if (r.increment(s, base(s), rng))
+ base.Increment(s, rng);
}
inline void Decrement(const std::vector<WordID>& s, MT19937 *rng) {
if (r.decrement(s, rng))
- base /= p0(s);
+ base.Decrement(s, rng);
}
inline prob_t Likelihood() const {
prob_t p; p.logeq(r.log_crp_prob());
- p *= base;
+ p *= base.Likelihood();
return p;
}
- void Summary() const;
-
- private:
- inline double logp0(const std::vector<WordID>& s) const {
- return Md::log_poisson(s.size(), mean_length) + s.size() * u0;
+ void Summary() const {
+ std::cerr << "PYPWordModel: generations=" << r.num_customers()
+ << " PYP(d=" << r.discount() << ",s=" << r.strength() << ')' << std::endl;
+ for (typename CCRP<std::vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it) {
+ std::cerr << " " << it->second.total_dish_count_
+ << " (on " << it->second.table_counts_.size() << " tables) "
+ << TD::GetString(it->first) << std::endl;
+ }
}
- inline prob_t p0(const std::vector<WordID>& s) const {
- prob_t p; p.logeq(logp0(s));
- return p;
- }
+ private:
- prob_t base; // keeps track of the draws from the base distribution
+ Base& base; // keeps track of the draws from the base distribution
CCRP<std::vector<WordID> > r;
- const double u0; // uniform log prob of generating a letter
- const double mean_length; // mean length of a word in the base distribution
};
#endif
diff --git a/gi/pf/quasi_model2.h b/gi/pf/quasi_model2.h
index 588c8f84..4075affe 100644
--- a/gi/pf/quasi_model2.h
+++ b/gi/pf/quasi_model2.h
@@ -9,6 +9,7 @@
#include "array2d.h"
#include "slice_sampler.h"
#include "m.h"
+#include "have_64_bits.h"
struct AlignmentObservation {
AlignmentObservation() : src_len(), trg_len(), j(), a_j() {}
@@ -20,13 +21,23 @@ struct AlignmentObservation {
unsigned short a_j;
};
+#ifdef HAVE_64_BITS
inline size_t hash_value(const AlignmentObservation& o) {
return reinterpret_cast<const size_t&>(o);
}
-
inline bool operator==(const AlignmentObservation& a, const AlignmentObservation& b) {
return hash_value(a) == hash_value(b);
}
+#else
+inline size_t hash_value(const AlignmentObservation& o) {
+ size_t h = 1;
+ boost::hash_combine(h, o.src_len);
+ boost::hash_combine(h, o.trg_len);
+ boost::hash_combine(h, o.j);
+ boost::hash_combine(h, o.a_j);
+ return h;
+}
+#endif
struct QuasiModel2 {
explicit QuasiModel2(double alpha, double pnull = 0.1) :
diff --git a/gi/pf/tied_resampler.h b/gi/pf/tied_resampler.h
index 6f45fbce..a4f4af36 100644
--- a/gi/pf/tied_resampler.h
+++ b/gi/pf/tied_resampler.h
@@ -78,10 +78,8 @@ struct TiedResampler {
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
std::cerr << "TiedCRPs(d=" << discount << ",s="
<< strength << ") = " << LogLikelihood(discount, strength) << std::endl;
- for (typename std::set<CRP*>::iterator it = crps.begin(); it != crps.end(); ++it) {
- (*it)->set_discount(discount);
- (*it)->set_strength(strength);
- }
+ for (typename std::set<CRP*>::iterator it = crps.begin(); it != crps.end(); ++it)
+ (*it)->set_hyperparameters(discount, strength);
}
private:
std::set<CRP*> crps;
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-26 13:12:16 +0000