Merge branch 'master' of https://github.com/redpony/cdec

234 files changed, 0 insertions, 36886 deletions

diff --git a/gi/clda/src/Makefile.am b/gi/clda/src/Makefile.am
deleted file mode 100644
index cdca1f97..00000000
--- a/gi/clda/src/Makefile.am
+++ /dev/null
@@ -1,6 +0,0 @@
-bin_PROGRAMS = clda
-
-clda_SOURCES = clda.cc
-
-AM_CPPFLAGS = -W -Wall -Wno-sign-compare -funroll-loops -I$(top_srcdir)/utils $(GTEST_CPPFLAGS)
-AM_LDFLAGS = $(top_srcdir)/utils/libutils.a -lz
diff --git a/gi/clda/src/ccrp.h b/gi/clda/src/ccrp.h
deleted file mode 100644
index a7c2825c..00000000
--- a/gi/clda/src/ccrp.h
+++ /dev/null
@@ -1,291 +0,0 @@
-#ifndef _CCRP_H_
-#define _CCRP_H_
-
-#include <numeric>
-#include <cassert>
-#include <cmath>
-#include <list>
-#include <iostream>
-#include <vector>
-#include <tr1/unordered_map>
-#include <boost/functional/hash.hpp>
-#include "sampler.h"
-#include "slice_sampler.h"
-
-// Chinese restaurant process (Pitman-Yor parameters) with table tracking.
-
-template <typename Dish, typename DishHash = boost::hash<Dish> >
-class CCRP {
- public:
-  CCRP(double disc, double conc) :
-    num_tables_(),
-    num_customers_(),
-    discount_(disc),
-    concentration_(conc),
-    discount_prior_alpha_(std::numeric_limits<double>::quiet_NaN()),
-    discount_prior_beta_(std::numeric_limits<double>::quiet_NaN()),
-    concentration_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
-    concentration_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
-
-  CCRP(double d_alpha, double d_beta, double c_shape, double c_rate, double d = 0.1, double c = 10.0) :
-    num_tables_(),
-    num_customers_(),
-    discount_(d),
-    concentration_(c),
-    discount_prior_alpha_(d_alpha),
-    discount_prior_beta_(d_beta),
-    concentration_prior_shape_(c_shape),
-    concentration_prior_rate_(c_rate) {}
-
-  double discount() const { return discount_; }
-  double concentration() const { return concentration_; }
-
-  bool has_discount_prior() const {
-    return !std::isnan(discount_prior_alpha_);
-  }
-
-  bool has_concentration_prior() const {
-    return !std::isnan(concentration_prior_shape_);
-  }
-
-  void clear() {
-    num_tables_ = 0;
-    num_customers_ = 0;
-    dish_locs_.clear();
-  }
-
-  unsigned num_tables(const Dish& dish) const {
-    const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
-    if (it == dish_locs_.end()) return 0;
-    return it->second.table_counts_.size();
-  }
-
-  unsigned num_customers() const {
-    return num_customers_;
-  }
-
-  unsigned num_customers(const Dish& dish) const {
-    const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
-    if (it == dish_locs_.end()) return 0;
-    return it->total_dish_count_;
-  }
-
-  // returns +1 or 0 indicating whether a new table was opened
-  int increment(const Dish& dish, const double& p0, MT19937* rng) {
-    DishLocations& loc = dish_locs_[dish];
-    bool share_table = false;
-    if (loc.total_dish_count_) {
-      const double p_empty = (concentration_ + num_tables_ * discount_) * p0;
-      const double p_share = (loc.total_dish_count_ - loc.table_counts_.size() * discount_);
-      share_table = rng->SelectSample(p_empty, p_share);
-    }
-    if (share_table) {
-      double r = rng->next() * (loc.total_dish_count_ - loc.table_counts_.size() * discount_);
-      for (typename std::list<unsigned>::iterator ti = loc.table_counts_.begin();
-           ti != loc.table_counts_.end(); ++ti) {
-        r -= (*ti - discount_);
-        if (r <= 0.0) {
-          ++(*ti);
-          break;
-        }
-      }
-      if (r > 0.0) {
-        std::cerr << "Serious error: r=" << r << std::endl;
-        Print(&std::cerr);
-        assert(r <= 0.0);
-      }
-    } else {
-      loc.table_counts_.push_back(1u);
-      ++num_tables_;
-    }
-    ++loc.total_dish_count_;
-    ++num_customers_;
-    return (share_table ? 0 : 1);
-  }
-
-  // returns -1 or 0, indicating whether a table was closed
-  int decrement(const Dish& dish, MT19937* rng) {
-    DishLocations& loc = dish_locs_[dish];
-    assert(loc.total_dish_count_);
-    if (loc.total_dish_count_ == 1) {
-      dish_locs_.erase(dish);
-      --num_tables_;
-      --num_customers_;
-      return -1;
-    } else {
-      int delta = 0;
-      // sample customer to remove UNIFORMLY. that is, do NOT use the discount
-      // here. if you do, it will introduce (unwanted) bias!
-      double r = rng->next() * loc.total_dish_count_;
-      --loc.total_dish_count_;
-      for (typename std::list<unsigned>::iterator ti = loc.table_counts_.begin();
-           ti != loc.table_counts_.end(); ++ti) {
-        r -= *ti;
-        if (r <= 0.0) {
-          if ((--(*ti)) == 0) {
-            --num_tables_;
-            delta = -1;
-            loc.table_counts_.erase(ti);
-          }
-          break;
-        }
-      }
-      if (r > 0.0) {
-        std::cerr << "Serious error: r=" << r << std::endl;
-        Print(&std::cerr);
-        assert(r <= 0.0);
-      }
-      --num_customers_;
-      return delta;
-    }
-  }
-
-  double prob(const Dish& dish, const double& p0) const {
-    const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
-    const double r = num_tables_ * discount_ + concentration_;
-    if (it == dish_locs_.end()) {
-      return r * p0 / (num_customers_ + concentration_);
-    } else {
-      return (it->second.total_dish_count_ - discount_ * it->second.table_counts_.size() + r * p0) /
-               (num_customers_ + concentration_);
-    }
-  }
-
-  double log_crp_prob() const {
-    return log_crp_prob(discount_, concentration_);
-  }
-
-  static double log_beta_density(const double& x, const double& alpha, const double& beta) {
-    assert(x > 0.0);
-    assert(x < 1.0);
-    assert(alpha > 0.0);
-    assert(beta > 0.0);
-    const double lp = (alpha-1)*log(x)+(beta-1)*log(1-x)+lgamma(alpha+beta)-lgamma(alpha)-lgamma(beta);
-    return lp;
-  }
-
-  static double log_gamma_density(const double& x, const double& shape, const double& rate) {
-    assert(x >= 0.0);
-    assert(shape > 0.0);
-    assert(rate > 0.0);
-    const double lp = (shape-1)*log(x) - shape*log(rate) - x/rate - lgamma(shape);
-    return lp;
-  }
-
-  // taken from http://en.wikipedia.org/wiki/Chinese_restaurant_process
-  // does not include P_0's
-  double log_crp_prob(const double& discount, const double& concentration) const {
-    double lp = 0.0;
-    if (has_discount_prior())
-      lp = log_beta_density(discount, discount_prior_alpha_, discount_prior_beta_);
-    if (has_concentration_prior())
-      lp += log_gamma_density(concentration, concentration_prior_shape_, concentration_prior_rate_);
-    assert(lp <= 0.0);
-    if (num_customers_) {
-      if (discount > 0.0) {
-        const double r = lgamma(1.0 - discount);
-        lp += lgamma(concentration) - lgamma(concentration + num_customers_)
-             + num_tables_ * log(discount) + lgamma(concentration / discount + num_tables_)
-             - lgamma(concentration / discount);
-        assert(std::isfinite(lp));
-        for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin();
-             it != dish_locs_.end(); ++it) {
-          const DishLocations& cur = it->second;
-          for (std::list<unsigned>::const_iterator ti = cur.table_counts_.begin(); ti != cur.table_counts_.end(); ++ti) {
-            lp += lgamma(*ti - discount) - r;
-          }
-        }
-      } else {
-        assert(!"not implemented yet");
-      }
-    }
-    assert(std::isfinite(lp));
-    return lp;
-  }
-
-  void resample_hyperparameters(MT19937* rng) {
-    assert(has_discount_prior() || has_concentration_prior());
-    DiscountResampler dr(*this);
-    ConcentrationResampler cr(*this);
-    const int niterations = 10;
-    double gamma_upper = std::numeric_limits<double>::infinity();
-    for (int iter = 0; iter < 5; ++iter) {
-      if (has_concentration_prior()) {
-        concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
-                               gamma_upper, 0.0, niterations, 100*niterations);
-      }
-      if (has_discount_prior()) {
-        discount_ = slice_sampler1d(dr, discount_, *rng, std::numeric_limits<double>::min(),
-                               1.0, 0.0, niterations, 100*niterations);
-      }
-    }
-    concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
-                             gamma_upper, 0.0, niterations, 100*niterations);
-  }
-
-  struct DiscountResampler {
-    DiscountResampler(const CCRP& crp) : crp_(crp) {}
-    const CCRP& crp_;
-    double operator()(const double& proposed_discount) const {
-      return crp_.log_crp_prob(proposed_discount, crp_.concentration_);
-    }
-  };
-
-  struct ConcentrationResampler {
-    ConcentrationResampler(const CCRP& crp) : crp_(crp) {}
-    const CCRP& crp_;
-    double operator()(const double& proposed_concentration) const {
-      return crp_.log_crp_prob(crp_.discount_, proposed_concentration);
-    }
-  };
-
-  struct DishLocations {
-    DishLocations() : total_dish_count_() {}
-    unsigned total_dish_count_;        // customers at all tables with this dish
-    std::list<unsigned> table_counts_; // list<> gives O(1) deletion and insertion, which we want
-                                       // .size() is the number of tables for this dish
-  };
-
-  void Print(std::ostream* out) const {
-    for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin();
-         it != dish_locs_.end(); ++it) {
-      (*out) << it->first << " (" << it->second.total_dish_count_ << " on " << it->second.table_counts_.size() << " tables): ";
-      for (typename std::list<unsigned>::const_iterator i = it->second.table_counts_.begin();
-           i != it->second.table_counts_.end(); ++i) {
-        (*out) << " " << *i;
-      }
-      (*out) << std::endl;
-    }
-  }
-
-  typedef typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator const_iterator;
-  const_iterator begin() const {
-    return dish_locs_.begin();
-  }
-  const_iterator end() const {
-    return dish_locs_.end();
-  }
-
-  unsigned num_tables_;
-  unsigned num_customers_;
-  std::tr1::unordered_map<Dish, DishLocations, DishHash> dish_locs_;
-
-  double discount_;
-  double concentration_;
-
-  // optional beta prior on discount_ (NaN if no prior)
-  double discount_prior_alpha_;
-  double discount_prior_beta_;
-
-  // optional gamma prior on concentration_ (NaN if no prior)
-  double concentration_prior_shape_;
-  double concentration_prior_rate_;
-};
-
-template <typename T,typename H>
-std::ostream& operator<<(std::ostream& o, const CCRP<T,H>& c) {
-  c.Print(&o);
-  return o;
-}
-
-#endif
diff --git a/gi/clda/src/clda.cc b/gi/clda/src/clda.cc
deleted file mode 100644
index f548997f..00000000
--- a/gi/clda/src/clda.cc
+++ /dev/null
@@ -1,148 +0,0 @@
-#include <iostream>
-#include <vector>
-#include <map>
-#include <string>
-
-#include "timer.h"
-#include "crp.h"
-#include "ccrp.h"
-#include "sampler.h"
-#include "tdict.h"
-const size_t MAX_DOC_LEN_CHARS = 10000000;
-
-using namespace std;
-
-void ShowTopWordsForTopic(const map<WordID, int>& counts) {
-  multimap<int, WordID> ms;
-  for (map<WordID,int>::const_iterator it = counts.begin(); it != counts.end(); ++it)
-    ms.insert(make_pair(it->second, it->first));
-  int cc = 0;
-  for (multimap<int, WordID>::reverse_iterator it = ms.rbegin(); it != ms.rend(); ++it) {
-    cerr << it->first << ':' << TD::Convert(it->second) << " ";
-    ++cc;
-    if (cc==20) break;
-  }
-  cerr << endl;
-}
-
-int main(int argc, char** argv) {
-  if (argc != 3) {
-    cerr << "Usage: " << argv[0] << " num-classes num-samples\n";
-    return 1;
-  }
-  const int num_classes = atoi(argv[1]);
-  const int num_iterations = atoi(argv[2]);
-  const int burnin_size = num_iterations * 0.9;
-  if (num_classes < 2) {
-    cerr << "Must request more than 1 class\n";
-    return 1;
-  }
-  if (num_iterations < 5) {
-    cerr << "Must request more than 5 iterations\n";
-    return 1;
-  }
-  cerr << "CLASSES: " << num_classes << endl;
-  char* buf = new char[MAX_DOC_LEN_CHARS];
-  vector<vector<int> > wji;   // w[j][i] - observed word i of doc j
-  vector<vector<int> > zji;   // z[j][i] - topic assignment for word i of doc j
-  cerr << "READING DOCUMENTS\n";
-  while(cin) {
-    cin.getline(buf, MAX_DOC_LEN_CHARS);
-    if (buf[0] == 0) continue;
-    wji.push_back(vector<WordID>());
-    TD::ConvertSentence(buf, &wji.back());
-  }
-  cerr << "READ " << wji.size() << " DOCUMENTS\n";
-  MT19937 rng;
-  cerr << "INITIALIZING RANDOM TOPIC ASSIGNMENTS\n";
-  zji.resize(wji.size());
-  double disc = 0.1;
-  double beta = 10.0;
-  double alpha = 50.0;
-  const double uniform_topic = 1.0 / num_classes;
-  const double uniform_word = 1.0 / TD::NumWords();
-  vector<CCRP<int> > dr(zji.size(), CCRP<int>(1,1,1,1,disc, beta)); // dr[i] describes the probability of using a topic in document i
-  vector<CCRP<int> > wr(num_classes, CCRP<int>(1,1,1,1,disc, alpha)); // wr[k] describes the probability of generating a word in topic k
-  for (int j = 0; j < zji.size(); ++j) {
-    const size_t num_words = wji[j].size();
-    vector<int>& zj = zji[j];
-    const vector<int>& wj = wji[j];
-    zj.resize(num_words);
-    for (int i = 0; i < num_words; ++i) {
-      int random_topic = rng.next() * num_classes;
-      if (random_topic == num_classes) { --random_topic; }
-      zj[i] = random_topic;
-      const int word = wj[i];
-      dr[j].increment(random_topic, uniform_topic, &rng);
-      wr[random_topic].increment(word, uniform_word, &rng);
-    }
-  }
-  cerr << "SAMPLING\n";
-  vector<map<WordID, int> > t2w(num_classes);
-  Timer timer;
-  SampleSet<double> ss;
-  ss.resize(num_classes);
-  double total_time = 0;
-  for (int iter = 0; iter < num_iterations; ++iter) {
-    cerr << '.';
-    if (iter && iter % 10 == 0) {
-      total_time += timer.Elapsed();
-      timer.Reset();
-      double llh = 0;
-#if 1
-      for (int j = 0; j < dr.size(); ++j)
-        dr[j].resample_hyperparameters(&rng);
-      for (int j = 0; j < wr.size(); ++j)
-        wr[j].resample_hyperparameters(&rng);
-#endif
-
-      for (int j = 0; j < dr.size(); ++j)
-        llh += dr[j].log_crp_prob();
-      for (int j = 0; j < wr.size(); ++j)
-        llh += wr[j].log_crp_prob();
-      cerr << " [LLH=" << llh << " I=" << iter << "]\n";
-    }
-    for (int j = 0; j < zji.size(); ++j) {
-      const size_t num_words = wji[j].size();
-      vector<int>& zj = zji[j];
-      const vector<int>& wj = wji[j];
-      for (int i = 0; i < num_words; ++i) {
-        const int word = wj[i];
-        const int cur_topic = zj[i];
-        dr[j].decrement(cur_topic, &rng);
-        wr[cur_topic].decrement(word, &rng);
- 
-        for (int k = 0; k < num_classes; ++k) {
-          ss[k]= dr[j].prob(k, uniform_topic) * wr[k].prob(word, uniform_word);
-        }
-        const int new_topic = rng.SelectSample(ss);
-        dr[j].increment(new_topic, uniform_topic, &rng);
-        wr[new_topic].increment(word, uniform_word, &rng);
-        zj[i] = new_topic;
-        if (iter > burnin_size) {
-          ++t2w[cur_topic][word];
-        }
-      }
-    }
-  }
-  for (int i = 0; i < num_classes; ++i) {
-    cerr << "---------------------------------\n";
-    cerr << " final PYP(" << wr[i].discount() << "," << wr[i].concentration() << ")\n";
-    ShowTopWordsForTopic(t2w[i]);
-  }
-  cerr << "-------------\n";
-#if 0
-  for (int j = 0; j < zji.size(); ++j) {
-    const size_t num_words = wji[j].size();
-    vector<int>& zj = zji[j];
-    const vector<int>& wj = wji[j];
-    zj.resize(num_words);
-    for (int i = 0; i < num_words; ++i) {
-      cerr << TD::Convert(wji[j][i]) << '(' << zj[i] << ") ";
-    }
-    cerr << endl;
-  }
-#endif
-  return 0;
-}
-
diff --git a/gi/clda/src/crp.h b/gi/clda/src/crp.h
deleted file mode 100644
index 9d35857e..00000000
--- a/gi/clda/src/crp.h
+++ /dev/null
@@ -1,50 +0,0 @@
-#ifndef _CRP_H_
-#define _CRP_H_
-
-// shamelessly adapted from code by Phil Blunsom and Trevor Cohn
-
-#include <boost/functional/hash.hpp>
-#include <tr1/unordered_map>
-
-#include "prob.h"
-
-template <typename DishType, typename Hash = boost::hash<DishType> >
-class CRP {
- public:
-  CRP(double alpha) : alpha_(alpha), palpha_(alpha), total_customers_() {}
-  void increment(const DishType& dish);
-  void decrement(const DishType& dish);
-  void erase(const DishType& dish) {
-    counts_.erase(dish);
-  }
-  inline int count(const DishType& dish) const {
-    const typename MapType::const_iterator i = counts_.find(dish);
-    if (i == counts_.end()) return 0; else return i->second;
-  }
-  inline prob_t prob(const DishType& dish, const prob_t& p0) const {
-    return (prob_t(count(dish)) + palpha_ * p0) / prob_t(total_customers_ + alpha_);
-  }
- private:
-  typedef std::tr1::unordered_map<DishType, int, Hash> MapType;
-  MapType counts_;
-  const double alpha_;
-  const prob_t palpha_;
-  int total_customers_;
-};
-
-template <typename Dish, typename Hash>
-void CRP<Dish,Hash>::increment(const Dish& dish) {
-  ++counts_[dish];
-  ++total_customers_;
-}
-
-template <typename Dish, typename Hash>
-void CRP<Dish,Hash>::decrement(const Dish& dish) {
-  typename MapType::iterator i = counts_.find(dish);
-  assert(i != counts_.end());
-  if (--i->second == 0)
-    counts_.erase(i);
-  --total_customers_;
-}
-
-#endif
diff --git a/gi/clda/src/slice_sampler.h b/gi/clda/src/slice_sampler.h
deleted file mode 100644
index aa48a169..00000000
--- a/gi/clda/src/slice_sampler.h
+++ /dev/null
@@ -1,191 +0,0 @@
-//! slice-sampler.h is an MCMC slice sampler
-//!
-//! Mark Johnson, 1st August 2008
-
-#ifndef SLICE_SAMPLER_H
-#define SLICE_SAMPLER_H
-
-#include <algorithm>
-#include <cassert>
-#include <cmath>
-#include <iostream>
-#include <limits>
-
-//! slice_sampler_rfc_type{} returns the value of a user-specified
-//! function if the argument is within range, or - infinity otherwise
-//
-template <typename F, typename Fn, typename U>
-struct slice_sampler_rfc_type {
-  F min_x, max_x;
-  const Fn& f;
-  U max_nfeval, nfeval;
-  slice_sampler_rfc_type(F min_x, F max_x, const Fn& f, U max_nfeval) 
-    : min_x(min_x), max_x(max_x), f(f), max_nfeval(max_nfeval), nfeval(0) { }
-    
-  F operator() (F x) {
-    if (min_x < x && x < max_x) {
-      assert(++nfeval <= max_nfeval);
-      F fx = f(x);
-      assert(std::isfinite(fx));
-      return fx;
-    }
-      return -std::numeric_limits<F>::infinity();
-  }
-};  // slice_sampler_rfc_type{}
-
-//! slice_sampler1d() implements the univariate "range doubling" slice sampler
-//! described in Neal (2003) "Slice Sampling", The Annals of Statistics 31(3), 705-767.
-//
-template <typename F, typename LogF, typename Uniform01>
-F slice_sampler1d(const LogF& logF0,               //!< log of function to sample
-		  F x,                             //!< starting point
-		  Uniform01& u01,                  //!< uniform [0,1) random number generator
-		  F min_x = -std::numeric_limits<F>::infinity(),  //!< minimum value of support
-		  F max_x = std::numeric_limits<F>::infinity(),   //!< maximum value of support
-		  F w = 0.0,                       //!< guess at initial width
-		  unsigned nsamples=1,             //!< number of samples to draw
-		  unsigned max_nfeval=200)         //!< max number of function evaluations
-{
-  typedef unsigned U;
-  slice_sampler_rfc_type<F,LogF,U> logF(min_x, max_x, logF0, max_nfeval);
-
-  assert(std::isfinite(x));
-
-  if (w <= 0.0) {                           // set w to a default width 
-    if (min_x > -std::numeric_limits<F>::infinity() && max_x < std::numeric_limits<F>::infinity())
-      w = (max_x - min_x)/4;
-    else
-      w = std::max(((x < 0.0) ? -x : x)/4, (F) 0.1);
-  }
-  assert(std::isfinite(w));
-
-  F logFx = logF(x);
-  for (U sample = 0; sample < nsamples; ++sample) {
-    F logY = logFx + log(u01()+1e-100);     //! slice logFx at this value
-    assert(std::isfinite(logY));
-
-    F xl = x - w*u01();                     //! lower bound on slice interval
-    F logFxl = logF(xl);
-    F xr = xl + w;                          //! upper bound on slice interval
-    F logFxr = logF(xr);
-
-    while (logY < logFxl || logY < logFxr)  // doubling procedure
-      if (u01() < 0.5) 
-	logFxl = logF(xl -= xr - xl);
-      else
-	logFxr = logF(xr += xr - xl);
-	
-    F xl1 = xl;
-    F xr1 = xr;
-    while (true) {                          // shrinking procedure
-      F x1 = xl1 + u01()*(xr1 - xl1);
-      if (logY < logF(x1)) {
-	F xl2 = xl;                         // acceptance procedure
-	F xr2 = xr; 
-	bool d = false;
-	while (xr2 - xl2 > 1.1*w) {
-	  F xm = (xl2 + xr2)/2;
-	  if ((x < xm && x1 >= xm) || (x >= xm && x1 < xm))
-	    d = true;
-	  if (x1 < xm)
-	    xr2 = xm;
-	  else
-	    xl2 = xm;
-	  if (d && logY >= logF(xl2) && logY >= logF(xr2))
-	    goto unacceptable;
-	}
-	x = x1;
-	goto acceptable;
-      }
-      goto acceptable;
-    unacceptable:
-      if (x1 < x)                           // rest of shrinking procedure
-	xl1 = x1;
-      else 
-	xr1 = x1;
-    }
-  acceptable:
-    w = (4*w + (xr1 - xl1))/5;              // update width estimate
-  }
-  return x;
-}
-
-/*
-//! slice_sampler1d() implements a 1-d MCMC slice sampler.
-//! It should be correct for unimodal distributions, but
-//! not for multimodal ones.
-//
-template <typename F, typename LogP, typename Uniform01>
-F slice_sampler1d(const LogP& logP,     //!< log of distribution to sample
-		  F x,                  //!< initial sample
-		  Uniform01& u01,       //!< uniform random number generator
-		  F min_x = -std::numeric_limits<F>::infinity(),  //!< minimum value of support
-		  F max_x = std::numeric_limits<F>::infinity(),   //!< maximum value of support
-		  F w = 0.0,            //!< guess at initial width
-		  unsigned nsamples=1,  //!< number of samples to draw
-		  unsigned max_nfeval=200)  //!< max number of function evaluations
-{
-  typedef unsigned U;
-  assert(std::isfinite(x));
-  if (w <= 0.0) {
-    if (min_x > -std::numeric_limits<F>::infinity() && max_x < std::numeric_limits<F>::infinity())
-      w = (max_x - min_x)/4;
-    else
-      w = std::max(((x < 0.0) ? -x : x)/4, 0.1);
-  }
-  // TRACE4(x, min_x, max_x, w);
-  F logPx = logP(x);
-  assert(std::isfinite(logPx));
-  U nfeval = 1;
-  for (U sample = 0; sample < nsamples; ++sample) {
-    F x0 = x;
-    F logU = logPx + log(u01()+1e-100);
-    assert(std::isfinite(logU));
-    F r = u01();
-    F xl = std::max(min_x, x - r*w);
-    F xr = std::min(max_x, x + (1-r)*w);
-    // TRACE3(x, logPx, logU);
-    while (xl > min_x && logP(xl) > logU) {
-      xl -= w;
-      w *= 2;
-      ++nfeval;
-      if (nfeval >= max_nfeval)
-	std::cerr << "## Error: nfeval = " << nfeval << ", max_nfeval = " << max_nfeval << ", sample = " << sample << ", nsamples = " << nsamples << ", r = " << r << ", w = " << w << ", xl = " << xl << std::endl;
-      assert(nfeval < max_nfeval);
-    }
-    xl = std::max(xl, min_x);
-    while (xr < max_x && logP(xr) > logU) {
-      xr += w;
-      w *= 2;
-      ++nfeval;
-      if (nfeval >= max_nfeval)
-	std::cerr << "## Error: nfeval = " << nfeval << ", max_nfeval = " << max_nfeval << ", sample = " << sample << ", nsamples = " << nsamples << ", r = " << r << ", w = " << w << ", xr = " << xr << std::endl;
-      assert(nfeval < max_nfeval);
-    }
-    xr = std::min(xr, max_x);
-    while (true) {
-      r = u01();
-      x = r*xl + (1-r)*xr;
-      assert(std::isfinite(x));
-      logPx = logP(x);
-      // TRACE4(logPx, x, xl, xr);
-      assert(std::isfinite(logPx));
-      ++nfeval;
-      if (nfeval >= max_nfeval)
-	std::cerr << "## Error: nfeval = " << nfeval << ", max_nfeval = " << max_nfeval << ", sample = " << sample << ", nsamples = " << nsamples << ", r = " << r << ", w = " << w << ", xl = " << xl << ", xr = " << xr << ", x = " << x << std::endl;
-      assert(nfeval < max_nfeval);
-      if (logPx > logU)
-        break;
-      else if (x > x0)
-          xr = x;
-        else
-          xl = x;
-    }
-    // w = (4*w + (xr-xl))/5;   // gradually adjust w
-  }
-  // TRACE2(logPx, x);
-  return x;
-}  // slice_sampler1d()
-*/
-
-#endif  // SLICE_SAMPLER_H
diff --git a/gi/clda/src/timer.h b/gi/clda/src/timer.h
deleted file mode 100644
index 123d9a94..00000000
--- a/gi/clda/src/timer.h
+++ /dev/null
@@ -1,20 +0,0 @@
-#ifndef _TIMER_STATS_H_
-#define _TIMER_STATS_H_
-
-#include <ctime>
-
-struct Timer {
-  Timer() { Reset(); }
-  void Reset() {
-    start_t = clock();
-  }
-  double Elapsed() const {
-    const clock_t end_t = clock();
-    const double elapsed = (end_t - start_t) / 1000000.0;
-    return elapsed;
-  }
- private:
-  std::clock_t start_t;
-};
-
-#endif
diff --git a/gi/evaluation/conditional_entropy.py b/gi/evaluation/conditional_entropy.py
deleted file mode 100644
index 356d3b1d..00000000
--- a/gi/evaluation/conditional_entropy.py
+++ /dev/null
@@ -1,61 +0,0 @@
-#!/usr/bin/env python
-
-import sys, math, itertools, getopt
-
-def usage():
-    print >>sys.stderr, 'Usage:', sys.argv[0], '[-s slash_threshold] input-1 input-2'
-    sys.exit(0)
-
-optlist, args = getopt.getopt(sys.argv[1:], 'hs:')
-slash_threshold = None
-for opt, arg in optlist:
-    if opt == '-s':
-        slash_threshold = int(arg)
-    else:
-        usage()
-if len(args) != 2:
-    usage()
-
-ginfile = open(args[0])
-pinfile = open(args[1])
-
-# evaluating: H(G | P) = sum_{g,p} p(g,p) log { p(p) / p(g,p) }
-#                      = sum_{g,p} c(g,p)/N { log c(p) - log N - log c(g,p) + log N }
-#                      = 1/N sum_{g,p} c(g,p) { log c(p) - log c(g,p) }
-# where G = gold, P = predicted, N = number of events
-
-N = 0
-gold_frequencies = {}
-predict_frequencies = {}
-joint_frequencies = {}
-
-for gline, pline in itertools.izip(ginfile, pinfile):
-    gparts = gline.split('||| ')[1].split()
-    pparts = pline.split('||| ')[1].split()
-    assert len(gparts) == len(pparts)
-
-    for gpart, ppart in zip(gparts, pparts):
-        gtag = gpart.split(':',1)[1]
-        ptag = ppart.split(':',1)[1]
-
-        if slash_threshold == None or gtag.count('/') + gtag.count('\\') <= slash_threshold:
-            joint_frequencies.setdefault((gtag, ptag), 0)
-            joint_frequencies[gtag,ptag] += 1
-
-            predict_frequencies.setdefault(ptag, 0)
-            predict_frequencies[ptag] += 1
-
-            gold_frequencies.setdefault(gtag, 0)
-            gold_frequencies[gtag] += 1
-
-            N += 1
-
-hg2p = 0
-hp2g = 0
-for (gtag, ptag), cgp in joint_frequencies.items():
-    hp2g += cgp * (math.log(predict_frequencies[ptag], 2) - math.log(cgp, 2))
-    hg2p += cgp * (math.log(gold_frequencies[gtag], 2) - math.log(cgp, 2))
-hg2p /= N
-hp2g /= N
-
-print 'H(P|G)', hg2p, 'H(G|P)', hp2g, 'VI', hg2p + hp2g
diff --git a/gi/evaluation/confusion_matrix.py b/gi/evaluation/confusion_matrix.py
deleted file mode 100644
index 2dd7aa47..00000000
--- a/gi/evaluation/confusion_matrix.py
+++ /dev/null
@@ -1,123 +0,0 @@
-#!/usr/bin/env python
-
-import sys, math, itertools, getopt
-
-def usage():
-    print >>sys.stderr, 'Usage:', sys.argv[0], '[-s slash_threshold] [-p output] [-m] input-1 input-2'
-    sys.exit(0)
-
-optlist, args = getopt.getopt(sys.argv[1:], 'hs:mp:')
-slash_threshold = None
-output_fname = None
-show_matrix = False
-for opt, arg in optlist:
-    if opt == '-s':
-        slash_threshold = int(arg)
-    elif opt == '-p':
-        output_fname = arg
-    elif opt == '-m':
-        show_matrix = True
-    else:
-        usage()
-if len(args) != 2 or (not show_matrix and not output_fname):
-    usage()
-
-ginfile = open(args[0])
-pinfile = open(args[1])
-
-if output_fname:
-    try:
-        import Image, ImageDraw
-    except ImportError:
-        print >>sys.stderr, "Error: Python Image Library not available. Did you forget to set your PYTHONPATH environment variable?" 
-        sys.exit(1)
-
-N = 0
-gold_frequencies = {}
-predict_frequencies = {}
-joint_frequencies = {}
-
-for gline, pline in itertools.izip(ginfile, pinfile):
-    gparts = gline.split('||| ')[1].split()
-    pparts = pline.split('||| ')[1].split()
-    assert len(gparts) == len(pparts)
-
-    for gpart, ppart in zip(gparts, pparts):
-        gtag = gpart.split(':',1)[1]
-        ptag = ppart.split(':',1)[1]
-
-        if slash_threshold == None or gtag.count('/') + gtag.count('\\') <= slash_threshold:
-            joint_frequencies.setdefault((gtag, ptag), 0)
-            joint_frequencies[gtag,ptag] += 1
-
-            predict_frequencies.setdefault(ptag, 0)
-            predict_frequencies[ptag] += 1
-
-            gold_frequencies.setdefault(gtag, 0)
-            gold_frequencies[gtag] += 1
-
-            N += 1
-
-# find top tags
-gtags = gold_frequencies.items()
-gtags.sort(lambda x,y: x[1]-y[1])
-gtags.reverse()
-#gtags = gtags[:50]
-
-preds = predict_frequencies.items()
-preds.sort(lambda x,y: x[1]-y[1])
-preds.reverse()
-
-if show_matrix:
-    print '%7s %7s' % ('pred', 'cnt'),
-    for gtag, gcount in gtags: print '%7s' % gtag,
-    print
-    print '=' * 80
-
-    for ptag, pcount in preds:
-        print '%7s %7d' % (ptag, pcount),
-        for gtag, gcount in gtags:
-            print '%7d' % joint_frequencies.get((gtag, ptag), 0),
-        print
-
-    print '%7s %7d' % ('total', N),
-    for gtag, gcount in gtags: print '%7d' % gcount,
-    print
-
-if output_fname:
-    offset=10
-
-    image = Image.new("RGB", (len(preds), len(gtags)), (255, 255, 255))
-    #hsl(hue, saturation%, lightness%)
-
-    # re-sort preds to get a better diagonal
-    ptags=[]
-    if True:
-        ptags = map(lambda (p,c): p, preds)
-    else:
-        remaining = set(predict_frequencies.keys())
-        for y, (gtag, gcount) in enumerate(gtags):
-            best = (None, 0)
-            for ptag in remaining:
-                #pcount = predict_frequencies[ptag]
-                p = joint_frequencies.get((gtag, ptag), 0)# / float(pcount)
-                if p > best[1]: best = (ptag, p)
-            ptags.append(ptag)
-            remaining.remove(ptag)
-            if not remaining: break
-
-    print 'Predicted tag ordering:', ' '.join(ptags)
-    print 'Gold tag ordering:', ' '.join(map(lambda (t,c): t, gtags))
-    
-    draw = ImageDraw.Draw(image)
-    for x, ptag in enumerate(ptags):
-        pcount = predict_frequencies[ptag]
-        minval = math.log(offset)
-        maxval = math.log(pcount + offset)
-        for y, (gtag, gcount) in enumerate(gtags):
-            f = math.log(offset + joint_frequencies.get((gtag, ptag), 0))
-            z = int(240. * (maxval - f) / float(maxval - minval))
-            #print x, y, z, f, maxval
-            draw.point([(x,y)], fill='hsl(%d, 100%%, 50%%)' % z)
-    del draw
-    image.save(output_fname)
diff --git a/gi/evaluation/entropy.py b/gi/evaluation/entropy.py
deleted file mode 100644
index ec1ef502..00000000
--- a/gi/evaluation/entropy.py
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/usr/bin/env python
-
-import sys, math, itertools, getopt
-
-def usage():
-    print >>sys.stderr, 'Usage:', sys.argv[0], '[-s slash_threshold] input file'
-    sys.exit(0)
-
-optlist, args = getopt.getopt(sys.argv[1:], 'hs:')
-slash_threshold = None
-for opt, arg in optlist:
-    if opt == '-s':
-        slash_threshold = int(arg)
-    else:
-        usage()
-if len(args) != 1:
-    usage()
-
-infile = open(args[0])
-N = 0
-frequencies = {}
-
-for line in infile:
-
-    for part in line.split('||| ')[1].split():
-        tag = part.split(':',1)[1]
-
-        if slash_threshold == None or tag.count('/') + tag.count('\\') <= slash_threshold:
-            frequencies.setdefault(tag, 0)
-            frequencies[tag] += 1
-            N += 1
-
-h = 0
-for tag, c in frequencies.items():
-    h -= c * (math.log(c, 2) - math.log(N, 2))
-h /= N
-
-print 'entropy', h
diff --git a/gi/evaluation/extract_ccg_labels.py b/gi/evaluation/extract_ccg_labels.py
deleted file mode 100644
index e0034648..00000000
--- a/gi/evaluation/extract_ccg_labels.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python
-
-#
-# Takes spans input along with treebank and spits out CG style categories for each span.
-#   spans = output from CDEC's extools/extractor with --base_phrase_spans option
-#   treebank = PTB format, one tree per line
-# 
-# Output is in CDEC labelled-span format
-#
-
-import sys, itertools, tree
-
-tinfile = open(sys.argv[1])
-einfile = open(sys.argv[2])
-
-def number_leaves(node, next=0):
-    left, right = None, None
-    for child in node.children:
-        l, r = number_leaves(child, next)
-        next = max(next, r+1)
-        if left == None or l < left:
-            left = l
-        if right == None or r > right:
-            right = r
-
-    #print node, left, right, next
-    if left == None or right == None:
-        assert not node.children
-        left = right = next
-
-    node.left = left
-    node.right = right
-
-    return left, right
-
-def ancestor(node, indices):
-    #print node, node.left, node.right, indices
-    # returns the deepest node covering all the indices
-    if min(indices) >= node.left and max(indices) <= node.right:
-        # try the children
-        for child in node.children:
-            x = ancestor(child, indices)
-            if x: return x
-        return node
-    else:
-        return None
-
-def frontier(node, indices):
-    #print 'frontier for node', node, 'indices', indices
-    if node.left > max(indices) or node.right < min(indices):
-        #print '\toutside'
-        return [node]
-    elif node.children:
-        #print '\tcovering at least part'
-        ns = []
-        for child in node.children:
-            n = frontier(child, indices)
-            ns.extend(n)
-        return ns
-    else:
-        return [node]
-
-def project_heads(node):
-    #print 'project_heads', node
-    is_head = node.data.tag.endswith('-HEAD')
-    if node.children:
-        found = 0
-        for child in node.children:
-            x = project_heads(child)
-            if x:
-                node.data.tag = x
-                found += 1
-        assert found == 1
-    elif is_head:
-        node.data.tag = node.data.tag[:-len('-HEAD')]
-
-    if is_head:
-        return node.data.tag
-    else:
-        return None
-
-for tline, eline in itertools.izip(tinfile, einfile):
-    if tline.strip() != '(())':
-        if tline.startswith('( '):
-            tline = tline[2:-1].strip()
-        tr = tree.parse_PST(tline)
-	if tr != None:
-		number_leaves(tr)
-		#project_heads(tr) # assumes Bikel-style head annotation for the input trees
-    else:
-        tr = None
-    
-    parts = eline.strip().split(" ||| ")
-    zh, en = parts[:2]
-    spans = parts[-1]
-    print '|||',
-    for span in spans.split():
-        sps = span.split(":")
-        i, j, x, y = map(int, sps[0].split("-"))
-
-        if tr:
-            a = ancestor(tr, range(x,y))
-	    try:
-		fs = frontier(a, range(x,y))
-	    except:
-		print >>sys.stderr, "problem with line", tline.strip(), "--", eline.strip()
-		raise
-
-            #print x, y
-            #print 'ancestor', a
-            #print 'frontier', fs
-
-            cat = a.data.tag
-            for f in fs:
-                if f.right < x:
-                    cat += '\\' + f.data.tag
-                else:
-                    break
-            fs.reverse()
-            for f in fs:
-                if f.left >= y:
-                    cat += '/' + f.data.tag
-                else:
-                    break
-        else:
-            cat = 'FAIL'
-            
-        print '%d-%d:%s' % (x, y, cat),
-    print
diff --git a/gi/evaluation/tree.py b/gi/evaluation/tree.py
deleted file mode 100644
index 702d80b6..00000000
--- a/gi/evaluation/tree.py
+++ /dev/null
@@ -1,485 +0,0 @@
-import re, sys
-
-class Symbol:
-    def __init__(self, nonterm, term=None, var=None):
-        assert not (term != None and var != None)
-        self.tag = nonterm
-        self.token = term
-        self.variable = var
-
-    def is_variable(self):
-        return self.variable != None
-
-    def __eq__(self, other):
-        return self.tag == other.tag and self.token == other.token and self.variable == other.variable
-
-    def __ne__(self, other):
-        return not (self == other)
-
-    def __hash__(self):
-        return hash((self.tag, self.token, self.variable))
-
-    def __repr__(self):
-        return str(self)
-
-    def __cmp__(self, other):
-        return cmp((self.tag, self.token, self.variable),
-                   (other.tag, other.token, other.variable))
-
-    def __str__(self):
-        parts = []
-	if False: # DEPENDENCY
-	    if self.token:
-		parts.append(str(self.token))
-	    elif self.variable != None:
-		parts.append('#%d' % self.variable)
-	    if self.tag:
-		parts.append(str(self.tag))
-	    return '/'.join(parts)
-	else:
-	    if self.tag:
-		parts.append(str(self.tag))
-	    if self.token:
-		parts.append(str(self.token))
-	    elif self.variable != None:
-		parts.append('#%d' % self.variable)
-	    return ' '.join(parts)
-
-class TreeNode:
-    def __init__(self, data, children=None, order=-1):
-        self.data = data
-        self.children = []
-        self.order = order
-        self.parent = None
-        if children: self.children = children
-
-    def insert(self, child):
-        self.children.append(child)
-        child.parent = self
-
-    def leaves(self):
-        ls = []
-        for node in self.xtraversal():
-            if not node.children:
-                ls.append(node.data)
-        return ls
-
-    def leaf_nodes(self):
-        ls = []
-        for node in self.xtraversal():
-            if not node.children:
-                ls.append(node)
-        return ls
-
-    def max_depth(self):
-        d = 1
-        for child in self.children:
-            d = max(d, 1 + child.max_depth())
-        if not self.children and self.data.token:
-            d = 2
-        return d
-
-    def max_width(self):
-        w = 0
-        for child in self.children:
-           w += child.max_width()
-        return max(1, w)
-
-    def num_internal_nodes(self):
-        if self.children:
-            n = 1
-            for child in self.children:
-                n += child.num_internal_nodes()
-            return n
-        elif self.data.token:
-            return 1
-        else:
-            return 0
-
-    def postorder_traversal(self, visit):
-        """
-	Postorder traversal; no guarantee that terminals will be read in the
-	correct order for dep. trees.
-        """
-        for child in self.children:
-            child.traversal(visit)
-	visit(self)
-
-    def traversal(self, visit):
-        """
-        Preorder for phrase structure trees, and inorder for dependency trees.
-        In both cases the terminals will be read off in the correct order.
-        """
-        visited_self = False
-        if self.order <= 0:
-            visited_self = True
-            visit(self)
-
-        for i, child in enumerate(self.children):
-            child.traversal(visit)
-            if i + 1 == self.order:
-                visited_self = True
-                visit(self)
-
-        assert visited_self
-
-    def xpostorder_traversal(self):
-        for child in self.children:
-            for node in child.xpostorder_traversal():
-                yield node
-        yield self
-
-    def xtraversal(self):
-        visited_self = False
-        if self.order <= 0:
-            visited_self = True
-            yield self
-
-        for i, child in enumerate(self.children):
-            for d in child.xtraversal():
-                yield d
-
-            if i + 1 == self.order:
-                visited_self = True
-                yield self
-
-        assert visited_self
-
-    def xpostorder_traversal(self):
-        for i, child in enumerate(self.children):
-            for d in child.xpostorder_traversal():
-                yield d
-        yield self
-
-    def edges(self):
-        es = []
-        self.traverse_edges(lambda h,c: es.append((h,c)))
-        return es
-
-    def traverse_edges(self, visit):
-        for child in self.children:
-            visit(self.data, child.data)
-            child.traverse_edges(visit)
-
-    def subtrees(self, include_self=False):
-        st = []
-        if include_self:
-            stack = [self]
-        else:
-            stack = self.children[:]
-
-        while stack:
-            node = stack.pop()
-            st.append(node)
-            stack.extend(node.children)
-        return st
-
-    def find_parent(self, node):
-        try:
-            index = self.children.index(node)
-            return self, index
-        except ValueError:
-            for child in self.children:
-                if isinstance(child, TreeNode):
-                    r = child.find_parent(node)
-                    if r: return r
-        return None
-
-    def is_ancestor_of(self, node):
-        if self == node:
-            return True
-        for child in self.children:
-            if child.is_ancestor_of(child):
-                return True
-        return False
-
-    def find(self, node):
-        if self == node:
-            return self
-        for child in self.children:
-            if isinstance(child, TreeNode):
-                r = child.find(node)
-                if r: return r
-            else:
-                if child == node:
-                   return r
-        return None
-
-    def equals_ignorecase(self, other):
-        if not isinstance(other, TreeNode):
-            return False
-        if self.data != other.data:
-            return False
-        if len(self.children) != len(other.children):
-            return False
-        for mc, oc in zip(self.children, other.children):
-            if isinstance(mc, TreeNode):
-                if not mc.equals_ignorecase(oc):
-                    return False
-            else:
-                if mc.lower() != oc.lower():
-                    return False
-        return True
-
-    def node_number(self, numbering, next=0):
-        if self.order <= 0:
-            numbering[id(self)] = next
-            next += 1
-
-        for i, child in enumerate(self.children):
-            next = child.node_number(numbering, next)
-            if i + 1 == self.order:
-                numbering[id(self)] = next
-                next += 1
-
-        return next
-
-    def display_conll(self, out):
-        numbering = {}
-        self.node_number(numbering)
-        next = 0
-        self.children[0].traversal(lambda x: \
-            out.write('%d\t%s\t%s\t%s\t%s\t_\t%d\tLAB\n' \
-             % (numbering[id(x)], x.data.token, x.data.token, 
-                x.data.tag, x.data.tag, numbering[id(x.parent)])))
-        out.write('\n')
-
-    def size(self):
-        sz = 1 
-        for child in self.children:
-            sz += child.size()
-        return sz
-
-    def __eq__(self, other):
-        if isinstance(other, TreeNode) and self.data == other.data \
-                and self.children == other.children:
-            return True
-        return False
-
-    def __cmp__(self, other):
-        if not isinstance(other, TreeNode): return 1
-        n = cmp(self.data, other.data)
-        if n != 0: return n
-        n = len(self.children) - len(other.children)
-        if n != 0: return n
-        for sc, oc in zip(self.children, other.children):
-            n = cmp(sc, oc)
-            if n != 0: return n
-        return 0
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __hash__(self):
-        return hash((self.data, tuple(self.children)))
-
-    def __repr__(self):
-        return str(self)
-
-    def __str__(self):
-        s = '('
-        space = False
-        if self.order <= 0:
-            s += str(self.data)
-            space = True
-        for i, child in enumerate(self.children):
-            if space: s += ' '
-            s += str(child)
-            space = True
-            if i+1 == self.order:
-                s += ' ' + str(self.data)
-        return s + ')'
-
-def read_PSTs(fname):
-    infile = open(fname)
-    trees = []
-    for line in infile:
-        trees.append(parse_PST(line.strip()))
-    infile.close()
-    return trees
-
-def parse_PST_multiline(infile, hash_is_var=True):
-    buf = ''
-    num_open = 0
-    while True:
-        line = infile.readline()
-        if not line:
-            return None
-        buf += ' ' + line.rstrip()
-        num_open += line.count('(') - line.count(')')
-        if num_open == 0:
-            break
-
-    return parse_PST(buf, hash_is_var)
-
-def parse_PST(line, hash_is_var=True):
-    line = line.rstrip()
-    if not line or line.lower() == 'null':
-        return None
-
-    # allow either (a/DT) or (DT a)
-    #parts_re = re.compile(r'(\(*)([^/)]*)(?:/([^)]*))?(\)*)$')
-
-    # only allow (DT a)
-    parts_re = re.compile(r'(\(*)([^)]*)(\)*)$')
-
-    root = TreeNode(Symbol('TOP'))
-    stack = [root]
-    for part in line.rstrip().split():
-        m = parts_re.match(part)
-        #opening, tok_or_tag, tag, closing = m.groups()
-        opening, tok_or_tag, closing = m.groups()
-	tag = None
-        #print 'token', part, 'bits', m.groups()
-        for i in opening:
-            node = TreeNode(Symbol(None))
-            stack[-1].insert(node)
-            stack.append(node)
-
-        if tag:
-            stack[-1].data.tag = tag
-            if hash_is_var and tok_or_tag.startswith('#'):
-                stack[-1].data.variable = int(tok_or_tag[1:])
-            else:
-                stack[-1].data.token = tok_or_tag
-        else:
-            if stack[-1].data.tag == None:
-                stack[-1].data.tag = tok_or_tag
-            else:
-                if hash_is_var and tok_or_tag.startswith('#'):
-                    try:
-                        stack[-1].data.variable = int(tok_or_tag[1:])
-                    except ValueError: # it's really a token!
