1 files changed, 0 insertions, 273 deletions
diff --git a/gi/pf/pyp_lm.cc b/gi/pf/pyp_lm.cc
deleted file mode 100644
index 605d8206..00000000
--- a/gi/pf/pyp_lm.cc
+++ /dev/null
@@ -1,273 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "gamma_poisson.h"
-#include "corpus_tools.h"
-#include "m.h"
-#include "tdict.h"
-#include "sampler.h"
-#include "ccrp.h"
-#include "tied_resampler.h"
-
-// A not very memory-efficient implementation of an N-gram LM based on PYPs
-// as described in Y.-W. Teh. (2006) A Hierarchical Bayesian Language Model
-// based on Pitman-Yor Processes. In Proc. ACL.
-
-// I use templates to handle the recursive formalation of the prior, so
-// the order of the model has to be specified here, at compile time:
-#define kORDER 3
-
-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,n",po::value<unsigned>()->default_value(300),"Number of samples")
-        ("train,i",po::value<string>(),"Training data file")
-        ("test,T",po::value<string>(),"Test data file")
-        ("discount_prior_a,a",po::value<double>()->default_value(1.0), "discount ~ Beta(a,b): a=this")
-        ("discount_prior_b,b",po::value<double>()->default_value(1.0), "discount ~ Beta(a,b): b=this")
-        ("strength_prior_s,s",po::value<double>()->default_value(1.0), "strength ~ Gamma(s,r): s=this")
-        ("strength_prior_r,r",po::value<double>()->default_value(1.0), "strength ~ Gamma(s,r): r=this")
-        ("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("train") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-// uniform distribution over a fixed vocabulary
-struct UniformVocabulary {
-  UniformVocabulary(unsigned vs, double, double, double, double) : p0(1.0 / vs), draws() {}
-  void increment(WordID, const vector<WordID>&, MT19937*) { ++draws; }
-  void decrement(WordID, const vector<WordID>&, MT19937*) { --draws; assert(draws >= 0); }
-  double prob(WordID, const vector<WordID>&) const { return p0; }
-  void resample_hyperparameters(MT19937*) {}
-  double log_likelihood() const { return draws * log(p0); }
-  const double p0;
-  int draws;
-};
-
-// Lord Rothschild. 1986. THE DISTRIBUTION OF ENGLISH DICTIONARY WORD LENGTHS.
-// Journal of Statistical Planning and Inference 14 (1986) 311-322
-struct PoissonLengthUniformCharWordModel {
-  explicit PoissonLengthUniformCharWordModel(unsigned vocab_size, double, double, double, double) : plen(5,5), uc(-log(95)), llh() {}
-  void increment(WordID w, const vector<WordID>& v, MT19937*) {
-    llh += log(prob(w, v)); // this isn't quite right
-    plen.increment(TD::Convert(w).size() - 1);
-  }
-  void decrement(WordID w, const vector<WordID>& v, MT19937*) {
-    plen.decrement(TD::Convert(w).size() - 1);
-    llh -= log(prob(w, v)); // this isn't quite right
-  }
-  double prob(WordID w, const vector<WordID>&) const {
-    const unsigned len = TD::Convert(w).size();
-    return plen.prob(len - 1) * exp(uc * len);
-  }
-  double log_likelihood() const { return llh; }
-  void resample_hyperparameters(MT19937*) {}
-  GammaPoisson plen;
-  const double uc;
-  double llh;
-};
-
-struct PYPAdaptedPoissonLengthUniformCharWordModel {
-  explicit PYPAdaptedPoissonLengthUniformCharWordModel(unsigned vocab_size, double, double, double, double) :
-    base(vocab_size,1,1,1,1),
-    crp(1,1,1,1) {}
-  void increment(WordID w, const vector<WordID>& v, MT19937* rng) {
-    double p0 = base.prob(w, v);
-    if (crp.increment(w, p0, rng))
-      base.increment(w, v, rng);
-  }
-  void decrement(WordID w, const vector<WordID>& v, MT19937* rng) {
-    if (crp.decrement(w, rng))
-      base.decrement(w, v, rng);
-  }
-  double prob(WordID w, const vector<WordID>& v) const {
-    double p0 = base.prob(w, v);
-    return crp.prob(w, p0);
-  }
-  double log_likelihood() const { return crp.log_crp_prob() + base.log_likelihood(); }
-  void resample_hyperparameters(MT19937* rng) { crp.resample_hyperparameters(rng); }
-  PoissonLengthUniformCharWordModel base;
-  CCRP<WordID> crp;
-};
-
-template <unsigned N> struct PYPLM;
-
-#if 1
-template<> struct PYPLM<0> : public UniformVocabulary {
-  PYPLM(unsigned vs, double a, double b, double c, double d) :
-    UniformVocabulary(vs, a, b, c, d) {}
-};
-#else
-#if 0
-template<> struct PYPLM<0> : public PoissonLengthUniformCharWordModel {
-  PYPLM(unsigned vs, double a, double b, double c, double d) :
-    PoissonLengthUniformCharWordModel(vs, a, b, c, d) {}
-};
-#else
-template<> struct PYPLM<0> : public PYPAdaptedPoissonLengthUniformCharWordModel {
-  PYPLM(unsigned vs, double a, double b, double c, double d) :
-    PYPAdaptedPoissonLengthUniformCharWordModel(vs, a, b, c, d) {}
-};
-#endif
-#endif
-
-// represents an N-gram LM