-                        #print >>sys.stderr, 'Warning: # used for token:', tok_or_tag
-                        stack[-1].data.token = tok_or_tag
-                else:
-                    stack[-1].data.token = tok_or_tag
-        
-        for i in closing:
-            stack.pop()
-
-    #assert str(root.children[0]) == line
-    return root.children[0]
-
-def read_DTs(fname):
-    infile = open(fname)
-    trees = []
-    while True:
-        t = parse_DT(infile)
-        if t: trees.append(t)
-        else: break
-    infile.close()
-    return trees
-
-def read_bracketed_DTs(fname):
-    infile = open(fname)
-    trees = []
-    for line in infile:
-        trees.append(parse_bracketed_DT(line))
-    infile.close()
-    return trees
-
-def parse_DT(infile):
-    tokens = [Symbol('ROOT')]
-    children = {}
-
-    for line in infile:
-        parts = line.rstrip().split()
-        #print parts
-        if not parts: break
-        index = len(tokens)
-        token = parts[1]
-        tag = parts[3]
-        parent = int(parts[6])
-        if token.startswith('#'):
-            tokens.append(Symbol(tag, var=int(token[1:])))
-        else:
-            tokens.append(Symbol(tag, token))
-        children.setdefault(parent, set()).add(index)
-
-    if len(tokens) == 1: return None
-
-    root = TreeNode(Symbol('ROOT'), [], 0)
-    schedule = []
-    for child in sorted(children[0]):
-        schedule.append((root, child))
-
-    while schedule:
-        parent, index = schedule[0]
-        del schedule[0]
-    
-        node = TreeNode(tokens[index])
-        node.order = 0
-        parent.insert(node)
-
-        for child in sorted(children.get(index, [])):
-            schedule.append((node, child))
-            if child < index:
-                node.order += 1
-
-    return root
-
-_bracket_split_re = re.compile(r'([(]*)([^)/]*)(?:/([^)]*))?([)]*)')
-
-def parse_bracketed_DT(line, insert_root=True):
-    line = line.rstrip()
-    if not line or line == 'NULL': return None
-    #print line
-
-    root = TreeNode(Symbol('ROOT'))
-    stack = [root]
-    for part in line.rstrip().split():
-        m = _bracket_split_re.match(part)
-
-        for c in m.group(1):
-            node = TreeNode(Symbol(None))
-            stack[-1].insert(node)
-            stack.append(node)
-
-        if m.group(3) != None:
-            if m.group(2).startswith('#'):
-                stack[-1].data.variable = int(m.group(2)[1:])
-            else:
-                stack[-1].data.token = m.group(2)
-            stack[-1].data.tag = m.group(3)
-        else:
-            stack[-1].data.tag = m.group(2)
-        stack[-1].order = len(stack[-1].children)
-        # FIXME: also check for vars
-
-        for c in m.group(4):
-            stack.pop()
-
-    assert len(stack) == 1
-    if not insert_root or root.children[0].data.tag == 'ROOT':
-        return root.children[0]
-    else:
-        return root
-
-_bracket_split_notag_re = re.compile(r'([(]*)([^)/]*)([)]*)')
-
-def parse_bracketed_untagged_DT(line):
-    line = line.rstrip()
-    if not line or line == 'NULL': return None
-
-    root = TreeNode(Symbol('TOP'))
-    stack = [root]
-    for part in line.rstrip().split():
-        m = _bracket_split_notag_re.match(part)
-
-        for c in m.group(1):
-            node = TreeNode(Symbol(None))
-            stack[-1].insert(node)
-            stack.append(node)
-
-        if stack[-1].data.token == None:
-            stack[-1].data.token = m.group(2)
-            stack[-1].order = len(stack[-1].children)
-        else:
-            child = TreeNode(Symbol(nonterm=None, term=m.group(2)))
-            stack[-1].insert(child)
-
-        for c in m.group(3):
-            stack.pop()
-
-    return root.children[0]
diff --git a/gi/markov_al/Makefile.am b/gi/markov_al/Makefile.am
deleted file mode 100644
index fe3e3349..00000000
--- a/gi/markov_al/Makefile.am
+++ /dev/null
@@ -1,6 +0,0 @@
-bin_PROGRAMS = ml
-
-ml_SOURCES = ml.cc
-
-AM_CPPFLAGS = -W -Wall -Wno-sign-compare -funroll-loops -I$(top_srcdir)/utils $(GTEST_CPPFLAGS) -I$(top_srcdir)/decoder
-AM_LDFLAGS = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
diff --git a/gi/markov_al/README b/gi/markov_al/README
deleted file mode 100644
index 9c10f7cd..00000000
--- a/gi/markov_al/README
+++ /dev/null
@@ -1,2 +0,0 @@
-Experimental translation models with Markovian dependencies.
-
diff --git a/gi/markov_al/ml.cc b/gi/markov_al/ml.cc
deleted file mode 100644
index 1e71edd6..00000000
--- a/gi/markov_al/ml.cc
+++ /dev/null
@@ -1,470 +0,0 @@
-#include <iostream>
-#include <tr1/unordered_map>
-
-#include <boost/shared_ptr.hpp>
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "tdict.h"
-#include "filelib.h"
-#include "sampler.h"
-#include "ccrp_onetable.h"
-#include "array2d.h"
-
-using namespace std;
-using namespace std::tr1;
-namespace po = boost::program_options;
-
-void PrintTopCustomers(const CCRP_OneTable<WordID>& crp) {
-  for (CCRP_OneTable<WordID>::const_iterator it = crp.begin(); it != crp.end(); ++it) {
-    cerr << "  " << TD::Convert(it->first) << " = " << it->second << endl;
-  }
-}
-
-void PrintAlignment(const vector<WordID>& src, const vector<WordID>& trg, const vector<unsigned char>& a) {
-  cerr << TD::GetString(src) << endl << TD::GetString(trg) << endl;
-  Array2D<bool> al(src.size(), trg.size());
-  for (int i = 0; i < a.size(); ++i)
-    if (a[i] != 255) al(a[i], i) = true;
-  cerr << al << endl;
-}
-
-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,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);
-  }
-}
-
-struct Unigram;
-struct Bigram {
-  Bigram() : trg(), cond() {}
-  Bigram(WordID prev, WordID cur, WordID t) : trg(t) { cond.first = prev; cond.second = cur; }
-  const pair<WordID,WordID>& ConditioningPair() const {
-    return cond;
-  }
-  WordID& prev_src() { return cond.first; }
-  WordID& cur_src() { return cond.second; }
-  const WordID& prev_src() const { return cond.first; }
-  const WordID& cur_src() const { return cond.second; }
-  WordID trg;
- private:
-  pair<WordID, WordID> cond;
-};
-
-struct Unigram {
-  Unigram() : cur_src(), trg() {}
-  Unigram(WordID s, WordID t) : cur_src(s), trg(t) {}
-  WordID cur_src;
-  WordID trg;
-};
-
-ostream& operator<<(ostream& os, const Bigram& b) {
-  os << "( " << TD::Convert(b.trg) << " | " << TD::Convert(b.prev_src()) << " , " << TD::Convert(b.cur_src()) << " )";
-  return os;
-}
-
-ostream& operator<<(ostream& os, const Unigram& u) {
-  os << "( " << TD::Convert(u.trg) << " | " << TD::Convert(u.cur_src) << " )";
-  return os;
-}
-
-bool operator==(const Bigram& a, const Bigram& b) {
-  return a.trg == b.trg && a.cur_src() == b.cur_src() && a.prev_src() == b.prev_src();
-}
-
-bool operator==(const Unigram& a, const Unigram& b) {
-  return a.trg == b.trg && a.cur_src == b.cur_src;
-}
-
-size_t hash_value(const Bigram& b) {
-  size_t h = boost::hash_value(b.prev_src());
-  boost::hash_combine(h, boost::hash_value(b.cur_src()));
-  boost::hash_combine(h, boost::hash_value(b.trg));
-  return h;
-}
-
-size_t hash_value(const Unigram& u) {
-  size_t h = boost::hash_value(u.cur_src);
-  boost::hash_combine(h, boost::hash_value(u.trg));
-  return h;
-}
-
-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;
-}
-
-struct UnigramModel {
-  UnigramModel(size_t src_voc_size, size_t trg_voc_size) :
-    unigrams(TD::NumWords() + 1, CCRP_OneTable<WordID>(1,1,1,1)),
-    p0(1.0 / trg_voc_size) {}
-
-  void increment(const Bigram& b) {
-    unigrams[b.cur_src()].increment(b.trg);
-  }
-
-  void decrement(const Bigram& b) {
-    unigrams[b.cur_src()].decrement(b.trg);
-  }
-
-  double prob(const Bigram& b) const {
-    const double q0 = unigrams[b.cur_src()].prob(b.trg, p0);
-    return q0;
-  }
-
-  double LogLikelihood() const {
-    double llh = 0;
-    for (unsigned i = 0; i < unigrams.size(); ++i) {
-      const CCRP_OneTable<WordID>& crp = unigrams[i];
-      if (crp.num_customers() > 0) {
-        llh += crp.log_crp_prob();
-        llh += crp.num_tables() * log(p0);
-      }
-    }
-    return llh;
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    for (unsigned i = 0; i < unigrams.size(); ++i)
-      unigrams[i].resample_hyperparameters(rng);
-  }
-
-  vector<CCRP_OneTable<WordID> > unigrams;  // unigrams[src].prob(trg, p0) = p(trg|src)
-
-  const double p0;
-};
-
-struct BigramModel {
-  BigramModel(size_t src_voc_size, size_t trg_voc_size) :
-    unigrams(TD::NumWords() + 1, CCRP_OneTable<WordID>(1,1,1,1)),
-    p0(1.0 / trg_voc_size) {}
-
-  void increment(const Bigram& b) {
-    BigramMap::iterator it = bigrams.find(b.ConditioningPair());
-    if (it == bigrams.end()) {
-      it = bigrams.insert(make_pair(b.ConditioningPair(), CCRP_OneTable<WordID>(1,1,1,1))).first;
-    }
-    if (it->second.increment(b.trg))
-      unigrams[b.cur_src()].increment(b.trg);
-  }
-
-  void decrement(const Bigram& b) {
-    BigramMap::iterator it = bigrams.find(b.ConditioningPair());
-    assert(it != bigrams.end());
-    if (it->second.decrement(b.trg)) {
-      unigrams[b.cur_src()].decrement(b.trg);
-      if (it->second.num_customers() == 0)
-        bigrams.erase(it);
-    }
-  }
-
-  double prob(const Bigram& b) const {
-    const double q0 = unigrams[b.cur_src()].prob(b.trg, p0);
-    const BigramMap::const_iterator it = bigrams.find(b.ConditioningPair());
-    if (it == bigrams.end()) return q0;
-    return it->second.prob(b.trg, q0);
-  }
-
-  double LogLikelihood() const {
-    double llh = 0;
-    for (unsigned i = 0; i < unigrams.size(); ++i) {
-      const CCRP_OneTable<WordID>& crp = unigrams[i];
-      if (crp.num_customers() > 0) {
-        llh += crp.log_crp_prob();
-        llh += crp.num_tables() * log(p0);
-      }
-    }
-    for (BigramMap::const_iterator it = bigrams.begin(); it != bigrams.end(); ++it) {
-      const CCRP_OneTable<WordID>& crp = it->second;
-      const WordID cur_src = it->first.second;
-      llh += crp.log_crp_prob();
-      for (CCRP_OneTable<WordID>::const_iterator bit = crp.begin(); bit != crp.end(); ++bit) {
-        llh += log(unigrams[cur_src].prob(bit->second, p0));
-      }
-    }
-    return llh;
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    for (unsigned i = 0; i < unigrams.size(); ++i)
-      unigrams[i].resample_hyperparameters(rng);
-    for (BigramMap::iterator it = bigrams.begin(); it != bigrams.end(); ++it)
-      it->second.resample_hyperparameters(rng);
-  }
-
-  typedef unordered_map<pair<WordID,WordID>, CCRP_OneTable<WordID>, boost::hash<pair<WordID,WordID> > > BigramMap;
-  BigramMap bigrams;  // bigrams[(src-1,src)].prob(trg, q0) = p(trg|src,src-1)
-  vector<CCRP_OneTable<WordID> > unigrams;  // unigrams[src].prob(trg, p0) = p(trg|src)
-
-  const double p0;
-};
-
-struct BigramAlignmentModel {
-  BigramAlignmentModel(size_t src_voc_size, size_t trg_voc_size) : bigrams(TD::NumWords() + 1, CCRP_OneTable<WordID>(1,1,1,1)), p0(1.0 / src_voc_size) {}
-  void increment(WordID prev, WordID next) {
-    bigrams[prev].increment(next);  // hierarchy?
-  }
-  void decrement(WordID prev, WordID next) {
-    bigrams[prev].decrement(next);  // hierarchy?
-  }
-  double prob(WordID prev, WordID next) {
-    return bigrams[prev].prob(next, p0);
-  }
-  double LogLikelihood() const {
-    double llh = 0;
-    for (unsigned i = 0; i < bigrams.size(); ++i) {
-      const CCRP_OneTable<WordID>& crp = bigrams[i];
-      if (crp.num_customers() > 0) {
-        llh += crp.log_crp_prob();
-        llh += crp.num_tables() * log(p0);
-      }
-    }
-    return llh;
-  }
-
-  vector<CCRP_OneTable<WordID> > bigrams;  // bigrams[prev].prob(next, p0) = p(next|prev)
-  const double p0;
-};
-
-struct Alignment {
-  vector<unsigned char> a;
-};
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  const unsigned samples = conf["samples"].as<unsigned>();
-
-  boost::shared_ptr<MT19937> prng;
-  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 size_t corpus_len = corpusf.size();
-  const WordID kNULL = TD::Convert("<eps>");
-  const WordID kBOS = TD::Convert("<s>");
-  const WordID kEOS = TD::Convert("</s>");
-  Bigram TT(kBOS, TD::Convert("我"), TD::Convert("i"));
-  Bigram TT2(kBOS, TD::Convert("要"), TD::Convert("i"));
-
-  UnigramModel model(vocabf.size(), vocabe.size());
-  vector<Alignment> alignments(corpus_len);
-  for (unsigned ci = 0; ci < corpus_len; ++ci) {
-    const vector<WordID>& src = corpusf[ci];
-    const vector<WordID>& trg = corpuse[ci];
-    vector<unsigned char>& alg = alignments[ci].a;
-    alg.resize(trg.size());
-    int lenp1 = src.size() + 1;
-    WordID prev_src = kBOS;
-    for (int j = 0; j < trg.size(); ++j) {
-      int samp = lenp1 * rng.next();
-      --samp;
-      if (samp < 0) samp = 255;
-      alg[j] = samp;
-      WordID cur_src = (samp == 255 ? kNULL : src[alg[j]]);
-      Bigram b(prev_src, cur_src, trg[j]);
-      model.increment(b);
-      prev_src = cur_src;
-    }
-    Bigram b(prev_src, kEOS, kEOS);
-    model.increment(b);
-  }
-  cerr << "Initial LLH: " << model.LogLikelihood() << endl;
-
-  SampleSet<double> ss;
-  for (unsigned si = 0; si < 50; ++si) {
-    for (unsigned ci = 0; ci < corpus_len; ++ci) {
-      const vector<WordID>& src = corpusf[ci];
-      const vector<WordID>& trg = corpuse[ci];
-      vector<unsigned char>& alg = alignments[ci].a;
-      WordID prev_src = kBOS;
-      for (unsigned j = 0; j < trg.size(); ++j) {
-        unsigned char& a_j = alg[j];
-        WordID cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]);
-        Bigram b(prev_src, cur_e_a_j, trg[j]);
-        //cerr << "DEC: " << b << "\t" << nextb << endl;
-        model.decrement(b);
-        ss.clear();
-        for (unsigned i = 0; i <= src.size(); ++i) {
-          const WordID cur_src = (i ? src[i-1] : kNULL);
-          b.cur_src() = cur_src;
-          ss.add(model.prob(b));
-        }
-        int sampled_a_j = rng.SelectSample(ss);
-        a_j = (sampled_a_j ? sampled_a_j - 1 : 255);
-        cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]);
-        b.cur_src() = cur_e_a_j;
-        //cerr << "INC: " << b << "\t" << nextb << endl;
-        model.increment(b);
-        prev_src = cur_e_a_j;
-      }
-    }
-    cerr << '.' << flush;
-    if (si % 10 == 9) {
-      cerr << "[LLH prev=" << model.LogLikelihood();
-      //model.ResampleHyperparameters(&rng);
-      cerr << " new=" << model.LogLikelihood() << "]\n";
-      //pair<WordID,WordID> xx = make_pair(kBOS, TD::Convert("我"));
-      //PrintTopCustomers(model.bigrams.find(xx)->second);
-      cerr << "p(" << TT << ") = " << model.prob(TT) << endl;
-      cerr << "p(" << TT2 << ") = " << model.prob(TT2) << endl;
-      PrintAlignment(corpusf[0], corpuse[0], alignments[0].a);
-    }
-  }
-  {
-  // MODEL 2
-  BigramModel model(vocabf.size(), vocabe.size());
-  BigramAlignmentModel amodel(vocabf.size(), vocabe.size());
-  for (unsigned ci = 0; ci < corpus_len; ++ci) {
-    const vector<WordID>& src = corpusf[ci];
-    const vector<WordID>& trg = corpuse[ci];
-    vector<unsigned char>& alg = alignments[ci].a;
-    WordID prev_src = kBOS;
-    for (int j = 0; j < trg.size(); ++j) {
-      WordID cur_src = (alg[j] == 255 ? kNULL : src[alg[j]]);
-      Bigram b(prev_src, cur_src, trg[j]);
-      model.increment(b);
-      amodel.increment(prev_src, cur_src);
-      prev_src = cur_src;
-    }
-    amodel.increment(prev_src, kEOS);
-    Bigram b(prev_src, kEOS, kEOS);
-    model.increment(b);
-  }
-  cerr << "Initial LLH: " << model.LogLikelihood() << " " << amodel.LogLikelihood() << endl;
-
-  SampleSet<double> ss;
-  for (unsigned si = 0; si < samples; ++si) {
-    for (unsigned ci = 0; ci < corpus_len; ++ci) {
-      const vector<WordID>& src = corpusf[ci];
-      const vector<WordID>& trg = corpuse[ci];
-      vector<unsigned char>& alg = alignments[ci].a;
-      WordID prev_src = kBOS;
-      for (unsigned j = 0; j < trg.size(); ++j) {
-        unsigned char& a_j = alg[j];
-        WordID cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]);
-        Bigram b(prev_src, cur_e_a_j, trg[j]);
-        WordID next_src = kEOS;
-        WordID next_trg = kEOS;
-        if (j < (trg.size() - 1)) {
-          next_src = (alg[j+1] == 255 ? kNULL : src[alg[j + 1]]);
-          next_trg = trg[j + 1];
-        }
-        Bigram nextb(cur_e_a_j, next_src, next_trg);
-        //cerr << "DEC: " << b << "\t" << nextb << endl;
-        model.decrement(b);
-        model.decrement(nextb);
-        amodel.decrement(prev_src, cur_e_a_j);
-        amodel.decrement(cur_e_a_j, next_src);
-        ss.clear();
-        for (unsigned i = 0; i <= src.size(); ++i) {
-          const WordID cur_src = (i ? src[i-1] : kNULL);
-          b.cur_src() = cur_src;
-          ss.add(model.prob(b) * model.prob(nextb) * amodel.prob(prev_src, cur_src) * amodel.prob(cur_src, next_src));
-          //cerr << log(ss[ss.size() - 1]) << "\t" << b << endl;
-        }
-        int sampled_a_j = rng.SelectSample(ss);
-        a_j = (sampled_a_j ? sampled_a_j - 1 : 255);
-        cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]);
-        b.cur_src() = cur_e_a_j;
-        nextb.prev_src() = cur_e_a_j;
-        //cerr << "INC: " << b << "\t" << nextb << endl;
-        //exit(1);
-        model.increment(b);
-        model.increment(nextb);
-        amodel.increment(prev_src, cur_e_a_j);
-        amodel.increment(cur_e_a_j, next_src);
-        prev_src = cur_e_a_j;
-      }
-    }
-    cerr << '.' << flush;
-    if (si % 10 == 9) {
-      cerr << "[LLH prev=" << (model.LogLikelihood() + amodel.LogLikelihood());
-      //model.ResampleHyperparameters(&rng);
-      cerr << " new=" << model.LogLikelihood() << "]\n";
-      pair<WordID,WordID> xx = make_pair(kBOS, TD::Convert("我"));
-      cerr << "p(" << TT << ") = " << model.prob(TT) << endl;
-      cerr << "p(" << TT2 << ") = " << model.prob(TT2) << endl;
-      pair<WordID,WordID> xx2 = make_pair(kBOS, TD::Convert("要"));
-      PrintTopCustomers(model.bigrams.find(xx)->second);
-      //PrintTopCustomers(amodel.bigrams[TD::Convert("<s>")]);
-      //PrintTopCustomers(model.unigrams[TD::Convert("<eps>")]);
-      PrintAlignment(corpusf[0], corpuse[0], alignments[0].a);
-    }
-  }
-  }
-  return 0;
-}
-
diff --git a/gi/morf-segmentation/filter_docs.pl b/gi/morf-segmentation/filter_docs.pl
deleted file mode 100755
index a78575da..00000000
--- a/gi/morf-segmentation/filter_docs.pl
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/perl
-
-#Filters the phrase&cluster document set to retain only documents that correspond to words or morphs, i.e. not crossing word boundaries.
-
-#Usage: filter_docs.pl [mark]
-#  STDIN: data in the doc.txt format (i.e. phrase\t blahblah ), most likely from cdec extractor
-#  STDOUT: the matching subset, same format
-
-use utf8;
-my $letter=qr/\p{L}\p{M}*/; # see http://www.regular-expressions.info/unicode.html
-
-my $morph=qr/$letter+/;
-
-my $m = "##"; # marker used to indicate morphemes
-if ((scalar @ARGV) >= 1) {
-   $m = $ARGV[0];
-   shift;
-}
-print STDERR "Using $m to filter for morphemes\n";
-
-my $expr = qr/^($morph\Q$m\E)? ?(\Q$m\E$morph\Q$m\E)* ?(\Q$m\E$morph)?\t/; #\Q and \E bounded sections are escaped
-while(<>) {
-   /$expr/ && print;
-}
diff --git a/gi/morf-segmentation/invalid_vocab.patterns b/gi/morf-segmentation/invalid_vocab.patterns
deleted file mode 100644
index 473ce1b1..00000000
--- a/gi/morf-segmentation/invalid_vocab.patterns
+++ /dev/null
@@ -1,6 +0,0 @@
-[[:digit:]]
-[] !"#$%&()*+,./:;<=>?@[\^_`{|}~]
-^'$
--$
-^-
-^$
diff --git a/gi/morf-segmentation/linestripper.py b/gi/morf-segmentation/linestripper.py
deleted file mode 100755
index 04e9044a..00000000
--- a/gi/morf-segmentation/linestripper.py
+++ /dev/null
@@ -1,40 +0,0 @@
-#!/usr/bin/python
-
-import sys
-
-#linestripper   file file maxlen [numlines]
-
-if len(sys.argv) < 3:
-  print "linestripper   file1 file2 maxlen [numlines]" 
-  print " outputs subset of file1 to stdout, ..of file2 to stderr"
-  sys.exit(1)
-
-
-f1 = open(sys.argv[1],'r')
-f2 = open(sys.argv[2],'r')
-
-maxlen=int(sys.argv[3])
-numlines = 0
-
-if len(sys.argv) > 4:
-  numlines = int(sys.argv[4])
-
-count=0
-for line1 in f1:
-  line2 = f2.readline()
-  
-  w1 = len(line1.strip().split())
-  w2 = len(line2.strip().split())
-
-  if w1 <= maxlen and w2 <= maxlen:
-    count = count + 1
-    sys.stdout.write(line1)
-    sys.stderr.write(line2)
- 
-  if numlines > 0 and count >= numlines:
-    break
-
-f1.close()
-f2.close()
-  
-
diff --git a/gi/morf-segmentation/morf-pipeline.pl b/gi/morf-segmentation/morf-pipeline.pl
deleted file mode 100755
index 46eb5b46..00000000
--- a/gi/morf-segmentation/morf-pipeline.pl
+++ /dev/null
@@ -1,486 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-use File::Copy;
-
-
-# Preprocessing pipeline to take care of word segmentation
-# Learns a segmentation model for each/either side of the parallel corpus using all train/dev/test data
-# Applies the segmentation where necessary.
-# Learns word alignments on the preprocessed training data.
-# Outputs script files used later to score output.
-
-
-my $SCRIPT_DIR; BEGIN { use Cwd qw/ abs_path cwd /; use File::Basename; $SCRIPT_DIR = dirname(abs_path($0)); push @INC, $SCRIPT_DIR; }
-
-use Getopt::Long "GetOptions";
-
-my $GZIP = 'gzip';
-my $ZCAT = 'gunzip -c';
-my $SED = 'sed -e';
-
-my $MORF_TRAIN = "$SCRIPT_DIR/morftrain.sh";
-my $MORF_SEGMENT = "$SCRIPT_DIR/morfsegment.py";
-
-my $LINESTRIPPER = "$SCRIPT_DIR/linestripper.py";
-my $ALIGNER = "/export/ws10smt/software/berkeleyaligner/berkeleyaligner.jar";
-#java -d64 -Xmx10g -jar $ALIGNER ++word-align.conf >> aligner.log
-assert_exec($MORF_TRAIN, $LINESTRIPPER, $MORF_SEGMENT, $ALIGNER);
-
-my $OUTPUT = './morfwork';
-my $PPL_SRC = 50;
-my $PPL_TRG = 50;
-my $MARKER = "#";
-my $MAX_WORDS = 40;
-my $SENTENCES;# = 100000;
-my $SPLIT_TYPE = ""; #possible values: s, t, st, or (empty string)
-my $NAME_SHORTCUT;
-
-usage() unless &GetOptions('max_words=i' => \$MAX_WORDS,
-                           'output=s' => \$OUTPUT,
-                           'ppl_src=i' => \$PPL_SRC,
-                           'ppl_trg=i' => \$PPL_TRG,
-                           'sentences=i' => \$SENTENCES,
-                           'marker=s' => \$MARKER,
-                           'split=s' => \$SPLIT_TYPE,
-                           'get_name_only' => \$NAME_SHORTCUT,
-                          );
-
-usage() unless scalar @ARGV >= 2;
-
-my %CORPUS; # for (src,trg) it has (orig, name, filtered, final)
-
-$CORPUS{'src'}{'orig'} = $ARGV[0];
-open F, "<$CORPUS{'src'}{'orig'}" or die "Can't read $CORPUS{'src'}{'orig'}: $!"; close F;
-$CORPUS{'src'}{'name'} = get_basename($CORPUS{'src'}{'orig'});
-
-$CORPUS{'trg'}{'orig'} = $ARGV[1];
-open F, "<$CORPUS{'trg'}{'orig'}" or die "Can't read $CORPUS{'trg'}{'orig'}: $!"; close F;
-$CORPUS{'trg'}{'name'} = get_basename($CORPUS{'trg'}{'orig'});
-
-my %DEV; # for (src,trg) has (orig, final.split final.unsplit
-if (@ARGV >= 4) {
-  $DEV{'src'}{'orig'} = $ARGV[2];
-  open F, "<$DEV{'src'}{'orig'}" or die "Can't read $DEV{'src'}{'orig'}: $!"; close F;
-  $DEV{'src'}{'name'} = get_basename($DEV{'src'}{'orig'});
-  $DEV{'trg'}{'orig'} = $ARGV[3];
-  open F, "<$DEV{'trg'}{'orig'}" or die "Can't read $DEV{'trg'}{'orig'}: $!"; close F;
-  $DEV{'trg'}{'name'} = get_basename($DEV{'trg'}{'orig'});
-}
-
-my %TEST; # for (src,trg) has (orig, name) 
-if (@ARGV >= 6) {
-  $TEST{'src'}{'orig'} = $ARGV[4];
-  open F, "<$TEST{'src'}{'orig'}" or die "Can't read $TEST{'src'}{'orig'}: $!"; close F;
-  $TEST{'src'}{'name'} = get_basename($TEST{'src'}{'orig'});
-  $TEST{'trg'}{'orig'} = $ARGV[5];
-  open F, "<$TEST{'trg'}{'orig'}" or die "Can't read $TEST{'trg'}{'orig'}: $!"; close F;
-  $TEST{'trg'}{'name'} = get_basename($TEST{'trg'}{'orig'});
-}
-
-my $SPLIT_SRC; #use these to check whether that part is being split
-my $SPLIT_TRG;
-
-#OUTPUT WILL GO IN THESE
-my $CORPUS_DIR = $OUTPUT . '/' . corpus_dir();            #subsampled corpus
-my $MODEL_SRC_DIR = $OUTPUT . '/' . model_dir("src"); #splitting..
-my $MODEL_TRG_DIR = $OUTPUT . '/' . model_dir("trg"); # .. models
-my $PROCESSED_DIR = $OUTPUT . '/' . processed_dir();      #segmented copora+alignments
-my $ALIGNMENT_DIR = $PROCESSED_DIR . '/alignments';
-
-$CORPUS{'src'}{'filtered'} = $CORPUS_DIR . "/$CORPUS{'src'}{'name'}";
-$CORPUS{'trg'}{'filtered'} = $CORPUS_DIR . "/$CORPUS{'trg'}{'name'}";
-
-print STDERR "Output: $OUTPUT\n";
-print STDERR "Corpus: $CORPUS_DIR\n";
-print STDERR "Model-src: $MODEL_SRC_DIR\n";
-print STDERR "Model-trg: $MODEL_TRG_DIR\n";
-print STDERR "Finaldir: $PROCESSED_DIR\n";
-
-safemkdir($OUTPUT) or die "Couldn't create output directory $OUTPUT: $!";
-safemkdir($CORPUS_DIR) or die "Couldn't create output directory $CORPUS_DIR: $!";
-filter_corpus();
-
-safemkdir($PROCESSED_DIR);
-safemkdir($ALIGNMENT_DIR);
-
-if ($SPLIT_SRC) {
-  safemkdir($MODEL_SRC_DIR) or die "Couldn't create output directory $MODEL_SRC_DIR: $!";
-  learn_segmentation("src");
-  apply_segmentation_side("src", $MODEL_SRC_DIR);  
-}
-
-#assume that unsplit hypotheses will be scored against an aritificially split target test set; thus obtain a target splitting model  
-#TODO: add a flag to override this behaviour
-safemkdir($MODEL_TRG_DIR) or die "Couldn't create output directory $MODEL_TRG_DIR: $!";
-learn_segmentation("trg");
-$TEST{'trg'}{'finalunsplit'} = "$PROCESSED_DIR/$TEST{'trg'}{'name'}";
-copy($TEST{'trg'}{'orig'}, $TEST{'trg'}{'finalunsplit'}) or die "Could not copy unsegmented test set";  
-
-if ($SPLIT_TRG) {
-  apply_segmentation_side("trg", $MODEL_TRG_DIR);  
-  } else {
-  $TEST{'trg'}{'finalsplit'} = "$PROCESSED_DIR/$TEST{'trg'}{'name'}.split";
-  apply_segmentation_any($MODEL_TRG_DIR, $TEST{'trg'}{'finalunsplit'}, $TEST{'trg'}{'finalsplit'});  
-}
-
-write_eval_sh("$PROCESSED_DIR/eval-devtest.sh");
-
-#copy corpora if they haven't been put in place by splitting operations
-place_missing_data_side('src');
-place_missing_data_side('trg');
-
-do_align();
-
-if ($CORPUS{'src'}{'orig'} && $DEV{'src'}{'orig'} && $TEST{'src'}{'orig'}) {
-  print STDERR "Putting the config file entry in $PROCESSED_DIR/exp.config\n";
-#format is:
-  # nlfr100k_unsplit /export/ws10smt/jan/nlfr/morfwork/s100k.w40.sp_0 corpus.nl-fr.al fr-3.lm.gz dev.nl dev.fr test2008.nl eval-devtest.sh
-  my $line = split_name() . " $PROCESSED_DIR corpus.src-trg.al LMFILE.lm.gz";
-  $line = $line . " $DEV{'src'}{'name'} $DEV{'trg'}{'name'}";
-  $line = $line . " " . get_basename($TEST{'src'}{$SPLIT_SRC ? "finalsplit" : "finalunsplit"}) . " eval-devtest.sh";
-  safesystem("echo '$line' > $PROCESSED_DIR/exp.config");
-}
-
-system("date");
-print STDERR "All done. You now need to train a language model (if target split), put it in the right dir and update the config file.\n\n";
-
-############################## BILINGUAL ###################################
-
-sub filter_corpus {
-  print STDERR "\n!!!FILTERING TRAINING COPRUS!!!\n";
-  if ( -f $CORPUS{'src'}{'filtered'} && -f $CORPUS{'trg'}{'filtered'}) {
-    print STDERR "$CORPUS{'src'}{'filtered'} and $CORPUS{'trg'}{'filtered'} exist, reusing...\n";
-    return;
-  }
-  my $args = "$CORPUS{'src'}{'orig'} $CORPUS{'trg'}{'orig'} $MAX_WORDS";
-  if ($SENTENCES) { $args = $args . " $SENTENCES"; } 
-  safesystem("$LINESTRIPPER $args 1> $CORPUS{'src'}{'filtered'} 2> $CORPUS{'trg'}{'filtered'}") or die "Failed to filter training corpus for length.";
-}
-
-sub learn_segmentation
-{
-  my $WHICH = shift;
-  my $corpus; my $dev; my $test; my $moddir;  my $ppl;
-
-  $corpus = $CORPUS{$WHICH}{'filtered'};
-  $dev = $DEV{$WHICH}{'orig'};
-  $test = $TEST{$WHICH}{'orig'};
-
-  if ($WHICH eq "src") {
-    $moddir = $MODEL_SRC_DIR;
-    $ppl = $PPL_SRC;
-  } else {
-    $moddir = $MODEL_TRG_DIR;
-    $ppl = $PPL_TRG;
-  }
-  my $cmd = "cat $corpus";
-  if ($dev) { $cmd = "$cmd $dev"; }
-  if ($test) { $cmd = "$cmd $test"; }
-  my $tmpfile = "$CORPUS_DIR/all.tmp.gz";
-  safesystem("$cmd | $GZIP > $tmpfile") or die "Failed to concatenate data for model learning..";
-  assert_marker($tmpfile);
-
-  learn_segmentation_side($tmpfile, $moddir, $ppl, $WHICH);
-  safesystem("rm $tmpfile");
-}
-
-sub do_align {
-  print STDERR "\n!!!WORD ALIGNMENT!!!\n";
-  system("date");
-
-  my $ALIGNMENTS = "$ALIGNMENT_DIR/training.align";
-  if ( -f $ALIGNMENTS ) {
-    print STDERR "$ALIGNMENTS  exists, reusing...\n";
-    return;
-  } 
-  my $conf_file = "$ALIGNMENT_DIR/word-align.conf";
-    
-  #decorate training files with identifiers to stop the aligner from training on dev and test when rerun in future.
-  safesystem("cd $PROCESSED_DIR && ln -s $CORPUS{'src'}{'name'} corpus.src") or die "Failed to symlink: $!";
-  safesystem("cd $PROCESSED_DIR && ln -s $CORPUS{'trg'}{'name'} corpus.trg") or die "Failed to symlink: $!";
-
-  write_wconf($conf_file, $PROCESSED_DIR);  
-  system("java -d64 -Xmx24g -jar $ALIGNER ++$conf_file > $ALIGNMENT_DIR/aligner.log");
-
-  if (! -f $ALIGNMENTS) { die "Failed to run word alignment.";}
-
-  my $cmd = "paste $PROCESSED_DIR/corpus.src $PROCESSED_DIR/corpus.trg $ALIGNMENTS";
-  $cmd = $cmd . " | sed 's/\\t/ \|\|\| /g' > $PROCESSED_DIR/corpus.src-trg.al";
-  safesystem($cmd) or die "Failed to paste into aligned corpus file.";
-
-}
-
-############################# MONOLINGUAL #################################
-
-#copy the necessary data files that weren't place by segmentation
-sub place_missing_data_side {
-  my $side = shift;
-
-  ifne_copy($CORPUS{$side}{'filtered'}, "$PROCESSED_DIR/$CORPUS{$side}{'name'}") ;
-
-  if ($DEV{$side}{'orig'} && ! -f "$PROCESSED_DIR/$DEV{$side}{'name'}") {
-    $DEV{$side}{'final'} = "$PROCESSED_DIR/$DEV{$side}{'name'}";
-    copy($DEV{$side}{'orig'}, $DEV{$side}{'final'}) or die "Copy failed: $!";
-  }
-
-  if ($TEST{$side}{'orig'} && ! -f "$PROCESSED_DIR/$TEST{$side}{'name'}" && ! $TEST{$side}{'finalunsplit'}) {
-    $TEST{$side}{'finalunsplit'} = "$PROCESSED_DIR/$TEST{$side}{'name'}";
-    copy($TEST{$side}{'orig'}, $TEST{$side}{'finalunsplit'}) or die "Copy failed: $!";
-  }
-
-}
-
-sub apply_segmentation_side {
-  my ($side, $moddir) = @_;
- 
-  print STDERR "\n!!!APPLYING SEGMENTATION MODEL ($side)!!!\n";
-  apply_segmentation_any($moddir, $CORPUS{$side}{'filtered'}, "$PROCESSED_DIR/$CORPUS{$side}{'name'}");
-  if ($DEV{$side}{'orig'}) {
-     $DEV{$side}{'final'} = "$PROCESSED_DIR/$DEV{$side}{'name'}";
-    apply_segmentation_any($moddir, $DEV{$side}{'orig'}, "$DEV{$side}{'final'}");
-  }
-  if ($TEST{$side}{'orig'}) {
-    $TEST{$side}{'finalsplit'} = "$PROCESSED_DIR/$TEST{$side}{'name'}.split";
-    apply_segmentation_any($moddir, $TEST{$side}{'orig'}, $TEST{$side}{'finalsplit'} );
-  } 
-
-}
-
-sub learn_segmentation_side {
-  my($INPUT_FILE, $SEGOUT_DIR, $PPL, $LANG) = @_;
-
-  print STDERR "\n!!!LEARNING SEGMENTATION MODEL ($LANG)!!!\n";
-  system("date");
-  my $SEG_FILE = $SEGOUT_DIR . "/segmentation.ready";
-   if ( -f $SEG_FILE) {
-    print STDERR "$SEG_FILE exists, reusing...\n";
-    return;
-  }
-  my $cmd = "$MORF_TRAIN $INPUT_FILE $SEGOUT_DIR $PPL \"$MARKER\"";
-  safesystem($cmd) or die "Failed to learn segmentation model";
-}
-
-sub apply_segmentation_any {
-  my($moddir, $datfile, $outfile) = @_;
-  if ( -f $outfile) {
-    print STDERR "$outfile exists, reusing...\n";
-    return;
-  }
-  
-  my $args = "$moddir/inputvocab.gz $moddir/segmentation.ready \"$MARKER\"";
-  safesystem("cat $datfile | $MORF_SEGMENT $args &> $outfile") or die "Could not segment $datfile";
-}
-
-##################### PATH FUNCTIONS ##########################
-
-sub beautify_numlines {
-  return ($SENTENCES ? $SENTENCES : "_all");
-}
-
-sub corpus_dir {
-  return "s" . beautify_numlines() . ".w" . $MAX_WORDS;
-}
-
-sub model_dir {
-  my $lang = shift;
-  if ($lang eq "src") { 
-    return corpus_dir() . ".PPL" . $PPL_SRC . ".src";
-  } elsif ($lang eq "trg") {
-    return corpus_dir() .  ".PPL" . $PPL_TRG . ".trg";
-  } else {
-    return "PPLundef";
-  }    
-}
-
-sub processed_dir {
-  return corpus_dir() . "." . split_name();
-}
-
-########################## HELPER FUNCTIONS ############################
-
-sub ifne_copy {
-  my ($src, $dest) = @_;
-  if (! -f $dest) {
-    copy($src, $dest) or die "Copy failed: $!";
-  }
-}
-
-sub split_name {
-  #parses SPLIT_TYPE, which can have the following values
-  # t|s|ts|st (last 2 are equiv)
-  # or is undefined when no splitting is done
-  my $name = "";
-  
-  if ($SPLIT_TYPE) { 
-    $SPLIT_SRC = lc($SPLIT_TYPE) =~ /s/;
-    $SPLIT_TRG = lc($SPLIT_TYPE) =~ /t/;
-    $name = $name . ($SPLIT_SRC ? $PPL_SRC : "0");
-    $name = $name . "_" . ($SPLIT_TRG ? $PPL_TRG : "0"); 
-  } else {
-    #no splitting
-    $name = "0";
-  }
-
-  return "sp_" . $name;
-  
-}
-
-sub usage {
-  print <<EOT;
-
-Usage: $0 [OPTIONS] corpus.src corpus.trg [dev.src dev.trg [test.src test.trg]]
-
-Learns a segmentation model and splits up corpora as necessary. Word alignments are trained on a specified subset of the training corpus.
-
-EOT
-  exit 1;
-};
-
-sub safemkdir {
-  my $dir = shift;
-  if (-d $dir) { return 1; }
-  return mkdir($dir);
-}
-
-sub assert_exec {
-  my @files = @_;
-  for my $file (@files) {
-    die "Can't find $file - did you run make?\n" unless -e $file;
-    die "Can't execute $file" unless -e $file;
-  }
-};
-sub safesystem {
-  print STDERR "Executing: @_\n";
-  system(@_);
-  if ($? == -1) {
-      print STDERR "ERROR: Failed to execute: @_\n  $!\n";
-      exit(1);
-  }
-  elsif ($? & 127) {
-      printf STDERR "ERROR: Execution of: @_\n  died with signal %d, %s coredump\n",
-          ($? & 127),  ($? & 128) ? 'with' : 'without';
-      exit(1);
-  }
-  else {
-    my $exitcode = $? >> 8;
-    print STDERR "Exit code: $exitcode\n" if $exitcode;
-    return ! $exitcode;
-  }
-}
-
-sub get_basename
-{
-  my $x = shift;
-  $x = `basename $x`;
-  $x =~ s/\n//;
-  return $x;
-}
-
-sub assert_marker {
-  my $file = shift;
-  my $result = `zcat $file| grep '$MARKER' | wc -l` or die "Cannot read $file: $!";
-  print $result; 
-  if (scalar($result) != 0) { die "Data contains marker '$MARKER'; use something else.";}
-}
-########################### Dynamic config files ##############################
-
-sub write_wconf {
-  my ($filename, $train_dir) = @_;
-  open WCONF, ">$filename" or die "Can't write $filename: $!";
-
-  print WCONF <<EOT;
-## ----------------------
-## This is an example training script for the Berkeley
-## word aligner.  In this configuration it uses two HMM
-## alignment models trained jointly and then decoded
-## using the competitive thresholding heuristic.
-
-##########################################
-# Training: Defines the training regimen
-##########################################
-forwardModels   MODEL1 HMM
-reverseModels   MODEL1 HMM
-mode    JOINT JOINT
-iters   5 5
-
-###############################################
-# Execution: Controls output and program flow
-###############################################
-execDir $ALIGNMENT_DIR
-create
-overwriteExecDir
-saveParams  true
-numThreads  1
-msPerLine   10000
-alignTraining
-
-#################
-# Language/Data
-#################
-foreignSuffix   src
-englishSuffix   trg
-
-# Choose the training sources, which can either be directories or files that list files/directories
-trainSources    $train_dir/
-#trainSources     $train_dir/sources
-testSources     
-sentences   MAX
-
-#################
-# 1-best output
-#################
-competitiveThresholding
-
-EOT
-  close WCONF;
-}
-
-sub write_eval_sh
-{
-  my ($filename) = @_;
-  open EVALFILE, ">$filename" or die "Can't write $filename: $!";
-
-  print EVALFILE <<EOT;
-#!/bin/bash
-
-EVAL_MAIN=/export/ws10smt/data/eval.sh
-marker="$MARKER"
-EOT
-
-  if ($SPLIT_TRG) {
-    print EVALFILE <<EOT;
-echo "OUTPUT EVALUATION"
-echo "-----------------"
-\$EVAL_MAIN "\$1" $TEST{'trg'}{'finalsplit'}
-
-echo "RECOMBINED OUTPUT EVALUATION"
-echo "----------------------------"
-cat "\$1" | sed -e "s/\$marker \$marker//g" -e "s/\$marker//g" > "\$1.recombined"
-
-\$EVAL_MAIN "\$1.recombined" $TEST{'trg'}{'finalunsplit'}
-EOT
-
-  } else {
-    print EVALFILE <<EOT;
-echo "ARTIFICIAL SPLIT EVALUATION"
-echo "--------------------------"
-
-#split the output translation
-cat "\$1" | $MORF_SEGMENT $MODEL_TRG_DIR/inputvocab.gz $MODEL_TRG_DIR/segmentation.ready "\$MARKER" > "\$1.split"
-
-\$EVAL_MAIN "\$1.split" $TEST{'trg'}{'finalsplit'}
-
-echo "DIRECT EVALUATION"
-echo "--------------------------"
-\$EVAL_MAIN "\$1" $TEST{'trg'}{'finalunsplit'}
-  
-EOT
-
-  }
-  close EVALFILE;
-
-}
-
-
-
-
diff --git a/gi/morf-segmentation/morfsegment.py b/gi/morf-segmentation/morfsegment.py
deleted file mode 100755
index 85b9d4fb..00000000
--- a/gi/morf-segmentation/morfsegment.py
+++ /dev/null
@@ -1,50 +0,0 @@
-#!/usr/bin/python
-
-import sys
-import gzip
-
-#usage: morfsegment.py inputvocab.gz segmentation.ready
-#  stdin: the data to segment
-#  stdout: the segmented data
-
-if len(sys.argv) < 3:
-  print "usage: morfsegment.py inputvocab.gz segmentation.ready [marker]"
-  print "  stdin: the data to segment"
-  print "  stdout: the segmented data"
-  sys.exit()
-
-#read index:
-split_index={}
-
-marker="##"
-
-if len(sys.argv) > 3:
-  marker=sys.argv[3]
-
-word_vocab=gzip.open(sys.argv[1], 'rb') #inputvocab.gz
-seg_vocab=open(sys.argv[2], 'r') #segm.ready..
-
-for seg in seg_vocab:
-  #seg = ver# #wonder\n
-  #wordline = 1 verwonder\n
-  word = word_vocab.readline().strip().split(' ')
-  assert(len(word) == 2)
-  word = word[1]
-  seg=seg.strip()
-
-  if seg != word:
-    split_index[word] = seg
-
-word_vocab.close()
-seg_vocab.close()
-
-for line in sys.stdin:
-  words = line.strip().split()
-
-  newsent = []
-  for word in words:
-    splitword = split_index.get(word, word)
-    newsent.append(splitword)
-
-  print ' '.join(newsent)
-
diff --git a/gi/morf-segmentation/morftrain.sh b/gi/morf-segmentation/morftrain.sh
deleted file mode 100755
index 9004922f..00000000
--- a/gi/morf-segmentation/morftrain.sh
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/bin/bash
-
-if [[ $# -lt 3 ]]; then
-	echo "Trains a morfessor model and places the result in writedir"
-	echo
-	echo "Usage: `basename $0` corpus_input_file writedir [PPL] [marker] [lines]"
-	echo -e "\tcorpus_input_file contains a sentence per line."
-	exit 1
-fi
-
-MORFESSOR_DIR="/export/ws10smt/software/morfessor_catmap0.9.2"
-SCRIPT_DIR=$(dirname `readlink -f $0`)
-
-MORFBINDIR="$MORFESSOR_DIR/bin"
-MORFMAKEFILE_TRAIN="$MORFESSOR_DIR/train/Makefile"
-VOCABEXT="$SCRIPT_DIR/vocabextractor.sh"
-
-MARKER="#"
-
-if [[ ! -f $VOCABEXT ]]; then
-  echo "$VOCABEXT doesn't exist!"
-  exit 1
-fi
-if [[ ! -f $MORFMAKEFILE_TRAIN ]]; then
-  echo "$MORFMAKEFILE_TRAIN doesn't exist!"
-  exit 1
-fi
-
-
-CORPUS="$1"
-WRITETODIR=$2
-
-if [[ ! -f $CORPUS ]]; then
-  echo "$CORPUS doesn't exist!"
-  exit 1
-fi
-
-PPL=10
-LINES=0
-if [[ $# -gt 2 ]]; then
-  PPL=$3
-fi
-if [[ $# -gt 3 ]]; then
-  MARKER="$4"
-fi
-if [[ $# -gt 4 ]]; then
-  LINES=$5
-fi
-
-mkdir -p $WRITETODIR
-
-#extract vocabulary to train on
-echo "Extracting vocabulary..."
-if [[ -f $WRITETODIR/inputvocab.gz ]]; then
-  echo " ....$WRITETODIR/inputvocab.gz exists, reusing."
-else
-  if [[ $LINES -gt 0 ]]; then
-    $VOCABEXT $CORPUS $LINES | gzip > $WRITETODIR/inputvocab.gz
-  else
-    $VOCABEXT $CORPUS | gzip > $WRITETODIR/inputvocab.gz
-  fi
-fi
-
-
-#train it
-echo "Training morf model..."
-if [[ -f $WRITETODIR/segmentation.final.gz ]]; then
-  echo " ....$WRITETODIR/segmentation.final.gz exists, reusing.."
-else
-  OLDPWD=`pwd`
-  cd $WRITETODIR
-  
-  #put the training Makefile in place, with appropriate modifications
-  sed -e "s/^GZIPPEDINPUTDATA = .*$/GZIPPEDINPUTDATA = inputvocab.gz/"  \
-    -e "s/^PPLTHRESH = .*$/PPLTHRESH = $PPL/" \
-    -e "s;^BINDIR = .*$;BINDIR = $MORFBINDIR;" \
-    $MORFMAKEFILE_TRAIN > ./Makefile
-
-  date
-  make > ./trainmorf.log 2>&1
-  cd $OLDPWD
-  
-  
-  echo "Post processing..."
-  #remove comments, counts and morph types
-  #mark morphs
-  
-  if [[ ! -f $WRITETODIR/segmentation.final.gz ]]; then
-     echo "Failed to learn segmentation model: $WRITETODIR/segmentation.final.gz not written"
-     exit 1
-  fi
-
-  zcat $WRITETODIR/segmentation.final.gz | \
-    awk '$1 !~ /^#/ {print}' | \
-    cut -d ' ' --complement -f 1 | \
-    sed -e "s/\/...//g" -e "s/ + /$MARKER $MARKER/g" \
-    > $WRITETODIR/segmentation.ready
-
-  if [[ ! -f $WRITETODIR/segmentation.ready ]]; then
-     echo "Failed to learn segmentation model: $WRITETODIR/segmentation.final.gz not written"
-     exit 1
-  fi
-
-
-
-  echo "Done training."
-  date
-fi
-echo "Segmentation model is $WRITETODIR/segmentation.ready."
-
diff --git a/gi/morf-segmentation/vocabextractor.sh b/gi/morf-segmentation/vocabextractor.sh
deleted file mode 100755
index 00ae7109..00000000
--- a/gi/morf-segmentation/vocabextractor.sh
+++ /dev/null
@@ -1,40 +0,0 @@
-#!/bin/bash
-
-d=$(dirname `readlink -f $0`)
-if [ $# -lt 1 ]; then
-	echo "Extracts unique words and their frequencies from a subset of a corpus."
-	echo
-	echo "Usage: `basename $0` input_file [number_of_lines] > output_file"
-	echo -e "\tinput_file contains a sentence per line."
-	echo
-	echo "Script also removes words from the vocabulary if they contain a digit or a special character. Output is printed to stdout in a format suitable for use with Morfessor."
-	echo
-	exit
-fi
-
-srcname=$1
-reallen=0
-
-if [[ $# -gt 1 ]]; then
-  reallen=$2
-fi
-
-pattern_file=$d/invalid_vocab.patterns
-
-if [[ ! -f $pattern_file ]]; then
-  echo "Pattern file missing"
-  exit 1 
-fi
-
-#this awk strips entries from the vocabulary if they contain invalid characters
-#invalid characters are digits and punctuation marks, and words beginning or ending with a dash
-#uniq -c extracts the unique words and counts the occurrences
-
-if [[ $reallen -eq 0 ]]; then
-	#when a zero is passed, use the whole file
-  zcat -f $srcname | sed 's/ /\n/g' | egrep -v -f $pattern_file | sort | uniq -c | sed 's/^  *//' 
-
-else
-	zcat -f $srcname | head -n $reallen | sed 's/ /\n/g' | egrep -v -f $pattern_file | sort | uniq -c | sed 's/^  *//'
-fi
-
diff --git a/gi/pf/Makefile.am b/gi/pf/Makefile.am
deleted file mode 100644
index 86f8e07b..00000000
--- a/gi/pf/Makefile.am
+++ /dev/null
@@ -1,44 +0,0 @@
-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 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
-
-align_lexonly_pyp_SOURCES = align-lexonly-pyp.cc
-align_lexonly_pyp_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
-
-align_tl_SOURCES = align-tl.cc
-align_tl_LDADD = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a -lz
-
-itg_SOURCES = itg.cc
-
-pyp_lm_SOURCES = pyp_lm.cc
-
-learn_cfg_SOURCES = learn_cfg.cc
-
-condnaive_SOURCES = condnaive.cc
-
-dpnaive_SOURCES = dpnaive.cc
-
-pfdist_SOURCES = pfdist.cc
-
-pfnaive_SOURCES = pfnaive.cc
-
-cbgi_SOURCES = cbgi.cc
-
-brat_SOURCES = brat.cc
-
-pfbrat_SOURCES = pfbrat.cc
-
-AM_CPPFLAGS = -W -Wall -Wno-sign-compare -funroll-loops -I$(top_srcdir)/utils $(GTEST_CPPFLAGS) -I$(top_srcdir)/decoder -I$(top_srcdir)/klm
-
-AM_LDFLAGS = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a $(top_srcdir)/utils/libutils.a -lz
diff --git a/gi/pf/README b/gi/pf/README
deleted file mode 100644
index 62e47541..00000000
--- a/gi/pf/README
+++ /dev/null
@@ -1,2 +0,0 @@
-Experimental Bayesian alignment tools. Nothing to see here.
-
diff --git a/gi/pf/align-lexonly-pyp.cc b/gi/pf/align-lexonly-pyp.cc
deleted file mode 100644
index e7509f57..00000000
--- a/gi/pf/align-lexonly-pyp.cc
+++ /dev/null
@@ -1,243 +0,0 @@
-#include <iostream>
-#include <queue>
-
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "tdict.h"
-#include "stringlib.h"
-#include "filelib.h"
-#include "array2d.h"
-#include "sampler.h"
-#include "corpus.h"
-#include "pyp_tm.h"
-#include "hpyp_tm.h"
-#include "quasi_model2.h"
-
-using namespace std;
-namespace po = boost::program_options;
-
-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")
-        ("infer_alignment_hyperparameters,I", "Infer alpha and p_null, otherwise fixed values will be assumed")
-        ("p_null,0", po::value<double>()->default_value(0.08), "probability of aligning to null")
-        ("align_alpha,a", po::value<double>()->default_value(4.0), "how 'tight' is the bias toward be along the diagonal?")
-        ("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,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);
-  }
-}
-
-MT19937* prng;
-
-struct LexicalAlignment {
-  unsigned char src_index;
-  bool is_transliteration;
-  vector<pair<short, short> > derivation;
-};
-
-struct AlignedSentencePair {
-  vector<WordID> src;
-  vector<WordID> trg;
-  vector<LexicalAlignment> a;
-  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, vocab_size, num_letters),
-      kNULL(TD::Convert("NULL")) {
-    assert(lets[kNULL].size() == 0);
-  }
-
-  vector<AlignedSentencePair>& corpus;
-  QuasiModel2 paj_model;
-  const bool infer_paj;
-  LexicalTranslationModel model;
-  const WordID kNULL;
-
-  void ResampleHyperparameters() {
-    model.ResampleHyperparameters(prng);
-    if (infer_paj) paj_model.ResampleHyperparameters(prng);
-  }
-
-  void InitializeRandom() {
-    cerr << "Initializing with random alignments ...\n";
-    for (unsigned i = 0; i < corpus.size(); ++i) {
-      AlignedSentencePair& asp = corpus[i];
-      asp.a.resize(asp.trg.size());
-      for (unsigned j = 0; j < asp.trg.size(); ++j) {
-        unsigned char& a_j = asp.a[j].src_index;
-        a_j = prng->next() * (1 + asp.src.size());
-        const WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-        model.Increment(f_a_j, asp.trg[j], &*prng);
-        paj_model.Increment(a_j, j, asp.src.size(), asp.trg.size());
-      }
-    }
-    cerr << "Corpus intialized randomly." << endl;
-    cerr << "LLH = " << Likelihood() << "    \t(Amodel=" << paj_model.Likelihood()
-         << " TModel=" << model.Likelihood() << ") contexts=" << model.UniqueConditioningContexts() << endl;
-  }
-
-  void ResampleCorpus() {
-    for (unsigned i = 0; i < corpus.size(); ++i) {
-      AlignedSentencePair& asp = corpus[i];
-      SampleSet<prob_t> ss; ss.resize(asp.src.size() + 1);
-      for (unsigned j = 0; j < asp.trg.size(); ++j) {
-        unsigned char& a_j = asp.a[j].src_index;
-        const WordID e_j = asp.trg[j];
-        WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-        model.Decrement(f_a_j, e_j, prng);
-        paj_model.Decrement(a_j, j, asp.src.size(), asp.trg.size());
-
-        for (unsigned prop_a_j = 0; prop_a_j <= asp.src.size(); ++prop_a_j) {
-          const WordID prop_f = (prop_a_j ? asp.src[prop_a_j - 1] : kNULL);
-          ss[prop_a_j] = model.Prob(prop_f, e_j);
-          ss[prop_a_j] *= paj_model.Prob(prop_a_j, j, asp.src.size(), asp.trg.size());
-        }
-        a_j = prng->SelectSample(ss);
-        f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-        model.Increment(f_a_j, e_j, prng);
-        paj_model.Increment(a_j, j, asp.src.size(), asp.trg.size());
-      }
-    }
-  }
-
-  prob_t Likelihood() const {
-    return model.Likelihood() * paj_model.Likelihood();
-  }
-};
-
-void ExtractLetters(const set<WordID>& v, vector<vector<WordID> >* l, set<WordID>* letset = NULL) {
-  for (set<WordID>::const_iterator it = v.begin(); it != v.end(); ++it) {
-    vector<WordID>& letters = (*l)[*it];
-    if (letters.size()) continue;   // if e and f have the same word
-
-    const string& w = TD::Convert(*it);
-    
-    size_t cur = 0;
-    while (cur < w.size()) {
-      const size_t len = UTF8Len(w[cur]);
-      letters.push_back(TD::Convert(w.substr(cur, len)));
-      if (letset) letset->insert(letters.back());
-      cur += len;
-    }
-  }
-}
-
-void Debug(const AlignedSentencePair& asp) {
-  cerr << TD::GetString(asp.src) << endl << TD::GetString(asp.trg) << endl;
-  Array2D<bool> a(asp.src.size(), asp.trg.size());
-  for (unsigned j = 0; j < asp.trg.size(); ++j) {
-    assert(asp.a[j].src_index <= asp.src.size());
-    if (asp.a[j].src_index) a(asp.a[j].src_index - 1, j) = true;
-  }
-  cerr << a << endl;
-}
-
-void AddSample(AlignedSentencePair* asp) {
-  for (unsigned j = 0; j < asp->trg.size(); ++j)
-    asp->posterior(asp->a[j].src_index, j)++;
-}
-
-void WriteAlignments(const AlignedSentencePair& asp) {
-  bool first = true;
-  for (unsigned j = 0; j < asp.trg.size(); ++j) {
-    int src_index = -1;
-    int mc = -1;
-    for (unsigned i = 0; i <= asp.src.size(); ++i) {
-      if (asp.posterior(i, j) > mc) {
-        mc = asp.posterior(i, j);
-        src_index = i;
-      }
-    }
-
-    if (src_index) {
-      if (first) first = false; else cout << ' ';
-      cout << (src_index - 1) << '-' << j;
-    }
-  }
-  cout << endl;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-
-  if (conf.count("random_seed"))
-    prng = new MT19937(conf["random_seed"].as<uint32_t>());
-  else
-    prng = new MT19937;
-
-  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());
-
-  vector<AlignedSentencePair> corpus(corpuse.size());
-  for (unsigned i = 0; i < corpuse.size(); ++i) {
-    corpus[i].src.swap(corpusf[i]);
-    corpus[i].trg.swap(corpuse[i]);
-    corpus[i].posterior.resize(corpus[i].src.size() + 1, corpus[i].trg.size());
-  }
-  corpusf.clear(); corpuse.clear();
-
-  vocabf.insert(TD::Convert("NULL"));
-  vector<vector<WordID> > letters(TD::NumWords());
-  set<WordID> letset;
-  ExtractLetters(vocabe, &letters, &letset);
-  ExtractLetters(vocabf, &letters, NULL);
-  letters[TD::Convert("NULL")].clear();
-
-  //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>();
-  for (int i = 0; i < samples; ++i) {
-    for (int j = 65; j < 67; ++j) Debug(corpus[j]);
-    if (i % 10 == 9) {
-      aligner.ResampleHyperparameters();
-      cerr << "LLH = " << aligner.Likelihood() << "    \t(Amodel=" << aligner.paj_model.Likelihood()
-           << " TModel=" << aligner.model.Likelihood() << ") contexts=" << aligner.model.UniqueConditioningContexts() << endl;
-    }
-    aligner.ResampleCorpus();
-    if (i > (samples / 5) && (i % 6 == 5)) for (int j = 0; j < corpus.size(); ++j) AddSample(&corpus[j]);
-  }
-  for (unsigned i = 0; i < corpus.size(); ++i)
-    WriteAlignments(corpus[i]);
-  aligner.model.Summary();
-
-  return 0;
-}
diff --git a/gi/pf/align-tl.cc b/gi/pf/align-tl.cc
deleted file mode 100644
index f6608f1d..00000000
--- a/gi/pf/align-tl.cc
+++ /dev/null
@@ -1,339 +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 "backward.h"
-#include "array2d.h"
-#include "base_distributions.h"
-#include "monotonic_pseg.h"
-#include "conditional_pseg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "stringlib.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "mfcr.h"
-#include "corpus.h"
-#include "ngram_base.h"
-#include "transliterations.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-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")
-        ("s2t", po::value<string>(), "character level source-to-target prior transliteration probabilities")
-        ("t2s", po::value<string>(), "character level target-to-source prior transliteration probabilities")
-        ("max_src_chunk", po::value<unsigned>()->default_value(4), "Maximum size of translitered chunk in source")
-        ("max_trg_chunk", po::value<unsigned>()->default_value(4), "Maximum size of translitered chunk in target")
-        ("expected_src_to_trg_ratio", po::value<double>()->default_value(1.0), "If a word is transliterated, what is the expected length ratio from source to target?")
-        ("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 LexicalAlignment {
-  unsigned char src_index;
-  bool is_transliteration;
-  vector<pair<short, short> > derivation;
-};
-
-struct AlignedSentencePair {
-  vector<WordID> src;
-  vector<WordID> trg;
-  vector<LexicalAlignment> a;
-  Array2D<short> posterior;
-};
-
-struct HierarchicalWordBase {
-  explicit HierarchicalWordBase(const unsigned vocab_e_size) :
-      base(prob_t::One()), r(1,1,1,1,0.66,50.0), u0(-log(vocab_e_size)), l(1,prob_t::One()), v(1, prob_t::Zero()) {}
-
-  void ResampleHyperparameters(MT19937* rng) {
-    r.resample_hyperparameters(rng);
-  }
-
-  inline double logp0(const vector<WordID>& s) const {
-    return Md::log_poisson(s.size(), 7.5) + s.size() * u0;
-  }
-
-  // return p0 of rule.e_
-  prob_t operator()(const TRule& rule) const {
-    v[0].logeq(logp0(rule.e_));
-    return r.prob(rule.e_, v.begin(), l.begin());
-  }
-
-  void Increment(const TRule& rule) {
-    v[0].logeq(logp0(rule.e_));
-    if (r.increment(rule.e_, v.begin(), l.begin(), &*prng).count) {
-      base *= v[0] * l[0];
-    }
-  }
-
-  void Decrement(const TRule& rule) {
-    if (r.decrement(rule.e_, &*prng).count) {
-      base /= prob_t(exp(logp0(rule.e_)));
-    }
-  }
-
-  prob_t Likelihood() const {
-    prob_t p; p.logeq(r.log_crp_prob());
-    p *= base;
-    return p;
-  }
-
-  void Summary() const {
-    cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << "  (d=" << r.discount() << ",s=" << r.strength() << ')' << endl;
-    for (MFCR<1,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;
-  }
-
-  prob_t base;
-  MFCR<1,vector<WordID> > r;
-  const double u0;
-  const vector<prob_t> l;
-  mutable vector<prob_t> v;
-};
-
-struct BasicLexicalAlignment {
-  explicit BasicLexicalAlignment(const vector<vector<WordID> >& lets,
-                                 const unsigned words_e,
-                                 const unsigned letters_e,
-                                 vector<AlignedSentencePair>* corp) :
-      letters(lets),
-      corpus(*corp),
-      //up0(words_e),
-      //up0("en.chars.1gram", letters_e),
-      //up0("en.words.1gram"),
-      up0(letters_e),
-      //up0("en.chars.2gram"),
-      tmodel(up0) {
-  }
-
-  void InstantiateRule(const WordID src,
-                       const WordID trg,
-                       TRule* rule) const {
-    static const WordID kX = TD::Convert("X") * -1;
-    rule->lhs_ = kX;
-    rule->e_ = letters[trg];
-    rule->f_ = letters[src];
-  }
-
-  void InitializeRandom() {
-    const WordID kNULL = TD::Convert("NULL");
-    cerr << "Initializing with random alignments ...\n";
-    for (unsigned i = 0; i < corpus.size(); ++i) {
-      AlignedSentencePair& asp = corpus[i];
-      asp.a.resize(asp.trg.size());
-      for (unsigned j = 0; j < asp.trg.size(); ++j) {
-        const unsigned char a_j = prng->next() * (1 + asp.src.size());
-        const WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-        TRule r;
-        InstantiateRule(f_a_j, asp.trg[j], &r);
-        asp.a[j].is_transliteration = false;
-        asp.a[j].src_index = a_j;
-        if (tmodel.IncrementRule(r, &*prng))
-          up0.Increment(r);
-      }
-    }
-    cerr << "  LLH = " << Likelihood() << endl;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = tmodel.Likelihood();
-    p *= up0.Likelihood();
-    return p;
-  }
-
-  void ResampleHyperparemeters() {
-    tmodel.ResampleHyperparameters(&*prng);
-    up0.ResampleHyperparameters(&*prng);
-    cerr << "  (base d=" << up0.r.discount() << ",s=" << up0.r.strength() << ")\n";
-  }
-
-  void ResampleCorpus();
-
-  const vector<vector<WordID> >& letters; // spelling dictionary
-  vector<AlignedSentencePair>& corpus;
-  //PhraseConditionalUninformativeBase up0;
-  //PhraseConditionalUninformativeUnigramBase up0;
-  //UnigramWordBase up0;
-  //HierarchicalUnigramBase up0;
-  HierarchicalWordBase up0;
-  //CompletelyUniformBase up0;
-  //FixedNgramBase up0;
-  //ConditionalTranslationModel<PhraseConditionalUninformativeBase> tmodel;
-  //ConditionalTranslationModel<PhraseConditionalUninformativeUnigramBase> tmodel;
-  //ConditionalTranslationModel<UnigramWordBase> tmodel;
-  //ConditionalTranslationModel<HierarchicalUnigramBase> tmodel;
-  MConditionalTranslationModel<HierarchicalWordBase> tmodel;
-  //ConditionalTranslationModel<FixedNgramBase> tmodel;
-  //ConditionalTranslationModel<CompletelyUniformBase> tmodel;
-};
-
-void BasicLexicalAlignment::ResampleCorpus() {
-  static const WordID kNULL = TD::Convert("NULL");
-  for (unsigned i = 0; i < corpus.size(); ++i) {
-    AlignedSentencePair& asp = corpus[i];
-    SampleSet<prob_t> ss; ss.resize(asp.src.size() + 1);
-    for (unsigned j = 0; j < asp.trg.size(); ++j) {
-      TRule r;
-      unsigned char& a_j = asp.a[j].src_index;
-      WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-      InstantiateRule(f_a_j, asp.trg[j], &r);
-      if (tmodel.DecrementRule(r, &*prng))
-        up0.Decrement(r);
-
-      for (unsigned prop_a_j = 0; prop_a_j <= asp.src.size(); ++prop_a_j) {
-        const WordID prop_f = (prop_a_j ? asp.src[prop_a_j - 1] : kNULL);
-        InstantiateRule(prop_f, asp.trg[j], &r);
-        ss[prop_a_j] = tmodel.RuleProbability(r);
-      }
-      a_j = prng->SelectSample(ss);
-      f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-      InstantiateRule(f_a_j, asp.trg[j], &r);
-      if (tmodel.IncrementRule(r, &*prng))
-        up0.Increment(r);
-    }
-  }
-  cerr << "  LLH = " << Likelihood() << endl;
-}
-
-void ExtractLetters(const set<WordID>& v, vector<vector<WordID> >* l, set<WordID>* letset = NULL) {
-  for (set<WordID>::const_iterator it = v.begin(); it != v.end(); ++it) {
-    vector<WordID>& letters = (*l)[*it];
-    if (letters.size()) continue;   // if e and f have the same word
-
-    const string& w = TD::Convert(*it);
-    
-    size_t cur = 0;
-    while (cur < w.size()) {
-      const size_t len = UTF8Len(w[cur]);
-      letters.push_back(TD::Convert(w.substr(cur, len)));
-      if (letset) letset->insert(letters.back());
-      cur += len;
-    }
-  }
-}
-
-void Debug(const AlignedSentencePair& asp) {
-  cerr << TD::GetString(asp.src) << endl << TD::GetString(asp.trg) << endl;
-  Array2D<bool> a(asp.src.size(), asp.trg.size());
-  for (unsigned j = 0; j < asp.trg.size(); ++j)
-    if (asp.a[j].src_index) a(asp.a[j].src_index - 1, j) = true;
-  cerr << a << endl;
-}
-
-void AddSample(AlignedSentencePair* asp) {
-  for (unsigned j = 0; j < asp->trg.size(); ++j)
-    asp->posterior(asp->a[j].src_index, j)++;
-}
-
-void WriteAlignments(const AlignedSentencePair& asp) {
-  bool first = true;
-  for (unsigned j = 0; j < asp.trg.size(); ++j) {
-    int src_index = -1;
-    int mc = -1;
-    for (unsigned i = 0; i <= asp.src.size(); ++i) {
-      if (asp.posterior(i, j) > mc) {
-        mc = asp.posterior(i, j);
-        src_index = i;
-      }
-    }
-
-    if (src_index) {
-      if (first) first = false; else cout << ' ';
-      cout << (src_index - 1) << '-' << j;
-    }
-  }
-  cout << endl;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-
-  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());
-
-  vector<AlignedSentencePair> corpus(corpuse.size());
-  for (unsigned i = 0; i < corpuse.size(); ++i) {
-    corpus[i].src.swap(corpusf[i]);
-    corpus[i].trg.swap(corpuse[i]);
-    corpus[i].posterior.resize(corpus[i].src.size() + 1, corpus[i].trg.size());
-  }
-  corpusf.clear(); corpuse.clear();
-
-  vocabf.insert(TD::Convert("NULL"));
-  vector<vector<WordID> > letters(TD::NumWords() + 1);
-  set<WordID> letset;
-  ExtractLetters(vocabe, &letters, &letset);
-  ExtractLetters(vocabf, &letters, NULL);
-  letters[TD::Convert("NULL")].clear();
-
-  // TODO configure this
-  const int max_src_chunk = conf["max_src_chunk"].as<unsigned>();
-  const int max_trg_chunk = conf["max_trg_chunk"].as<unsigned>();
-  const double s2t_rat = conf["expected_src_to_trg_ratio"].as<double>();
-  const BackwardEstimator be(conf["s2t"].as<string>(), conf["t2s"].as<string>());
-  Transliterations tl(max_src_chunk, max_trg_chunk, s2t_rat, be); 
-
-  cerr << "Initializing transliteration graph structures ...\n";
-  for (int i = 0; i < corpus.size(); ++i) {
-    const vector<int>& src = corpus[i].src;
-    const vector<int>& trg = corpus[i].trg;
-    for (int j = 0; j < src.size(); ++j) {
-      const vector<int>& src_let = letters[src[j]];
-      for (int k = 0; k < trg.size(); ++k) {
-        const vector<int>& trg_let = letters[trg[k]];
-        tl.Initialize(src[j], src_let, trg[k], trg_let);
-        //if (src_let.size() < min_trans_src)
-        //  tl.Forbid(src[j], src_let, trg[k], trg_let);
-      }
-    }
-  }
-  cerr << endl;
-  tl.GraphSummary();
-
-  return 0;
-}
diff --git a/gi/pf/backward.cc b/gi/pf/backward.cc
deleted file mode 100644
index b92629fd..00000000
--- a/gi/pf/backward.cc
+++ /dev/null
@@ -1,89 +0,0 @@
-#include "backward.h"
-
-#include <queue>
-#include <utility>
-
-#include "array2d.h"
-#include "reachability.h"
-#include "base_distributions.h"
-
-using namespace std;
-
-BackwardEstimator::BackwardEstimator(const string& s2t,
-                    const string& t2s) : m1(new Model1(s2t)), m1inv(new Model1(t2s)) {}
-
-BackwardEstimator::~BackwardEstimator() {
-  delete m1; m1 = NULL;
-  delete m1inv; m1inv = NULL;
-}
-
-float BackwardEstimator::ComputeBackwardProb(const std::vector<WordID>& src,
-                                             const std::vector<WordID>& trg,
-                                             unsigned src_covered,
-                                             unsigned trg_covered,
-                                             double s2t_ratio) const {
-  if (src_covered == src.size() || trg_covered == trg.size()) {
-    assert(src_covered == src.size());
-    assert(trg_covered == trg.size());
-    return 0;
-  }
-  static const WordID kNULL = TD::Convert("<eps>");
-  const prob_t uniform_alignment(1.0 / (src.size() - src_covered + 1));
-  // TODO factor in expected length ratio
-  prob_t e; e.logeq(Md::log_poisson(trg.size() - trg_covered, (src.size() - src_covered) * s2t_ratio)); // p(trg len remaining | src len remaining)
-  for (unsigned j = trg_covered; j < trg.size(); ++j) {
-    prob_t p = (*m1)(kNULL, trg[j]) + prob_t(1e-12);
-    for (unsigned i = src_covered; i < src.size(); ++i)
-      p += (*m1)(src[i], trg[j]);
-    if (p.is_0()) {
-      cerr << "ERROR: p(" << TD::Convert(trg[j]) << " | " << TD::GetString(src) << ") = 0!\n";
-      assert(!"failed");
-    }
-    p *= uniform_alignment;
-    e *= p;
-  }
-  // TODO factor in expected length ratio
-  const prob_t inv_uniform(1.0 / (trg.size() - trg_covered + 1.0));
-  prob_t inv;
-  inv.logeq(Md::log_poisson(src.size() - src_covered, (trg.size() - trg_covered) / s2t_ratio));
-  for (unsigned i = src_covered; i < src.size(); ++i) {
-    prob_t p = (*m1inv)(kNULL, src[i]) + prob_t(1e-12);
-    for (unsigned j = trg_covered; j < trg.size(); ++j)
-      p += (*m1inv)(trg[j], src[i]);
-    if (p.is_0()) {
-      cerr << "ERROR: p_inv(" << TD::Convert(src[i]) << " | " << TD::GetString(trg) << ") = 0!\n";
-      assert(!"failed");
-    }
-    p *= inv_uniform;
-    inv *= p;
-  }
-  return (log(e) + log(inv)) / 2;
-}
-
-void BackwardEstimator::InitializeGrid(const vector<WordID>& src,
-                      const vector<WordID>& trg,
-                      const Reachability& r,
-                      double s2t_ratio,
-                      float* grid) const {
-  queue<pair<int,int> > q;
-  q.push(make_pair(0,0));
-  Array2D<bool> done(src.size()+1, trg.size()+1, false);
-  //cerr << TD::GetString(src) << " ||| " << TD::GetString(trg) << endl;
-  while(!q.empty()) {
-    const pair<int,int> n = q.front();
-    q.pop();
-    if (done(n.first,n.second)) continue;
-    done(n.first,n.second) = true;
-
-    float lp = ComputeBackwardProb(src, trg, n.first, n.second, s2t_ratio);
-    if (n.first == 0 && n.second == 0) grid[0] = lp;
-    //cerr << "  " << n.first << "," << n.second << "\t" << lp << endl;
-
-    if (n.first == src.size() || n.second == trg.size()) continue;
-    const vector<pair<short,short> >& edges = r.valid_deltas[n.first][n.second];
-    for (int i = 0; i < edges.size(); ++i)
-      q.push(make_pair(n.first + edges[i].first, n.second + edges[i].second));
-  }
-  //static int cc = 0; ++cc; if (cc == 80) exit(1);
-}
-
diff --git a/gi/pf/backward.h b/gi/pf/backward.h
deleted file mode 100644
index e67eff0c..00000000
--- a/gi/pf/backward.h
+++ /dev/null
@@ -1,33 +0,0 @@
-#ifndef _BACKWARD_H_
-#define _BACKWARD_H_
-
-#include <vector>
-#include <string>
-#include "wordid.h"
-
-struct Reachability;
-struct Model1;
-
-struct BackwardEstimator {
-  BackwardEstimator(const std::string& s2t,
-                    const std::string& t2s);
-  ~BackwardEstimator();
-
-  void InitializeGrid(const std::vector<WordID>& src,
-                      const std::vector<WordID>& trg,
-                      const Reachability& r,
-                      double src2trg_ratio,
-                      float* grid) const;
-
- private:
-  float ComputeBackwardProb(const std::vector<WordID>& src,
-                            const std::vector<WordID>& trg,
-                            unsigned src_covered,
-                            unsigned trg_covered,
-                            double src2trg_ratio) const;
-
-  Model1* m1;
-  Model1* m1inv;
-};
-
-#endif
diff --git a/gi/pf/base_distributions.cc b/gi/pf/base_distributions.cc
deleted file mode 100644
index 57e0bbe1..00000000
--- a/gi/pf/base_distributions.cc
+++ /dev/null
@@ -1,241 +0,0 @@
-#include "base_distributions.h"
-
-#include <iostream>
-
-#include "filelib.h"
-
-using namespace std;
-
-TableLookupBase::TableLookupBase(const string& fname) {
-  cerr << "TableLookupBase reading from " << fname << " ..." << endl;
-  ReadFile rf(fname);
-  istream& in = *rf.stream();
-  string line;
-  unsigned lc = 0;
-  const WordID kDIV = TD::Convert("|||");
-  vector<WordID> tmp;
-  vector<int> le, lf;
-  TRule x;
-  x.lhs_ = -TD::Convert("X");
-  bool flag = false;
-  while(getline(in, line)) {
-    ++lc;
-    if (lc % 1000000 == 0) { cerr << " [" << lc << ']' << endl; flag = false; }
-    else if (lc % 25000 == 0) { cerr << '.' << flush; flag = true; }
-    tmp.clear();
-    TD::ConvertSentence(line, &tmp);
-    x.f_.clear();
-    x.e_.clear();
-    size_t pos = 0;
-    int cc = 0;
-    while(pos < tmp.size()) {
-      const WordID cur = tmp[pos++];
-      if (cur == kDIV) {
-        ++cc;
-      } else if (cc == 0) {
-        x.f_.push_back(cur);    
-      } else if (cc == 1) {
-        x.e_.push_back(cur);
-      } else if (cc == 2) {
-        table[x].logeq(atof(TD::Convert(cur).c_str()));
-        ++cc;
-      } else {
-        if (flag) cerr << endl;
-        cerr << "Bad format in " << lc << ": " << line << endl; abort();
-      }
-    }
-    if (cc != 3) {
-      if (flag) cerr << endl;
-      cerr << "Bad format in " << lc << ": " << line << endl; abort();
-    }
-  }
-  if (flag) cerr << endl;
-  cerr << " read " << lc << " entries\n";
-}
-
-prob_t PhraseConditionalUninformativeUnigramBase::p0(const vector<WordID>& vsrc,
-                                                     const vector<WordID>& vtrg,
-                                                     int start_src, int start_trg) const {
-  const int flen = vsrc.size() - start_src;
-  const int elen = vtrg.size() - start_trg;
-  prob_t p;
-  p.logeq(Md::log_poisson(elen, flen + 0.01));       // elen | flen          ~Pois(flen + 0.01)
-  //p.logeq(log_poisson(elen, 1));       // elen | flen          ~Pois(flen + 0.01)
-  for (int i = 0; i < elen; ++i)
-    p *= u(vtrg[i + start_trg]);                        // draw e_i             ~Uniform
-  return p;
-}
-
-prob_t PhraseConditionalUninformativeBase::p0(const vector<WordID>& vsrc,
-                                              const vector<WordID>& vtrg,
-                                              int start_src, int start_trg) const {
-  const int flen = vsrc.size() - start_src;
-  const int elen = vtrg.size() - start_trg;
-  prob_t p;
-  //p.logeq(log_poisson(elen, flen + 0.01));       // elen | flen          ~Pois(flen + 0.01)
-  p.logeq(Md::log_poisson(elen, 1));       // elen | flen          ~Pois(flen + 0.01)
-  for (int i = 0; i < elen; ++i)
-    p *= kUNIFORM_TARGET;                        // draw e_i             ~Uniform
-  return p;
-}
-
-void Model1::LoadModel1(const string& fname) {
-  cerr << "Loading Model 1 parameters from " << fname << " ..." << endl;
-  ReadFile rf(fname);
-  istream& in = *rf.stream();
-  string line;
-  unsigned lc = 0;
-  while(getline(in, line)) {
-    ++lc;
-    int cur = 0;
-    int start = 0;
-    while(cur < line.size() && line[cur] != ' ') { ++cur; }
-    assert(cur != line.size());
-    line[cur] = 0;
-    const WordID src = TD::Convert(&line[0]);
-    ++cur;
-    start = cur;
-    while(cur < line.size() && line[cur] != ' ') { ++cur; }
-    assert(cur != line.size());
-    line[cur] = 0;
-    WordID trg = TD::Convert(&line[start]);
-    const double logprob = strtod(&line[cur + 1], NULL);
-    if (src >= ttable.size()) ttable.resize(src + 1);
-    ttable[src][trg].logeq(logprob);
-  }
-  cerr << "  read " << lc << " parameters.\n";
-}
-
-prob_t PhraseConditionalBase::p0(const vector<WordID>& vsrc,
-                                 const vector<WordID>& vtrg,
-                                 int start_src, int start_trg) const {
-  const int flen = vsrc.size() - start_src;
-  const int elen = vtrg.size() - start_trg;
-  prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
-  prob_t p;
-  p.logeq(Md::log_poisson(elen, flen + 0.01));       // elen | flen          ~Pois(flen + 0.01)
-  for (int i = 0; i < elen; ++i) {               // for each position i in e-RHS
-    const WordID trg = vtrg[i + start_trg];
-    prob_t tp = prob_t::Zero();
-    for (int j = -1; j < flen; ++j) {
-      const WordID src = j < 0 ? 0 : vsrc[j + start_src];
-      tp += kM1MIXTURE * model1(src, trg);
-      tp += kUNIFORM_MIXTURE * kUNIFORM_TARGET;
-    }
-    tp *= uniform_src_alignment;                 //     draw a_i         ~uniform
-    p *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-  }
-  if (p.is_0()) {
-    cerr << "Zero! " << vsrc << "\nTRG=" << vtrg << endl;
-    abort();
-  }
-  return p;
-}
-
-prob_t PhraseJointBase::p0(const vector<WordID>& vsrc,
-                           const vector<WordID>& vtrg,
-                           int start_src, int start_trg) const {
-  const int flen = vsrc.size() - start_src;
-  const int elen = vtrg.size() - start_trg;
-  prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
-  prob_t p;
-  p.logeq(Md::log_poisson(flen, 1.0));               // flen                 ~Pois(1)
-                                                 // elen | flen          ~Pois(flen + 0.01)
-  prob_t ptrglen; ptrglen.logeq(Md::log_poisson(elen, flen + 0.01));
-  p *= ptrglen;
-  p *= kUNIFORM_SOURCE.pow(flen);                // each f in F ~Uniform
-  for (int i = 0; i < elen; ++i) {               // for each position i in E
-    const WordID trg = vtrg[i + start_trg];
-    prob_t tp = prob_t::Zero();
-    for (int j = -1; j < flen; ++j) {
-      const WordID src = j < 0 ? 0 : vsrc[j + start_src];
-      tp += kM1MIXTURE * model1(src, trg);
-      tp += kUNIFORM_MIXTURE * kUNIFORM_TARGET;
-    }
-    tp *= uniform_src_alignment;                 //     draw a_i         ~uniform
-    p *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-  }
-  if (p.is_0()) {
-    cerr << "Zero! " << vsrc << "\nTRG=" << vtrg << endl;
-    abort();
-  }
-  return p;
-}
-
-prob_t PhraseJointBase_BiDir::p0(const vector<WordID>& vsrc,
-                                 const vector<WordID>& vtrg,
-                                 int start_src, int start_trg) const {
-  const int flen = vsrc.size() - start_src;
-  const int elen = vtrg.size() - start_trg;
-  prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
-  prob_t uniform_trg_alignment; uniform_trg_alignment.logeq(-log(elen + 1));
-
-  prob_t p1;
-  p1.logeq(Md::log_poisson(flen, 1.0));               // flen                 ~Pois(1)
-                                                 // elen | flen          ~Pois(flen + 0.01)
-  prob_t ptrglen; ptrglen.logeq(Md::log_poisson(elen, flen + 0.01));
-  p1 *= ptrglen;
-  p1 *= kUNIFORM_SOURCE.pow(flen);                // each f in F ~Uniform
-  for (int i = 0; i < elen; ++i) {               // for each position i in E
-    const WordID trg = vtrg[i + start_trg];
-    prob_t tp = prob_t::Zero();
-    for (int j = -1; j < flen; ++j) {
-      const WordID src = j < 0 ? 0 : vsrc[j + start_src];
-      tp += kM1MIXTURE * model1(src, trg);
-      tp += kUNIFORM_MIXTURE * kUNIFORM_TARGET;
-    }
-    tp *= uniform_src_alignment;                 //     draw a_i         ~uniform
-    p1 *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-  }
-  if (p1.is_0()) {
-    cerr << "Zero! " << vsrc << "\nTRG=" << vtrg << endl;
-    abort();
-  }
-
-  prob_t p2;
-  p2.logeq(Md::log_poisson(elen, 1.0));               // elen                 ~Pois(1)
-                                                 // flen | elen          ~Pois(flen + 0.01)
-  prob_t psrclen; psrclen.logeq(Md::log_poisson(flen, elen + 0.01));
-  p2 *= psrclen;
-  p2 *= kUNIFORM_TARGET.pow(elen);                // each f in F ~Uniform
-  for (int i = 0; i < flen; ++i) {               // for each position i in E
-    const WordID src = vsrc[i + start_src];
-    prob_t tp = prob_t::Zero();
-    for (int j = -1; j < elen; ++j) {
-      const WordID trg = j < 0 ? 0 : vtrg[j + start_trg];
-      tp += kM1MIXTURE * invmodel1(trg, src);
-      tp += kUNIFORM_MIXTURE * kUNIFORM_SOURCE;
-    }
-    tp *= uniform_trg_alignment;                 //     draw a_i         ~uniform
-    p2 *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-  }
-  if (p2.is_0()) {
-    cerr << "Zero! " << vsrc << "\nTRG=" << vtrg << endl;
-    abort();
-  }
-
-  static const prob_t kHALF(0.5);
-  return (p1 + p2) * kHALF;
-}
-
-JumpBase::JumpBase() : p(200) {
-  for (unsigned src_len = 1; src_len < 200; ++src_len) {
-    map<int, prob_t>& cpd = p[src_len];
-    int min_jump = 1 - src_len;
-    int max_jump = src_len;
-    prob_t z;
-    for (int j = min_jump; j <= max_jump; ++j) {
-      prob_t& cp = cpd[j];
-      if (j < 0)
-        cp.logeq(Md::log_poisson(1.5-j, 1));
-      else if (j > 0)
-        cp.logeq(Md::log_poisson(j, 1));
-      cp.poweq(0.2);
-      z += cp;
-    }
-    for (int j = min_jump; j <= max_jump; ++j) {
-      cpd[j] /= z;
-    }
-  }
-}
-
diff --git a/gi/pf/base_distributions.h b/gi/pf/base_distributions.h
deleted file mode 100644
index 41b513f8..00000000
--- a/gi/pf/base_distributions.h
+++ /dev/null
@@ -1,238 +0,0 @@
-#ifndef _BASE_MEASURES_H_
-#define _BASE_MEASURES_H_
-
-#include <vector>
-#include <map>
-#include <string>
-#include <cmath>
-#include <iostream>
-#include <cassert>
-
-#include "unigrams.h"
-#include "trule.h"
-#include "prob.h"
-#include "tdict.h"
-#include "sampler.h"
-#include "m.h"
-#include "os_phrase.h"
-
-struct Model1 {
-  explicit Model1(const std::string& fname) :
-      kNULL(TD::Convert("<eps>")),
-      kZERO() {
-    LoadModel1(fname);
-  }
-
-  void LoadModel1(const std::string& fname);
-
-  // returns prob 0 if src or trg is not found
-  const prob_t& operator()(WordID src, WordID trg) const {
-    if (src == 0) src = kNULL;
-    if (src < ttable.size()) {
-      const std::map<WordID, prob_t>& cpd = ttable[src];
-      const std::map<WordID, prob_t>::const_iterator it = cpd.find(trg);
-      if (it != cpd.end())
-        return it->second;
-    }
-    return kZERO;
-  }
-
-  const WordID kNULL;
-  const prob_t kZERO;
-  std::vector<std::map<WordID, prob_t> > ttable;
-};
-
-struct PoissonUniformUninformativeBase {
-  explicit PoissonUniformUninformativeBase(const unsigned ves) : kUNIFORM(1.0 / ves) {}
-  prob_t operator()(const TRule& r) const {
-    prob_t p; p.logeq(Md::log_poisson(r.e_.size(), 1.0));
-    prob_t q = kUNIFORM; q.poweq(r.e_.size());
-    p *= q;
-    return p;
-  }
-  void Summary() const {}
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  const prob_t kUNIFORM;
-};
-
-struct CompletelyUniformBase {
-  explicit CompletelyUniformBase(const unsigned ves) : kUNIFORM(1.0 / ves) {}
-  prob_t operator()(const TRule&) const {
-    return kUNIFORM;
-  }
-  void Summary() const {}
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  const prob_t kUNIFORM;
-};
-
-struct UnigramWordBase {
-  explicit UnigramWordBase(const std::string& fname) : un(fname) {}
-  prob_t operator()(const TRule& r) const {
-    return un(r.e_);
-  }
-  const UnigramWordModel un;
-};
-
-struct RuleHasher {
-  size_t operator()(const TRule& r) const {
-    return hash_value(r);
-  }
-};
-
-struct TableLookupBase {
-  TableLookupBase(const std::string& fname);
-
-  prob_t operator()(const TRule& rule) const {
-    const std::tr1::unordered_map<TRule,prob_t,RuleHasher>::const_iterator it = table.find(rule);
-    if (it == table.end()) {
-      std::cerr << rule << " not found\n";
-      abort();
-    }
-    return it->second;
-  }
-
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  void Summary() const {}
-
-  std::tr1::unordered_map<TRule,prob_t,RuleHasher> table;
-};
-
-struct PhraseConditionalUninformativeBase {
-  explicit PhraseConditionalUninformativeBase(const unsigned vocab_e_size) :
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  void Summary() const {}
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  const prob_t kUNIFORM_TARGET;
-};
-
-struct PhraseConditionalUninformativeUnigramBase {
-  explicit PhraseConditionalUninformativeUnigramBase(const std::string& file, const unsigned vocab_e_size) : u(file, vocab_e_size) {}
-
-  // return p0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const UnigramModel u;
-};
-
-struct PhraseConditionalBase {
-  explicit PhraseConditionalBase(const Model1& m1, const double m1mixture, const unsigned vocab_e_size) :
-      model1(m1),
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const Model1& model1;
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_TARGET;
-};
-
-struct PhraseJointBase {
-  explicit PhraseJointBase(const Model1& m1, const double m1mixture, const unsigned vocab_e_size, const unsigned vocab_f_size) :
-      model1(m1),
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_SOURCE(1.0 / vocab_f_size),
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ , rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const Model1& model1;
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_SOURCE;
-  const prob_t kUNIFORM_TARGET;
-};
-
-struct PhraseJointBase_BiDir {
-  explicit PhraseJointBase_BiDir(const Model1& m1,
-                                 const Model1& im1,
-                                 const double m1mixture,
-                                 const unsigned vocab_e_size,
-                                 const unsigned vocab_f_size) :
-      model1(m1),
-      invmodel1(im1),
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_SOURCE(1.0 / vocab_f_size),
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ , rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const Model1& model1;
-  const Model1& invmodel1;
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_SOURCE;
-  const prob_t kUNIFORM_TARGET;
-};
-
-// base distribution for jump size multinomials
-// basically p(0) = 0 and then, p(1) is max, and then
-// you drop as you move to the max jump distance
-struct JumpBase {
-  JumpBase();
-
-  const prob_t& operator()(int jump, unsigned src_len) const {
-    assert(jump != 0);
-    const std::map<int, prob_t>::const_iterator it = p[src_len].find(jump);
-    assert(it != p[src_len].end());
-    return it->second;
-  }
-  std::vector<std::map<int, prob_t> > p;
-};
-
-
-#endif
diff --git a/gi/pf/bayes_lattice_score.cc b/gi/pf/bayes_lattice_score.cc
deleted file mode 100644
index 70cb8dc2..00000000
--- a/gi/pf/bayes_lattice_score.cc
+++ /dev/null
@@ -1,309 +0,0 @@
-#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
deleted file mode 100644
index 832f22cf..00000000
--- a/gi/pf/brat.cc
+++ /dev/null
@@ -1,543 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/multi_array.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#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 "cfg_wfst_composer.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-static unsigned kMAX_SRC_PHRASE;
-static unsigned kMAX_TRG_PHRASE;
-struct FSTState;
-
-double log_poisson(unsigned x, const double& lambda) {
-  assert(lambda > 0.0);
-  return log(lambda) * x - lgamma(x + 1) - lambda;
-}
-
-struct ConditionalBase {
-  explicit ConditionalBase(const double m1mixture, const unsigned vocab_e_size, const string& model1fname) :
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_TARGET(1.0 / vocab_e_size),
-      kNULL(TD::Convert("<eps>")) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-    LoadModel1(model1fname);
-  }
-
-  void LoadModel1(const string& fname) {
-    cerr << "Loading Model 1 parameters from " << fname << " ..." << endl;
-    ReadFile rf(fname);
-    istream& in = *rf.stream();
-    string line;
-    unsigned lc = 0;
-    while(getline(in, line)) {
-      ++lc;
-      int cur = 0;
-      int start = 0;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      const WordID src = TD::Convert(&line[0]);
-      ++cur;
-      start = cur;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      WordID trg = TD::Convert(&line[start]);
-      const double logprob = strtod(&line[cur + 1], NULL);
-      if (src >= ttable.size()) ttable.resize(src + 1);
-      ttable[src][trg].logeq(logprob);
-    }
-    cerr << "  read " << lc << " parameters.\n";
-  }
-
-  // return logp0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    const int flen = rule.f_.size();
-    const int elen = rule.e_.size();
-    prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
-    prob_t p;
-    p.logeq(log_poisson(elen, flen + 0.01));       // elen | flen          ~Pois(flen + 0.01)
-    for (int i = 0; i < elen; ++i) {               // for each position i in e-RHS
-      const WordID trg = rule.e_[i];
-      prob_t tp = prob_t::Zero();
-      for (int j = -1; j < flen; ++j) {
-        const WordID src = j < 0 ? kNULL : rule.f_[j];
-        const map<WordID, prob_t>::const_iterator it = ttable[src].find(trg);
-        if (it != ttable[src].end()) {
-          tp += kM1MIXTURE * it->second;
-        }
-        tp += kUNIFORM_MIXTURE * kUNIFORM_TARGET;
-      }
-      tp *= uniform_src_alignment;                 //     draw a_i         ~uniform
-      p *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-    }
-    return p;
-  }
-
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_TARGET;
-  const WordID kNULL;
-  vector<map<WordID, prob_t> > ttable;
-};
-
-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(3),"Maximum length of source language phrases")
-        ("max_trg_phrase",po::value<unsigned>()->default_value(3),"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);
-  }
-}
-
-void ReadParallelCorpus(const string& filename,
-                vector<vector<WordID> >* f,
-                vector<vector<int> >* e,
-                set<int>* vocab_f,
-                set<int>* 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;
-}
-
-struct UniphraseLM {
-  UniphraseLM(const vector<vector<int> >& corpus,
-              const set<int>& vocab,
-              const po::variables_map& conf) :
-    phrases_(1,1),
-    gen_(1,1),
-    corpus_(corpus),
-    uniform_word_(1.0 / vocab.size()),
-    gen_p0_(0.5),
-    p_end_(0.5),
-    use_poisson_(conf.count("poisson_length") > 0) {}
-
-  void ResampleHyperparameters(MT19937* rng) {
-    phrases_.resample_hyperparameters(rng);
-    gen_.resample_hyperparameters(rng);
-    cerr << " " << phrases_.alpha();
-  }
-
-  CCRP_NoTable<vector<int> > phrases_;
-  CCRP_NoTable<bool> gen_;
-  vector<vector<bool> > z_;   // z_[i] is there a phrase boundary after the ith word
-  const vector<vector<int> >& corpus_;
-  const double uniform_word_;
-  const double gen_p0_;
-  const double p_end_; // in base length distribution, p of the end of a phrase
-  const bool use_poisson_;
-};
-
-struct Reachability {
-  boost::multi_array<bool, 4> edges;  // edges[src_covered][trg_covered][x][trg_delta] is this edge worth exploring?