-template <unsigned N> struct PYPLM {
-  PYPLM(unsigned vs, double da, double db, double ss, double sr) :
-      backoff(vs, da, db, ss, sr),
-      tr(da, db, ss, sr, 0.8, 1.0),
-      lookup(N-1) {}
-  void increment(WordID w, const vector<WordID>& context, MT19937* rng) {
-    const double bo = backoff.prob(w, context);
-    for (unsigned i = 0; i < N-1; ++i)
-      lookup[i] = context[context.size() - 1 - i];
-    typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::iterator it = p.find(lookup);
-    if (it == p.end()) {
-      it = p.insert(make_pair(lookup, CCRP<WordID>(0.5,1))).first;
-      tr.Add(&it->second);  // add to resampler
-    }
-    if (it->second.increment(w, bo, rng))
-      backoff.increment(w, context, rng);
-  }
-  void decrement(WordID w, const vector<WordID>& context, MT19937* rng) {
-    for (unsigned i = 0; i < N-1; ++i)
-      lookup[i] = context[context.size() - 1 - i];
-    typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::iterator it = p.find(lookup);
-    assert(it != p.end());
-    if (it->second.decrement(w, rng))
-      backoff.decrement(w, context, rng);
-  }
-  double prob(WordID w, const vector<WordID>& context) const {
-    const double bo = backoff.prob(w, context);
-    for (unsigned i = 0; i < N-1; ++i)
-      lookup[i] = context[context.size() - 1 - i];
-    typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::const_iterator it = p.find(lookup);
-    if (it == p.end()) return bo;
-    return it->second.prob(w, bo);
-  }
-
-  double log_likelihood() const {
-    double llh = backoff.log_likelihood();
-    typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::const_iterator it;
-    for (it = p.begin(); it != p.end(); ++it)
-      llh += it->second.log_crp_prob();
-    llh += tr.LogLikelihood();
-    return llh;
-  }
-
-  void resample_hyperparameters(MT19937* rng) {
-    tr.ResampleHyperparameters(rng);
-    backoff.resample_hyperparameters(rng);
-  }
-
-  PYPLM<N-1> backoff;
-  TiedResampler<CCRP<WordID> > tr;
-  double discount_a, discount_b, strength_s, strength_r;
-  double d, strength;
-  mutable vector<WordID> lookup;  // thread-local
-  unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > > p;
-};
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-
-  InitCommandLine(argc, argv, &conf);
-  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<vector<WordID> > corpuse;
-  set<WordID> vocabe;
-  const WordID kEOS = TD::Convert("</s>");
-  cerr << "Reading corpus...\n";
-  CorpusTools::ReadFromFile(conf["train"].as<string>(), &corpuse, &vocabe);
-  cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
-  vector<vector<WordID> > test;
-  if (conf.count("test"))
-    CorpusTools::ReadFromFile(conf["test"].as<string>(), &test);
-  else
-    test = corpuse;
-  PYPLM<kORDER> lm(vocabe.size(),
-                   conf["discount_prior_a"].as<double>(),
-                   conf["discount_prior_b"].as<double>(),
-                   conf["strength_prior_s"].as<double>(),
-                   conf["strength_prior_r"].as<double>());
-  vector<WordID> ctx(kORDER - 1, TD::Convert("<s>"));
-  for (int SS=0; SS < samples; ++SS) {
-    for (int ci = 0; ci < corpuse.size(); ++ci) {
-      ctx.resize(kORDER - 1);
-      const vector<WordID>& s = corpuse[ci];
-      for (int i = 0; i <= s.size(); ++i) {
-        WordID w = (i < s.size() ? s[i] : kEOS);
-        if (SS > 0) lm.decrement(w, ctx, &rng);
-        lm.increment(w, ctx, &rng);
-        ctx.push_back(w);
-      }
-    }
-    if (SS % 10 == 9) {
-      cerr << " [LLH=" << lm.log_likelihood() << "]" << endl;
-      if (SS % 30 == 29) lm.resample_hyperparameters(&rng);
-    } else { cerr << '.' << flush; }
-  }
-  double llh = 0;
-  unsigned cnt = 0;
-  unsigned oovs = 0;
-  for (int ci = 0; ci < test.size(); ++ci) {
-    ctx.resize(kORDER - 1);
-    const vector<WordID>& s = test[ci];
-    for (int i = 0; i <= s.size(); ++i) {
-      WordID w = (i < s.size() ? s[i] : kEOS);
-      double lp = log(lm.prob(w, ctx)) / log(2);
-      if (i < s.size() && vocabe.count(w) == 0) {
-        cerr << "**OOV ";
-        ++oovs;
-        lp = 0;
-      }
-      cerr << "p(" << TD::Convert(w) << " |";
-      for (int j = ctx.size() + 1 - kORDER; j < ctx.size(); ++j)
-        cerr << ' ' << TD::Convert(ctx[j]);
-      cerr << ") = " << lp << endl;
-      ctx.push_back(w);
-      llh -= lp;
-      cnt++;
-    }
-  }
-  cerr << "  Log_10 prob: " << (-llh * log(2) / log(10)) << endl;
-  cerr << "        Count: " << cnt << endl;
-  cerr << "         OOVs: " << oovs << endl;
-  cerr << "Cross-entropy: " << (llh / cnt) << endl;
-  cerr << "   Perplexity: " << pow(2, llh / cnt) << endl;
-  return 0;
-}
-
-