-  boost::multi_array<short, 2> max_src_delta; // msd[src_covered][trg_covered] -- the largest src delta that's valid
-
-  Reachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) :
-      edges(boost::extents[srclen][trglen][src_max_phrase_len+1][trg_max_phrase_len+1]),
-      max_src_delta(boost::extents[srclen][trglen]) {
-    ComputeReachability(srclen, trglen, src_max_phrase_len, trg_max_phrase_len);
-  }
-
- private:
-  struct SState {
-    SState() : prev_src_covered(), prev_trg_covered() {}
-    SState(int i, int j) : prev_src_covered(i), prev_trg_covered(j) {}
-    int prev_src_covered;
-    int prev_trg_covered;
-  };
-
-  struct NState {
-    NState() : next_src_covered(), next_trg_covered() {}
-    NState(int i, int j) : next_src_covered(i), next_trg_covered(j) {}
-    int next_src_covered;
-    int next_trg_covered;
-  };
-
-  void ComputeReachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) {
-    typedef boost::multi_array<vector<SState>, 2> array_type;
-    array_type a(boost::extents[srclen + 1][trglen + 1]);
-    a[0][0].push_back(SState());
-    for (int i = 0; i < srclen; ++i) {
-      for (int j = 0; j < trglen; ++j) {
-        if (a[i][j].size() == 0) continue;
-        const SState prev(i,j);
-        for (int k = 1; k <= src_max_phrase_len; ++k) {
-          if ((i + k) > srclen) continue;
-          for (int l = 1; l <= trg_max_phrase_len; ++l) {
-            if ((j + l) > trglen) continue;
-            a[i + k][j + l].push_back(prev);
-          }
-        }
-      }
-    }
-    a[0][0].clear();
-    cerr << "Final cell contains " << a[srclen][trglen].size() << " back pointers\n";
-    assert(a[srclen][trglen].size() > 0);
-
-    typedef boost::multi_array<bool, 2> rarray_type;
-    rarray_type r(boost::extents[srclen + 1][trglen + 1]);
-//    typedef boost::multi_array<vector<NState>, 2> narray_type;
-//    narray_type b(boost::extents[srclen + 1][trglen + 1]);
-    r[srclen][trglen] = true;
-    for (int i = srclen; i >= 0; --i) {
-      for (int j = trglen; j >= 0; --j) {
-        vector<SState>& prevs = a[i][j];
-        if (!r[i][j]) { prevs.clear(); }
-//        const NState nstate(i,j);
-        for (int k = 0; k < prevs.size(); ++k) {
-          r[prevs[k].prev_src_covered][prevs[k].prev_trg_covered] = true;
-          int src_delta = i - prevs[k].prev_src_covered;
-          edges[prevs[k].prev_src_covered][prevs[k].prev_trg_covered][src_delta][j - prevs[k].prev_trg_covered] = true;
-          short &msd = max_src_delta[prevs[k].prev_src_covered][prevs[k].prev_trg_covered];
-          if (src_delta > msd) msd = src_delta;
-//          b[prevs[k].prev_src_covered][prevs[k].prev_trg_covered].push_back(nstate);
-        }
-      }
-    }
-    assert(!edges[0][0][1][0]);
-    assert(!edges[0][0][0][1]);
-    assert(!edges[0][0][0][0]);
-    cerr << "  MAX SRC DELTA[0][0] = " << max_src_delta[0][0] << endl;
-    assert(max_src_delta[0][0] > 0);
-    //cerr << "First cell contains " << b[0][0].size() << " forward pointers\n";
-    //for (int i = 0; i < b[0][0].size(); ++i) {
-    //  cerr << "  -> (" << b[0][0][i].next_src_covered << "," << b[0][0][i].next_trg_covered << ")\n";
-    //}
-  }
-};
-
-ostream& operator<<(ostream& os, const FSTState& q);
-struct FSTState {
-  explicit FSTState(int src_size) :
-      trg_covered_(),
-      src_covered_(),
-      src_coverage_(src_size) {}
-
-  FSTState(short trg_covered, short src_covered, const vector<bool>& src_coverage, const vector<short>& src_prefix) :
-      trg_covered_(trg_covered),
-      src_covered_(src_covered),
-      src_coverage_(src_coverage),
-      src_prefix_(src_prefix) {
-    if (src_coverage_.size() == src_covered) {
-      assert(src_prefix.size() == 0);
-    }
-  }
-
-  // if we extend by the word at src_position, what are
-  // the next states that are reachable and lie on a valid
-  // path to the final state?
-  vector<FSTState> Extensions(int src_position, int src_len, int trg_len, const Reachability& r) const {
-    assert(src_position < src_coverage_.size());
-    if (src_coverage_[src_position]) {
-      cerr << "Trying to extend " << *this << " with position " << src_position << endl;
-      abort();
-    }
-    vector<bool> ncvg = src_coverage_;
-    ncvg[src_position] = true;
-
-    vector<FSTState> res;
-    const int trg_remaining = trg_len - trg_covered_;
-    if (trg_remaining <= 0) {
-      cerr << "Target appears to have been covered: " << *this << " (trg_len=" << trg_len << ",trg_covered=" << trg_covered_ << ")" << endl;
-      abort();
-    }
-    const int src_remaining = src_len - src_covered_;
-    if (src_remaining <= 0) {
-      cerr << "Source appears to have been covered: " << *this << endl;
-      abort();
-    }
-
-    for (int tc = 1; tc <= kMAX_TRG_PHRASE; ++tc) {
-      if (r.edges[src_covered_][trg_covered_][src_prefix_.size() + 1][tc]) {
-        int nc = src_prefix_.size() + 1 + src_covered_;
-        res.push_back(FSTState(trg_covered_ + tc, nc, ncvg, vector<short>()));
-      }
-    }
-
-    if ((src_prefix_.size() + 1) < r.max_src_delta[src_covered_][trg_covered_]) {
-      vector<short> nsp = src_prefix_;
-      nsp.push_back(src_position);
-      res.push_back(FSTState(trg_covered_, src_covered_, ncvg, nsp));
-    }
-
-    if (res.size() == 0) {
-      cerr << *this << " can't be extended!\n";
-      abort();
-    }
-    return res;
-  }
-
-  short trg_covered_, src_covered_;
-  vector<bool> src_coverage_;
-  vector<short> src_prefix_;
-};
-bool operator<(const FSTState& q, const FSTState& r) {
-  if (q.trg_covered_ != r.trg_covered_) return q.trg_covered_ < r.trg_covered_;
-  if (q.src_covered_!= r.src_covered_) return q.src_covered_ < r.src_covered_;
-  if (q.src_coverage_ != r.src_coverage_) return q.src_coverage_ < r.src_coverage_;
-  return q.src_prefix_ < r.src_prefix_;
-}
-
-ostream& operator<<(ostream& os, const FSTState& q) {
-  os << "[" << q.trg_covered_ << " : ";
-  for (int i = 0; i < q.src_coverage_.size(); ++i)
-    os << q.src_coverage_[i];
-  os << " : <";
-  for (int i = 0; i < q.src_prefix_.size(); ++i) {
-    if (i != 0) os << ' ';
-    os << q.src_prefix_[i];
-  }
-  return os << ">]";
-}
-
-struct MyModel {
-  MyModel(ConditionalBase& rcp0) : rp0(rcp0) {}
-  typedef unordered_map<vector<WordID>, CCRP_NoTable<TRule>, boost::hash<vector<WordID> > > SrcToRuleCRPMap;
-
-  void DecrementRule(const TRule& rule) {
-    SrcToRuleCRPMap::iterator it = rules.find(rule.f_);
-    assert(it != rules.end());
-    it->second.decrement(rule);
-    if (it->second.num_customers() == 0) rules.erase(it);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    SrcToRuleCRPMap::iterator it = rules.find(rule.f_);
-    if (it == rules.end()) {
-      CCRP_NoTable<TRule> crp(1,1);
-      it = rules.insert(make_pair(rule.f_, crp)).first;
-    }
-    it->second.increment(rule);
-  }
-
-  // conditioned on rule.f_
-  prob_t RuleConditionalProbability(const TRule& rule) const {
-    const prob_t base = rp0(rule);
-    SrcToRuleCRPMap::const_iterator it = rules.find(rule.f_);
-    if (it == rules.end()) {
-      return base;
-    } else {
-      const double lp = it->second.logprob(rule, log(base));
-      prob_t q; q.logeq(lp);
-      return q;
-    }
-  }
-
-  const ConditionalBase& rp0;
-  SrcToRuleCRPMap rules;
-};
-
-struct MyFST : public WFST {
-  MyFST(const vector<WordID>& ssrc, const vector<WordID>& strg, MyModel* m) :
-      src(ssrc), trg(strg),
-      r(src.size(),trg.size(),kMAX_SRC_PHRASE, kMAX_TRG_PHRASE),
-      model(m) {
-    FSTState in(src.size());
-    cerr << " INIT: " << in << endl;
-    init = GetNode(in);
-    for (int i = 0; i < in.src_coverage_.size(); ++i) in.src_coverage_[i] = true;
-    in.src_covered_ = src.size();
-    in.trg_covered_ = trg.size();
-    cerr << "FINAL: " << in << endl;
-    final = GetNode(in);
-  }
-  virtual const WFSTNode* Final() const;
-  virtual const WFSTNode* Initial() const;
-
-  const WFSTNode* GetNode(const FSTState& q);
-  map<FSTState, boost::shared_ptr<WFSTNode> > m;
-  const vector<WordID>& src;
-  const vector<WordID>& trg;
-  Reachability r;
-  const WFSTNode* init;
-  const WFSTNode* final;
-  MyModel* model;
-};
-
-struct MyNode : public WFSTNode {
-  MyNode(const FSTState& q, MyFST* fst) : state(q), container(fst) {}
-  virtual vector<pair<const WFSTNode*, TRulePtr> > ExtendInput(unsigned srcindex) const;
-  const FSTState state;
-  mutable MyFST* container;
-};
-
-vector<pair<const WFSTNode*, TRulePtr> > MyNode::ExtendInput(unsigned srcindex) const {
-  cerr << "EXTEND " << state << " with " << srcindex << endl;
-  vector<FSTState> ext = state.Extensions(srcindex, container->src.size(), container->trg.size(), container->r);
-  vector<pair<const WFSTNode*,TRulePtr> > res(ext.size());
-  for (unsigned i = 0; i < ext.size(); ++i) {
-    res[i].first = container->GetNode(ext[i]);
-    if (ext[i].src_prefix_.size() == 0) {
-      const unsigned trg_from = state.trg_covered_;
-      const unsigned trg_to = ext[i].trg_covered_;
-      const unsigned prev_prfx_size = state.src_prefix_.size();
-      res[i].second.reset(new TRule);
-      res[i].second->lhs_ = -TD::Convert("X");
-      vector<WordID>& src = res[i].second->f_;
-      vector<WordID>& trg = res[i].second->e_;
-      src.resize(prev_prfx_size + 1);
-      for (unsigned j = 0; j < prev_prfx_size; ++j)
-        src[j] = container->src[state.src_prefix_[j]];
-      src[prev_prfx_size] = container->src[srcindex];
-      for (unsigned j = trg_from; j < trg_to; ++j)
-        trg.push_back(container->trg[j]);
-      res[i].second->scores_.set_value(FD::Convert("Proposal"), log(container->model->RuleConditionalProbability(*res[i].second)));
-    }
-  }
-  return res;
-}
-
-const WFSTNode* MyFST::GetNode(const FSTState& q) {
-  boost::shared_ptr<WFSTNode>& res = m[q];
-  if (!res) {
-    res.reset(new MyNode(q, this));
-  }
-  return &*res;
-}
-
-const WFSTNode* MyFST::Final() const {
-  return final;
-}
-
-const WFSTNode* MyFST::Initial() const {
-  return init;
-}
-
-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;
-  }
-  boost::shared_ptr<MT19937> prng;
-  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;
-  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());
-
-  ConditionalBase lp0(conf["model1_interpolation_weight"].as<double>(),
-                      vocabe.size(),
-                      conf["model1"].as<string>());
-  MyModel m(lp0);
-
-  TRule x("[X] ||| kAnwntR myN ||| at the convent ||| 0");
-  m.IncrementRule(x);
-  TRule y("[X] ||| nY dyN ||| gave ||| 0");
-  m.IncrementRule(y);
-
-
-  MyFST fst(corpusf[0], corpuse[0], &m);
-  ifstream in("./kimura.g");
-  assert(in);
-  CFG_WFSTComposer comp(fst);
-  Hypergraph hg;
-  bool succeed = comp.Compose(&in, &hg);
-  hg.PrintGraphviz();
-  if (succeed) { cerr << "SUCCESS.\n"; } else { cerr << "FAILURE REPORTED.\n"; }
-
-#if 0
-  ifstream in2("./amnabooks.g");
-  assert(in2);
-  MyFST fst2(corpusf[1], corpuse[1], &m);
-  CFG_WFSTComposer comp2(fst2);
-  Hypergraph hg2;
-  bool succeed2 = comp2.Compose(&in2, &hg2);
-  if (succeed2) { cerr << "SUCCESS.\n"; } else { cerr << "FAILURE REPORTED.\n"; }
-#endif
-
-  SparseVector<double> w; w.set_value(FD::Convert("Proposal"), 1.0);
-  hg.Reweight(w);
-  cerr << ViterbiFTree(hg) << endl;
-  return 0;
-}
-
diff --git a/gi/pf/cbgi.cc b/gi/pf/cbgi.cc
deleted file mode 100644
index 97f1ba34..00000000
--- a/gi/pf/cbgi.cc
+++ /dev/null
@@ -1,330 +0,0 @@
-#include <queue>
-#include <sstream>
-#include <iostream>
-
-#include <boost/unordered_map.hpp>
-#include <boost/functional/hash.hpp>
-
-#include "sampler.h"
-#include "filelib.h"
-#include "hg_io.h"
-#include "hg.h"
-#include "ccrp_nt.h"
-#include "trule.h"
-#include "inside_outside.h"
-
-using namespace std;
-using namespace std::tr1;
-
-double log_poisson(unsigned x, const double& lambda) {
-  assert(lambda > 0.0);
-  return log(lambda) * x - lgamma(x + 1) - lambda;
-}
-
-double log_decay(unsigned x, const double& b) {
-  assert(b > 1.0);
-  assert(x > 0);
-  return log(b - 1) - x * log(b);
-}
-
-struct SimpleBase {
-  SimpleBase(unsigned esize, unsigned fsize, unsigned ntsize = 144) :
-    uniform_e(-log(esize)),
-    uniform_f(-log(fsize)),
-    uniform_nt(-log(ntsize)) {
-  }
-
-  // binomial coefficient
-  static double choose(unsigned n, unsigned k) {
-    return exp(lgamma(n + 1) - lgamma(k + 1) - lgamma(n - k + 1));
-  }
-
-  // count the number of patterns of terminals and NTs in the rule, given elen and flen
-  static double log_number_of_patterns(const unsigned flen, const unsigned elen) {
-    static vector<vector<double> > counts;
-    if (elen >= counts.size()) counts.resize(elen + 1);
-    if (flen >= counts[elen].size()) counts[elen].resize(flen + 1);
-    double& count = counts[elen][flen];
-    if (count) return log(count);
-    const unsigned max_arity = min(elen, flen);
-    for (unsigned a = 0; a <= max_arity; ++a)
-      count += choose(elen, a) * choose(flen, a);
-    return log(count);
-  }
-
-  // return logp0 of rule | LHS
-  double operator()(const TRule& rule) const {
-    const unsigned flen = rule.f_.size();
-    const unsigned elen = rule.e_.size();
-#if 0
-    double p = 0;
-    p += log_poisson(flen, 0.5);                   // flen                 ~Pois(0.5)
-    p += log_poisson(elen, flen);                  // elen | flen          ~Pois(flen)
-    p -= log_number_of_patterns(flen, elen);       // pattern | flen,elen  ~Uniform
-    for (unsigned i = 0; i < flen; ++i) {          // for each position in f-RHS
-      if (rule.f_[i] <= 0)                         //   according to pattern
-        p += uniform_nt;                           //     draw NT          ~Uniform
-      else
-        p += uniform_f;                            //     draw f terminal  ~Uniform
-    }
-    p -= lgamma(rule.Arity() + 1);                 // draw permutation     ~Uniform 
-    for (unsigned i = 0; i < elen; ++i) {          // for each position in e-RHS
-      if (rule.e_[i] > 0)                          //   according to pattern
-        p += uniform_e;                            //     draw e|f term    ~Uniform
-        // TODO this should prob be model 1
-    }
-#else
-    double p = 0;
-    bool is_abstract = rule.f_[0] <= 0;
-    p += log(0.5);
-    if (is_abstract) {
-      if (flen == 2) p += log(0.99); else p += log(0.01);
-    } else {
-      p += log_decay(flen, 3);
-    }
-
-    for (unsigned i = 0; i < flen; ++i) {          // for each position in f-RHS
-      if (rule.f_[i] <= 0)                         //   according to pattern
-        p += uniform_nt;                           //     draw NT          ~Uniform
-      else
-        p += uniform_f;                            //     draw f terminal  ~Uniform
-    }
-#endif
-    return p;
-  }
-  const double uniform_e;
-  const double uniform_f;
-  const double uniform_nt;
-  vector<double> arities;
-};
-
-MT19937* rng = NULL;
-
-template <typename Base>
-struct MHSamplerEdgeProb {
-  MHSamplerEdgeProb(const Hypergraph& hg,
-                  const map<int, CCRP_NoTable<TRule> >& rdp,
-                  const Base& logp0,
-                  const bool exclude_multiword_terminals) : edge_probs(hg.edges_.size()) {
-    for (int i = 0; i < edge_probs.size(); ++i) {
-      const TRule& rule = *hg.edges_[i].rule_;
-      const map<int, CCRP_NoTable<TRule> >::const_iterator it = rdp.find(rule.lhs_);
-      assert(it != rdp.end());
-      const CCRP_NoTable<TRule>& crp = it->second;
-      edge_probs[i].logeq(crp.logprob(rule, logp0(rule)));
-      if (exclude_multiword_terminals && rule.f_[0] > 0 && rule.f_.size() > 1)
-        edge_probs[i] = prob_t::Zero();
-    }
-  }
-  inline prob_t operator()(const Hypergraph::Edge& e) const {
-    return edge_probs[e.id_];
-  }
-  prob_t DerivationProb(const vector<int>& d) const {
-    prob_t p = prob_t::One();
-    for (unsigned i = 0; i < d.size(); ++i)
-      p *= edge_probs[d[i]];
-    return p;
-  }
-  vector<prob_t> edge_probs;
-};
-
-template <typename Base>
-struct ModelAndData {
-  ModelAndData() :
-     base_lh(prob_t::One()),
-     logp0(10000, 10000),
-     mh_samples(),
-     mh_rejects() {}
-
-  void SampleCorpus(const string& hgpath, int i);
-  void ResampleHyperparameters() {
-    for (map<int, CCRP_NoTable<TRule> >::iterator it = rules.begin(); it != rules.end(); ++it)
-      it->second.resample_hyperparameters(rng);
-  }
-
-  CCRP_NoTable<TRule>& RuleCRP(int lhs) {
-    map<int, CCRP_NoTable<TRule> >::iterator it = rules.find(lhs);
-    if (it == rules.end()) {
-      rules.insert(make_pair(lhs, CCRP_NoTable<TRule>(1,1)));
-      it = rules.find(lhs);
-    }
-    return it->second;
-  }
-
-  void IncrementRule(const TRule& rule) {
-    CCRP_NoTable<TRule>& crp = RuleCRP(rule.lhs_);
-    if (crp.increment(rule)) {
-      prob_t p; p.logeq(logp0(rule));
-      base_lh *= p;
-    }
-  }
-
-  void DecrementRule(const TRule& rule) {
-    CCRP_NoTable<TRule>& crp = RuleCRP(rule.lhs_);
-    if (crp.decrement(rule)) {
-      prob_t p; p.logeq(logp0(rule));
-      base_lh /= p;
-    }
-  }
-
-  void DecrementDerivation(const Hypergraph& hg, const vector<int>& d) {
-    for (unsigned i = 0; i < d.size(); ++i) {
-      const TRule& rule = *hg.edges_[d[i]].rule_;
-      DecrementRule(rule);
-    }
-  }
-
-  void IncrementDerivation(const Hypergraph& hg, const vector<int>& d) {
-    for (unsigned i = 0; i < d.size(); ++i) {
-      const TRule& rule = *hg.edges_[d[i]].rule_;
-      IncrementRule(rule);
-    }
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = prob_t::One();
-    for (map<int, CCRP_NoTable<TRule> >::const_iterator it = rules.begin(); it != rules.end(); ++it) {
-      prob_t q; q.logeq(it->second.log_crp_prob());
-      p *= q;
-    }
-    p *= base_lh;
-    return p;
-  }
-
-  void ResampleDerivation(const Hypergraph& hg, vector<int>* sampled_derivation);
-
-  map<int, CCRP_NoTable<TRule> > rules;  // [lhs] -> distribution over RHSs
-  prob_t base_lh;
-  SimpleBase logp0;
-  vector<vector<int> > samples;   // sampled derivations
-  unsigned int mh_samples;
-  unsigned int mh_rejects;
-};
-
-template <typename Base>
-void ModelAndData<Base>::SampleCorpus(const string& hgpath, int n) {
-  vector<Hypergraph> hgs(n); hgs.clear();
-  boost::unordered_map<TRule, unsigned> acc;
-  map<int, unsigned> tot;
-  for (int i = 0; i < n; ++i) {
-    ostringstream os;
-    os << hgpath << '/' << i << ".json.gz";
-    if (!FileExists(os.str())) continue;
-    hgs.push_back(Hypergraph());
-    ReadFile rf(os.str());
-    HypergraphIO::ReadFromJSON(rf.stream(), &hgs.back());
-  }
-  cerr << "Read " << hgs.size() << " alignment hypergraphs.\n";
-  samples.resize(hgs.size());
-  const unsigned SAMPLES = 2000;
-  const unsigned burnin = 3 * SAMPLES / 4;
-  const unsigned every = 20;
-  for (unsigned s = 0; s < SAMPLES; ++s) {
-    if (s % 10 == 0) {
-      if (s > 0) { cerr << endl; ResampleHyperparameters(); }
-      cerr << "[" << s << " LLH=" << log(Likelihood()) << " REJECTS=" << ((double)mh_rejects / mh_samples) << " LHS's=" << rules.size() << " base=" << log(base_lh) << "] ";
-    }
-    cerr << '.';
-    for (unsigned i = 0; i < hgs.size(); ++i) {
-      ResampleDerivation(hgs[i], &samples[i]);
-      if (s > burnin && s % every == 0) {
-        for (unsigned j = 0; j < samples[i].size(); ++j) {
-          const TRule& rule = *hgs[i].edges_[samples[i][j]].rule_;
-          ++acc[rule];
-          ++tot[rule.lhs_];
-        }
-      }
-    }
-  }
-  cerr << endl;
-  for (boost::unordered_map<TRule,unsigned>::iterator it = acc.begin(); it != acc.end(); ++it) {
-    cout << it->first << " MyProb=" << log(it->second)-log(tot[it->first.lhs_]) << endl;
-  }
-}
-
-template <typename Base>
-void ModelAndData<Base>::ResampleDerivation(const Hypergraph& hg, vector<int>* sampled_deriv) {
-  vector<int> cur;
-  cur.swap(*sampled_deriv);
-
-  const prob_t p_cur = Likelihood();
-  DecrementDerivation(hg, cur);
-  if (cur.empty()) {
-    // first iteration, create restaurants
-    for (int i = 0; i < hg.edges_.size(); ++i)
-      RuleCRP(hg.edges_[i].rule_->lhs_);
-  }
-  MHSamplerEdgeProb<SimpleBase> wf(hg, rules, logp0, cur.empty());
-//  MHSamplerEdgeProb<SimpleBase> wf(hg, rules, logp0, false);
-  const prob_t q_cur = wf.DerivationProb(cur);
-  vector<prob_t> node_probs;
-  Inside<prob_t, MHSamplerEdgeProb<SimpleBase> >(hg, &node_probs, wf);
-  queue<unsigned> q;
-  q.push(hg.nodes_.size() - 3);
-  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 = wf.edge_probs[edge.id_];             // edge proposal 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]);
-    }
-  }
-  IncrementDerivation(hg, *sampled_deriv);
-
-//  cerr << "sampled derivation contains " << sampled_deriv->size() << " edges\n";
-//  cerr << "DERIV:\n";
-//  for (int i = 0; i < sampled_deriv->size(); ++i) {
-//    cerr << "  " << hg.edges_[(*sampled_deriv)[i]].rule_->AsString() << endl;
-//  }
-
-  if (cur.empty()) return;  // accept first sample
-
-  ++mh_samples;
-  // only need to do MH if proposal is different to current state
-  if (cur != *sampled_deriv) {
-    const prob_t q_prop = wf.DerivationProb(*sampled_deriv);
-    const prob_t p_prop = Likelihood();
-    if (!rng->AcceptMetropolisHastings(p_prop, p_cur, q_prop, q_cur)) {
-      ++mh_rejects;
-      DecrementDerivation(hg, *sampled_deriv);
-      IncrementDerivation(hg, cur);
-      swap(cur, *sampled_deriv);
-    }
-  }
-}
-
-int main(int argc, char** argv) {
-  rng = new MT19937;
-  ModelAndData<SimpleBase> m;
-  m.SampleCorpus("./hgs", 50);
-  // m.SampleCorpus("./btec/hgs", 5000);
-  return 0;
-}
-
diff --git a/gi/pf/cfg_wfst_composer.cc b/gi/pf/cfg_wfst_composer.cc
deleted file mode 100644
index 21d5ec5b..00000000
--- a/gi/pf/cfg_wfst_composer.cc
+++ /dev/null
@@ -1,731 +0,0 @@
-#include "cfg_wfst_composer.h"
-
-#include <iostream>
-#include <fstream>
-#include <map>
-#include <queue>
-#include <tr1/unordered_map>
-#include <tr1/unordered_set>
-
-#include <boost/shared_ptr.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-#include "fast_lexical_cast.hpp"
-
-#include "phrasetable_fst.h"
-#include "sparse_vector.h"
-#include "tdict.h"
-#include "hg.h"
-#include "hg_remove_eps.h"
-
-namespace po = boost::program_options;
-using namespace std;
-using namespace std::tr1;
-
-WFSTNode::~WFSTNode() {}
-WFST::~WFST() {}
-
-// Define the following macro if you want to see lots of debugging output
-// when you run the chart parser
-#undef DEBUG_CHART_PARSER
-
-// A few constants used by the chart parser ///////////////
-static const int kMAX_NODES = 2000000;
-static const string kPHRASE_STRING = "X";
-static bool constants_need_init = true;
-static WordID kUNIQUE_START;
-static WordID kPHRASE;
-static TRulePtr kX1X2;
-static TRulePtr kX1;
-static WordID kEPS;
-static TRulePtr kEPSRule;
-
-static void InitializeConstants() {
-  if (constants_need_init) {
-    kPHRASE = TD::Convert(kPHRASE_STRING) * -1;
-    kUNIQUE_START = TD::Convert("S") * -1;
-    kX1X2.reset(new TRule("[X] ||| [X,1] [X,2] ||| [X,1] [X,2]"));
-    kX1.reset(new TRule("[X] ||| [X,1] ||| [X,1]"));
-    kEPSRule.reset(new TRule("[X] ||| <eps> ||| <eps>"));
-    kEPS = TD::Convert("<eps>");
-    constants_need_init = false;
-  }
-}
-////////////////////////////////////////////////////////////
-
-class EGrammarNode {
-  friend bool CFG_WFSTComposer::Compose(const Hypergraph& src_forest, Hypergraph* trg_forest);
-  friend void AddGrammarRule(const string& r, map<WordID, EGrammarNode>* g);
- public:
-#ifdef DEBUG_CHART_PARSER
-  string hint;
-#endif
-  EGrammarNode() : is_some_rule_complete(false), is_root(false) {}
-  const map<WordID, EGrammarNode>& GetTerminals() const { return tptr; }
-  const map<WordID, EGrammarNode>& GetNonTerminals() const { return ntptr; }
-  bool HasNonTerminals() const { return (!ntptr.empty()); }
-  bool HasTerminals() const { return (!tptr.empty()); }
-  bool RuleCompletes() const {
-    return (is_some_rule_complete || (ntptr.empty() && tptr.empty()));
-  }
-  bool GrammarContinues() const {
-    return !(ntptr.empty() && tptr.empty());
-  }
-  bool IsRoot() const {
-    return is_root;
-  }
-  // these are the features associated with the rule from the start
-  // node up to this point.  If you use these features, you must
-  // not Extend() this rule.
-  const SparseVector<double>& GetCFGProductionFeatures() const {
-    return input_features;
-  }
-
-  const EGrammarNode* Extend(const WordID& t) const {
-    if (t < 0) {
-      map<WordID, EGrammarNode>::const_iterator it = ntptr.find(t);
-      if (it == ntptr.end()) return NULL;
-      return &it->second;
-    } else {
-      map<WordID, EGrammarNode>::const_iterator it = tptr.find(t);
-      if (it == tptr.end()) return NULL;
-      return &it->second;
-    }
-  }
-
- private:
-  map<WordID, EGrammarNode> tptr;
-  map<WordID, EGrammarNode> ntptr;
-  SparseVector<double> input_features;
-  bool is_some_rule_complete;
-  bool is_root;
-};
-typedef map<WordID, EGrammarNode> EGrammar;    // indexed by the rule LHS
-
-// edges are immutable once created
-struct Edge {
-#ifdef DEBUG_CHART_PARSER
-  static int id_count;
-  const int id;
-#endif
-  const WordID cat;                   // lhs side of rule proved/being proved
-  const EGrammarNode* const dot;      // dot position
-  const WFSTNode* const q;             // start of span
-  const WFSTNode* const r;             // end of span
-  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
-  boost::shared_ptr<SparseVector<double> > features; // features from CFG rule
-
-  bool IsPassive() const {
-    // when a rule is completed, this value will be set
-    return static_cast<bool>(features);
-  }
-  bool IsActive() const { return !IsPassive(); }
-  bool IsInitial() const {
-    return !(active_parent || passive_parent);
-  }
-  bool IsCreatedByScan() const {
-    return active_parent && !passive_parent && !dot->IsRoot();
-  }
-  bool IsCreatedByPredict() const {
-    return dot->IsRoot();
-  }
-  bool IsCreatedByComplete() const {
-    return active_parent && passive_parent;
-  }
-
-  // constructor for PREDICT
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* q_and_r) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(q_and_r), r(q_and_r), active_parent(NULL), passive_parent(NULL), tps() {}
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* q_and_r, const Edge* act_parent) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(q_and_r), r(q_and_r), active_parent(act_parent), passive_parent(NULL), tps() {}
-
-  // constructors for SCAN
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* i, const WFSTNode* j,
-       const Edge* act_par, const TRulePtr& translations) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(i), r(j), active_parent(act_par), passive_parent(NULL), tps(translations) {}
-
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* i, const WFSTNode* j,
-       const Edge* act_par, const TRulePtr& translations,
-       const SparseVector<double>& feats) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(i), r(j), active_parent(act_par), passive_parent(NULL), tps(translations),
-    features(new SparseVector<double>(feats)) {}
-
-  // constructors for COMPLETE
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* i, const WFSTNode* j,
-       const Edge* act_par, const Edge *pas_par) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(i), r(j), active_parent(act_par), passive_parent(pas_par), tps() {
-      assert(pas_par->IsPassive());
-      assert(act_par->IsActive());
-    }
-
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* i, const WFSTNode* j,
-       const Edge* act_par, const Edge *pas_par, const SparseVector<double>& feats) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(i), r(j), active_parent(act_par), passive_parent(pas_par), tps(),
-    features(new SparseVector<double>(feats)) {
-      assert(pas_par->IsPassive());
-      assert(act_par->IsActive());
-    }
-
-  // constructor for COMPLETE query
-  Edge(const WFSTNode* _r) :
-#ifdef DEBUG_CHART_PARSER
-    id(0),
-#endif
-    cat(0), dot(NULL), q(NULL),
-    r(_r), active_parent(NULL), passive_parent(NULL), tps() {}
-  // constructor for MERGE quere
-  Edge(const WFSTNode* _q, int) :
-#ifdef DEBUG_CHART_PARSER
-    id(0),
-#endif
-    cat(0), dot(NULL), q(_q),
-    r(NULL), active_parent(NULL), passive_parent(NULL), tps() {}
-};
-#ifdef DEBUG_CHART_PARSER
-int Edge::id_count = 0;
-#endif
-
-ostream& operator<<(ostream& os, const Edge& e) {
-  string type = "PREDICT";
-  if (e.IsCreatedByScan())
-    type = "SCAN";
-  else if (e.IsCreatedByComplete())
-    type = "COMPLETE";
-  os << "["
-#ifdef DEBUG_CHART_PARSER
-     << '(' << e.id << ") "
-#else
-     << '(' << &e << ") "
-#endif
-     << "q=" << e.q << ", r=" << e.r
-     << ", cat="<< TD::Convert(e.cat*-1) << ", dot="
-     << e.dot
-#ifdef DEBUG_CHART_PARSER
-     << e.dot->hint
-#endif
-     << (e.IsActive() ? ", Active" : ", Passive")
-     << ", " << type;
-#ifdef DEBUG_CHART_PARSER
-  if (e.active_parent) { os << ", act.parent=(" << e.active_parent->id << ')'; }
-  if (e.passive_parent) { os << ", psv.parent=(" << e.passive_parent->id << ')'; }
-#endif
-  if (e.tps) { os << ", tps=" << e.tps->AsString(); }
-  return os << ']';
-}
-
-struct Traversal {
-  const Edge* const edge;      // result from the active / passive combination
-  const Edge* const active;
-  const Edge* const passive;
-  Traversal(const Edge* me, const Edge* a, const Edge* p) : edge(me), active(a), passive(p) {}
-};
-
-struct UniqueTraversalHash {
-  size_t operator()(const Traversal* t) const {
-    size_t x = 5381;
-    x = ((x << 5) + x) ^ reinterpret_cast<size_t>(t->active);
-    x = ((x << 5) + x) ^ reinterpret_cast<size_t>(t->passive);
-    x = ((x << 5) + x) ^ t->edge->IsActive();
-    return x;
-  }
-};
-
-struct UniqueTraversalEquals {
-  size_t operator()(const Traversal* a, const Traversal* b) const {
-    return (a->passive == b->passive && a->active == b->active && a->edge->IsActive() == b->edge->IsActive());
-  }
-};
-
-struct UniqueEdgeHash {
-  size_t operator()(const Edge* e) const {
-    size_t x = 5381;
-    if (e->IsActive()) {
-      x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->dot);
-      x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->q);
-      x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->r);
-      x = ((x << 5) + x) ^ static_cast<size_t>(e->cat);
-      x += 13;
-    } else {  // with passive edges, we don't care about the dot
-      x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->q);
-      x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->r);
-      x = ((x << 5) + x) ^ static_cast<size_t>(e->cat);
-    }
-    return x;
-  }
-};
-
-struct UniqueEdgeEquals {
-  bool operator()(const Edge* a, const Edge* b) const {
-    if (a->IsActive() != b->IsActive()) return false;
-    if (a->IsActive()) {
-      return (a->cat == b->cat) && (a->dot == b->dot) && (a->q == b->q) && (a->r == b->r);
-    } else {
-      return (a->cat == b->cat) && (a->q == b->q) && (a->r == b->r);
-    }
-  }
-};
-
-struct REdgeHash {
-  size_t operator()(const Edge* e) const {
-    size_t x = 5381;
-    x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->r);
-    return x;
-  }
-};
-
-struct REdgeEquals {
-  bool operator()(const Edge* a, const Edge* b) const {
-    return (a->r == b->r);
-  }
-};
-
-struct QEdgeHash {
-  size_t operator()(const Edge* e) const {
-    size_t x = 5381;
-    x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->q);
-    return x;
-  }
-};
-
-struct QEdgeEquals {
-  bool operator()(const Edge* a, const Edge* b) const {
-    return (a->q == b->q);
-  }
-};
-
-struct EdgeQueue {
-  queue<const Edge*> q;
-  EdgeQueue() {}
-  void clear() { while(!q.empty()) q.pop(); }
-  bool HasWork() const { return !q.empty(); }
-  const Edge* Next() { const Edge* res = q.front(); q.pop(); return res; }
-  void AddEdge(const Edge* s) { q.push(s); }
-};
-
-class CFG_WFSTComposerImpl {
- public:
-  CFG_WFSTComposerImpl(WordID start_cat,
-                       const WFSTNode* q_0,
-                       const WFSTNode* q_final) : start_cat_(start_cat), q_0_(q_0), q_final_(q_final) {}
-
-  // returns false if the intersection is empty
-  bool Compose(const EGrammar& g, Hypergraph* forest) {
-    goal_node = NULL;
-    EGrammar::const_iterator sit = g.find(start_cat_);
-    forest->ReserveNodes(kMAX_NODES);
-    assert(sit != g.end());
-    Edge* init = new Edge(start_cat_, &sit->second, q_0_);
-    assert(IncorporateNewEdge(init));
-    while (exp_agenda.HasWork() || agenda.HasWork()) {
-      while(exp_agenda.HasWork()) {
-        const Edge* edge = exp_agenda.Next();
-        FinishEdge(edge, forest);
-      }
-      if (agenda.HasWork()) {
-        const Edge* edge = agenda.Next();
-#ifdef DEBUG_CHART_PARSER
-        cerr << "processing (" << edge->id << ')' << endl;
-#endif
-        if (edge->IsActive()) {
-          if (edge->dot->HasTerminals())
-            DoScan(edge);
-          if (edge->dot->HasNonTerminals()) {
-            DoMergeWithPassives(edge);
-            DoPredict(edge, g);
-          }
-        } else {
-          DoComplete(edge);
-        }
-      }
-    }
-    if (goal_node) {
-      forest->PruneUnreachable(goal_node->id_);
-      RemoveEpsilons(forest, kEPS);
-    }
-    FreeAll();
-    return goal_node;
-  }
-
-  void FreeAll() {
-    for (int i = 0; i < free_list_.size(); ++i)
-      delete free_list_[i];
-    free_list_.clear();
-    for (int i = 0; i < traversal_free_list_.size(); ++i)
-      delete traversal_free_list_[i];
-    traversal_free_list_.clear();
-    all_traversals.clear();
-    exp_agenda.clear();
-    agenda.clear();
-    tps2node.clear();
-    edge2node.clear();
-    all_edges.clear();
-    passive_edges.clear();
-    active_edges.clear();
-  }
-
-  ~CFG_WFSTComposerImpl() {
-    FreeAll();
-  }
-
-  // returns the total number of edges created during composition
-  int EdgesCreated() const {
-    return free_list_.size();
-  }
-
- private:
-  void DoScan(const Edge* edge) {
-    // here, we assume that the FST will potentially have many more outgoing
-    // edges than the grammar, which will be just a couple.  If you want to
-    // efficiently handle the case where both are relatively large, this code
-    // will need to change how the intersection is done.  The best general
-    // solution would probably be the Baeza-Yates double binary search.
-
-    const EGrammarNode* dot = edge->dot;
-    const WFSTNode* r = edge->r;
-    const map<WordID, EGrammarNode>& terms = dot->GetTerminals();
-    for (map<WordID, EGrammarNode>::const_iterator git = terms.begin();
-         git != terms.end(); ++git) {
-
-      if (!(TD::Convert(git->first)[0] >= '0' && TD::Convert(git->first)[0] <= '9')) {
-        std::cerr << "TERMINAL SYMBOL: " << TD::Convert(git->first) << endl;
-        abort();
-      }
-      std::vector<std::pair<const WFSTNode*, TRulePtr> > extensions = r->ExtendInput(atoi(TD::Convert(git->first).c_str()));
-      for (unsigned nsi = 0; nsi < extensions.size(); ++nsi) {
-        const WFSTNode* next_r = extensions[nsi].first;
-        const EGrammarNode* next_dot = &git->second;
-        const bool grammar_continues = next_dot->GrammarContinues();
-        const bool rule_completes    = next_dot->RuleCompletes();
-        if (extensions[nsi].second)
-          cerr << "!!! " << extensions[nsi].second->AsString() << endl;
-        // cerr << "  rule completes: " << rule_completes << " after consuming " << TD::Convert(git->first) << endl;
-        assert(grammar_continues || rule_completes);
-        const SparseVector<double>& input_features = next_dot->GetCFGProductionFeatures();
-        if (rule_completes)
-          IncorporateNewEdge(new Edge(edge->cat, next_dot, edge->q, next_r, edge, extensions[nsi].second, input_features));
-        if (grammar_continues)
-          IncorporateNewEdge(new Edge(edge->cat, next_dot, edge->q, next_r, edge, extensions[nsi].second));
-      }
-    }
-  }
-
-  void DoPredict(const Edge* edge, const EGrammar& g) {
-    const EGrammarNode* dot = edge->dot;
-    const map<WordID, EGrammarNode>& non_terms = dot->GetNonTerminals();
-    for (map<WordID, EGrammarNode>::const_iterator git = non_terms.begin();
-         git != non_terms.end(); ++git) {
-      const WordID nt_to_predict = git->first;
-      //cerr << edge->id << " -- " << TD::Convert(nt_to_predict*-1) << endl;
-      EGrammar::const_iterator egi = g.find(nt_to_predict);
-      if (egi == g.end()) {
-        cerr << "[ERROR] Can't find any grammar rules with a LHS of type "
-             << TD::Convert(-1*nt_to_predict) << '!' << endl;
-        continue;
-      }
-      assert(edge->IsActive());
-      const EGrammarNode* new_dot = &egi->second;
-      Edge* new_edge = new Edge(nt_to_predict, new_dot, edge->r, edge);
-      IncorporateNewEdge(new_edge);
-    }
-  }
-
-  void DoComplete(const Edge* passive) {
-#ifdef DEBUG_CHART_PARSER
-    cerr << "  complete: " << *passive << endl;
-#endif
-    const WordID completed_nt = passive->cat;
-    const WFSTNode* q = passive->q;
-    const WFSTNode* next_r = passive->r;
-    const Edge query(q);
-    const pair<unordered_multiset<const Edge*, REdgeHash, REdgeEquals>::iterator,
-         unordered_multiset<const Edge*, REdgeHash, REdgeEquals>::iterator > p =
-      active_edges.equal_range(&query);
-    for (unordered_multiset<const Edge*, REdgeHash, REdgeEquals>::iterator it = p.first;
-         it != p.second; ++it) {
-      const Edge* active = *it;
-#ifdef DEBUG_CHART_PARSER
-      cerr << "    pos: " << *active << endl;
-#endif
-      const EGrammarNode* next_dot = active->dot->Extend(completed_nt);
-      if (!next_dot) continue;
-      const SparseVector<double>& input_features = next_dot->GetCFGProductionFeatures();
-      // add up to 2 rules
-      if (next_dot->RuleCompletes())
-        IncorporateNewEdge(new Edge(active->cat, next_dot, active->q, next_r, active, passive, input_features));
-      if (next_dot->GrammarContinues())
-        IncorporateNewEdge(new Edge(active->cat, next_dot, active->q, next_r, active, passive));
-    }
-  }
-
-  void DoMergeWithPassives(const Edge* active) {
-    // edge is active, has non-terminals, we need to find the passives that can extend it
-    assert(active->IsActive());
-    assert(active->dot->HasNonTerminals());
-#ifdef DEBUG_CHART_PARSER
-    cerr << "  merge active with passives: ACT=" << *active << endl;
-#endif
-    const Edge query(active->r, 1);
-    const pair<unordered_multiset<const Edge*, QEdgeHash, QEdgeEquals>::iterator,
-         unordered_multiset<const Edge*, QEdgeHash, QEdgeEquals>::iterator > p =
-      passive_edges.equal_range(&query);
-    for (unordered_multiset<const Edge*, QEdgeHash, QEdgeEquals>::iterator it = p.first;
-         it != p.second; ++it) {
-      const Edge* passive = *it;
-      const EGrammarNode* next_dot = active->dot->Extend(passive->cat);
-      if (!next_dot) continue;
-      const WFSTNode* next_r = passive->r;
-      const SparseVector<double>& input_features = next_dot->GetCFGProductionFeatures();
-      if (next_dot->RuleCompletes())
-        IncorporateNewEdge(new Edge(active->cat, next_dot, active->q, next_r, active, passive, input_features));
-      if (next_dot->GrammarContinues())
-        IncorporateNewEdge(new Edge(active->cat, next_dot, active->q, next_r, active, passive));
-    }
-  }
-
-  // take ownership of edge memory, add to various indexes, etc
-  // returns true if this edge is new
-  bool IncorporateNewEdge(Edge* edge) {
-    free_list_.push_back(edge);
-    if (edge->passive_parent && edge->active_parent) {
-      Traversal* t = new Traversal(edge, edge->active_parent, edge->passive_parent);
-      traversal_free_list_.push_back(t);
-      if (all_traversals.find(t) != all_traversals.end()) {
-        return false;
-      } else {
-        all_traversals.insert(t);
-      }
-    }
-    exp_agenda.AddEdge(edge);
-    return true;
-  }
-
-  bool FinishEdge(const Edge* edge, Hypergraph* hg) {
-    bool is_new = false;
-    if (all_edges.find(edge) == all_edges.end()) {
-#ifdef DEBUG_CHART_PARSER
-      cerr << *edge << " is NEW\n";
-#endif
-      all_edges.insert(edge);
-      is_new = true;
-      if (edge->IsPassive()) passive_edges.insert(edge);
-      if (edge->IsActive()) active_edges.insert(edge);
-      agenda.AddEdge(edge);
-    } else {
-#ifdef DEBUG_CHART_PARSER
-      cerr << *edge << " is NOT NEW.\n";
-#endif
-    }
-    AddEdgeToTranslationForest(edge, hg);
-    return is_new;
-  }
-
-  // build the translation forest
-  void AddEdgeToTranslationForest(const Edge* edge, Hypergraph* hg) {
-    assert(hg->nodes_.size() < kMAX_NODES);
-    Hypergraph::Node* tps = NULL;
-    // first add any target language rules
-    if (edge->tps) {
-      Hypergraph::Node*& node = tps2node[(size_t)edge->tps.get()];
-      if (!node) {
-        // cerr << "Creating phrases for " << edge->tps << endl;
-        const TRulePtr& rule = edge->tps;
-        node = hg->AddNode(kPHRASE);
-        Hypergraph::Edge* hg_edge = hg->AddEdge(rule, Hypergraph::TailNodeVector());
-        hg_edge->feature_values_ += rule->GetFeatureValues();
-        hg->ConnectEdgeToHeadNode(hg_edge, node);
-      }
-      tps = node;
-    }
-    Hypergraph::Node*& head_node = edge2node[edge];
-    if (!head_node)
-      head_node = hg->AddNode(kPHRASE);
-    if (edge->cat == start_cat_ && edge->q == q_0_ && edge->r == q_final_ && edge->IsPassive()) {
-      assert(goal_node == NULL || goal_node == head_node);
-      goal_node = head_node;
-    }
-    Hypergraph::TailNodeVector tail;
-    SparseVector<double> extra;
-    if (edge->IsCreatedByPredict()) {
-      // extra.set_value(FD::Convert("predict"), 1);
-    } else if (edge->IsCreatedByScan()) {
-      tail.push_back(edge2node[edge->active_parent]->id_);
-      if (tps) {
-        tail.push_back(tps->id_);
-      }
-      //extra.set_value(FD::Convert("scan"), 1);
-    } else if (edge->IsCreatedByComplete()) {
-      tail.push_back(edge2node[edge->active_parent]->id_);
-      tail.push_back(edge2node[edge->passive_parent]->id_);
-      //extra.set_value(FD::Convert("complete"), 1);
-    } else {
-      assert(!"unexpected edge type!");
-    }
-    //cerr << head_node->id_ << "<--" << *edge << endl;
-
-#ifdef DEBUG_CHART_PARSER
-      for (int i = 0; i < tail.size(); ++i)
-        if (tail[i] == head_node->id_) {
-          cerr << "ERROR: " << *edge << "\n   i=" << i << endl;
-          if (i == 1) { cerr << "\tP: " << *edge->passive_parent << endl; }
-          if (i == 0) { cerr << "\tA: " << *edge->active_parent << endl; }
-          assert(!"self-loop found!");
-        }
-#endif
-    Hypergraph::Edge* hg_edge = NULL;
-    if (tail.size() == 0) {
-      hg_edge = hg->AddEdge(kEPSRule, tail);
-    } else if (tail.size() == 1) {
-      hg_edge = hg->AddEdge(kX1, tail);
-    } else if (tail.size() == 2) {
-      hg_edge = hg->AddEdge(kX1X2, tail);
-    }
-    if (edge->features)
-      hg_edge->feature_values_ += *edge->features;
-    hg_edge->feature_values_ += extra;
-    hg->ConnectEdgeToHeadNode(hg_edge, head_node);
-  }
-
-  Hypergraph::Node* goal_node;
-  EdgeQueue exp_agenda;
-  EdgeQueue agenda;
-  unordered_map<size_t, Hypergraph::Node*> tps2node;
-  unordered_map<const Edge*, Hypergraph::Node*, UniqueEdgeHash, UniqueEdgeEquals> edge2node;
-  unordered_set<const Traversal*, UniqueTraversalHash, UniqueTraversalEquals> all_traversals;
-  unordered_set<const Edge*, UniqueEdgeHash, UniqueEdgeEquals> all_edges;
-  unordered_multiset<const Edge*, QEdgeHash, QEdgeEquals> passive_edges;
-  unordered_multiset<const Edge*, REdgeHash, REdgeEquals> active_edges;
-  vector<Edge*> free_list_;
-  vector<Traversal*> traversal_free_list_;
-  const WordID start_cat_;
-  const WFSTNode* const q_0_;
-  const WFSTNode* const q_final_;
-};
-
-#ifdef DEBUG_CHART_PARSER
-static string TrimRule(const string& r) {
-  size_t start = r.find(" |||") + 5;
-  size_t end = r.rfind(" |||");
-  return r.substr(start, end - start);
-}
-#endif
-
-void AddGrammarRule(const string& r, EGrammar* g) {
-  const size_t pos = r.find(" ||| ");
-  if (pos == string::npos || r[0] != '[') {
-    cerr << "Bad rule: " << r << endl;
-    return;
-  }
-  const size_t rpos = r.rfind(" ||| ");
-  string feats;
-  string rs = r;
-  if (rpos != pos) {
-    feats = r.substr(rpos + 5);
-    rs = r.substr(0, rpos);
-  }
-  string rhs = rs.substr(pos + 5);
-  string trule = rs + " ||| " + rhs + " ||| " + feats;
-  TRule tr(trule);
-  cerr << "X: " << tr.e_[0] << endl;
-#ifdef DEBUG_CHART_PARSER
-  string hint_last_rule;
-#endif
-  EGrammarNode* cur = &(*g)[tr.GetLHS()];
-  cur->is_root = true;
-  for (int i = 0; i < tr.FLength(); ++i) {
-    WordID sym = tr.f()[i];
-#ifdef DEBUG_CHART_PARSER
-    hint_last_rule = TD::Convert(sym < 0 ? -sym : sym);
-    cur->hint += " <@@> (*" + hint_last_rule + ") " + TrimRule(tr.AsString());
-#endif
-    if (sym < 0)
-      cur = &cur->ntptr[sym];
-    else
-      cur = &cur->tptr[sym];
-  }
-#ifdef DEBUG_CHART_PARSER
-  cur->hint += " <@@> (" + hint_last_rule + "*) " + TrimRule(tr.AsString());
-#endif
-  cur->is_some_rule_complete = true;
-  cur->input_features = tr.GetFeatureValues();
-}
-
-CFG_WFSTComposer::~CFG_WFSTComposer() {
-  delete pimpl_;
-}
-
-CFG_WFSTComposer::CFG_WFSTComposer(const WFST& wfst) {
-  InitializeConstants();
-  pimpl_ = new CFG_WFSTComposerImpl(kUNIQUE_START, wfst.Initial(), wfst.Final());
-}
-
-bool CFG_WFSTComposer::Compose(const Hypergraph& src_forest, Hypergraph* trg_forest) {
-  // first, convert the src forest into an EGrammar
-  EGrammar g;
-  const int nedges = src_forest.edges_.size();
-  const int nnodes = src_forest.nodes_.size();
-  vector<int> cats(nnodes);
-  bool assign_cats = false;
-  for (int i = 0; i < nnodes; ++i)
-    if (assign_cats) {
-      cats[i] = TD::Convert("CAT_" + boost::lexical_cast<string>(i)) * -1;
-    } else {
-      cats[i] = src_forest.nodes_[i].cat_;
-    }
-  // construct the grammar
-  for (int i = 0; i < nedges; ++i) {
-    const Hypergraph::Edge& edge = src_forest.edges_[i];
-    const vector<WordID>& src = edge.rule_->f();
-    EGrammarNode* cur = &g[cats[edge.head_node_]];
-    cur->is_root = true;
-    int ntc = 0;
-    for (int j = 0; j < src.size(); ++j) {
-      WordID sym = src[j];
-      if (sym <= 0) {
-        sym = cats[edge.tail_nodes_[ntc]];
-        ++ntc;
-        cur = &cur->ntptr[sym];
-      } else {
-        cur = &cur->tptr[sym];
-      }
-    }
-    cur->is_some_rule_complete = true;
-    cur->input_features = edge.feature_values_;
-  }
-  EGrammarNode& goal_rule = g[kUNIQUE_START];
-  assert((goal_rule.ntptr.size() == 1 && goal_rule.tptr.size() == 0) ||
-         (goal_rule.ntptr.size() == 0 && goal_rule.tptr.size() == 1));
-
-  return pimpl_->Compose(g, trg_forest);
-}
-
-bool CFG_WFSTComposer::Compose(istream* in, Hypergraph* trg_forest) {
-  EGrammar g;
-  while(*in) {
-    string line;
-    getline(*in, line);
-    if (line.empty()) continue;
-    AddGrammarRule(line, &g);
-  }
-
-  return pimpl_->Compose(g, trg_forest);
-}
diff --git a/gi/pf/cfg_wfst_composer.h b/gi/pf/cfg_wfst_composer.h
deleted file mode 100644
index cf47f459..00000000
--- a/gi/pf/cfg_wfst_composer.h
+++ /dev/null
@@ -1,46 +0,0 @@
-#ifndef _CFG_WFST_COMPOSER_H_
-#define _CFG_WFST_COMPOSER_H_
-
-#include <iostream>
-#include <vector>
-#include <utility>
-
-#include "trule.h"
-#include "wordid.h"
-
-class CFG_WFSTComposerImpl;
-class Hypergraph;
-
-struct WFSTNode {
-  virtual ~WFSTNode();
-  // returns the next states reachable by consuming srcindex (which identifies a word)
-  // paired with the output string generated by taking that transition.
-  virtual std::vector<std::pair<const WFSTNode*,TRulePtr> > ExtendInput(unsigned srcindex) const = 0;
-};
-
-struct WFST {
-  virtual ~WFST();
-  virtual const WFSTNode* Final() const = 0;
-  virtual const WFSTNode* Initial() const = 0;
-};
-
-class CFG_WFSTComposer {
- public:
-  ~CFG_WFSTComposer();
-  explicit CFG_WFSTComposer(const WFST& wfst);
-  bool Compose(const Hypergraph& in_forest, Hypergraph* trg_forest);
-
-  // reads the grammar from a file. There must be a single top-level
-  // S -> X rule.  Anything else is possible. Format is:
-  // [S] ||| [SS,1]
-  // [SS] ||| [NP,1] [VP,2] ||| Feature1=0.2 Feature2=-2.3
-  // [SS] ||| [VP,1] [NP,2] ||| Feature1=0.8
-  // [NP] ||| [DET,1] [N,2] ||| Feature3=2
-  // ...
-  bool Compose(std::istream* grammar_file, Hypergraph* trg_forest);
-
- private:
-  CFG_WFSTComposerImpl* pimpl_;
-};
-
-#endif
diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
deleted file mode 100644
index 81ddb206..00000000
--- a/gi/pf/conditional_pseg.h
+++ /dev/null
@@ -1,275 +0,0 @@
-#ifndef _CONDITIONAL_PSEG_H_
-#define _CONDITIONAL_PSEG_H_
-
-#include <vector>
-#include <tr1/unordered_map>
-#include <boost/functional/hash.hpp>
-#include <iostream>
-
-#include "m.h"
-#include "prob.h"
-#include "ccrp_nt.h"
-#include "mfcr.h"
-#include "trule.h"
-#include "base_distributions.h"
-#include "tdict.h"
-
-template <typename ConditionalBaseMeasure>
-struct MConditionalTranslationModel {
-  explicit MConditionalTranslationModel(ConditionalBaseMeasure& rcp0) :
-    rp0(rcp0), d(0.5), strength(1.0), lambdas(1, prob_t::One()), p0s(1) {}
-
-  void Summary() const {
-    std::cerr << "Number of conditioning contexts: " << r.size() << std::endl;
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      std::cerr << TD::GetString(it->first) << "   \t(d=" << it->second.discount() << ",s=" << it->second.strength() << ") --------------------------" << std::endl;
-      for (MFCR<1,TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
-        std::cerr << "   " << i2->second.total_dish_count_ << '\t' << i2->first << std::endl;
-    }
-  }
-
-  double log_likelihood(const double& dd, const double& aa) const {
-    if (aa <= -dd) return -std::numeric_limits<double>::infinity();
-    //double llh = Md::log_beta_density(dd, 10, 3) + Md::log_gamma_density(aa, 1, 1);
-    double llh = Md::log_beta_density(dd, 1, 1) +
-                 Md::log_gamma_density(dd + aa, 1, 1);
-    typename std::tr1::unordered_map<std::vector<WordID>, MFCR<1,TRule>, boost::hash<std::vector<WordID> > >::const_iterator it;
-    for (it = r.begin(); it != r.end(); ++it)
-      llh += it->second.log_crp_prob(dd, aa);
-    return llh;
-  }
-
-  struct DiscountResampler {
-    DiscountResampler(const MConditionalTranslationModel& m) : m_(m) {}
-    const MConditionalTranslationModel& m_;
-    double operator()(const double& proposed_discount) const {
-      return m_.log_likelihood(proposed_discount, m_.strength);
-    }
-  };
-
-  struct AlphaResampler {
-    AlphaResampler(const MConditionalTranslationModel& m) : m_(m) {}
-    const MConditionalTranslationModel& m_;
-    double operator()(const double& proposed_strength) const {
-      return m_.log_likelihood(m_.d, proposed_strength);
-    }
-  };
-
-  void ResampleHyperparameters(MT19937* rng) {
-    typename std::tr1::unordered_map<std::vector<WordID>, MFCR<1,TRule>, boost::hash<std::vector<WordID> > >::iterator it;
-#if 1
-    for (it = r.begin(); it != r.end(); ++it) {
-      it->second.resample_hyperparameters(rng);
-    }
-#else
-    const unsigned nloop = 5;
-    const unsigned niterations = 10;
-    DiscountResampler dr(*this);
-    AlphaResampler ar(*this);
-    for (int iter = 0; iter < nloop; ++iter) {
-      strength = slice_sampler1d(ar, strength, *rng, -d + std::numeric_limits<double>::min(),
-                              std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-      double min_discount = std::numeric_limits<double>::min();
-      if (strength < 0.0) min_discount -= strength;
-      d = slice_sampler1d(dr, d, *rng, min_discount,
-                          1.0, 0.0, niterations, 100*niterations);
-    }
-    strength = slice_sampler1d(ar, strength, *rng, -d,
-                            std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-    std::cerr << "MConditionalTranslationModel(d=" << d << ",s=" << strength << ") = " << log_likelihood(d, strength) << std::endl;
-    for (it = r.begin(); it != r.end(); ++it) {
-      it->second.set_discount(d);
-      it->second.set_strength(strength);
-    }
-#endif
-  }
-
-  int DecrementRule(const TRule& rule, MT19937* rng) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    assert(it != r.end());
-    const TableCount delta = it->second.decrement(rule, rng);
-    if (delta.count) {
-      if (it->second.num_customers() == 0) r.erase(it);
-    }
-    return delta.count;
-  }
-
-  int IncrementRule(const TRule& rule, MT19937* rng) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      //it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(d, strength))).first;
-      it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(1,1,1,1,0.6, -0.12))).first;
-    }
-    p0s[0] = rp0(rule); 
-    TableCount delta = it->second.increment(rule, p0s.begin(), lambdas.begin(), rng);
-    return delta.count;
-  }
-
-  prob_t RuleProbability(const TRule& rule) const {
-    prob_t p;
-    RuleModelHash::const_iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      p = rp0(rule);
-    } else {
-      p0s[0] = rp0(rule);
-      p = it->second.prob(rule, p0s.begin(), lambdas.begin());
-    }
-    return p;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p; p.logeq(log_likelihood(d, strength));
-    return p;
-  }
-
-  const ConditionalBaseMeasure& rp0;
-  typedef std::tr1::unordered_map<std::vector<WordID>,
-                                  MFCR<1, TRule>,
-                                  boost::hash<std::vector<WordID> > > RuleModelHash;
-  RuleModelHash r;
-  double d, strength;
-  std::vector<prob_t> lambdas;
-  mutable std::vector<prob_t> p0s;
-};
-
-template <typename ConditionalBaseMeasure>
-struct ConditionalTranslationModel {
-  explicit ConditionalTranslationModel(ConditionalBaseMeasure& rcp0) :
-    rp0(rcp0) {}
-
-  void Summary() const {
-    std::cerr << "Number of conditioning contexts: " << r.size() << std::endl;
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      std::cerr << TD::GetString(it->first) << "   \t(\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
-      for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
-        std::cerr << "   " << i2->second << '\t' << i2->first << std::endl;
-    }
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    for (RuleModelHash::iterator it = r.begin(); it != r.end(); ++it)
-      it->second.resample_hyperparameters(rng);
-  } 
-
-  int DecrementRule(const TRule& rule) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    assert(it != r.end());    
-    int count = it->second.decrement(rule);
-    if (count) {
-      if (it->second.num_customers() == 0) r.erase(it);
-    }
-    return count;
-  }
-
-  int IncrementRule(const TRule& rule) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      it = r.insert(make_pair(rule.f_, CCRP_NoTable<TRule>(1.0, 1.0, 8.0))).first;
-    } 
-    int count = it->second.increment(rule);
-    return count;
-  }
-
-  void IncrementRules(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      IncrementRule(*rules[i]);
-  }
-
-  void DecrementRules(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      DecrementRule(*rules[i]);
-  }
-
-  prob_t RuleProbability(const TRule& rule) const {
-    prob_t p;
-    RuleModelHash::const_iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      p.logeq(log(rp0(rule)));
-    } else {
-      p.logeq(it->second.logprob(rule, log(rp0(rule))));
-    }
-    return p;
-  }
-
-  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;
-      for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
-        p *= rp0(i2->first);
-    }
-    return p;
-  }
-
-  const ConditionalBaseMeasure& rp0;
-  typedef std::tr1::unordered_map<std::vector<WordID>,
-                                  CCRP_NoTable<TRule>,
-                                  boost::hash<std::vector<WordID> > > RuleModelHash;
-  RuleModelHash r;
-};
-
-template <typename ConditionalBaseMeasure>
-struct ConditionalParallelSegementationModel {
-  explicit ConditionalParallelSegementationModel(ConditionalBaseMeasure& rcp0) :
-    tmodel(rcp0), base(prob_t::One()), aligns(1,1) {}
-
-  ConditionalTranslationModel<ConditionalBaseMeasure> tmodel;
-
-  void DecrementRule(const TRule& rule) {
-    tmodel.DecrementRule(rule);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    tmodel.IncrementRule(rule);
-  }
-
-  void IncrementRulesAndAlignments(const std::vector<TRulePtr>& rules) {
-    tmodel.IncrementRules(rules);
-    for (int i = 0; i < rules.size(); ++i) {
-      IncrementAlign(rules[i]->f_.size());
-    }
-  }
-
-  void DecrementRulesAndAlignments(const std::vector<TRulePtr>& rules) {
-    tmodel.DecrementRules(rules);
-    for (int i = 0; i < rules.size(); ++i) {
-      DecrementAlign(rules[i]->f_.size());
-    }
-  }
-
-  prob_t RuleProbability(const TRule& rule) const {
-    return tmodel.RuleProbability(rule);
-  }
-
-  void IncrementAlign(unsigned span) {
-    if (aligns.increment(span)) {
-      // TODO
-    }
-  }
-
-  void DecrementAlign(unsigned span) {
-    if (aligns.decrement(span)) {
-      // TODO
-    }
-  }
-
-  prob_t AlignProbability(unsigned span) const {
-    prob_t p;
-    p.logeq(aligns.logprob(span, Md::log_poisson(span, 1.0)));
-    return p;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p; p.logeq(aligns.log_crp_prob());
-    p *= base;
-    p *= tmodel.Likelihood();
-    return p;
-  }
-
-  prob_t base;
-  CCRP_NoTable<unsigned> aligns;
-};
-
-#endif
-
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;
-}
-
diff --git a/gi/pf/corpus.cc b/gi/pf/corpus.cc
deleted file mode 100644
index cb6e4ed7..00000000
--- a/gi/pf/corpus.cc
+++ /dev/null
@@ -1,62 +0,0 @@
-#include "corpus.h"
-
-#include <set>
-#include <vector>
-#include <string>
-
-#include "tdict.h"
-#include "filelib.h"
-
-using namespace std;
-
-namespace corpus {
-
-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();
-  ReadFile rf(filename);
-  istream* in = rf.stream();
-  assert(*in);
-  string line;
-  unsigned lc = 0;
-  const WordID kDIV = TD::Convert("|||");
-  vector<WordID> tmp;
-  while(getline(*in, line)) {
-    ++lc;
-    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 {
-        if (cur == kDIV) {
-          cerr << "ERROR in " << lc << ": " << line << endl << endl;
-          abort();
-        }
-        le.push_back(cur);
-        vocab_e->insert(cur);
-      }
-    }
-    assert(isf == false);
-  }
-}
-
-}
-
diff --git a/gi/pf/corpus.h b/gi/pf/corpus.h
deleted file mode 100644
index e7febdb7..00000000
--- a/gi/pf/corpus.h
+++ /dev/null
@@ -1,19 +0,0 @@
-#ifndef _CORPUS_H_
-#define _CORPUS_H_
-
-#include <string>
-#include <vector>
-#include <set>
-#include "wordid.h"
-
-namespace corpus {
-
-void ReadParallelCorpus(const std::string& filename,
-                std::vector<std::vector<WordID> >* f,
-                std::vector<std::vector<WordID> >* e,
-                std::set<WordID>* vocab_f,
-                std::set<WordID>* vocab_e);
-
-}
-
-#endif
diff --git a/gi/pf/dpnaive.cc b/gi/pf/dpnaive.cc
deleted file mode 100644
index 75ccad72..00000000
--- a/gi/pf/dpnaive.cc
+++ /dev/null
@@ -1,301 +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 "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)")
-        ("inverse_model1,M",po::value<string>(),"Inverse 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;
-
-template <typename Base>
-struct ModelAndData {
-  explicit ModelAndData(MonotonicParallelSegementationModel<PhraseJointBase_BiDir>& m, const Base& b, const vector<vector<int> >& ce, const vector<vector<int> >& cf, const set<int>& ve, const set<int>& vf) :
-     model(m),
-     rng(&*prng),
-     p0(b),
-     baseprob(prob_t::One()),
-     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.DecrementContinue();
-    }
-    model.DecrementStop();
-  }
-
-  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.IncrementContinue();
-    }
-    model.IncrementStop();
-  }
-
-  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 = model.StopProbability();
-    if (d.size() < 2) return p;
-    TRule x;
-    const prob_t p_cont = model.ContinueProbability();
-    for (int i = 1; i < d.size(); ++i) {
-      InstantiateRule(d[i], d[i-1], sentf, sente, &x);
-      p *= p_cont;
-      p *= model.RuleProbability(x);
-    }
-    return p;
-  }
-
-  void Sample();
-
-  MonotonicParallelSegementationModel<PhraseJointBase_BiDir>& model;
-  MT19937* rng;
-  const Base& p0;
-  prob_t baseprob; // cached value of generating the table table labels from p0
-                   // this can't be used if we go to a hierarchical prior!
-  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;
-};
-
-template <typename Base>
-void ModelAndData<Base>::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]);
-      }
-    }
-    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();
-      const prob_t q_stop = model.StopProbability();
-      const prob_t q_cont = model.ContinueProbability();
-      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();
-            for (int l = 1; l <= MAXL; ++l) {
-              if (j + l > sente.size()) break;
-              x.e_.push_back(sente[j + l - 1]);
-              const bool stop_now = ((j + l) == sente.size()) && ((i + k) == sentf.size());
-              const prob_t& cp = stop_now ? q_stop : q_cont;
-              trans[i][j][k - 1][l - 1] = model.RuleProbability(x) * cp;
-              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("inverse_model1")) {
-    cerr << argv[0] << "Please use --inverse_model1 to specify inverse 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>());
-  Model1 invm1(conf["inverse_model1"].as<string>());
-//  PhraseJointBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
-  PhraseJointBase_BiDir alp0(m1, invm1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
-  MonotonicParallelSegementationModel<PhraseJointBase_BiDir> m(alp0);
-
-  ModelAndData<PhraseJointBase_BiDir> posterior(m, alp0, corpuse, corpusf, vocabe, vocabf);
-  posterior.Sample();
-
-  return 0;
-}
-
diff --git a/gi/pf/guess-translits.pl b/gi/pf/guess-translits.pl
deleted file mode 100755
index d00c2168..00000000
--- a/gi/pf/guess-translits.pl
+++ /dev/null
@@ -1,72 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-use utf8;
-
-my $MIN_PMI = -3;
-
-my %fs;
-my %es;
-my %ef;
-
-die "Usage: $0 < input.utf8.txt\n" if scalar @ARGV > 0;
-
-binmode(STDIN,":utf8");
-binmode(STDOUT,":utf8");
-binmode(STDERR,":utf8");
-
-my $tot = 0;
-print STDERR "Reading alignments from STDIN ...\n";
-while(<STDIN>) {
-  chomp;
-  my ($fsent, $esent, $alsent) = split / \|\|\| /;
-  die "Format should be 'foreign sentence ||| english sentence ||| 0-0 1-1 ...'\n" unless defined $fsent && defined $esent && defined $alsent;
-
-  my @fws = split /\s+/, $fsent;  
-  my @ews = split /\s+/, $esent;
-  my @as = split /\s+/, $alsent;
-  my %a2b;
-  my %b2a;
-  for my $ap (@as) {
-    my ($a,$b) = split /-/, $ap;
-    die "BAD INPUT: $_\n" unless defined $a && defined $b;
-    $a2b{$a}->{$b} = 1;
-    $b2a{$b}->{$a} = 1;
-  }
-  for my $a (keys %a2b) {
-    my $bref = $a2b{$a};
-    next unless scalar keys %$bref < 2;
-    my $b = (keys %$bref)[0];
-    next unless scalar keys %{$b2a{$b}} < 2;
-    my $f = $fws[$a];
-    next unless defined $f;
-    next unless length($f) > 3;
-    my $e = $ews[$b];
-    next unless defined $e;
-    next unless length($e) > 3;
-
-    $ef{$f}->{$e}++;
-    $es{$e}++;
-    $fs{$f}++;
-    $tot++;
-  }  
-}
-my $ltot = log($tot);
-my $num = 0;
-print STDERR "Extracting pairs for PMI > $MIN_PMI ...\n";
-for my $f (keys %fs) {
-  my $logf = log($fs{$f});
-  my $esref = $ef{$f};
-  for my $e (keys %$esref) {
-    my $loge = log($es{$e});
-    my $ef = $esref->{$e};
-    my $logef = log($ef);
-    my $pmi = $logef - ($loge + $logf);
-    next if $pmi < $MIN_PMI;
-    my @flets = split //, $f;
-    my @elets = split //, $e;
-    print "@flets ||| @elets\n";
-    $num++;
-  }
-}
-print STDERR "Extracted $num pairs.\n";
-print STDERR "Recommend running:\n   ../../training/model1 -v -d -t -99999 output.txt\n";
diff --git a/gi/pf/hpyp_tm.cc b/gi/pf/hpyp_tm.cc
deleted file mode 100644
index f362d3f8..00000000
--- a/gi/pf/hpyp_tm.cc
+++ /dev/null
@@ -1,133 +0,0 @@
-#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 << 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
deleted file mode 100644
index af3215ba..00000000
--- a/gi/pf/hpyp_tm.h
+++ /dev/null
@@ -1,38 +0,0 @@
-#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
deleted file mode 100644
index 29ec3860..00000000
--- a/gi/pf/itg.cc
+++ /dev/null
@@ -1,275 +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 "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;
-
-ostream& operator<<(ostream& os, const vector<WordID>& p) {
-  os << '[';
-  for (int i = 0; i < p.size(); ++i)
-    os << (i==0 ? "" : " ") << TD::Convert(p[i]);
-  return os << ']';
-}
-
-struct UnigramModel {
-  explicit UnigramModel(const string& fname, unsigned vocab_size, double p0null = 0.05) :
-      use_uniform_(fname.size() == 0),
-      p0null_(p0null),
-      uniform_((1.0 - p0null) / vocab_size),
-      probs_(TD::NumWords() + 1) {
-    if (fname.size() > 0) LoadUnigrams(fname);
-    probs_[0] = p0null_;
-  }
-
-// 
-// \data\
-// ngram 1=9295
-// 
-// \1-grams:
-// -3.191193	"
-
-  void LoadUnigrams(const string& fname) {
-    cerr << "Loading unigram probabilities from " << fname << " ..." << endl;
-    ReadFile rf(fname);
-    string line;
-    istream& in = *rf.stream();
-    assert(in);
-    getline(in, line);
-    assert(line.empty());
-    getline(in, line);
-    assert(line == "\\data\\");
-    getline(in, line);
-    size_t pos = line.find("ngram 1=");
-    assert(pos == 0);
-    assert(line.size() > 8);
-    const size_t num_unigrams = atoi(&line[8]);
-    getline(in, line);
-    assert(line.empty());
-    getline(in, line);
-    assert(line == "\\1-grams:");
-    for (size_t i = 0; i < num_unigrams; ++i) {
-      getline(in, line);
-      assert(line.size() > 0);
-      pos = line.find('\t');
-      assert(pos > 0);
-      assert(pos + 1 < line.size());
-      const WordID w = TD::Convert(line.substr(pos + 1));
-      line[pos] = 0;
-      float p = atof(&line[0]);
-      const prob_t pnon_null(1.0 - p0null_.as_float());
-      if (w < probs_.size()) probs_[w].logeq(p * log(10) + log(pnon_null)); else abort();
-    }
-  }
-
-  const prob_t& operator()(const WordID& w) const {
-    if (!w) return p0null_;
-    if (use_uniform_) return uniform_;
-    return probs_[w];
-  }
-
-  const bool use_uniform_;
-  const prob_t p0null_;
-  const prob_t uniform_;
-  vector<prob_t> probs_;
-};
-
-struct Model1 {
-  explicit Model1(const string& fname) :
-      kNULL(TD::Convert("<eps>")),
-      kZERO() {
-    LoadModel1(fname);
-  }
-
-  void LoadModel1(const string& fname) {
-    cerr << "Loading Model 1 parameters from " << fname << " ..." << endl;
-    ReadFile rf(fname);
-    istream& in = *rf.stream();
-    string line;
-    unsigned lc = 0;
-    while(getline(in, line)) {
-      ++lc;
-      int cur = 0;
-      int start = 0;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      const WordID src = TD::Convert(&line[0]);
-      ++cur;
-      start = cur;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      WordID trg = TD::Convert(&line[start]);
-      const double logprob = strtod(&line[cur + 1], NULL);
-      if (src >= ttable.size()) ttable.resize(src + 1);
-      ttable[src][trg].logeq(logprob);
-    }
-    cerr << "  read " << lc << " parameters.\n";
-  }
-
-  // returns prob 0 if src or trg is not found!
-  const prob_t& operator()(WordID src, WordID trg) const {
-    if (src == 0) src = kNULL;
-    if (src < ttable.size()) {
-      const map<WordID, prob_t>& cpd = ttable[src];
-      const map<WordID, prob_t>::const_iterator it = cpd.find(trg);
-      if (it != cpd.end())
-        return it->second;
-    }
-    return kZERO;
-  }
-
-  const WordID kNULL;
-  const prob_t kZERO;
-  vector<map<WordID, prob_t> > ttable;
-};
-
-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(25),"Number of particles")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("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")
-        ("src_unigram,u",po::value<string>()->default_value(""),"Source unigram distribution; empty for uniform")
-        ("trg_unigram,U",po::value<string>()->default_value(""),"Target unigram distribution; empty for uniform")
-        ("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;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  const unsigned particles = conf["particles"].as<unsigned>();
-  const unsigned samples = conf["samples"].as<unsigned>();
-  TD::Convert("<s>");
-  TD::Convert("</s>");
-  TD::Convert("<unk>");
-  if (!conf.count("model1")) {
-    cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
-    return 1;
-  }
-  boost::shared_ptr<MT19937> prng;
-  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());
-  UnigramModel src_unigram(conf["src_unigram"].as<string>(), vocabf.size());
-  UnigramModel trg_unigram(conf["trg_unigram"].as<string>(), vocabe.size());
-  const prob_t kHALF(0.5);
-
-  const string kEMPTY = "NULL";
-  const int kLHS = -TD::Convert("X");
-  Model1 m1(conf["model1"].as<string>());
-  Model1 invm1(conf["inverse_model1"].as<string>());
-  for (int si = 0; si < conf["samples"].as<unsigned>(); ++si) {
-    cerr << '.' << flush;
-    for (int ci = 0; ci < corpusf.size(); ++ci) {
-      const vector<WordID>& trg = corpuse[ci];
-      const vector<WordID>& src = corpusf[ci];
-      for (int i = 0; i <= trg.size(); ++i) {
-        const WordID e_i = i > 0 ? trg[i-1] : 0;
-        for (int j = 0; j <= src.size(); ++j) {
-          const WordID f_j = j > 0 ? src[j-1] : 0;
-          if (e_i == 0 && f_j == 0) continue;
-          prob_t je = kHALF * src_unigram(f_j) * m1(f_j,e_i) + kHALF * trg_unigram(e_i) * invm1(e_i,f_j);
-          cerr << "p( " << (e_i ? TD::Convert(e_i) : kEMPTY) << " , " << (f_j ? TD::Convert(f_j) : kEMPTY) << " ) = " << log(je) << endl;
-          if (e_i && f_j)
-            cout << "[X] ||| " << TD::Convert(f_j) << " ||| " << TD::Convert(e_i) << " ||| LogProb=" << log(je) << endl;
-        }
-      }
-    }
-  }
-}
-
diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
deleted file mode 100644
index 1d5126e4..00000000
--- a/gi/pf/learn_cfg.cc
+++ /dev/null
@@ -1,428 +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 "inside_outside.h"
-#include "hg.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;
-vector<int> nt_vocab;
-vector<int> nt_id_to_index;
-static unsigned kMAX_RULE_SIZE = 0;
-static unsigned kMAX_ARITY = 0;
-static bool kALLOW_MIXED = true;  // allow rules with mixed terminals and NTs
-static bool kHIERARCHICAL_PRIOR = false;
-
-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_rule_size,m", po::value<unsigned>()->default_value(0), "Maximum rule size (0 for unlimited)")
-        ("max_arity,a", po::value<unsigned>()->default_value(0), "Maximum number of nonterminals in a rule (0 for unlimited)")
-        ("no_mixed_rules,M", "Do not mix terminals and nonterminals in a rule RHS")
-        ("nonterminals,n", po::value<unsigned>()->default_value(1), "Size of nonterminal vocabulary")
-        ("hierarchical_prior,h", "Use hierarchical prior")
-        ("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<vector<WordID> >* e,
-                    set<WordID>* vocab_e) {
-  e->clear();
-  vocab_e->clear();
-  istream* in;
-  if (filename == "-")
-    in = &cin;
-  else
-    in = new ifstream(filename.c_str());
-  assert(*in);
-  string line;
-  unsigned toks = 0;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    e->push_back(vector<int>());
-    vector<int>& le = e->back();
-    TD::ConvertSentence(line, &le);
-    for (unsigned i = 0; i < le.size(); ++i)
-      vocab_e->insert(le[i]);
-    toks += le.size();
-  }
-  if (in != &cin) delete in;
-  return toks;
-}
-
-struct Grid {
-  // a b c d e
-  // 0 - 0 - -
-  vector<int> grid;
-};
-
-struct BaseRuleModel {
-  explicit BaseRuleModel(unsigned term_size,
-                         unsigned nonterm_size = 1) :
-      unif_term(1.0 / term_size),
-      unif_nonterm(1.0 / nonterm_size) {}
-  prob_t operator()(const TRule& r) const {
-    prob_t p; p.logeq(Md::log_poisson(1.0, r.f_.size()));
-    const prob_t term_prob((2.0 + 0.01*r.f_.size()) / (r.f_.size() + 2));
-    const prob_t nonterm_prob(1.0 - term_prob.as_float());
-    for (unsigned i = 0; i < r.f_.size(); ++i) {
-      if (r.f_[i] <= 0) {     // nonterminal
-        if (kALLOW_MIXED) p *= nonterm_prob;
-        p *= unif_nonterm;
-      } else {                // terminal
-        if (kALLOW_MIXED) p *= term_prob;
-        p *= unif_term;
-      }
-    }
-    return p;
-  }
-  const prob_t unif_term, unif_nonterm;
-};
-
-struct HieroLMModel {
-  explicit HieroLMModel(unsigned vocab_size, unsigned num_nts = 1) :
-      base(vocab_size, num_nts),
-      q0(1,1,1,1),
-      nts(num_nts, CCRP<TRule>(1,1,1,1)) {}
-
-  prob_t Prob(const TRule& r) const {
-    return nts[nt_id_to_index[-r.lhs_]].prob(r, p0(r));
-  }
-
-  inline prob_t p0(const TRule& r) const {
-    if (kHIERARCHICAL_PRIOR)
-      return q0.prob(r, base(r));
-    else
-      return base(r);
-  }
-
-  int Increment(const TRule& r, MT19937* rng) {
-    const int delta = nts[nt_id_to_index[-r.lhs_]].increment(r, p0(r), rng);
-    if (kHIERARCHICAL_PRIOR && delta)
-      q0.increment(r, base(r), rng);
-    return delta;
-    // return x.increment(r);
-  }
-
-  int Decrement(const TRule& r, MT19937* rng) {
-    const int delta = nts[nt_id_to_index[-r.lhs_]].decrement(r, rng);
-    if (kHIERARCHICAL_PRIOR && delta)
-      q0.decrement(r, rng);
-    return delta;
-    //return x.decrement(r);
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = prob_t::One();
-    for (unsigned i = 0; i < nts.size(); ++i) {
-      prob_t q; q.logeq(nts[i].log_crp_prob());
-      p *= q;
-      for (CCRP<TRule>::const_iterator it = nts[i].begin(); it != nts[i].end(); ++it) {
-        prob_t tp = p0(it->first);
-        tp.poweq(it->second.num_tables());
-        p *= tp;
-      }
-    }
-    if (kHIERARCHICAL_PRIOR) {
-      prob_t q; q.logeq(q0.log_crp_prob());
-      p *= q;
-      for (CCRP<TRule>::const_iterator it = q0.begin(); it != q0.end(); ++it) {
-        prob_t tp = base(it->first);
-        tp.poweq(it->second.num_tables());
-        p *= tp;
-      }
-    }
-    //for (CCRP_OneTable<TRule>::const_iterator it = x.begin(); it != x.end(); ++it)
-    //    p *= base(it->first);
-    return p;
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    for (unsigned i = 0; i < nts.size(); ++i)
-      nts[i].resample_hyperparameters(rng);
-    if (kHIERARCHICAL_PRIOR) {
-      q0.resample_hyperparameters(rng);
-      cerr << "[base d=" << q0.discount() << ", s=" << q0.strength() << "]";
-    }
-    cerr << " d=" << nts[0].discount() << ", s=" << nts[0].strength() << endl;
-  }
-
-  const BaseRuleModel base;
-  CCRP<TRule> q0;
-  vector<CCRP<TRule> > nts;
-  //CCRP_OneTable<TRule> x;
-};
-
-vector<GrammarIter* > tofreelist;
-
-HieroLMModel* plm;
-
-struct NPGrammarIter : public GrammarIter, public RuleBin {
-  NPGrammarIter() : arity() { tofreelist.push_back(this); }
-  NPGrammarIter(const TRulePtr& inr, const int a, int symbol) : arity(a) {
-    if (inr) {
-      r.reset(new TRule(*inr));
-    } else {
-      r.reset(new TRule);
-    }
-    TRule& rr = *r;
-    rr.lhs_ = nt_vocab[0];
-    rr.f_.push_back(symbol);
-    rr.e_.push_back(symbol < 0 ? (1-int(arity)) : symbol);
-    tofreelist.push_back(this);
-  }
-  inline static unsigned NextArity(int cur_a, int symbol) {
-    return cur_a + (symbol <= 0 ? 1 : 0);
-  }
-  virtual int GetNumRules() const {
-    if (r) return nt_vocab.size(); else return 0;
-  }
-  virtual TRulePtr GetIthRule(int i) const {
-    if (i == 0) return r;
-    TRulePtr nr(new TRule(*r));
-    nr->lhs_ = nt_vocab[i];
-    return nr;
-  }
-  virtual int Arity() const {
-    return arity;
-  }
-  virtual const RuleBin* GetRules() const {
-    if (!r) return NULL; else return this;
-  }
-  virtual const GrammarIter* Extend(int symbol) const {
-    const int next_arity = NextArity(arity, symbol);
-    if (kMAX_ARITY && next_arity > kMAX_ARITY)
-      return NULL;
-    if (!kALLOW_MIXED && r) {
-      bool t1 = r->f_.front() <= 0;
-      bool t2 = symbol <= 0;
-      if (t1 != t2) return NULL;
-    }
-    if (!kMAX_RULE_SIZE || !r || (r->f_.size() < kMAX_RULE_SIZE))
-      return new NPGrammarIter(r, next_arity, symbol);
-    else
-      return NULL;
-  }
-  const unsigned char arity;
-  TRulePtr r;
-};
-
-struct NPGrammar : public Grammar {
-  virtual const GrammarIter* GetRoot() const {
-    return new NPGrammarIter;
-  }
-};
-
-prob_t TotalProb(const Hypergraph& hg) {
-  return Inside<prob_t, EdgeProb>(hg);
-}
-
-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, HieroLMModel* 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, HieroLMModel* plm, MT19937* rng) {
-  for (unsigned i = 0; i < d.size(); ++i)
-    plm->Decrement(*hg.edges_[d[i]].rule_, rng);
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-
-  InitCommandLine(argc, argv, &conf);
-  nt_vocab.resize(conf["nonterminals"].as<unsigned>());
-  assert(nt_vocab.size() > 0);
-  assert(nt_vocab.size() < 26);
-  {
-    string nt = "X";
-    for (unsigned i = 0; i < nt_vocab.size(); ++i) {
-      if (nt_vocab.size() > 1) nt[0] = ('A' + i);
-      int pid = TD::Convert(nt);
-      nt_vocab[i] = -pid;
-      if (pid >= nt_id_to_index.size()) {
-        nt_id_to_index.resize(pid + 1, -1);
-      }
-      nt_id_to_index[pid] = i;
-    }
-  }
-  vector<GrammarPtr> grammars;
-  grammars.push_back(GrammarPtr(new NPGrammar));
-
-  const unsigned samples = conf["samples"].as<unsigned>();
-  kMAX_RULE_SIZE = conf["max_rule_size"].as<unsigned>();
-  if (kMAX_RULE_SIZE == 1) {
-    cerr << "Invalid maximum rule size: must be 0 or >1\n";
-    return 1;
-  }
-  kMAX_ARITY = conf["max_arity"].as<unsigned>();
-  if (kMAX_ARITY == 1) {
-    cerr << "Invalid maximum arity: must be 0 or >1\n";
-    return 1;
-  }
-  kALLOW_MIXED = !conf.count("no_mixed_rules");
-
-  kHIERARCHICAL_PRIOR = conf.count("hierarchical_prior");
-
-  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;
-  cerr << "Reading corpus...\n";
-  const unsigned toks = ReadCorpus(conf["input"].as<string>(), &corpuse, &vocabe);
-  cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
-  HieroLMModel lm(vocabe.size(), nt_vocab.size());
-
-  plm = &lm;
-  ExhaustiveBottomUpParser parser(TD::Convert(-nt_vocab[0]), grammars);
-
-  Hypergraph hg;
-  const int kGoal = -TD::Convert("Goal");
-  const int kLP = FD::Convert("LogProb");
-  SparseVector<double> v; v.set_value(kLP, 1.0);
-  vector<vector<unsigned> > derivs(corpuse.size());
-  vector<Lattice> cl(corpuse.size());
-  for (int ci = 0; ci < corpuse.size(); ++ci) {
-    vector<int>& src = corpuse[ci];
-    Lattice& lat = cl[ci];
-    lat.resize(src.size());
-    for (unsigned i = 0; i < src.size(); ++i)
-      lat[i].push_back(LatticeArc(src[i], 0.0, 1));
-  }
-  for (int SS=0; SS < samples; ++SS) {
-    const bool is_last = ((samples - 1) == SS);
-    prob_t dlh = prob_t::One();
-    for (int ci = 0; ci < corpuse.size(); ++ci) {
-      const vector<int>& src = corpuse[ci];
-      const Lattice& lat = cl[ci];
-      cerr << TD::GetString(src) << endl;
-      hg.clear();
-      parser.Parse(lat, &hg);  // exhaustive parse
-      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.lhs_ == kGoal)
-          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 (tofreelist.size() > 200000) {
-        cerr << "Freeing ... ";
-        for (unsigned i = 0; i < tofreelist.size(); ++i)
-          delete tofreelist[i];
-        tofreelist.clear();
-        cerr << "Freed.\n";
-      }
-    }
-    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;
-    }
-  }
-  for (unsigned i = 0; i < nt_vocab.size(); ++i)
-    cerr << lm.nts[i] << endl;
-  return 0;
-}
-
diff --git a/gi/pf/make-freq-bins.pl b/gi/pf/make-freq-bins.pl
deleted file mode 100755
index fdcd3555..00000000
--- a/gi/pf/make-freq-bins.pl
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-
-my $BASE = 6;
-my $CUTOFF = 3;
-
-my %d;
-my $num = 0;
-while(<>){
- chomp;
- my @words = split /\s+/;
- for my $w (@words) {$d{$w}++; $num++;}
-}
-
-my @vocab = sort {$d{$b} <=> $d{$a}} keys %d;
-
-for (my $i=0; $i<scalar @vocab; $i++) {
-  my $most = $d{$vocab[$i]};
-  my $least = 1;
-
-  my $nl = -int(log($most / $num) / log($BASE) + $CUTOFF);
-  if ($nl < 0) { $nl = 0; }
-  print "$vocab[$i] $nl\n"
-}
-
-
diff --git a/gi/pf/mh_test.cc b/gi/pf/mh_test.cc
deleted file mode 100644
index 296e7285..00000000
--- a/gi/pf/mh_test.cc
+++ /dev/null
@@ -1,148 +0,0 @@
-#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/monotonic_pseg.h b/gi/pf/monotonic_pseg.h
deleted file mode 100644
index 10d171fe..00000000
--- a/gi/pf/monotonic_pseg.h
+++ /dev/null
@@ -1,89 +0,0 @@
-#ifndef _MONOTONIC_PSEG_H_
-#define _MONOTONIC_PSEG_H_
-
-#include <vector>
-
-#include "prob.h"
-#include "ccrp_nt.h"
-#include "trule.h"
-#include "base_distributions.h"
-
-template <typename BaseMeasure>
-struct MonotonicParallelSegementationModel {
-  explicit MonotonicParallelSegementationModel(BaseMeasure& rcp0) :
-    rp0(rcp0), base(prob_t::One()), rules(1,1), stop(1.0) {}
-
-  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 IncrementRulesAndStops(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      IncrementRule(*rules[i]);
-    if (rules.size()) IncrementContinue(rules.size() - 1);
-    IncrementStop();
-  }
-
-  void DecrementRulesAndStops(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      DecrementRule(*rules[i]);
-    if (rules.size()) {
-      DecrementContinue(rules.size() - 1);
-      DecrementStop();
-    }
-  }
-
-  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;
-    q.logeq(stop.log_crp_prob());
-    p *= q;
-    return p;
-  }
-
-  void IncrementStop() {
-    stop.increment(true);
-  }
-
-  void IncrementContinue(int n = 1) {
-    for (int i = 0; i < n; ++i)
-      stop.increment(false);
-  }
-
-  void DecrementStop() {
-    stop.decrement(true);
-  }
-
-  void DecrementContinue(int n = 1) {
-    for (int i = 0; i < n; ++i)
-      stop.decrement(false);
-  }
-
-  prob_t StopProbability() const {
-    return prob_t(stop.prob(true, 0.5));
-  }
-
-  prob_t ContinueProbability() const {
-    return prob_t(stop.prob(false, 0.5));
-  }
-
-  const BaseMeasure& rp0;
-  prob_t base;
-  CCRP_NoTable<TRule> rules;
-  CCRP_NoTable<bool> stop;
-};
-
-#endif
-
diff --git a/gi/pf/ngram_base.cc b/gi/pf/ngram_base.cc
deleted file mode 100644
index 1299f06f..00000000
--- a/gi/pf/ngram_base.cc
+++ /dev/null
@@ -1,69 +0,0 @@
-#include "ngram_base.h"
-
-#include "lm/model.hh"
-#include "tdict.h"
-
-using namespace std;
-
-namespace {
-struct GICSVMapper : public lm::EnumerateVocab {
-  GICSVMapper(vector<lm::WordIndex>* out) : out_(out), kLM_UNKNOWN_TOKEN(0) { out_->clear(); }
-  void Add(lm::WordIndex index, const StringPiece &str) {
-    const WordID cdec_id = TD::Convert(str.as_string());
-    if (cdec_id >= out_->size())
-      out_->resize(cdec_id + 1, kLM_UNKNOWN_TOKEN);
-    (*out_)[cdec_id] = index;
-  }
-  vector<lm::WordIndex>* out_;
-  const lm::WordIndex kLM_UNKNOWN_TOKEN;
-};
-}
-
-struct FixedNgramBaseImpl {
-  FixedNgramBaseImpl(const string& param) {
-    GICSVMapper vm(&cdec2klm_map_);
-    lm::ngram::Config conf;
-    conf.enumerate_vocab = &vm;
-    cerr << "Reading character LM from " << param << endl;
-    model = new lm::ngram::ProbingModel(param.c_str(), conf);
-    order = model->Order();
-    kEOS = MapWord(TD::Convert("</s>"));
-    assert(kEOS > 0);
-  }
-
-  lm::WordIndex MapWord(const WordID w) const {
-    if (w < cdec2klm_map_.size()) return cdec2klm_map_[w];
-    return 0;
-  }
-
-  ~FixedNgramBaseImpl() { delete model; }
-
-  prob_t StringProbability(const vector<WordID>& s) const {
-    lm::ngram::State state = model->BeginSentenceState();
-    double prob = 0;
-    for (unsigned i = 0; i < s.size(); ++i) {
-      const lm::ngram::State scopy(state);
-      prob += model->Score(scopy, MapWord(s[i]), state);
-    }
-    const lm::ngram::State scopy(state);
-    prob += model->Score(scopy, kEOS, state);
-    prob_t p; p.logeq(prob * log(10));
-    return p;
-  }
-
-  lm::ngram::ProbingModel* model;
-  unsigned order;
-  vector<lm::WordIndex> cdec2klm_map_;
-  lm::WordIndex kEOS;
-};
-
-FixedNgramBase::~FixedNgramBase() { delete impl; }
-
-FixedNgramBase::FixedNgramBase(const string& lmfname) {
-  impl = new FixedNgramBaseImpl(lmfname);
-}
-
-prob_t FixedNgramBase::StringProbability(const vector<WordID>& s) const {
-  return impl->StringProbability(s);
-}
-
diff --git a/gi/pf/ngram_base.h b/gi/pf/ngram_base.h
deleted file mode 100644
index 4ea999f3..00000000
--- a/gi/pf/ngram_base.h
+++ /dev/null
@@ -1,25 +0,0 @@
-#ifndef _NGRAM_BASE_H_
-#define _NGRAM_BASE_H_
-
-#include <string>
-#include <vector>
-#include "trule.h"
-#include "wordid.h"
-#include "prob.h"
-
-struct FixedNgramBaseImpl;
-struct FixedNgramBase {
-  FixedNgramBase(const std::string& lmfname);
-  ~FixedNgramBase();
-  prob_t StringProbability(const std::vector<WordID>& s) const;
-
-  prob_t operator()(const TRule& rule) const {
-    return StringProbability(rule.e_);
-  }
-
- private:
-  FixedNgramBaseImpl* impl;
-
-};
-
-#endif
diff --git a/gi/pf/nuisance_test.cc b/gi/pf/nuisance_test.cc
deleted file mode 100644
index fc0af9cb..00000000
--- a/gi/pf/nuisance_test.cc
+++ /dev/null
@@ -1,161 +0,0 @@
-#include "ccrp.h"
-
-#include <vector>
-#include <iostream>
-
-#include "tdict.h"
-#include "transliterations.h"
-
-using namespace std;
-
-MT19937 rng;
-
-ostream& operator<<(ostream&os, const vector<int>& v) {
-  os << '[' << v[0];
-  if (v.size() == 2) os << ' ' << v[1];
-  return os << ']';
-}
-
-struct Base {
-  Base() : llh(), v(2), v1(1), v2(1), crp(0.25, 0.5) {}
-  inline double p0(const vector<int>& x) const {
-    double p = 0.75;
-    if (x.size() == 2) p = 0.25;
-    p *= 1.0 / 3.0;
-    if (x.size() == 2) p *= 1.0 / 3.0;
-    return p;
-  }
-  double est_deriv_prob(int a, int b, int seg) const {
-    assert(a > 0 && a < 4);  // a \in {1,2,3}
-    assert(b > 0 && b < 4);  // b \in {1,2,3}
-    assert(seg == 0 || seg == 1);   // seg \in {0,1}
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      return crp.prob(v, p0(v));
-    } else {
-      v1[0] = a;
-      v2[0] = b;
-      return crp.prob(v1, p0(v1)) * crp.prob(v2, p0(v2));
-    }
-  }
-  double est_marginal_prob(int a, int b) const {
-    return est_deriv_prob(a,b,0) + est_deriv_prob(a,b,1);
-  }
-  int increment(int a, int b, double* pw = NULL) {
-    double p1 = est_deriv_prob(a, b, 0);
-    double p2 = est_deriv_prob(a, b, 1);
-    //p1 = 0.5; p2 = 0.5;
-    int seg = rng.SelectSample(p1,p2);
-    double tmp = 0;
-    if (!pw) pw = &tmp;
-    double& w = *pw;
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      w = crp.prob(v, p0(v)) / p1;
-      if (crp.increment(v, p0(v), &rng)) {
-        llh += log(p0(v));
-      }
-    } else {
-      v1[0] = a;
-      w = crp.prob(v1, p0(v1)) / p2;
-      if (crp.increment(v1, p0(v1), &rng)) {
-        llh += log(p0(v1));
-      }
-      v2[0] = b;
-      w *= crp.prob(v2, p0(v2));
-      if (crp.increment(v2, p0(v2), &rng)) {
-        llh += log(p0(v2));
-      }
-    }
-    return seg;
-  }
-  void increment(int a, int b, int seg) {
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      if (crp.increment(v, p0(v), &rng)) {
-        llh += log(p0(v));
-      }
-    } else {
-      v1[0] = a;
-      if (crp.increment(v1, p0(v1), &rng)) {
-        llh += log(p0(v1));
-      }
-      v2[0] = b;
-      if (crp.increment(v2, p0(v2), &rng)) {
-        llh += log(p0(v2));
-      }
-    }
-  }
-  void decrement(int a, int b, int seg) {
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      if (crp.decrement(v, &rng)) {
-        llh -= log(p0(v));
-      }
-    } else {
-      v1[0] = a;
-      if (crp.decrement(v1, &rng)) {
-        llh -= log(p0(v1));
-      }
-      v2[0] = b;
-      if (crp.decrement(v2, &rng)) {
-        llh -= log(p0(v2));
-      }
-    }
-  }
-  double log_likelihood() const {
-    return llh + crp.log_crp_prob();
-  }
-  double llh;
-  mutable vector<int> v, v1, v2;
-  CCRP<vector<int> > crp;
-};
-
-int main(int argc, char** argv) {
-  double tl = 0;
-  const int ITERS = 1000;
-  const int PARTICLES = 20;
-  const int DATAPOINTS = 50;
-  WordID x = TD::Convert("souvenons");
-  WordID y = TD::Convert("remember");
-  vector<WordID> src; TD::ConvertSentence("s o u v e n o n s", &src);
-  vector<WordID> trg; TD::ConvertSentence("r e m e m b e r", &trg);
-//  Transliterations xx;
-//  xx.Initialize(x, src, y, trg);
-//  return 1;
-
- for (int j = 0; j < ITERS; ++j) {
-  Base b;
-  vector<int> segs(DATAPOINTS);
-  SampleSet<double> ss;
-  vector<int> sss;
-  for (int i = 0; i < DATAPOINTS; i++) {
-    ss.clear();
-    sss.clear();
-    int x = ((i / 10) % 3) + 1;
-    int y = (i % 3) + 1;
-    //double ep = b.est_marginal_prob(x,y);
-    //cerr << "est p(" << x << "," << y << ") = " << ep << endl;
-    for (int n = 0; n < PARTICLES; ++n) {
-      double w;
-      int seg = b.increment(x,y,&w);
-      //cerr << seg << " w=" << w << endl;
-      ss.add(w);
-      sss.push_back(seg);
-      b.decrement(x,y,seg);
-    }
-    int seg = sss[rng.SelectSample(ss)];
-    b.increment(x, y, seg);
-    //cerr << "Selected: " << seg << endl;
-    //return 1;
-    segs[i] = seg;
-  }
-  tl += b.log_likelihood();
- }
-  cerr << "LLH=" << tl / ITERS << endl;
-}
-
diff --git a/gi/pf/os_phrase.h b/gi/pf/os_phrase.h
deleted file mode 100644
index dfe40cb1..00000000
--- a/gi/pf/os_phrase.h
+++ /dev/null
@@ -1,15 +0,0 @@
-#ifndef _OS_PHRASE_H_
-#define _OS_PHRASE_H_
-
-#include <iostream>
-#include <vector>
-#include "tdict.h"
-
-inline std::ostream& operator<<(std::ostream& os, const std::vector<WordID>& p) {
-  os << '[';
-  for (int i = 0; i < p.size(); ++i)
-    os << (i==0 ? "" : " ") << TD::Convert(p[i]);
-  return os << ']';
-}
-
-#endif
diff --git a/gi/pf/pf.h b/gi/pf/pf.h
deleted file mode 100644
index ede7cda8..00000000
--- a/gi/pf/pf.h
+++ /dev/null
@@ -1,84 +0,0 @@
-#ifndef _PF_H_
-#define _PF_H_
-
-#include <cassert>
-#include <vector>
-#include "sampler.h"
-#include "prob.h"
-
-template <typename ParticleType>
-struct ParticleRenormalizer {
-  void operator()(std::vector<ParticleType>* pv) const {
-    if (pv->empty()) return;
-    prob_t z = prob_t::Zero();
-    for (unsigned i = 0; i < pv->size(); ++i)
-      z += (*pv)[i].weight;
-    assert(z > prob_t::Zero());
-    for (unsigned i = 0; i < pv->size(); ++i)
-      (*pv)[i].weight /= z;
-  }
-};
-
-template <typename ParticleType>
-struct MultinomialResampleFilter {
-  explicit MultinomialResampleFilter(MT19937* rng) : rng_(rng) {}
-
-  void operator()(std::vector<ParticleType>* pv) {
-    if (pv->empty()) return;
-    std::vector<ParticleType>& ps = *pv;
-    SampleSet<prob_t> ss;
-    for (int i = 0; i < ps.size(); ++i)
-      ss.add(ps[i].weight);
-    std::vector<ParticleType> 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[rng_->SelectSample(ss)]);
-      nps[i].weight = uniform_weight;
-    }
-    nps.swap(ps);
-  }
-
- private:
-  MT19937* rng_;
-};
-
-template <typename ParticleType>
-struct SystematicResampleFilter {
-  explicit SystematicResampleFilter(MT19937* rng) : rng_(rng), renorm_() {}
-
-  void operator()(std::vector<ParticleType>* pv) {
-    if (pv->empty()) return;
-    renorm_(pv);
-    std::vector<ParticleType>& ps = *pv;
-    std::vector<ParticleType> nps; nps.reserve(ps.size());
-    double lower = 0, upper = 0;
-    const double skip = 1.0 / ps.size();
-    double u_j = rng_->next() * skip;
-    //std::cerr << "u_0: " << u_j << std::endl;
-    int j = 0;
-    for (unsigned i = 0; i < ps.size(); ++i) {
-      upper += ps[i].weight.as_float();
-      //std::cerr << "lower: " << lower << " upper: " << upper << std::endl;
-      // how many children does ps[i] have?
-      while (u_j < lower) { u_j += skip; ++j; }
-      while (u_j >= lower && u_j <= upper) {
-        assert(j < ps.size());
-        nps.push_back(ps[i]);
-        u_j += skip;
-        //std::cerr << " add u_j=" << u_j << std::endl;
-        ++j;
-      }
-      lower = upper;
-    }
-    //std::cerr << ps.size() << " " << nps.size() << "\n";
-    assert(ps.size() == nps.size());
-    //exit(1);
-    ps.swap(nps);
-  }
-
- private:
-  MT19937* rng_;
-  ParticleRenormalizer<ParticleType> renorm_;
-};
-
-#endif
diff --git a/gi/pf/pf_test.cc b/gi/pf/pf_test.cc
deleted file mode 100644
index 296e7285..00000000
--- a/gi/pf/pf_test.cc
+++ /dev/null
@@ -1,148 +0,0 @@
-#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
deleted file mode 100644
index 832f22cf..00000000
--- a/gi/pf/pfbrat.cc
+++ /dev/null
@@ -1,543 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/multi_array.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#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 "cfg_wfst_composer.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-static unsigned kMAX_SRC_PHRASE;
-static unsigned kMAX_TRG_PHRASE;
-struct FSTState;
-
-double log_poisson(unsigned x, const double& lambda) {
-  assert(lambda > 0.0);
-  return log(lambda) * x - lgamma(x + 1) - lambda;
-}
-
-struct ConditionalBase {
-  explicit ConditionalBase(const double m1mixture, const unsigned vocab_e_size, const string& model1fname) :
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_TARGET(1.0 / vocab_e_size),
-      kNULL(TD::Convert("<eps>")) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-    LoadModel1(model1fname);
-  }
-
-  void LoadModel1(const string& fname) {
-    cerr << "Loading Model 1 parameters from " << fname << " ..." << endl;
-    ReadFile rf(fname);
-    istream& in = *rf.stream();
-    string line;
-    unsigned lc = 0;
-    while(getline(in, line)) {
-      ++lc;
-      int cur = 0;
-      int start = 0;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      const WordID src = TD::Convert(&line[0]);
-      ++cur;
-      start = cur;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      WordID trg = TD::Convert(&line[start]);
-      const double logprob = strtod(&line[cur + 1], NULL);
-      if (src >= ttable.size()) ttable.resize(src + 1);
-      ttable[src][trg].logeq(logprob);
-    }
-    cerr << "  read " << lc << " parameters.\n";
-  }
-
-  // return logp0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    const int flen = rule.f_.size();
-    const int elen = rule.e_.size();
-    prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
-    prob_t p;
-    p.logeq(log_poisson(elen, flen + 0.01));       // elen | flen          ~Pois(flen + 0.01)
-    for (int i = 0; i < elen; ++i) {               // for each position i in e-RHS
-      const WordID trg = rule.e_[i];
-      prob_t tp = prob_t::Zero();
-      for (int j = -1; j < flen; ++j) {
-        const WordID src = j < 0 ? kNULL : rule.f_[j];
-        const map<WordID, prob_t>::const_iterator it = ttable[src].find(trg);
-        if (it != ttable[src].end()) {
-          tp += kM1MIXTURE * it->second;
-        }
-        tp += kUNIFORM_MIXTURE * kUNIFORM_TARGET;
-      }
-      tp *= uniform_src_alignment;                 //     draw a_i         ~uniform
-      p *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-    }
-    return p;
-  }
-
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_TARGET;
-  const WordID kNULL;
-  vector<map<WordID, prob_t> > ttable;
-};
-
-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(3),"Maximum length of source language phrases")
-        ("max_trg_phrase",po::value<unsigned>()->default_value(3),"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);
-  }
-}
-
-void ReadParallelCorpus(const string& filename,
-                vector<vector<WordID> >* f,
-                vector<vector<int> >* e,
-                set<int>* vocab_f,
-                set<int>* 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;
-}
-
-struct UniphraseLM {
-  UniphraseLM(const vector<vector<int> >& corpus,
-              const set<int>& vocab,
-              const po::variables_map& conf) :
-    phrases_(1,1),
-    gen_(1,1),
-    corpus_(corpus),
-    uniform_word_(1.0 / vocab.size()),
-    gen_p0_(0.5),
-    p_end_(0.5),
-    use_poisson_(conf.count("poisson_length") > 0) {}
-
-  void ResampleHyperparameters(MT19937* rng) {
-    phrases_.resample_hyperparameters(rng);
-    gen_.resample_hyperparameters(rng);
-    cerr << " " << phrases_.alpha();
-  }
-
-  CCRP_NoTable<vector<int> > phrases_;
-  CCRP_NoTable<bool> gen_;
-  vector<vector<bool> > z_;   // z_[i] is there a phrase boundary after the ith word
-  const vector<vector<int> >& corpus_;
-  const double uniform_word_;
-  const double gen_p0_;
-  const double p_end_; // in base length distribution, p of the end of a phrase
-  const bool use_poisson_;
-};
-
-struct Reachability {
-  boost::multi_array<bool, 4> edges;  // edges[src_covered][trg_covered][x][trg_delta] is this edge worth exploring?
-  boost::multi_array<short, 2> max_src_delta; // msd[src_covered][trg_covered] -- the largest src delta that's valid
-
-  Reachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) :
-      edges(boost::extents[srclen][trglen][src_max_phrase_len+1][trg_max_phrase_len+1]),
-      max_src_delta(boost::extents[srclen][trglen]) {
-    ComputeReachability(srclen, trglen, src_max_phrase_len, trg_max_phrase_len);
-  }
-
- private:
-  struct SState {
-    SState() : prev_src_covered(), prev_trg_covered() {}
-    SState(int i, int j) : prev_src_covered(i), prev_trg_covered(j) {}
-    int prev_src_covered;
-    int prev_trg_covered;
-  };
-
-  struct NState {
-    NState() : next_src_covered(), next_trg_covered() {}
-    NState(int i, int j) : next_src_covered(i), next_trg_covered(j) {}
-    int next_src_covered;
-    int next_trg_covered;
-  };
-
-  void ComputeReachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) {
-    typedef boost::multi_array<vector<SState>, 2> array_type;
-    array_type a(boost::extents[srclen + 1][trglen + 1]);
-    a[0][0].push_back(SState());
-    for (int i = 0; i < srclen; ++i) {
-      for (int j = 0; j < trglen; ++j) {
-        if (a[i][j].size() == 0) continue;
-        const SState prev(i,j);
-        for (int k = 1; k <= src_max_phrase_len; ++k) {
-          if ((i + k) > srclen) continue;
-          for (int l = 1; l <= trg_max_phrase_len; ++l) {
-            if ((j + l) > trglen) continue;
-            a[i + k][j + l].push_back(prev);
-          }
-        }
-      }
-    }
-    a[0][0].clear();
-    cerr << "Final cell contains " << a[srclen][trglen].size() << " back pointers\n";
-    assert(a[srclen][trglen].size() > 0);
-
-    typedef boost::multi_array<bool, 2> rarray_type;
-    rarray_type r(boost::extents[srclen + 1][trglen + 1]);
-//    typedef boost::multi_array<vector<NState>, 2> narray_type;
-//    narray_type b(boost::extents[srclen + 1][trglen + 1]);
-    r[srclen][trglen] = true;
-    for (int i = srclen; i >= 0; --i) {
-      for (int j = trglen; j >= 0; --j) {
-        vector<SState>& prevs = a[i][j];
-        if (!r[i][j]) { prevs.clear(); }
-//        const NState nstate(i,j);
-        for (int k = 0; k < prevs.size(); ++k) {
-          r[prevs[k].prev_src_covered][prevs[k].prev_trg_covered] = true;
-          int src_delta = i - prevs[k].prev_src_covered;
-          edges[prevs[k].prev_src_covered][prevs[k].prev_trg_covered][src_delta][j - prevs[k].prev_trg_covered] = true;
-          short &msd = max_src_delta[prevs[k].prev_src_covered][prevs[k].prev_trg_covered];
-          if (src_delta > msd) msd = src_delta;
-//          b[prevs[k].prev_src_covered][prevs[k].prev_trg_covered].push_back(nstate);
-        }
-      }
-    }
-    assert(!edges[0][0][1][0]);
-    assert(!edges[0][0][0][1]);
-    assert(!edges[0][0][0][0]);
-    cerr << "  MAX SRC DELTA[0][0] = " << max_src_delta[0][0] << endl;
-    assert(max_src_delta[0][0] > 0);
-    //cerr << "First cell contains " << b[0][0].size() << " forward pointers\n";
-    //for (int i = 0; i < b[0][0].size(); ++i) {
-    //  cerr << "  -> (" << b[0][0][i].next_src_covered << "," << b[0][0][i].next_trg_covered << ")\n";
-    //}
-  }
-};
-
-ostream& operator<<(ostream& os, const FSTState& q);
-struct FSTState {
-  explicit FSTState(int src_size) :
-      trg_covered_(),
-      src_covered_(),
-      src_coverage_(src_size) {}
-
-  FSTState(short trg_covered, short src_covered, const vector<bool>& src_coverage, const vector<short>& src_prefix) :
-      trg_covered_(trg_covered),
-      src_covered_(src_covered),
-      src_coverage_(src_coverage),
-      src_prefix_(src_prefix) {
-    if (src_coverage_.size() == src_covered) {
-      assert(src_prefix.size() == 0);
-    }
-  }
-
-  // if we extend by the word at src_position, what are
-  // the next states that are reachable and lie on a valid
-  // path to the final state?
-  vector<FSTState> Extensions(int src_position, int src_len, int trg_len, const Reachability& r) const {
-    assert(src_position < src_coverage_.size());
-    if (src_coverage_[src_position]) {
-      cerr << "Trying to extend " << *this << " with position " << src_position << endl;
-      abort();
-    }
-    vector<bool> ncvg = src_coverage_;
-    ncvg[src_position] = true;
-
-    vector<FSTState> res;
-    const int trg_remaining = trg_len - trg_covered_;
-    if (trg_remaining <= 0) {
-      cerr << "Target appears to have been covered: " << *this << " (trg_len=" << trg_len << ",trg_covered=" << trg_covered_ << ")" << endl;
-      abort();
-    }
-    const int src_remaining = src_len - src_covered_;
-    if (src_remaining <= 0) {
-      cerr << "Source appears to have been covered: " << *this << endl;
-      abort();
-    }
-
-    for (int tc = 1; tc <= kMAX_TRG_PHRASE; ++tc) {
-      if (r.edges[src_covered_][trg_covered_][src_prefix_.size() + 1][tc]) {
-        int nc = src_prefix_.size() + 1 + src_covered_;
-        res.push_back(FSTState(trg_covered_ + tc, nc, ncvg, vector<short>()));
-      }
-    }
-
-    if ((src_prefix_.size() + 1) < r.max_src_delta[src_covered_][trg_covered_]) {
-      vector<short> nsp = src_prefix_;
-      nsp.push_back(src_position);
-      res.push_back(FSTState(trg_covered_, src_covered_, ncvg, nsp));
-    }
-
-    if (res.size() == 0) {
-      cerr << *this << " can't be extended!\n";
-      abort();
-    }
-    return res;
-  }
-
-  short trg_covered_, src_covered_;
-  vector<bool> src_coverage_;
-  vector<short> src_prefix_;
-};
-bool operator<(const FSTState& q, const FSTState& r) {
-  if (q.trg_covered_ != r.trg_covered_) return q.trg_covered_ < r.trg_covered_;
-  if (q.src_covered_!= r.src_covered_) return q.src_covered_ < r.src_covered_;
-  if (q.src_coverage_ != r.src_coverage_) return q.src_coverage_ < r.src_coverage_;
-  return q.src_prefix_ < r.src_prefix_;
-}
-
-ostream& operator<<(ostream& os, const FSTState& q) {
-  os << "[" << q.trg_covered_ << " : ";
-  for (int i = 0; i < q.src_coverage_.size(); ++i)
-    os << q.src_coverage_[i];
-  os << " : <";
-  for (int i = 0; i < q.src_prefix_.size(); ++i) {
-    if (i != 0) os << ' ';
-    os << q.src_prefix_[i];
-  }
-  return os << ">]";
-}
-
-struct MyModel {
-  MyModel(ConditionalBase& rcp0) : rp0(rcp0) {}
-  typedef unordered_map<vector<WordID>, CCRP_NoTable<TRule>, boost::hash<vector<WordID> > > SrcToRuleCRPMap;
-
-  void DecrementRule(const TRule& rule) {
-    SrcToRuleCRPMap::iterator it = rules.find(rule.f_);
-    assert(it != rules.end());
-    it->second.decrement(rule);
-    if (it->second.num_customers() == 0) rules.erase(it);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    SrcToRuleCRPMap::iterator it = rules.find(rule.f_);
-    if (it == rules.end()) {
-      CCRP_NoTable<TRule> crp(1,1);
-      it = rules.insert(make_pair(rule.f_, crp)).first;
-    }
-    it->second.increment(rule);
-  }
-
-  // conditioned on rule.f_
-  prob_t RuleConditionalProbability(const TRule& rule) const {
-    const prob_t base = rp0(rule);
-    SrcToRuleCRPMap::const_iterator it = rules.find(rule.f_);
-    if (it == rules.end()) {
-      return base;
-    } else {
-      const double lp = it->second.logprob(rule, log(base));
-      prob_t q; q.logeq(lp);
-      return q;
-    }
-  }
-
-  const ConditionalBase& rp0;
-  SrcToRuleCRPMap rules;
-};
-
-struct MyFST : public WFST {
-  MyFST(const vector<WordID>& ssrc, const vector<WordID>& strg, MyModel* m) :
-      src(ssrc), trg(strg),
-      r(src.size(),trg.size(),kMAX_SRC_PHRASE, kMAX_TRG_PHRASE),
-      model(m) {
-    FSTState in(src.size());
-    cerr << " INIT: " << in << endl;
-    init = GetNode(in);
-    for (int i = 0; i < in.src_coverage_.size(); ++i) in.src_coverage_[i] = true;
-    in.src_covered_ = src.size();
-    in.trg_covered_ = trg.size();
-    cerr << "FINAL: " << in << endl;
-    final = GetNode(in);
-  }
-  virtual const WFSTNode* Final() const;
-  virtual const WFSTNode* Initial() const;
-
-  const WFSTNode* GetNode(const FSTState& q);
-  map<FSTState, boost::shared_ptr<WFSTNode> > m;
-  const vector<WordID>& src;
-  const vector<WordID>& trg;
-  Reachability r;
-  const WFSTNode* init;
-  const WFSTNode* final;
-  MyModel* model;
-};
-
-struct MyNode : public WFSTNode {
-  MyNode(const FSTState& q, MyFST* fst) : state(q), container(fst) {}
-  virtual vector<pair<const WFSTNode*, TRulePtr> > ExtendInput(unsigned srcindex) const;
-  const FSTState state;
-  mutable MyFST* container;
-};
-
-vector<pair<const WFSTNode*, TRulePtr> > MyNode::ExtendInput(unsigned srcindex) const {
-  cerr << "EXTEND " << state << " with " << srcindex << endl;
-  vector<FSTState> ext = state.Extensions(srcindex, container->src.size(), container->trg.size(), container->r);
-  vector<pair<const WFSTNode*,TRulePtr> > res(ext.size());
-  for (unsigned i = 0; i < ext.size(); ++i) {
-    res[i].first = container->GetNode(ext[i]);
-    if (ext[i].src_prefix_.size() == 0) {
-      const unsigned trg_from = state.trg_covered_;
-      const unsigned trg_to = ext[i].trg_covered_;
-      const unsigned prev_prfx_size = state.src_prefix_.size();
-      res[i].second.reset(new TRule);
-      res[i].second->lhs_ = -TD::Convert("X");
-      vector<WordID>& src = res[i].second->f_;
-      vector<WordID>& trg = res[i].second->e_;
-      src.resize(prev_prfx_size + 1);
-      for (unsigned j = 0; j < prev_prfx_size; ++j)
-        src[j] = container->src[state.src_prefix_[j]];
-      src[prev_prfx_size] = container->src[srcindex];
-      for (unsigned j = trg_from; j < trg_to; ++j)
-        trg.push_back(container->trg[j]);
-      res[i].second->scores_.set_value(FD::Convert("Proposal"), log(container->model->RuleConditionalProbability(*res[i].second)));
-    }
-  }
-  return res;
-}
-
-const WFSTNode* MyFST::GetNode(const FSTState& q) {
-  boost::shared_ptr<WFSTNode>& res = m[q];
-  if (!res) {
-    res.reset(new MyNode(q, this));
-  }
-  return &*res;
-}
-
-const WFSTNode* MyFST::Final() const {
-  return final;
-}
-
-const WFSTNode* MyFST::Initial() const {
-  return init;
-}
-
-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;
-  }
-  boost::shared_ptr<MT19937> prng;
-  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;
-  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());
-
-  ConditionalBase lp0(conf["model1_interpolation_weight"].as<double>(),
-                      vocabe.size(),
-                      conf["model1"].as<string>());
-  MyModel m(lp0);
-
-  TRule x("[X] ||| kAnwntR myN ||| at the convent ||| 0");
-  m.IncrementRule(x);
-  TRule y("[X] ||| nY dyN ||| gave ||| 0");
-  m.IncrementRule(y);
-
-
-  MyFST fst(corpusf[0], corpuse[0], &m);
-  ifstream in("./kimura.g");
-  assert(in);
-  CFG_WFSTComposer comp(fst);
-  Hypergraph hg;
-  bool succeed = comp.Compose(&in, &hg);
-  hg.PrintGraphviz();
-  if (succeed) { cerr << "SUCCESS.\n"; } else { cerr << "FAILURE REPORTED.\n"; }
-
-#if 0
-  ifstream in2("./amnabooks.g");
-  assert(in2);
-  MyFST fst2(corpusf[1], corpuse[1], &m);
-  CFG_WFSTComposer comp2(fst2);
-  Hypergraph hg2;
-  bool succeed2 = comp2.Compose(&in2, &hg2);
-  if (succeed2) { cerr << "SUCCESS.\n"; } else { cerr << "FAILURE REPORTED.\n"; }
-#endif
-
-  SparseVector<double> w; w.set_value(FD::Convert("Proposal"), 1.0);
-  hg.Reweight(w);
-  cerr << ViterbiFTree(hg) << endl;
-  return 0;
-}
-
diff --git a/gi/pf/pfdist.cc b/gi/pf/pfdist.cc
deleted file mode 100644
index a3e46064..00000000
--- a/gi/pf/pfdist.cc
+++ /dev/null
@@ -1,598 +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 "pf.h"
-#include "base_distributions.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;
-
-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")
-        ("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(Md::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(Md::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(Md::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;
-}
-
-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
-
-  MultinomialResampleFilter<Particle> filter(&rng);
-  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)
-          filter(&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;
-}
-
diff --git a/gi/pf/pfdist.new.cc b/gi/pf/pfdist.new.cc
deleted file mode 100644
index 3169eb75..00000000
--- a/gi/pf/pfdist.new.cc
+++ /dev/null
@@ -1,620 +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 "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(25),"Number of particles")
-        ("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>();
-
-  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;
-          const int rejuv_freq = 1;
-          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;
-}
-
diff --git a/gi/pf/pfnaive.cc b/gi/pf/pfnaive.cc
deleted file mode 100644
index 958ec4e2..00000000
--- a/gi/pf/pfnaive.cc
+++ /dev/null
@@ -1,284 +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 "pf.h"
-#include "base_distributions.h"
-#include "monotonic_pseg.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"
-#include "corpus.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")
-        ("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);
-  }
-}
-
-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()(unsigned src_cov, unsigned trg_cov) const {
-    assert(src_cov <= src_.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 = src_cov; i < src_.size(); ++i)
-        r.push_back(src_[i]);
-      r.push_back(0);  // NULL word
-      const prob_t uniform_alignment(1.0 / r.size());
-      e.logeq(Md::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(Md::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<unsigned, map<unsigned, prob_t> > cache_;
-};
-
-struct Particle {
-  Particle() : weight(prob_t::One()), src_cov(), trg_cov() {}
-  prob_t weight;
-  prob_t gamma_last;
-  vector<TRulePtr> rules;
-  int src_cov;
-  int trg_cov;
-};
-
-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 << "[src_cov=" << p.src_cov << " trg_cov=" << p.trg_cov << " num_rules=" << p.rules.size() << "  w=" << log(p.weight) << ']';
-  return o;
-}
-
-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";
-  corpus::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>());
-
-  PhraseJointBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
-  PhraseJointBase_BiDir alp0(m1, invm1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
-  MonotonicParallelSegementationModel<PhraseJointBase_BiDir> m(alp0);
-  TRule xx("[X] ||| ms. kimura ||| MS. KIMURA ||| X=0");
-  cerr << xx << endl << lp0(xx) << " " << alp0(xx) << endl;
-  TRule xx12("[X] ||| . ||| PHARMACY . ||| X=0");
-  TRule xx21("[X] ||| pharmacy . ||| . ||| X=0");
-//  TRule xx22("[X] ||| . ||| . ||| X=0");
-  TRule xx22("[X] ||| . ||| THE . ||| X=0");
-  cerr << xx12 << "\t" << lp0(xx12) << " " << alp0(xx12) << endl;
-  cerr << xx21 << "\t" << lp0(xx21) << " " << alp0(xx21) << endl;
-  cerr << xx22 << "\t" << lp0(xx22) << " " << alp0(xx22) << endl;
-
-  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;
-  SystematicResampleFilter<Particle> filter(&rng);
-  // MultinomialResampleFilter<Particle> filter(&rng);
-  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.DecrementRulesAndStops(ps[ci].rules);
-      const prob_t q_stop = m.StopProbability();
-      const prob_t q_cont = m.ContinueProbability();
-      cerr << "P(stop)=" << q_stop << "\tP(continue)=" <<q_cont << endl;
-
-      BackwardEstimateSym be(m1, invm1, src, trg);
-      const Reachability& r = reaches[ci];
-      vector<Particle> lps(particles);
-
-      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)
-          filter(&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;
-
-            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;
-
-                int i = p.src_cov;
-                assert(ss.size() < tmp_p.size());  // if fails increase tmp_p size
-                Particle& np = tmp_p[ss.size()];
-                np = p;
-                x.f_.clear();
-                for (int j = 0; j < src_len; ++j)
-                  x.f_.push_back(src[i + j]);
-                np.src_cov += x.f_.size();
-                np.trg_cov += x.e_.size();
-                const bool stop_now = (np.src_cov == src_len && np.trg_cov == trg_len);
-                prob_t rp = m.RuleProbability(x) * (stop_now ? q_stop : q_cont);
-                np.gamma_last = rp;
-                const prob_t u = pow(np.gamma_last * pow(be(np.src_cov, np.trg_cov), 1.2), 0.1);
-                //cerr << "**rule=" << x << endl;
-                //cerr << "  u=" << log(u) << "  rule=" << rp << endl;
-                ss.add(u);
-                np.rules.push_back(TRulePtr(new TRule(x)));
-                z += u;
-              }
-            }
-            //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;
-        prob_t wv = prob_t::Zero();
-        for (int pp = 0; pp < lps.size(); ++pp)
-          wv += lps[pp].weight;
-        for (int pp = 0; pp < lps.size(); ++pp)
-          lps[pp].weight /= wv;
-      }
-      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.IncrementRulesAndStops(lps[sampled].rules);
-      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;
-  }
-  return 0;
-}
-
diff --git a/gi/pf/poisson_uniform_word_model.h b/gi/pf/poisson_uniform_word_model.h
deleted file mode 100644
index 76204a0e..00000000
--- a/gi/pf/poisson_uniform_word_model.h
+++ /dev/null
@@ -1,50 +0,0 @@
-#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
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;
-}
-
-
diff --git a/gi/pf/pyp_tm.cc b/gi/pf/pyp_tm.cc
deleted file mode 100644
index 37b9a604..00000000
--- a/gi/pf/pyp_tm.cc
+++ /dev/null
@@ -1,128 +0,0 @@
-#include "pyp_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 << '\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;
-};
-
-PYPLexicalTranslation::PYPLexicalTranslation(const vector<vector<WordID> >& lets,
-                                             const unsigned vocab_size,
-                                             const unsigned num_letters) :
-    letters(lets),
-    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();
-}
-
-prob_t PYPLexicalTranslation::Likelihood() const {
-  return tmodel->Likelihood() * base.Likelihood();
-}
-
-void PYPLexicalTranslation::ResampleHyperparameters(MT19937* rng) {
-  tmodel->ResampleHyperparameters(rng);
-}
-
-unsigned PYPLexicalTranslation::UniqueConditioningContexts() const {
-  return tmodel->UniqueConditioningContexts();
-}
-
-prob_t PYPLexicalTranslation::Prob(WordID src, WordID trg) const {
-  return tmodel->Prob(src, letters[trg]);
-}
-
-void PYPLexicalTranslation::Increment(WordID src, WordID trg, MT19937* rng) {
-  tmodel->Increment(src, letters[trg], rng);
-}
-
-void PYPLexicalTranslation::Decrement(WordID src, WordID trg, MT19937* rng) {
-  tmodel->Decrement(src, letters[trg], rng);
-}
-
diff --git a/gi/pf/pyp_tm.h b/gi/pf/pyp_tm.h
deleted file mode 100644
index 2b076a25..00000000
--- a/gi/pf/pyp_tm.h
+++ /dev/null
@@ -1,36 +0,0 @@
-#ifndef PYP_LEX_TRANS
-#define PYP_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 <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;
-
-  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
-  ConditionalPYPWordModel<PoissonUniformWordModel, FreqBinner>* tmodel;  // translation distributions
-                      // (model English word | French word)
-  const WordID kX;
-};
-
-#endif
diff --git a/gi/pf/pyp_word_model.h b/gi/pf/pyp_word_model.h
deleted file mode 100644
index 0bebb751..00000000
--- a/gi/pf/pyp_word_model.h
+++ /dev/null
@@ -1,61 +0,0 @@
-#ifndef _PYP_WORD_MODEL_H_
-#define _PYP_WORD_MODEL_H_
-
-#include <iostream>
-#include <cmath>
-#include <vector>
-#include "prob.h"
-#include "ccrp.h"
-#include "m.h"
-#include "tdict.h"
-#include "os_phrase.h"
-
-// PYP(d,s,poisson-uniform) represented as a CRP
-template <class Base>
-struct PYPWordModel {
-  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, base(s));
-  }
-
-  inline void Increment(const std::vector<WordID>& s, MT19937* rng) {
-    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.Decrement(s, rng);
-  }
-
-  inline prob_t Likelihood() const {
-    prob_t p; p.logeq(r.log_crp_prob());
-    p *= base.Likelihood();
-    return p;
-  }
-
-  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
-                << TD::GetString(it->first) << std::endl;
-    }
-  }
-
- private:
-
-  Base& base;  // keeps track of the draws from the base distribution
-  CCRP<std::vector<WordID> > r;
-};
-
-#endif
diff --git a/gi/pf/quasi_model2.h b/gi/pf/quasi_model2.h
deleted file mode 100644
index 4075affe..00000000
--- a/gi/pf/quasi_model2.h
+++ /dev/null
@@ -1,177 +0,0 @@
-#ifndef _QUASI_MODEL2_H_
-#define _QUASI_MODEL2_H_
-
-#include <vector>
-#include <cmath>
-#include <tr1/unordered_map>
-#include "boost/functional.hpp"
-#include "prob.h"
-#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() {}
-  AlignmentObservation(unsigned sl, unsigned tl, unsigned tw, unsigned sw) :
-      src_len(sl), trg_len(tl), j(tw), a_j(sw) {}
-  unsigned short src_len;
-  unsigned short trg_len;
-  unsigned short j;
-  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) :
-      alpha_(alpha),
-      pnull_(pnull),
-      pnotnull_(1 - pnull) {}
-
-  // a_j = 0 => NULL; src_len does *not* include null
-  prob_t Prob(unsigned a_j, unsigned j, unsigned src_len, unsigned trg_len) const {
-    if (!a_j) return pnull_;
-    return pnotnull_ *
-       prob_t(UnnormalizedProb(a_j, j, src_len, trg_len, alpha_) / GetOrComputeZ(j, src_len, trg_len));
-  }
-
-  void Increment(unsigned a_j, unsigned j, unsigned src_len, unsigned trg_len) {
-    assert(a_j <= src_len);
-    assert(j < trg_len);
-    ++obs_[AlignmentObservation(src_len, trg_len, j, a_j)];
-  }
-
-  void Decrement(unsigned a_j, unsigned j, unsigned src_len, unsigned trg_len) {
-    const AlignmentObservation ao(src_len, trg_len, j, a_j);
-    int &cc = obs_[ao];
-    assert(cc > 0);
-    --cc;
-    if (!cc) obs_.erase(ao);
-  }
-
-  struct PNullResampler {
-    PNullResampler(const QuasiModel2& m) : m_(m) {}
-    const QuasiModel2& m_;
-    double operator()(const double& proposed_pnull) const {
-      return log(m_.Likelihood(m_.alpha_, proposed_pnull));
-    }
-  };
-
-  struct AlphaResampler {
-    AlphaResampler(const QuasiModel2& m) : m_(m) {}
-    const QuasiModel2& m_;
-    double operator()(const double& proposed_alpha) const {
-      return log(m_.Likelihood(proposed_alpha, m_.pnull_.as_float()));
-    }
-  };
-
-  void ResampleHyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
-    const PNullResampler dr(*this);
-    const AlphaResampler ar(*this);
-    for (unsigned i = 0; i < nloop; ++i) {
-      double pnull = slice_sampler1d(dr, pnull_.as_float(), *rng, 0.00000001,
-                            1.0, 0.0, niterations, 100*niterations);
-      pnull_ = prob_t(pnull);
-      alpha_ = slice_sampler1d(ar, alpha_, *rng, 0.00000001,
-                              std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-    }
-    std::cerr << "QuasiModel2(alpha=" << alpha_ << ",p_null="
-              << pnull_.as_float() << ") = " << Likelihood() << std::endl;
-    zcache_.clear();
-  }
-
-  prob_t Likelihood() const {
-    return Likelihood(alpha_, pnull_.as_float());
-  }
-
-  prob_t Likelihood(double alpha, double ppnull) const {
-    const prob_t pnull(ppnull);
-    const prob_t pnotnull(1 - ppnull);
-
-    prob_t p;
-    p.logeq(Md::log_gamma_density(alpha, 0.1, 25));  // TODO configure
-    assert(!p.is_0());
-    prob_t prob_of_ppnull; prob_of_ppnull.logeq(Md::log_beta_density(ppnull, 2, 10));
-    assert(!prob_of_ppnull.is_0());
-    p *= prob_of_ppnull;
-    for (ObsCount::const_iterator it = obs_.begin(); it != obs_.end(); ++it) {
-      const AlignmentObservation& ao = it->first;
-      if (ao.a_j) {
-        prob_t u = XUnnormalizedProb(ao.a_j, ao.j, ao.src_len, ao.trg_len, alpha);
-        prob_t z = XComputeZ(ao.j, ao.src_len, ao.trg_len, alpha);
-        prob_t pa(u / z);
-        pa *= pnotnull;
-        pa.poweq(it->second);
-        p *= pa;
-      } else {
-        p *= pnull.pow(it->second);
-      }
-    }
-    return p;
-  }
-
- private:
-  static prob_t XUnnormalizedProb(unsigned a_j, unsigned j, unsigned src_len, unsigned trg_len, double alpha) {
-    prob_t p;
-    p.logeq(-fabs(double(a_j - 1) / src_len - double(j) / trg_len) * alpha);
-    return p;
-  }
-
-  static prob_t XComputeZ(unsigned j, unsigned src_len, unsigned trg_len, double alpha) {
-    prob_t z = prob_t::Zero();
-    for (int a_j = 1; a_j <= src_len; ++a_j)
-      z += XUnnormalizedProb(a_j, j, src_len, trg_len, alpha);
-    return z;
-  }
-
-  static double UnnormalizedProb(unsigned a_j, unsigned j, unsigned src_len, unsigned trg_len, double alpha) {
-    return exp(-fabs(double(a_j - 1) / src_len - double(j) / trg_len) * alpha);
-  }
-
-  static double ComputeZ(unsigned j, unsigned src_len, unsigned trg_len, double alpha) {
-    double z = 0;
-    for (int a_j = 1; a_j <= src_len; ++a_j)
-      z += UnnormalizedProb(a_j, j, src_len, trg_len, alpha);
-    return z;
-  }
-
-  const double& GetOrComputeZ(unsigned j, unsigned src_len, unsigned trg_len) const {
-    if (src_len >= zcache_.size())
-      zcache_.resize(src_len + 1);
-    if (trg_len >= zcache_[src_len].size())
-      zcache_[src_len].resize(trg_len + 1);
-    std::vector<double>& zv = zcache_[src_len][trg_len];
-    if (zv.size() == 0)
-      zv.resize(trg_len);
-    double& z = zv[j];
-    if (!z)
-      z = ComputeZ(j, src_len, trg_len, alpha_);
-    return z;
-  }
-
-  double alpha_;
-  prob_t pnull_;
-  prob_t pnotnull_;
-  mutable std::vector<std::vector<std::vector<double> > > zcache_;
-  typedef std::tr1::unordered_map<AlignmentObservation, int, boost::hash<AlignmentObservation> > ObsCount;
-  ObsCount obs_;
-};
-
-#endif
diff --git a/gi/pf/reachability.cc b/gi/pf/reachability.cc
deleted file mode 100644
index 7d0d04ac..00000000
--- a/gi/pf/reachability.cc
+++ /dev/null
@@ -1,74 +0,0 @@
-#include "reachability.h"
-
-#include <vector>
-#include <iostream>
-
-using namespace std;
-
-struct SState {
-  SState() : prev_src_covered(), prev_trg_covered() {}
-  SState(int i, int j) : prev_src_covered(i), prev_trg_covered(j) {}
-  int prev_src_covered;
-  int prev_trg_covered;
-};
-
-void Reachability::ComputeReachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) {
-    typedef boost::multi_array<vector<SState>, 2> array_type;
-    array_type a(boost::extents[srclen + 1][trglen + 1]);
-    a[0][0].push_back(SState());
-    for (int i = 0; i < srclen; ++i) {
-      for (int j = 0; j < trglen; ++j) {
-        if (a[i][j].size() == 0) continue;
-        const SState prev(i,j);
-        for (int k = 1; k <= src_max_phrase_len; ++k) {
-          if ((i + k) > srclen) continue;
-          for (int l = 1; l <= trg_max_phrase_len; ++l) {
-            if ((j + l) > trglen) continue;
-            a[i + k][j + l].push_back(prev);
-          }
-        }
-      }
-    }
-    a[0][0].clear();
-    //cerr << srclen << "," << trglen << ": Final cell contains " << a[srclen][trglen].size() << " back pointers\n";
-    if (a[srclen][trglen].empty()) {
-      cerr << "Sequence pair with lengths (" << srclen << ',' << trglen << ") violates reachability constraints\n";
-      nodes = 0;
-      return;
-    }
-
-    typedef boost::multi_array<bool, 2> rarray_type;
-    rarray_type r(boost::extents[srclen + 1][trglen + 1]);
-    r[srclen][trglen] = true;
-    nodes = 0;
-    for (int i = srclen; i >= 0; --i) {
-      for (int j = trglen; j >= 0; --j) {
-        vector<SState>& prevs = a[i][j];
-        if (!r[i][j]) { prevs.clear(); }
-        for (int k = 0; k < prevs.size(); ++k) {
-          r[prevs[k].prev_src_covered][prevs[k].prev_trg_covered] = true;
-          int src_delta = i - prevs[k].prev_src_covered;
-          edges[prevs[k].prev_src_covered][prevs[k].prev_trg_covered][src_delta][j - prevs[k].prev_trg_covered] = true;
-          valid_deltas[prevs[k].prev_src_covered][prevs[k].prev_trg_covered].push_back(make_pair<short,short>(src_delta,j - prevs[k].prev_trg_covered));
-          short &msd = max_src_delta[prevs[k].prev_src_covered][prevs[k].prev_trg_covered];
-          if (src_delta > msd) msd = src_delta;
-        }
-      }
-    }
-    assert(!edges[0][0][1][0]);
-    assert(!edges[0][0][0][1]);
-    assert(!edges[0][0][0][0]);
-    assert(max_src_delta[0][0] > 0);
-    nodes = 0;
-    for (int i = 0; i < srclen; ++i) {
-      for (int j = 0; j < trglen; ++j) {
-        if (valid_deltas[i][j].size() > 0) {
-          node_addresses[i][j] = nodes++;
-        } else {
-          node_addresses[i][j] = -1;
-        }
-      }
-    }
-    cerr << "Sequence pair with lengths (" << srclen << ',' << trglen << ") has " << valid_deltas[0][0].size() << " out edges in its root node, " << nodes << " nodes in total, and outside estimate matrix will require " << sizeof(float)*nodes << " bytes\n";
-  }
-
diff --git a/gi/pf/reachability.h b/gi/pf/reachability.h
deleted file mode 100644
index 1e22c76a..00000000
--- a/gi/pf/reachability.h
+++ /dev/null
@@ -1,34 +0,0 @@
-#ifndef _REACHABILITY_H_
-#define _REACHABILITY_H_
-
-#include "boost/multi_array.hpp"
-
-// determines minimum and maximum lengths of outgoing edges from all
-// coverage positions such that the alignment path respects src and
-// trg maximum phrase sizes
-//
-// runs in O(n^2 * src_max * trg_max) time but should be relatively fast
-//
-// currently forbids 0 -> n and n -> 0 alignments
-
-struct Reachability {
-  unsigned nodes;
-  boost::multi_array<bool, 4> edges;  // edges[src_covered][trg_covered][src_delta][trg_delta] is this edge worth exploring?
-  boost::multi_array<short, 2> max_src_delta; // msd[src_covered][trg_covered] -- the largest src delta that's valid
-  boost::multi_array<short, 2> node_addresses; // na[src_covered][trg_covered] -- the index of the node in a one-dimensional array (of size "nodes")
-  boost::multi_array<std::vector<std::pair<short,short> >, 2> valid_deltas; // valid_deltas[src_covered][trg_covered] list of valid transitions leaving a particular node
-
-  Reachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) :
-      nodes(),
-      edges(boost::extents[srclen][trglen][src_max_phrase_len+1][trg_max_phrase_len+1]),
-      max_src_delta(boost::extents[srclen][trglen]),
-      node_addresses(boost::extents[srclen][trglen]),
-      valid_deltas(boost::extents[srclen][trglen]) {
-    ComputeReachability(srclen, trglen, src_max_phrase_len, trg_max_phrase_len);
-  }
-
- private:
-  void ComputeReachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len);
-};
-
-#endif
diff --git a/gi/pf/tied_resampler.h b/gi/pf/tied_resampler.h
deleted file mode 100644
index a4f4af36..00000000
--- a/gi/pf/tied_resampler.h
+++ /dev/null
@@ -1,122 +0,0 @@
-#ifndef _TIED_RESAMPLER_H_
-#define _TIED_RESAMPLER_H_
-
-#include <set>
-#include <vector>
-#include "sampler.h"
-#include "slice_sampler.h"
-#include "m.h"
-
-template <class CRP>
-struct TiedResampler {
-  explicit TiedResampler(double da, double db, double ss, double sr, double d=0.5, double s=1.0) :
-      d_alpha(da),
-      d_beta(db),
-      s_shape(ss),
-      s_rate(sr),
-      discount(d),
-      strength(s) {}
-
-  void Add(CRP* crp) {
-    crps.insert(crp);
-    crp->set_discount(discount);
-    crp->set_strength(strength);
-    assert(!crp->has_discount_prior());
-    assert(!crp->has_strength_prior());
-  }
-
-  void Remove(CRP* crp) {
-    crps.erase(crp);
-  }
-
-  size_t size() const {
-    return crps.size();
-  }
-
-  double LogLikelihood(double d, double s) const {
-    if (s <= -d) return -std::numeric_limits<double>::infinity();
-    double llh = Md::log_beta_density(d, d_alpha, d_beta) +
-                 Md::log_gamma_density(d + s, s_shape, s_rate);
-    for (typename std::set<CRP*>::iterator it = crps.begin(); it != crps.end(); ++it)
-      llh += (*it)->log_crp_prob(d, s);
-    return llh;
-  }
-
-  double LogLikelihood() const {
-    return LogLikelihood(discount, strength);
-  }
-
-  struct DiscountResampler {
-    DiscountResampler(const TiedResampler& m) : m_(m) {}
-    const TiedResampler& m_;
-    double operator()(const double& proposed_discount) const {
-      return m_.LogLikelihood(proposed_discount, m_.strength);
-    }
-  };
-
-  struct AlphaResampler {
-    AlphaResampler(const TiedResampler& m) : m_(m) {}
-    const TiedResampler& m_;
-    double operator()(const double& proposed_strength) const {
-      return m_.LogLikelihood(m_.discount, proposed_strength);
-    }
-  };
-
-  void ResampleHyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
-    if (size() == 0) { std::cerr << "EMPTY - not resampling\n"; return; }
-    const DiscountResampler dr(*this);
-    const AlphaResampler ar(*this);
-    for (int iter = 0; iter < nloop; ++iter) {
-      strength = slice_sampler1d(ar, strength, *rng, -discount + std::numeric_limits<double>::min(),
-                              std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-      double min_discount = std::numeric_limits<double>::min();
-      if (strength < 0.0) min_discount -= strength;
-      discount = slice_sampler1d(dr, discount, *rng, min_discount,
-                          1.0, 0.0, niterations, 100*niterations);
-    }
-    strength = slice_sampler1d(ar, strength, *rng, -discount + std::numeric_limits<double>::min(),
-                            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_hyperparameters(discount, strength);
-  }
- private:
-  std::set<CRP*> crps;
-  const double d_alpha, d_beta, s_shape, s_rate;
-  double discount, strength;
-};
-
-// split according to some criterion
-template <class CRP>
-struct BinTiedResampler {
-  explicit BinTiedResampler(unsigned nbins) :
-      resamplers(nbins, TiedResampler<CRP>(1,1,1,1)) {}
-
-  void Add(unsigned bin, CRP* crp) {
-    resamplers[bin].Add(crp);
-  }
-
-  void Remove(unsigned bin, CRP* crp) {
-    resamplers[bin].Remove(crp);
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    for (unsigned i = 0; i < resamplers.size(); ++i) {
-      std::cerr << "BIN " << i << " (" << resamplers[i].size() << " CRPs): " << std::flush;
-      resamplers[i].ResampleHyperparameters(rng);
-    }
-  }
-
-  double LogLikelihood() const {
-    double llh = 0;
-    for (unsigned i = 0; i < resamplers.size(); ++i)
-      llh += resamplers[i].LogLikelihood();
-    return llh;
-  }
-
- private:
-  std::vector<TiedResampler<CRP> > resamplers;
-};
-
-#endif
diff --git a/gi/pf/tpf.cc b/gi/pf/tpf.cc
deleted file mode 100644
index 7348d21c..00000000
--- a/gi/pf/tpf.cc
+++ /dev/null
@@ -1,99 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include "sampler.h"
-
-using namespace std;
-using namespace tr1;
-
-shared_ptr<MT19937> prng;
-
-struct Particle {
-  Particle() : weight(prob_t::One()) {}
-  vector<int> states;
-  prob_t weight;
-  prob_t gamma_last;
-};
-
-ostream& operator<<(ostream& os, const Particle& p) {
-  os << "[";
-  for (int i = 0; i < p.states.size(); ++i) os << p.states[i] << ' ';
-  os << "| w=" << log(p.weight) << ']';
-  return os;
-}
-
-void Rejuvenate(vector<Particle>& pps) {
-  SampleSet<prob_t> ss;
-  vector<Particle> nps(pps.size());
-  for (int i = 0; i < pps.size(); ++i) {
-//    cerr << pps[i] << endl;
-    ss.add(pps[i].weight);
-  }
-//  cerr << "REJUVINATING...\n";
-  for (int i = 0; i < pps.size(); ++i) {
-    nps[i] = pps[prng->SelectSample(ss)];
-    nps[i].weight = prob_t(1.0 / pps.size());
-//    cerr << nps[i] << endl;
-  }
-  nps.swap(pps);
-//  exit(1);
-}
-
-int main(int argc, char** argv) {
-  const unsigned particles = 100;
-  prng.reset(new MT19937);
-  MT19937& rng = *prng;
-
-  // q(a) = 0.8
-  // q(b) = 0.8
-  // q(c) = 0.4
-  SampleSet<double> ssq;
-  ssq.add(0.4);
-  ssq.add(0.6);
-  ssq.add(0);
-  double qz = 1;
-
-  // p(a) = 0.2
-  // p(b) = 0.8
-  vector<double> p(3);
-  p[0] = 0.2;
-  p[1] = 0.8;
-  p[2] = 0;
-
-  vector<int> counts(3);
-  int tot = 0;
-
-  vector<Particle> pps(particles);
-  SampleSet<prob_t> ppss;
-  int LEN = 12;
-  int PP = 1;
-  while (pps[0].states.size() < LEN) {
-    for (int pi = 0; pi < particles; ++pi) {
-      Particle& prt = pps[pi];
-
-      bool redo = true;
-      const Particle savedp = prt;
-      while (redo) {
-        redo = false;
-        for (int i = 0; i < PP; ++i) {
-          int s = rng.SelectSample(ssq);
-          double gamma_last = p[s];
-          if (!gamma_last) { redo = true; break; }
-          double q = ssq[s] / qz;
-          prt.states.push_back(s);
-          prt.weight *= prob_t(gamma_last / q);
-        }
-        if (redo) { prt = savedp; continue; }
-      }
-    }
-    Rejuvenate(pps);
-  }
-  ppss.clear();
-  for (int i = 0; i < particles; ++i) { ppss.add(pps[i].weight); }
-  int sp = rng.SelectSample(ppss);
-  cerr << pps[sp] << endl;
-
-  return 0;
-}
-
diff --git a/gi/pf/transliterations.cc b/gi/pf/transliterations.cc
deleted file mode 100644
index b2996f65..00000000
--- a/gi/pf/transliterations.cc
+++ /dev/null
@@ -1,334 +0,0 @@
-#include "transliterations.h"
-
-#include <iostream>
-#include <vector>
-
-#include "boost/shared_ptr.hpp"
-
-#include "backward.h"
-#include "filelib.h"
-#include "tdict.h"
-#include "trule.h"
-#include "filelib.h"
-#include "ccrp_nt.h"
-#include "m.h"
-#include "reachability.h"
-
-using namespace std;
-using namespace std::tr1;
-
-struct TruncatedConditionalLengthModel {
-  TruncatedConditionalLengthModel(unsigned max_src_size, unsigned max_trg_size, double expected_src_to_trg_ratio) :
-      plens(max_src_size+1, vector<prob_t>(max_trg_size+1, 0.0)) {
-    for (unsigned i = 1; i <= max_src_size; ++i) {
-      prob_t z = prob_t::Zero();
-      for (unsigned j = 1; j <= max_trg_size; ++j)
-        z += (plens[i][j] = prob_t(0.01 + exp(Md::log_poisson(j, i * expected_src_to_trg_ratio))));
-      for (unsigned j = 1; j <= max_trg_size; ++j)
-        plens[i][j] /= z;
-      //for (unsigned j = 1; j <= max_trg_size; ++j)
-      //  cerr << "P(trg_len=" << j << " | src_len=" << i << ") = " << plens[i][j] << endl;
-    }
-  }
-
-  // return p(tlen | slen) for *chunks* not full words
-  inline const prob_t& operator()(int slen, int tlen) const {
-    return plens[slen][tlen];
-  }
-
-  vector<vector<prob_t> > plens;
-};
-
-struct CondBaseDist {
-  CondBaseDist(unsigned max_src_size, unsigned max_trg_size, double expected_src_to_trg_ratio) :
-    tclm(max_src_size, max_trg_size, expected_src_to_trg_ratio) {}
-
-  prob_t operator()(const vector<WordID>& src, unsigned sf, unsigned st,
-                    const vector<WordID>& trg, unsigned tf, unsigned tt) const {
-    prob_t p = tclm(st - sf, tt - tf);  // target len | source length ~ TCLM(source len)
-    assert(!"not impl");
-    return p;
-  }
-  inline prob_t operator()(const vector<WordID>& src, const vector<WordID>& trg) const {
-    return (*this)(src, 0, src.size(), trg, 0, trg.size());
-  }
-  TruncatedConditionalLengthModel tclm;
-};
-
-// represents transliteration phrase probabilities, e.g.
-//   p( a l - | A l ) , p( o | A w ) , ...
-struct TransliterationChunkConditionalModel {
-  explicit TransliterationChunkConditionalModel(const CondBaseDist& pp0) :
-      d(0.0),
-      strength(1.0),
-      rp0(pp0) {
-  }
-
-  void Summary() const {
-    std::cerr << "Number of conditioning contexts: " << r.size() << std::endl;
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      std::cerr << TD::GetString(it->first) << "   \t(\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
-      for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
-        std::cerr << "   " << i2->second << '\t' << i2->first << std::endl;
-    }
-  }
-
-  int DecrementRule(const TRule& rule) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    assert(it != r.end());    
-    int count = it->second.decrement(rule);
-    if (count) {
-      if (it->second.num_customers() == 0) r.erase(it);
-    }
-    return count;
-  }
-
-  int IncrementRule(const TRule& rule) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      it = r.insert(make_pair(rule.f_, CCRP_NoTable<TRule>(strength))).first;
-    } 
-    int count = it->second.increment(rule);
-    return count;
-  }
-
-  void IncrementRules(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      IncrementRule(*rules[i]);
-  }
-
-  void DecrementRules(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      DecrementRule(*rules[i]);
-  }
-
-  prob_t RuleProbability(const TRule& rule) const {
-    prob_t p;
-    RuleModelHash::const_iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      p = rp0(rule.f_, rule.e_);
-    } else {
-      p = it->second.prob(rule, rp0(rule.f_, rule.e_));
-    }
-    return p;
-  }
-
-  double LogLikelihood(const double& dd, const double& aa) const {
-    if (aa <= -dd) return -std::numeric_limits<double>::infinity();
-    //double llh = Md::log_beta_density(dd, 10, 3) + Md::log_gamma_density(aa, 1, 1);
-    double llh = //Md::log_beta_density(dd, 1, 1) +
-                 Md::log_gamma_density(dd + aa, 1, 1);
-    std::tr1::unordered_map<std::vector<WordID>, CCRP_NoTable<TRule>, boost::hash<std::vector<WordID> > >::const_iterator it;
-    for (it = r.begin(); it != r.end(); ++it)
-      llh += it->second.log_crp_prob(aa);
-    return llh;
-  }
-
-  struct AlphaResampler {
-    AlphaResampler(const TransliterationChunkConditionalModel& m) : m_(m) {}
-    const TransliterationChunkConditionalModel& m_;
-    double operator()(const double& proposed_strength) const {
-      return m_.LogLikelihood(m_.d, proposed_strength);
-    }
-  };
-
-  void ResampleHyperparameters(MT19937* rng) {
-    std::tr1::unordered_map<std::vector<WordID>, CCRP_NoTable<TRule>, boost::hash<std::vector<WordID> > >::iterator it;
-    //const unsigned nloop = 5;
-    const unsigned niterations = 10;
-    //DiscountResampler dr(*this);
-    AlphaResampler ar(*this);
-#if 0
-    for (int iter = 0; iter < nloop; ++iter) {
-      strength = slice_sampler1d(ar, strength, *rng, -d + std::numeric_limits<double>::min(),
-                              std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-      double min_discount = std::numeric_limits<double>::min();
-      if (strength < 0.0) min_discount -= strength;
-      d = slice_sampler1d(dr, d, *rng, min_discount,
-                          1.0, 0.0, niterations, 100*niterations);
-    }
-#endif
-    strength = slice_sampler1d(ar, strength, *rng, -d,
-                            std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-    std::cerr << "CTMModel(alpha=" << strength << ") = " << LogLikelihood(d, strength) << std::endl;
-    for (it = r.begin(); it != r.end(); ++it) {
-#if 0
-      it->second.set_discount(d);
-#endif
-      it->second.set_alpha(strength);
-    }
-  }
-
-  prob_t Likelihood() const {
-    prob_t p; p.logeq(LogLikelihood(d, strength));
-    return p;
-  }
-
-  const CondBaseDist& rp0;
-  typedef std::tr1::unordered_map<std::vector<WordID>,
-                                  CCRP_NoTable<TRule>,
-                                  boost::hash<std::vector<WordID> > > RuleModelHash;
-  RuleModelHash r;
-  double d, strength;
-};
-
-struct GraphStructure {
-  GraphStructure() : r() {}
-  // leak memory - these are basically static
-  const Reachability* r;
-  bool IsReachable() const { return r->nodes > 0; }
-};
-
-struct ProbabilityEstimates {
-  ProbabilityEstimates() : gs(), backward() {}
-  explicit ProbabilityEstimates(const GraphStructure& g) :
-      gs(&g), backward() {
-    if (g.r->nodes > 0)
-      backward = new float[g.r->nodes];
-  }
-  // leak memory, these are static
-
-  // returns an estimate of the marginal probability
-  double MarginalEstimate() const {
-    if (!backward) return 0;
-    return backward[0];
-  }
-
-  // returns an backward estimate
-  double Backward(int src_covered, int trg_covered) const {
-    if (!backward) return 0;
-    int ind = gs->r->node_addresses[src_covered][trg_covered];
-    if (ind < 0) return 0;
-    return backward[ind];
-  }
-
-  prob_t estp;
-  float* backward;
- private:
-  const GraphStructure* gs;
-};
-
-struct TransliterationsImpl {
-  TransliterationsImpl(int max_src, int max_trg, double sr, const BackwardEstimator& b) :
-      cp0(max_src, max_trg, sr),
-      tccm(cp0),
-      be(b),
-      kMAX_SRC_CHUNK(max_src),
-      kMAX_TRG_CHUNK(max_trg),
-      kS2T_RATIO(sr),
-      tot_pairs(), tot_mem() {
-  }
-  const CondBaseDist cp0;
-  TransliterationChunkConditionalModel tccm;
-  const BackwardEstimator& be;
-
-  void Initialize(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) {
-    const size_t src_len = src_lets.size();
-    const size_t trg_len = trg_lets.size();
-
-    // init graph structure
-    if (src_len >= graphs.size()) graphs.resize(src_len + 1);
-    if (trg_len >= graphs[src_len].size()) graphs[src_len].resize(trg_len + 1);
-    GraphStructure& gs = graphs[src_len][trg_len];
-    if (!gs.r) {
-      double rat = exp(fabs(log(trg_len / (src_len * kS2T_RATIO))));
-      if (rat > 1.5 || (rat > 2.4 && src_len < 6)) {
-        cerr << " ** Forbidding transliterations of size " << src_len << "," << trg_len << ": " << rat << endl;
-        gs.r = new Reachability(src_len, trg_len, 0, 0);
-      } else {
-        gs.r = new Reachability(src_len, trg_len, kMAX_SRC_CHUNK, kMAX_TRG_CHUNK);
-      }
-    }
-
-    const Reachability& r = *gs.r;
-
-    // init backward estimates
-    if (src >= ests.size()) ests.resize(src + 1);
-    unordered_map<WordID, ProbabilityEstimates>::iterator it = ests[src].find(trg);
-    if (it != ests[src].end()) return; // already initialized
-
-    it = ests[src].insert(make_pair(trg, ProbabilityEstimates(gs))).first;
-    ProbabilityEstimates& est = it->second;
-    if (!gs.r->nodes) return;  // not derivable subject to length constraints
-
-    be.InitializeGrid(src_lets, trg_lets, r, kS2T_RATIO, est.backward);
-    cerr << TD::GetString(src_lets) << " ||| " << TD::GetString(trg_lets) << " ||| " << (est.backward[0] / trg_lets.size()) << endl;
-    tot_pairs++;
-    tot_mem += sizeof(float) * gs.r->nodes;
-  }
-
-  void Forbid(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) {
-    const size_t src_len = src_lets.size();
-    const size_t trg_len = trg_lets.size();
-    // TODO
-  }
-
-  prob_t EstimateProbability(WordID s, const vector<WordID>& src, WordID t, const vector<WordID>& trg) const {
-    assert(src.size() < graphs.size());
-    const vector<GraphStructure>& tv = graphs[src.size()];
-    assert(trg.size() < tv.size());
-    const GraphStructure& gs = tv[trg.size()];
-    if (gs.r->nodes == 0)
-      return prob_t::Zero();
-    const unordered_map<WordID, ProbabilityEstimates>::const_iterator it = ests[s].find(t);
-    assert(it != ests[s].end());
-    return it->second.estp;
-  }
-
-  void GraphSummary() const {
-    double to = 0;
-    double tn = 0;
-    double tt = 0;
-    for (int i = 0; i < graphs.size(); ++i) {
-      const vector<GraphStructure>& vt = graphs[i];
-      for (int j = 0; j < vt.size(); ++j) {
-        const GraphStructure& gs = vt[j];
-        if (!gs.r) continue;
-        tt++;
-        for (int k = 0; k < i; ++k) {
-          for (int l = 0; l < j; ++l) {
-            size_t c = gs.r->valid_deltas[k][l].size();
-            if (c) {
-              tn += 1;
-              to += c;
-            }
-          }
-        }
-      }
-    }
-    cerr << "     Average nodes = " << (tn / tt) << endl;
-    cerr << "Average out-degree = " << (to / tn) << endl;
-    cerr << " Unique structures = " << tt << endl;
-    cerr << "      Unique pairs = " << tot_pairs << endl;
-    cerr << "          BEs size = " << (tot_mem / (1024.0*1024.0)) << " MB" << endl;
-  }
-
-  const int kMAX_SRC_CHUNK;
-  const int kMAX_TRG_CHUNK;
-  const double kS2T_RATIO;
-  unsigned tot_pairs;
-  size_t tot_mem;
-  vector<vector<GraphStructure> > graphs; // graphs[src_len][trg_len]
-  vector<unordered_map<WordID, ProbabilityEstimates> > ests; // ests[src][trg]
-};
-
-Transliterations::Transliterations(int max_src, int max_trg, double sr, const BackwardEstimator& be) :
-    pimpl_(new TransliterationsImpl(max_src, max_trg, sr, be)) {}
-Transliterations::~Transliterations() { delete pimpl_; }
-
-void Transliterations::Initialize(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) {
-  pimpl_->Initialize(src, src_lets, trg, trg_lets);
-}
-
-prob_t Transliterations::EstimateProbability(WordID s, const vector<WordID>& src, WordID t, const vector<WordID>& trg) const {
-  return pimpl_->EstimateProbability(s, src,t, trg);
-}
-
-void Transliterations::Forbid(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) {
-  pimpl_->Forbid(src, src_lets, trg, trg_lets);
-}
-
-void Transliterations::GraphSummary() const {
-  pimpl_->GraphSummary();
-}
-
diff --git a/gi/pf/transliterations.h b/gi/pf/transliterations.h
deleted file mode 100644
index 49d14684..00000000
--- a/gi/pf/transliterations.h
+++ /dev/null
@@ -1,24 +0,0 @@
-#ifndef _TRANSLITERATIONS_H_
-#define _TRANSLITERATIONS_H_
-
-#include <vector>
-#include "wordid.h"
-#include "prob.h"
-
-struct BackwardEstimator;
-struct TransliterationsImpl;
-struct Transliterations {
-  // max_src and max_trg indicate how big the transliteration phrases can be
-  // see reachability.h for information about filter_ratio
-  explicit Transliterations(int max_src, int max_trg, double s2t_rat, const BackwardEstimator& be);
-  ~Transliterations();
-  void Initialize(WordID src, const std::vector<WordID>& src_lets, WordID trg, const std::vector<WordID>& trg_lets);
-  void Forbid(WordID src, const std::vector<WordID>& src_lets, WordID trg, const std::vector<WordID>& trg_lets);
-  void GraphSummary() const;
-  prob_t EstimateProbability(WordID s, const std::vector<WordID>& src, WordID t, const std::vector<WordID>& trg) const;
- private:
-  TransliterationsImpl* pimpl_;
-};
-
-#endif
-
diff --git a/gi/pf/unigrams.cc b/gi/pf/unigrams.cc
deleted file mode 100644
index 40829775..00000000
--- a/gi/pf/unigrams.cc
+++ /dev/null
@@ -1,80 +0,0 @@
-#include "unigrams.h"
-
-#include <string>
-#include <cmath>
-
-#include "stringlib.h"
-#include "filelib.h"
-
-using namespace std;
-
-void UnigramModel::LoadUnigrams(const string& fname) {
-  cerr << "Loading unigram probabilities from " << fname << " ..." << endl;
-  ReadFile rf(fname);
-  string line;
-  istream& in = *rf.stream();
-  assert(in);
-  getline(in, line);
-  assert(line.empty());
-  getline(in, line);
-  assert(line == "\\data\\");
-  getline(in, line);
-  size_t pos = line.find("ngram 1=");
-  assert(pos == 0);
-  assert(line.size() > 8);
-  const size_t num_unigrams = atoi(&line[8]);
-  getline(in, line);
-  assert(line.empty());
-  getline(in, line);
-  assert(line == "\\1-grams:");
-  for (size_t i = 0; i < num_unigrams; ++i) {
-    getline(in, line);
-    assert(line.size() > 0);
-    pos = line.find('\t');
-    assert(pos > 0);
-    assert(pos + 1 < line.size());
-    const WordID w = TD::Convert(line.substr(pos + 1));
-    line[pos] = 0;
-    float p = atof(&line[0]);
-    if (w < probs_.size()) probs_[w].logeq(p * log(10)); else cerr << "WARNING: don't know about '" << TD::Convert(w) << "'\n";
-  }
-}
-
-void UnigramWordModel::LoadUnigrams(const string& fname) {
-  cerr << "Loading unigram probabilities from " << fname << " ..." << endl;
-  ReadFile rf(fname);
-  string line;
-  istream& in = *rf.stream();
-  assert(in);
-  getline(in, line);
-  assert(line.empty());
-  getline(in, line);
-  assert(line == "\\data\\");
-  getline(in, line);
-  size_t pos = line.find("ngram 1=");
-  assert(pos == 0);
-  assert(line.size() > 8);
-  const size_t num_unigrams = atoi(&line[8]);
-  getline(in, line);
-  assert(line.empty());
-  getline(in, line);
-  assert(line == "\\1-grams:");
-  for (size_t i = 0; i < num_unigrams; ++i) {
-    getline(in, line);
-    assert(line.size() > 0);
-    pos = line.find('\t');
-    assert(pos > 0);
-    assert(pos + 1 < line.size());
-    size_t cur = pos + 1;
-    vector<WordID> w;
-    while (cur < line.size()) {
-      const size_t len = UTF8Len(line[cur]);
-      w.push_back(TD::Convert(line.substr(cur, len)));
-      cur += len;
-    }
-    line[pos] = 0;
-    float p = atof(&line[0]);
-    probs_[w].logeq(p * log(10.0));
-  }
-}
-
diff --git a/gi/pf/unigrams.h b/gi/pf/unigrams.h
deleted file mode 100644
index 1660d1ed..00000000
--- a/gi/pf/unigrams.h
+++ /dev/null
@@ -1,69 +0,0 @@
-#ifndef _UNIGRAMS_H_
-#define _UNIGRAMS_H_
-
-#include <vector>
-#include <string>
-#include <tr1/unordered_map>
-#include <boost/functional.hpp>
-
-#include "wordid.h"
-#include "prob.h"
-#include "tdict.h"
-
-struct UnigramModel {
-  explicit UnigramModel(const std::string& fname, unsigned vocab_size) :
-      use_uniform_(fname.size() == 0),
-      uniform_(1.0 / vocab_size),
-      probs_() {
-    if (fname.size() > 0) {
-      probs_.resize(TD::NumWords() + 1);
-      LoadUnigrams(fname);
-    }
-  }
-
-  const prob_t& operator()(const WordID& w) const {
-    assert(w);
-    if (use_uniform_) return uniform_;
-    return probs_[w];
-  }
-
- private:
-  void LoadUnigrams(const std::string& fname);
-
-  const bool use_uniform_;
-  const prob_t uniform_;
-  std::vector<prob_t> probs_;
-};
-
-
-// reads an ARPA unigram file and converts words like 'cat' into a string 'c a t'
-struct UnigramWordModel {
-  explicit UnigramWordModel(const std::string& fname) :
-      use_uniform_(false),
-      uniform_(1.0),
-      probs_() {
-    LoadUnigrams(fname);
-  }
-
-  explicit UnigramWordModel(const unsigned vocab_size) :
-      use_uniform_(true),
-      uniform_(1.0 / vocab_size),
-      probs_() {}
-
-  const prob_t& operator()(const std::vector<WordID>& s) const {
-    if (use_uniform_) return uniform_;
-    const VectorProbHash::const_iterator it = probs_.find(s);
-    assert(it != probs_.end());
-    return it->second;
-  }
-
- private:
-  void LoadUnigrams(const std::string& fname);
-
-  const bool use_uniform_;
-  const prob_t uniform_;
-  typedef std::tr1::unordered_map<std::vector<WordID>, prob_t, boost::hash<std::vector<WordID> > > VectorProbHash;
-  VectorProbHash probs_;
-};
-
-#endif
diff --git a/gi/pipeline/OLD.clsp.config b/gi/pipeline/OLD.clsp.config
deleted file mode 100644
index cd0f9d65..00000000
--- a/gi/pipeline/OLD.clsp.config
+++ /dev/null
@@ -1,9 +0,0 @@
-# THIS FILE GIVES THE LOCATIONS OF THE CORPORA USED
-# name path aligned-corpus LM xfeats.grammar dev dev-refs test1 testt-eval.sh ...
-btec /export/ws10smt/data/btec/ split.zh-en.al lm/en.3gram.lm.gz xgrammar/grammar.gz devtest/devset1_2.zh devtest/devset1_2.lc.en* devtest/devset3.zh eval-devset3.sh
-fbis /export/ws10smt/data/chinese-english.fbis corpus.zh-en.al
-zhen /export/ws10smt/data/chinese-english corpus.zh-en.al
-aren /export/ws10smt/data/arabic-english corpus.ar-en.al
-uren /export/ws10smt/data/urdu-english corpus.ur-en.al
-nlfr /export/ws10smt/data/dutch-french corpus.nl-fr.al
-
diff --git a/gi/pipeline/OLD.evaluation-pipeline.pl b/gi/pipeline/OLD.evaluation-pipeline.pl
deleted file mode 100755
index 49c303eb..00000000
--- a/gi/pipeline/OLD.evaluation-pipeline.pl
+++ /dev/null
@@ -1,277 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-use Getopt::Long;
-use Cwd;
-my $CWD = getcwd;
-
-my $SCRIPT_DIR; BEGIN { use Cwd qw/ abs_path /; use File::Basename; $SCRIPT_DIR = dirname(abs_path($0)); push @INC, $SCRIPT_DIR; }
-
-my @DEFAULT_FEATS = qw(
-  LogRuleCount SingletonRule LexE2F LexF2E WordPenalty
-  LogFCount LanguageModel Glue GlueTop PassThrough SingletonF
-);
-
-my %init_weights = qw(
-  LogRuleCount 0.2
-  LexE2F -0.3
-  LexF2E -0.3
-  LogFCount 0.1
-  WordPenalty -1.5
-  LanguageModel 1.2
-  Glue -1.0
-  GlueTop 0.00001
-  PassThrough -10.0
-  SingletonRule -0.1
-  X_EGivenF -0.3
-  X_FGivenE -0.3
-  X_LogECount -1
-  X_LogFCount -0.1
-  X_LogRuleCount 0.3
-  X_SingletonE -0.1
-  X_SingletonF -0.1
-  X_SingletonRule -0.5
-);
-
-my $CDEC = "$SCRIPT_DIR/../../decoder/cdec";
-my $PARALLELIZE = "$SCRIPT_DIR/../../vest/parallelize.pl";
-my $EXTOOLS = "$SCRIPT_DIR/../../extools";
-die "Can't find extools: $EXTOOLS" unless -e $EXTOOLS && -d $EXTOOLS;
-my $VEST = "$SCRIPT_DIR/../../vest";
-die "Can't find vest: $VEST" unless -e $VEST && -d $VEST;
-my $DISTVEST = "$VEST/dist-vest.pl";
-my $FILTSCORE = "$EXTOOLS/filter_score_grammar";
-my $ADDXFEATS = "$SCRIPT_DIR/scripts/xfeats.pl";
-assert_exec($CDEC, $PARALLELIZE, $FILTSCORE, $DISTVEST, $ADDXFEATS);
-
-my $config = "$SCRIPT_DIR/OLD.clsp.config";
-print STDERR "CORPORA CONFIGURATION: $config\n";
-open CONF, "<$config" or die "Can't read $config: $!";
-my %paths;
-my %corpora;
-my %lms;
-my %devs;
-my %devrefs;
-my %tests;
-my %testevals;
-my %xgrammars;
-print STDERR "       LANGUAGE PAIRS:";
-while(<CONF>) {
-  chomp;
-  next if /^#/;
-  next if /^\s*$/;
-  s/^\s+//;
-  s/\s+$//;
-  my ($name, $path, $corpus, $lm, $xgrammar, $dev, $devref, @xtests) = split /\s+/;
-  $paths{$name} = $path;
-  $corpora{$name} = $corpus;
-  $lms{$name} = $lm;
-  $xgrammars{$name} = $xgrammar;
-  $devs{$name} = $dev;
-  $devrefs{$name} = $devref;
-  $tests{$name} = $xtests[0];
-  $testevals{$name} = $xtests[1];
-  print STDERR " $name";
-}
-print STDERR "\n";
-
-my %langpairs = map { $_ => 1 } qw( btec zhen fbis aren uren nlfr );
-
-my $outdir = "$CWD/exp";
-my $help;
-my $XFEATS;
-my $EXTRA_FILTER = '';
-my $dataDir = '/export/ws10smt/data';
-if (GetOptions(
-        "data=s" => \$dataDir,
-        "xfeats" => \$XFEATS,
-) == 0 || @ARGV!=2 || $help) {
-        print_help();
-        exit;
-}
-my $lp = $ARGV[0];
-my $grammar = $ARGV[1];
-print STDERR "   CORPUS REPO: $dataDir\n";
-print STDERR " LANGUAGE PAIR: $lp\n";
-die "I don't know about that language pair\n" unless $paths{$lp};
-my $corpdir = "$dataDir";
-if ($paths{$lp} =~ /^\//) { $corpdir = $paths{$lp}; } else { $corpdir .= '/' . $paths{$lp}; }
-die "I can't find the corpora directory: $corpdir" unless -d $corpdir;
-print STDERR "       GRAMMAR: $grammar\n";
-my $LANG_MODEL = mydircat($corpdir, $lms{$lp});
-print STDERR "            LM: $LANG_MODEL\n";
-my $CORPUS = mydircat($corpdir, $corpora{$lp});
-die "Can't find corpus: $CORPUS" unless -f $CORPUS;
-
-my $dev = mydircat($corpdir, $devs{$lp});
-my $drefs = $devrefs{$lp};
-die "Can't find dev: $dev\n" unless -f $dev;
-die "Dev refs not set" unless $drefs;
-$drefs = mydircat($corpdir, $drefs);
-
-my $test = mydircat($corpdir, $tests{$lp});
-my $teval = mydircat($corpdir, $testevals{$lp});
-die "Can't find test: $test\n" unless -f $test;
-assert_exec($teval);
-
-if ($XFEATS) {
-  my $xgram = mydircat($corpdir, $xgrammars{$lp});
-  die "Can't find x-grammar: $xgram" unless -f $xgram;
-  $EXTRA_FILTER = "$ADDXFEATS $xgram |";
-  print STDERR "ADDING X-FEATS FROM $xgram\n";
-}
-
-# MAKE DEV
-print STDERR "\nFILTERING FOR dev...\n";
-print STDERR "DEV: $dev (REFS=$drefs)\n";
-`mkdir -p $outdir`;
-my $devgrammar = filter($grammar, $dev, 'dev', $outdir);
-my $devini = mydircat($outdir, "cdec-dev.ini");
-write_cdec_ini($devini, $devgrammar);
-
-
-# MAKE TEST
-print STDERR "\nFILTERING FOR test...\n";
-print STDERR "TEST: $test (EVAL=$teval)\n";
-`mkdir -p $outdir`;
-my $testgrammar = filter($grammar, $test, 'test', $outdir);
-my $testini = mydircat($outdir, "cdec-test.ini");
-write_cdec_ini($testini, $testgrammar);
-
-
-# CREATE INIT WEIGHTS
-print STDERR "\nCREATING INITIAL WEIGHTS FILE: weights.init\n";
-my $weights = mydircat($outdir, "weights.init");
-write_random_weights_file($weights);
-
-
-# VEST
-print STDERR "\nMINIMUM ERROR TRAINING\n";
-my $tuned_weights = mydircat($outdir, 'weights.tuned');
-if (-f $tuned_weights) {
-  print STDERR "TUNED WEIGHTS $tuned_weights EXISTS: REUSING\n";
-} else {
-  my $cmd = "$DISTVEST --ref-files=$drefs --source-file=$dev --weights $weights $devini";
-  print STDERR "MERT COMMAND: $cmd\n";
-  `rm -rf $outdir/vest 2> /dev/null`;
-  chdir $outdir or die "Can't chdir to $outdir: $!";
-  $weights = `$cmd`;
-  die "MERT reported non-zero exit code" unless $? == 0;
-  chomp $weights;
-  safesystem($tuned_weights, "cp $weights $tuned_weights");
-  print STDERR "TUNED WEIGHTS: $tuned_weights\n";
-  die "$tuned_weights is missing!" unless -f $tuned_weights;
-}
-
-# DECODE
-print STDERR "\nDECODE TEST SET\n";
-my $decolog = mydircat($outdir, "test-decode.log");
-my $testtrans = mydircat($outdir, "test.trans");
-my $cmd = "cat $test | $PARALLELIZE -j 20 -e $decolog -- $CDEC -c $testini -w $tuned_weights > $testtrans";
-safesystem($testtrans, $cmd) or die "Failed to decode test set!";
-
-
-# EVALUATE
-print STDERR "\nEVALUATE TEST SET\n";
-print STDERR "TEST: $testtrans\n";
-$cmd = "$teval $testtrans";
-safesystem(undef, $cmd) or die "Failed to evaluate!";
-exit 0;
-
-
-sub write_random_weights_file {
-  my ($file, @extras) = @_;
-  open F, ">$file" or die "Can't write $file: $!";
-  my @feats = (@DEFAULT_FEATS, @extras);
-  if ($XFEATS) {
-    my @xfeats = qw(
-      X_LogRuleCount X_LogECount X_LogFCount X_EGivenF X_FGivenE X_SingletonRule X_SingletonE X_SingletonF
-    );
-    @feats = (@feats, @xfeats);
-  }
-  for my $feat (@feats) {
-    my $r = rand(1.6);
-    my $w = $init_weights{$feat} * $r;
-    if ($w == 0) { $w = 0.0001; print STDERR "WARNING: $feat had no initial weight!\n"; }
-    print F "$feat $w\n";
-  }
-  close F;
-}
-
-sub filter {
-  my ($grammar, $set, $name, $outdir) = @_;
-  my $outgrammar = mydircat($outdir, "$name.scfg.gz");
-  if (-f $outgrammar) { print STDERR "$outgrammar exists - REUSING!\n"; } else {
-    my $cmd = "gunzip -c $grammar | $FILTSCORE -c $CORPUS -t $set | $EXTRA_FILTER gzip > $outgrammar";
-    safesystem($outgrammar, $cmd) or die "Can't filter and score grammar!";
-  }
-  return $outgrammar;
-}
-
-sub mydircat {
- my ($base, $suffix) = @_;
- if ($suffix =~ /^\//) { return $suffix; }
- my $res = $base . '/' . $suffix;
- $res =~ s/\/\//\//g;
- return $res;
-}
-
-sub write_cdec_ini {
-  my ($filename, $grammar_path) = (@_);
-  open CDECINI, ">$filename" or die "Can't write $filename: $!";
-  print CDECINI <<EOT;
-formalism=scfg
-cubepruning_pop_limit=100
-add_pass_through_rules=true
-scfg_extra_glue_grammar=/export/ws10smt/data/glue/glue.scfg.gz
-grammar=$grammar_path
-feature_function=WordPenalty
-feature_function=LanguageModel -o 3 $LANG_MODEL
-EOT
-  close CDECINI;
-};
-
-sub print_help {
-  print STDERR<<EOT;
-
-Usage: $0 [OPTIONS] language-pair unfiltered-grammar.gz
-
-Given an induced grammar for an entire corpus (i.e., generated by
-local-gi-pipeline.pl), filter and featurize it for a dev and test set,
-run MERT, report scores.
-
-EOT
-}
-
-sub safesystem {
-  my $output = shift @_;
-  print STDERR "Executing: @_\n";
-  system(@_);
-  if ($? == -1) {
-      print STDERR "ERROR: Failed to execute: @_\n  $!\n";
-      if (defined $output && -e $output) { printf STDERR "Removing $output\n"; `rm -rf $output`; }
-      exit(1);
-  }
-  elsif ($? & 127) {
-      printf STDERR "ERROR: Execution of: @_\n  died with signal %d, %s coredump\n",
-          ($? & 127),  ($? & 128) ? 'with' : 'without';
-      if (defined $output && -e $output) { printf STDERR "Removing $output\n"; `rm -rf $output`; }
-      exit(1);
-  }
-  else {
-    my $exitcode = $? >> 8;
-    if ($exitcode) {
-      print STDERR "Exit code: $exitcode\n";
-      if (defined $output && -e $output) { printf STDERR "Removing $output\n"; `rm -rf $output`; }
-    }
-    return ! $exitcode;
-  }
-}
-
-sub assert_exec {
-  my @files = @_;
-  for my $file (@files) {
-    die "Can't find $file - did you run make?\n" unless -e $file;
-    die "Can't execute $file" unless -e $file;
-  }
-};
-
diff --git a/gi/pipeline/backoff-pipe.pl b/gi/pipeline/backoff-pipe.pl
deleted file mode 100644
index ac103c8b..00000000
--- a/gi/pipeline/backoff-pipe.pl
+++ /dev/null
@@ -1,215 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-
-use Getopt::Long "GetOptions";
-
-my @grammars;
-my $OUTPUTPREFIX = './giwork/bo.hier.grammar';
-safemkdir($OUTPUTPREFIX);
-my $backoff_levels = 1;
-my $glue_levels = 1;
-
-usage() unless &GetOptions('grmr=s@' => \ @grammars,
-                           'outprefix=s' => \ $OUTPUTPREFIX,
-                           'bo-lvls=i' => \ $backoff_levels,
-                           'glue-lvls=i' => \ $glue_levels,
-);
-                           
-my $OUTDIR = $OUTPUTPREFIX . '/hier';
-print STDERR "@grammars\n";
-
-
-my %grmr = ();
-foreach my $grammar (@grammars) {
-    $grammar =~ m/\/[^\/]*\.t(\d+)\.[^\/]*/;
-    $grmr{$1} = $grammar;
-}
-
-my @index = sort keys %grmr;
-$OUTDIR = $OUTDIR . join('-',@index);
-safemkdir($OUTDIR);
-my $BACKOFF_GRMR = $OUTDIR . '/backoff.hier.gz';
-safesystem("echo \"\" | gzip > $BACKOFF_GRMR");
-my $GLUE_GRMR = $OUTDIR . '/glue.hier.gz';
-safesystem("echo \"\" | gzip > $GLUE_GRMR");
-my $joinedgrammars = $OUTDIR . '/grammar.hier.gz';
-
-join_grammars();
-
-for my $i (0..(scalar @index)-2) {
-    my $freqs = extract_freqs($index[$i], $index[$i+1]);
-    if ($i < $backoff_levels) {
-        create_backoff_rules($index[$i],$index[$i+1],$freqs);
-    }
-    if ($i < $glue_levels) {
-        add_glue_rules($index[$i]);
-    }
-}
-
-output_grammar_info();
-
-
-sub usage {
-  print <<EOT;
-
-Usage: $0 [OPTIONS] corpus.fr-en-al
-
-Induces a grammar using Pitman-Yor topic modeling or Posterior Regularisation.
-
-EOT
-  exit 1;
-};
-
-sub safemkdir {
-  my $dir = shift;
-  if (-d $dir) { return 1; }
-  return mkdir($dir);
-}
-
-
-sub safesystem {
-  print STDERR "Executing: @_\n";
-  system(@_);
-  if ($? == -1) {
-      print STDERR "ERROR: Failed to execute: @_\n  $!\n";
-      exit(1);
-  }
-  elsif ($? & 127) {
-      printf STDERR "ERROR: Execution of: @_\n  died with signal %d, %s coredump\n",
-          ($? & 127),  ($? & 128) ? 'with' : 'without';
-      exit(1);
-  }
-  else {
-    my $exitcode = $? >> 8;
-    print STDERR "Exit code: $exitcode\n" if $exitcode;
-    return ! $exitcode;
-  }
-}
-
-
-sub join_grammars {
-    print STDERR "\n!!! JOINING GRAMMARS\n";
-    if(-e $joinedgrammars) {
-        print STDERR "$joinedgrammars exists, reusing...\n";
-        return;
-    }
-    safesystem("echo \"\" | gzip > $joinedgrammars");
-    foreach my $i (@index) {
-        my $g = $grmr{$i};
-        safesystem("zcat $g | sed -r -e 's/X([0-9]+)/X$i\\1/g' - | gzip > $g.2.gz");
-        safesystem("zcat $joinedgrammars $g.2.gz | gzip > $joinedgrammars.2.gz");
-        safesystem("mv $joinedgrammars.2.gz $joinedgrammars");
-    }
-}
-
-
-sub extract_freqs {
-    my($grmr1,$grmr2) = @_;
-    print STDERR "\n!!!EXTRACTING FREQUENCIES: $grmr1->$grmr2\n";
-    my $IN_COARSE = substr($grmr{$grmr1},0,index($grmr{$grmr1},".grammar/")) . "/labeled_spans.txt";
-    my $IN_FINE = substr($grmr{$grmr2},0,index($grmr{$grmr2},".grammar/")) . "/labeled_spans.txt";
-    my $OUT_SPANS = "$OUTDIR/labeled_spans.hier$grmr1-$grmr2.txt";
-    my $FREQS = "$OUTDIR/label_freq.hier$grmr1-$grmr2.txt";
-    if(-e $OUT_SPANS && -e $FREQS) {
-        print STDERR "$OUT_SPANS exists, reusing...\n";
-        print STDERR "$FREQS exists, reusing...\n";
-        return $FREQS;
-    }
-    
-    safesystem("paste -d ' ' $IN_COARSE $IN_FINE > $OUT_SPANS");
-    
-    my %FREQ_HIER = ();
-    my %finehier = ();
-    
-    open SPANS, $OUT_SPANS or die $!;
-    while (<SPANS>) {
-        my ($tmp, $coarse, $fine) = split /\|\|\|/;
-        my @coarse_spans = $coarse =~ /\d+-\d+:X(\d+)/g;
-        my @fine_spans = $fine =~ /\d+-\d+:X(\d+)/g;
-        
-        foreach my $i (0..(scalar @coarse_spans)-1) {
-            my $coarse_cat = $coarse_spans[$i];
-            my $fine_cat = $fine_spans[$i];
-            
-            $FREQ_HIER{$coarse_cat}{$fine_cat}++;
-        }
-    }
-    close SPANS;
-    foreach (values %FREQ_HIER) {
-        my $coarse_freq = $_;
-        my $total = 0;
-        $total+=$_ for (values %{ $coarse_freq });
-        $coarse_freq->{$_}=log($coarse_freq->{$_}/$total) for (keys %{ $coarse_freq });
-    }
-    open FREQS, ">", $FREQS or die $!;
-    foreach my $coarse_cat (keys %FREQ_HIER) {
-        print FREQS "$coarse_cat |||";
-        foreach my $fine_cat (keys %{$FREQ_HIER{$coarse_cat}}) {
-            my $freq = $FREQ_HIER{$coarse_cat}{$fine_cat};
-            print FREQS " $fine_cat:$freq";
-            if(! exists $finehier{$fine_cat} || $finehier{$fine_cat} < $freq) {
-               $finehier{$fine_cat} = $coarse_cat;
-            }  
-        }
-        print FREQS "\n";
-    }
-#    foreach my $fine_cat (keys %finehier) {
-#        print FREQS "$fine_cat -> $finehier{$fine_cat}\n";
-#    }
-    close FREQS;
-    return $FREQS;
-}
-
-
-sub create_backoff_rules {
-    print STDERR "\n!!! CREATING BACKOFF RULES\n";
-    my ($grmr1, $grmr2, $freq) = @_;
-    my $OUTFILE = "$OUTDIR/backoff.hier$grmr1-$grmr2.txt";
-    if(-e $OUTFILE) {
-        print STDERR "$OUTFILE exists, reusing...\n";
-        return;
-    }
-    open FREQS, $freq or die $!;
-    open TMP, ">", $OUTFILE or die $!;
-    while (<FREQS>) {
-        my $line = $_;
-        $line = m/^(\d+) \|\|\| (.+)$/;
-        my $coarse = $1;
-        $line = $2;
-        my @finefreq = $line =~ m/(\d+):(\S+)/g;
-        for(my $i = 0; $i < scalar @finefreq; $i+=2) {
-            my $finecat = $finefreq[$i];
-            my $finefreq = $finefreq[$i+1];
-            print TMP "[X$grmr1$coarse] ||| [X$grmr2$finecat,1]\t[1] ||| BackoffRule=$finefreq A=0-0\n";
-        }
-    }
-    close TMP;
-    close FREQS;
-    safesystem("zcat $BACKOFF_GRMR | cat - $OUTFILE | gzip > $BACKOFF_GRMR.2.gz");
-    safesystem("mv $BACKOFF_GRMR.2.gz $BACKOFF_GRMR");
-}
-
-sub add_glue_rules {
-    print STDERR "\n!!! CREATING GLUE RULES\n";
-    my ($grmr) = @_;
-    my $OUTFILE = "$OUTDIR/glue.$grmr.gz";
-    if (-e $OUTFILE) {
-        print STDERR "$OUTFILE exists, reusing...\n";
-        return;
-    }
-    open TMP, ">", $OUTFILE or die $!;
-    for my $i (0..($grmr-1)) {
-        print TMP "[S] ||| [S,1] [X$grmr$i,2] ||| [1] [2] ||| Glue=1\n";
-        print TMP "[S] ||| [X$grmr$i,1] ||| [1] ||| GlueTop=1\n";
-    }
-    close TMP;
-    safesystem("zcat $GLUE_GRMR | cat - $OUTFILE | gzip > $GLUE_GRMR.2.gz");
-    safesystem("mv $GLUE_GRMR.2.gz $GLUE_GRMR");
-}
-
-sub output_grammar_info {
-    print STDERR "\n!!! GRAMMAR INFORMATION\n";
-    print STDOUT "GRAMMAR: \t$joinedgrammars\n";
-    print STDOUT "GLUE: \t$GLUE_GRMR\n";
-    print STDOUT "BACKOFF: \t$BACKOFF_GRMR\n";
-}
diff --git a/gi/pipeline/blacklight.config b/gi/pipeline/blacklight.config
deleted file mode 100644
index fc59a604..00000000
--- a/gi/pipeline/blacklight.config
+++ /dev/null
@@ -1,9 +0,0 @@
-# THIS FILE GIVES THE LOCATIONS OF THE CORPORA USED
-# name path aligned-corpus LM dev dev-refs test1 testt-eval.sh ...
-/usr/users/0/cdyer/ws10smt/data
-btec /home/cdyer/ws10smt-data/btec/ split.zh-en.al lm/en.3gram.lm.gz devtest/devset1_2.zh devtest/devset1_2.lc.en* devtest/devset3.zh eval-devset3.sh
-zhen /home/cdyer/ws10smt-data/chinese-english corpus.zh-en.al lm/c2e.3gram.lm.gz dev_and_test/mt02.src.txt dev_and_test/mt02.ref.* dev_and_test/mt03.src.txt eval-mt03.sh
-aren /home/cdyer/ws10smt-data/arabic-english corpus.ar-en-al lm/a2e.3gram.lm.gz dev_and_test/dev.src.txt dev_and_test/dev.ref.txt.* dev_and_test/mt05.src.txt eval-mt05.sh
-uren /usr/users/0/cdyer/ws10smt/data/urdu-english corpus.ur-en.al lm/u2e.en.lm.gz dev/dev.ur dev/dev.en* devtest/devtest.ur eval-devtest.sh
-nlfr /home/cdyer/ws10smt-data/dutch-french corpus.nl-fr.al
-
diff --git a/gi/pipeline/clsp.config b/gi/pipeline/clsp.config
deleted file mode 100644
index c23d409f..00000000
--- a/gi/pipeline/clsp.config
+++ /dev/null
@@ -1,10 +0,0 @@
-# THIS FILE GIVES THE LOCATIONS OF THE CORPORA USED
-# name path aligned-corpus LM dev dev-refs test1 testt-eval.sh ...
-/export/ws10smt/data
-btec /export/ws10smt/data/btec/ split.zh-en.al lm/en.3gram.lm.gz devtest/devset1_2.zh devtest/devset1_2.lc.en* devtest/devset3.zh eval-devset3.sh
-fbis /export/ws10smt/data/chinese-english.fbis corpus.zh-en.al
-zhen /export/ws10smt/data/chinese-english corpus.zh-en.al lm/c2e.3gram.lm.gz dev_and_test/mt02.src.txt dev_and_test/mt02.ref.* dev_and_test/mt03.src.txt eval-mt03.sh
-aren /export/ws10smt/data/arabic-english corpus.ar-en-al lm/a2e.3gram.lm.gz dev_and_test/dev.src.txt dev_and_test/dev.ref.txt.* dev_and_test/mt05.src.txt eval-mt05.sh
-uren /export/ws10smt/data/urdu-english corpus.ur-en.al lm/u2e.en.lm.gz dev/dev.ur dev/dev.en* devtest/devtest.ur eval-devtest.sh
-nlfr /export/ws10smt/data/dutch-french corpus.nl-fr.al
-
diff --git a/gi/pipeline/evaluation-pipeline.pl b/gi/pipeline/evaluation-pipeline.pl
deleted file mode 100755
index 4b4529d9..00000000
--- a/gi/pipeline/evaluation-pipeline.pl
+++ /dev/null
@@ -1,364 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-use Getopt::Long;
-use Cwd;
-my $CWD = getcwd;
-
-my $SCRIPT_DIR; BEGIN { use Cwd qw/ abs_path /; use File::Basename; $SCRIPT_DIR = dirname(abs_path($0)); push @INC, $SCRIPT_DIR, "$SCRIPT_DIR/../../environment"; }
-use LocalConfig;
-
-my $JOBS = 15;
-my $PMEM = "9G";
-my $NUM_TRANSLATIONS = 50;
-my $GOAL = "S";
-
-# featurize_grammar may add multiple features from a single feature extractor
-# the key in this map is the extractor name, the value is a list of the extracted features
-my $feat_map = {
-  "LogRuleCount" => [ "LogRuleCount", "SingletonRule" ] ,
-#  "XFeatures" => [ "XFE","XEF" ] ,
-  "XFeatures" => [ "XFE","XEF","LabelledEF","LabelledFE"], # ,"XE_Singleton","XF_Singleton"] ,
-  "LabelledRuleConditionals" => [ "LabelledFE","LabelledEF" ] ,
-  "LexProb" => [ "LexE2F", "LexF2E" ] ,
-  "BackoffRule" => [ "BackoffRule" ] ,
-  "RulePenalty" => [ "RulePenalty" ] ,
-  "LHSProb" => [ "LHSProb" ] ,
-  "LabellingShape" => [ "LabellingShape" ] ,
-  "GenerativeProb" => [ "GenerativeProb" ] ,
-};
-
-my %init_weights = qw(
-  EGivenF -0.735245
-  FGivenE -0.219391
-  Glue -0.306709
-  GlueTop 0.0473331
-  LanguageModel 2.40403
-  LexE2F -0.266989
-  LexF2E -0.550373
-  LogECount -0.129853
-  LogFCount -0.194037
-  LogRuleCount 0.256706
-  BackoffRule 0.5
-  XFE -0.256706
-  XEF -0.256706
-  XF_Singleton -0.05
-  XE_Singleton -0.8
-  LabelledFE -0.256706
-  LabelledEF -0.256706
-  PassThrough -0.9304905
-  SingletonE -3.04161
-  SingletonF 0.0714027
-  SingletonRule -0.889377
-  WordPenalty -1.99495
-  RulePenalty -0.1
-  LabellingShape -0.1
-  LHSProb -0.1
-  GenerativeProb -0.1
-);
-
-
-# these features are included by default
-my @DEFAULT_FEATS = qw( PassThrough Glue GlueTop LanguageModel WordPenalty );
-
-
-
-my $FILTERBYF = "$SCRIPT_DIR/scripts/filter-by-f.pl";
-my $CDEC = "$SCRIPT_DIR/../../decoder/cdec";
-my $PARALLELIZE = "$SCRIPT_DIR/../../vest/parallelize.pl";
-my $EXTOOLS = "$SCRIPT_DIR/../../extools";
-die "Can't find extools: $EXTOOLS" unless -e $EXTOOLS && -d $EXTOOLS;
-my $VEST = "$SCRIPT_DIR/../../vest";
-die "Can't find vest: $VEST" unless -e $VEST && -d $VEST;
-my $DISTVEST = "$VEST/dist-vest.pl";
-my $FILTER = "$EXTOOLS/filter_grammar";
-my $FEATURIZE = "$EXTOOLS/featurize_grammar";
-assert_exec($CDEC, $PARALLELIZE, $FILTER, $FEATURIZE, $DISTVEST, $FILTERBYF);
-
-my $numtopics = 25;
-
-my $config = "$SCRIPT_DIR/" . (lc environment_name()) . '.config';
-print STDERR "CORPORA CONFIGURATION: $config\n";
-open CONF, "<$config" or die "Can't read $config: $!";
-my %paths;
-my %corpora;
-my %lms;
-my %devs;
-my %devrefs;
-my %tests;
-my %testevals;
-my $datadir;
-print STDERR "       LANGUAGE PAIRS:";
-while(<CONF>) {
-  chomp;
-  next if /^#/;
-  next if /^\s*$/;
-  s/^\s+//;
-  s/\s+$//;
-  if (! defined $datadir) { $datadir = $_; next; }
-  my ($name, $path, $corpus, $lm, $dev, $devref, @xtests) = split /\s+/;
-  $paths{$name} = $path;
-  $corpora{$name} = $corpus;
-  $lms{$name} = $lm;
-  $devs{$name} = $dev;
-  $devrefs{$name} = $devref;
-  $tests{$name} = $xtests[0];
-  $testevals{$name} = $xtests[1];
-  print STDERR " $name";
-}
-print STDERR "\n";
-
-my %langpairs = map { $_ => 1 } qw( btec zhen fbis aren uren nlfr );
-
-my $outdir = "$CWD/exp";
-my $help;
-my $FEATURIZER_OPTS = '';
-my $dataDir = '/export/ws10smt/data';
-my @features;
-my $bkoffgram;
-my $gluegram;
-my $oovgram;
-my $usefork;
-my $lmorder = 3;
-my $density;
-if (GetOptions(
-        "backoff-grammar=s" => \$bkoffgram,
-        "density-prune=f" => \$density,
-        "glue-grammar=s" => \$gluegram,
-        "oov-grammar=s" => \$oovgram,
-        "data=s" => \$dataDir,
-        "pmem=s" => \$PMEM,
-        "n=i" => \$NUM_TRANSLATIONS,
-        "features=s@" => \@features,
-        "use-fork" => \$usefork,
-        "jobs=i" => \$JOBS,
-        "out-dir=s" => \$outdir,
-        "lmorder=i" => \$lmorder,
-        "goal=s" => \$GOAL,
-) == 0 || @ARGV!=2 || $help) {
-        print_help();
-        exit;
-}
-my $DENSITY_PRUNE = '';
-if ($density) {
-  $DENSITY_PRUNE = "--density-prune $density";
-}
-if ($usefork) { $usefork="--use-fork"; } else { $usefork = ''; }
-my @fkeys = keys %$feat_map;
-die "You must specify one or more features with -f. Known features: @fkeys\n" unless scalar @features > 0;
-my @xfeats;
-for my $feat (@features) {
-  my $rs = $feat_map->{$feat};
-  if (!defined $rs) { die "DON'T KNOW ABOUT FEATURE $feat\n"; }
-  my @xfs = @$rs;
-  @xfeats = (@xfeats, @xfs);
-  $FEATURIZER_OPTS .= " -f $feat" unless $feat eq "BackoffRule";
-}
-print STDERR "X-FEATS: @xfeats\n";
-
-my $lp = $ARGV[0];
-my $grammar = $ARGV[1];
-print STDERR "   CORPUS REPO: $dataDir\n";
-print STDERR " LANGUAGE PAIR: $lp\n";
-die "I don't know about that language pair\n" unless $paths{$lp};
-my $corpdir = "$dataDir";
-if ($paths{$lp} =~ /^\//) { $corpdir = $paths{$lp}; } else { $corpdir .= '/' . $paths{$lp}; }
-die "I can't find the corpora directory: $corpdir" unless -d $corpdir;
-print STDERR "       GRAMMAR: $grammar\n";
-my $LANG_MODEL = mydircat($corpdir, $lms{$lp});
-print STDERR "            LM: $LANG_MODEL\n";
-my $CORPUS = mydircat($corpdir, $corpora{$lp});
-die "Can't find corpus: $CORPUS" unless -f $CORPUS;
-
-my $dev = mydircat($corpdir, $devs{$lp});
-my $drefs = $devrefs{$lp};
-die "Can't find dev: $dev\n" unless -f $dev;
-die "Dev refs not set" unless $drefs;
-$drefs = mydircat($corpdir, $drefs);
-
-my $test = mydircat($corpdir, $tests{$lp});
-my $teval = mydircat($corpdir, $testevals{$lp});
-#die "Can't find test: $test\n" unless -f $test;
-#assert_exec($teval);
-
-`mkdir -p $outdir`;
-
-# CREATE INIT WEIGHTS
-print STDERR "\nCREATING INITIAL WEIGHTS FILE: weights.init\n";
-my $weights = mydircat($outdir, "weights.init");
-write_random_weights_file($weights, @xfeats);
-
-my $bkoff_grmr;
-my $glue_grmr;
-if($bkoffgram) {
-    print STDERR "Placing backoff grammar…\n";
-    $bkoff_grmr = mydircat($outdir, "backoff.scfg.gz");
-    print STDERR "cp $bkoffgram $bkoff_grmr\n";
-    safesystem(undef,"cp $bkoffgram $bkoff_grmr");
-}
-if($gluegram) {
-    print STDERR "Placing glue grammar…\n";
-    $glue_grmr = mydircat($outdir, "glue.bo.scfg.gz");
-    print STDERR "cp $gluegram $glue_grmr\n";
-    safesystem(undef,"cp $gluegram $glue_grmr");
-}
-
-# MAKE DEV
-print STDERR "\nFILTERING FOR dev...\n";
-print STDERR "DEV: $dev (REFS=$drefs)\n";
-my $devgrammar = filter($grammar, $dev, 'dev', $outdir);
-my $devini = mydircat($outdir, "cdec-dev.ini");
-write_cdec_ini($devini, $devgrammar);
-
-
-# MAKE TEST
-print STDERR "\nFILTERING FOR test...\n";
-print STDERR "TEST: $test (EVAL=$teval)\n";
-`mkdir -p $outdir`;
-my $testgrammar = filter($grammar, $test, 'test', $outdir);
-my $testini = mydircat($outdir, "cdec-test.ini");
-write_cdec_ini($testini, $testgrammar);
-
-
-# VEST
-print STDERR "\nMINIMUM ERROR TRAINING\n";
-my $tuned_weights = mydircat($outdir, 'weights.tuned');
-if (-f $tuned_weights) {
-  print STDERR "TUNED WEIGHTS $tuned_weights EXISTS: REUSING\n";
-} else {
-  my $cmd = "$DISTVEST $usefork $DENSITY_PRUNE --decode-nodes $JOBS --pmem=$PMEM --ref-files=$drefs --source-file=$dev --weights $weights $devini";
-  print STDERR "MERT COMMAND: $cmd\n";
-  `rm -rf $outdir/vest 2> /dev/null`;
-  chdir $outdir or die "Can't chdir to $outdir: $!";
-  $weights = `$cmd`;
-  die "MERT reported non-zero exit code" unless $? == 0;
-  chomp $weights;
-  safesystem($tuned_weights, "cp $weights $tuned_weights");
-  print STDERR "TUNED WEIGHTS: $tuned_weights\n";
-  die "$tuned_weights is missing!" unless -f $tuned_weights;
-}
-
-# DECODE
-print STDERR "\nDECODE TEST SET\n";
-my $decolog = mydircat($outdir, "test-decode.log");
-my $testtrans = mydircat($outdir, "test.trans");
-my $cmd = "cat $test | $PARALLELIZE $usefork -j $JOBS -e $decolog -- $CDEC -c $testini -w $tuned_weights > $testtrans";
-safesystem($testtrans, $cmd) or die "Failed to decode test set!";
-
-
-# EVALUATE
-print STDERR "\nEVALUATE TEST SET\n";
-print STDERR "TEST: $testtrans\n";
-$cmd = "$teval $testtrans";
-safesystem(undef, $cmd) or die "Failed to evaluate!";
-exit 0;
-
-
-sub write_random_weights_file {
-  my ($file, @extras) = @_;
-  if (-f $file) {
-    print STDERR "$file exists - REUSING!\n";
-    return;
-  }
-  open F, ">$file" or die "Can't write $file: $!";
-  my @feats = (@DEFAULT_FEATS, @extras);
-  for my $feat (@feats) {
-    my $r = rand(0.4) + 0.8;
-    my $w = $init_weights{$feat} * $r;
-    if ($w == 0) { $w = 0.0001; print STDERR "WARNING: $feat had no initial weight!\n"; }
-    print F "$feat $w\n";
-  }
-  close F;
-}
-
-sub filter {
-  my ($grammar, $set, $name, $outdir) = @_;
-  my $out1 = mydircat($outdir, "$name.filt.gz");
-  my $out2 = mydircat($outdir, "$name.f_feat.gz");
-  my $outgrammar = mydircat($outdir, "$name.scfg.gz");
-  if (-f $outgrammar) { print STDERR "$outgrammar exists - REUSING!\n"; } else {
-    my $cmd = "gunzip -c $grammar | $FILTER -t $set | gzip > $out1";
-    safesystem($out1, $cmd) or die "Filtering failed.";
-    $cmd = "gunzip -c $out1 | $FEATURIZE $FEATURIZER_OPTS -g $out1 -c $CORPUS | gzip > $out2";
-    safesystem($out2, $cmd) or die "Featurizing failed";
-    $cmd = "$FILTERBYF $NUM_TRANSLATIONS $out2 $outgrammar";
-    safesystem($outgrammar, $cmd) or die "Secondary filtering failed";
-  }
-  return $outgrammar;
-}  
-
-sub mydircat {
- my ($base, $suffix) = @_;
- if ($suffix =~ /^\//) { return $suffix; }
- my $res = $base . '/' . $suffix;
- $res =~ s/\/\//\//g;
- return $res;
-}
-
-sub write_cdec_ini {
-  my ($filename, $grammar_path) = (@_);
-  open CDECINI, ">$filename" or die "Can't write $filename: $!";
-  my $glue = ($gluegram ? "$glue_grmr" : "$datadir/glue/glue.scfg.gz");
-  my $oov = ($oovgram ? "$oovgram" : "$datadir/oov.scfg.gz");
-  print CDECINI <<EOT;
-formalism=scfg
-cubepruning_pop_limit=100
-add_pass_through_rules=true
-scfg_extra_glue_grammar=$glue
-grammar=$oov
-grammar=$grammar_path
-scfg_default_nt=OOV
-scfg_no_hiero_glue_grammar=true
-feature_function=WordPenalty
-feature_function=LanguageModel -o $lmorder $LANG_MODEL
-goal=$GOAL
-EOT
-  print CDECINI "grammar=$bkoff_grmr\n" if $bkoffgram;
-  close CDECINI;
-};
-
-sub print_help {
-  print STDERR<<EOT;
-
-Usage: $0 [-c data-config-file] [-n N] language-pair grammar.bidir.gz [OPTIONS]
-
-Given an induced grammar for an entire corpus (i.e., generated by
-local-gi-pipeline.pl), filter and featurize it for a dev and test set,
-run MERT, report scores. Use -n to specify the number of translations
-to keep for a given source (30 is default).
-
-EOT
-}
-
-sub safesystem {
-  my $output = shift @_;
-  print STDERR "Executing: @_\n";
-  system(@_);
-  if ($? == -1) {
-      print STDERR "ERROR: Failed to execute: @_\n  $!\n";
-      if (defined $output && -e $output) { printf STDERR "Removing $output\n"; `rm -rf $output`; }
-      exit(1);
-  }
-  elsif ($? & 127) {
-      printf STDERR "ERROR: Execution of: @_\n  died with signal %d, %s coredump\n",
-          ($? & 127),  ($? & 128) ? 'with' : 'without';
-      if (defined $output && -e $output) { printf STDERR "Removing $output\n"; `rm -rf $output`; }
-      exit(1);
-  }
-  else {
-    my $exitcode = $? >> 8;
-    if ($exitcode) {
-      print STDERR "Exit code: $exitcode\n";
-      if (defined $output && -e $output) { printf STDERR "Removing $output\n"; `rm -rf $output`; }
-    }
-    return ! $exitcode;
-  }
-}
-
-sub assert_exec {
-  my @files = @_;
-  for my $file (@files) {
-    die "Can't find $file - did you run make?\n" unless -e $file;
-    die "Can't execute $file" unless -e $file;
-  }
-};
-
diff --git a/gi/pipeline/local-gi-pipeline.pl b/gi/pipeline/local-gi-pipeline.pl
deleted file mode 100755
index e31167a2..00000000
--- a/gi/pipeline/local-gi-pipeline.pl
+++ /dev/null
@@ -1,465 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-use File::Copy;
-
-my $SCRIPT_DIR; BEGIN { use Cwd qw/ abs_path cwd /; use File::Basename; $SCRIPT_DIR = dirname(abs_path($0)); push @INC, $SCRIPT_DIR; }
-
-use Getopt::Long "GetOptions";
-
-my $GZIP = 'gzip';
-my $ZCAT = 'gunzip -c';
-my $SED = 'sed -e';
-my $BASE_PHRASE_MAX_SIZE = 10;
-my $COMPLETE_CACHE = 1;
-my $ITEMS_IN_MEMORY = 10000000;  # cache size in extractors
-my $NUM_TOPICS = 50;
-my $NUM_TOPICS_COARSE;
-my $NUM_TOPICS_FINE = $NUM_TOPICS;
-my $NUM_SAMPLES = 1000;
-my $CONTEXT_SIZE = 1;
-my $BIDIR = 0;
-my $TOPICS_CONFIG = "pyp-topics.conf";
-my $LANGUAGE = "target";
-my $LABEL_THRESHOLD = "0";
-my $PRESERVE_PHRASES;
-
-my $MODEL = "pyp";
-my $NUM_ITERS = 100;
-my $PR_SCALE_P = 0;
-my $PR_SCALE_C = 0;
-my $PR_FLAGS = "";
-my $MORFMARK = "";
-
-my $EXTOOLS = "$SCRIPT_DIR/../../extools";
-die "Can't find extools: $EXTOOLS" unless -e $EXTOOLS && -d $EXTOOLS;
-my $PYPTOOLS = "$SCRIPT_DIR/../pyp-topics/src";
-die "Can't find pyp-topics: $PYPTOOLS" unless -e $PYPTOOLS && -d $PYPTOOLS;
-my $PYPSCRIPTS = "$SCRIPT_DIR/../pyp-topics/scripts";
-die "Can't find pyp-topics: $PYPSCRIPTS" unless -e $PYPSCRIPTS && -d $PYPSCRIPTS;
-my $PRTOOLS = "$SCRIPT_DIR/../posterior-regularisation";
-die "Can't find posterior-regularisation: $PRTOOLS" unless -e $PRTOOLS && -d $PRTOOLS;
-my $REDUCER = "$EXTOOLS/mr_stripe_rule_reduce";
-my $C2D = "$PYPSCRIPTS/contexts2documents.py";
-my $S2L = "$PYPSCRIPTS/spans2labels.py";
-my $SPLIT = "$SCRIPT_DIR/../posterior-regularisation/split-languages.py";
-
-my $PREM_TRAIN="$PRTOOLS/prjava/train-PR-cluster.sh";
-
-my $SORT_KEYS = "$SCRIPT_DIR/scripts/sort-by-key.sh";
-my $PATCH_CORPUS = "$SCRIPT_DIR/scripts/patch-corpus.pl";
-my $REMOVE_TAGS_CORPUS = "$SCRIPT_DIR/scripts/remove-tags-from-corpus.pl";
-my $REMOVE_TAGS_CONTEXT = "$SCRIPT_DIR/scripts/remove-tags-from-contexts.pl";
-my $EXTRACTOR = "$EXTOOLS/extractor";
-my $TOPIC_TRAIN = "$PYPTOOLS/pyp-contexts-train";
-my $MORF_DOC_FILTER = "$SCRIPT_DIR/../morf-segmentation/filter_docs.pl";
-
-assert_exec($PATCH_CORPUS, $SORT_KEYS, $REDUCER, $EXTRACTOR,
-            $S2L, $C2D, $TOPIC_TRAIN, $SPLIT, $REMOVE_TAGS_CONTEXT, $REMOVE_TAGS_CORPUS, $MORF_DOC_FILTER);
-
-my $BACKOFF_GRAMMAR;
-my $DEFAULT_CAT;
-my $HIER_CAT;
-my %FREQ_HIER = ();
-my $TAGGED_CORPUS;
-
-my $NAME_SHORTCUT;
-
-my $OUTPUT = './giwork';
-usage() unless &GetOptions('base_phrase_max_size=i' => \$BASE_PHRASE_MAX_SIZE,
-                           'backoff_grammar' => \$BACKOFF_GRAMMAR,
-                           'output=s' => \$OUTPUT,
-                           'model=s' => \$MODEL,
-                           'topics=i' => \$NUM_TOPICS_FINE,
-                           'coarse_topics=i' => \$NUM_TOPICS_COARSE,
-                           'trg_context=i' => \$CONTEXT_SIZE,
-                           'samples=i' => \$NUM_SAMPLES,
-                           'label_threshold=f' => \$LABEL_THRESHOLD,
-                           'use_default_cat' => \$DEFAULT_CAT,
-                           'topics-config=s' => \$TOPICS_CONFIG,
-                           'iterations=i' => \$NUM_ITERS,
-                           'pr-scale-phrase=f' => \$PR_SCALE_P,
-                           'pr-scale-context=f' => \$PR_SCALE_C,
-                           'pr-flags=s' => \$PR_FLAGS,
-                           'tagged_corpus=s' => \$TAGGED_CORPUS,
-                           'language=s' => \$LANGUAGE,
-                           'get_name_only' => \$NAME_SHORTCUT,
-                           'preserve_phrases' => \$PRESERVE_PHRASES,
-                           'morf=s' => \$MORFMARK,
-                          );
-if ($NAME_SHORTCUT) {
-  $NUM_TOPICS = $NUM_TOPICS_FINE;
-  print STDERR labeled_dir();
-  exit 0;
-}
-usage() unless scalar @ARGV == 1;
-my $CORPUS = $ARGV[0];
-open F, "<$CORPUS" or die "Can't read $CORPUS: $!"; close F;
-
-$NUM_TOPICS = $NUM_TOPICS_FINE;
-
-$HIER_CAT = ( $NUM_TOPICS_COARSE ? 1 : 0 );
-
-print STDERR "   Output: $OUTPUT\n";
-my $DATA_DIR = $OUTPUT . '/corpora';
-my $LEX_NAME = "corpus.f_e_a.$LANGUAGE.lex";
-my $CORPUS_LEX = $DATA_DIR . '/' . $LEX_NAME;  # corpus used to extract rules
-my $CORPUS_CLUSTER = $DATA_DIR . "/corpus.f_e_a.$LANGUAGE.cluster"; # corpus used for clustering (often identical)
-
-my $CONTEXT_DIR = $OUTPUT . '/' . context_dir();
-my $CLUSTER_DIR = $OUTPUT . '/' . cluster_dir();
-my $LABELED_DIR = $OUTPUT . '/' . labeled_dir();
-my $CLUSTER_DIR_C;
-my $CLUSTER_DIR_F;
-my $LABELED_DIR_C;
-my $LABELED_DIR_F;
-if($HIER_CAT) {
-    $CLUSTER_DIR_F = $CLUSTER_DIR;
-    $LABELED_DIR_F = $LABELED_DIR;
-    $NUM_TOPICS = $NUM_TOPICS_COARSE;
-    $CLUSTER_DIR_C = $OUTPUT . '/' . cluster_dir();
-    $LABELED_DIR_C = $OUTPUT . '/' . labeled_dir();
-    $NUM_TOPICS = $NUM_TOPICS_FINE;
-}
-my $GRAMMAR_DIR = $OUTPUT . '/' . grammar_dir();
-print STDERR "  Context: $CONTEXT_DIR\n  Cluster: $CLUSTER_DIR\n  Labeled: $LABELED_DIR\n  Grammar: $GRAMMAR_DIR\n";
-safemkdir($OUTPUT) or die "Couldn't create output directory $OUTPUT: $!";
-safemkdir($DATA_DIR) or die "Couldn't create output directory $DATA_DIR: $!";
-safemkdir($CONTEXT_DIR) or die "Couldn't create output directory $CONTEXT_DIR: $!";
-safemkdir($CLUSTER_DIR) or die "Couldn't create output directory $CLUSTER_DIR: $!";
-if($HIER_CAT) {
-    safemkdir($CLUSTER_DIR_C) or die "Couldn't create output directory $CLUSTER_DIR_C: $!";
-    safemkdir($LABELED_DIR_C) or die "Couldn't create output directory $LABELED_DIR_C: $!";
-}
-safemkdir($LABELED_DIR) or die "Couldn't create output directory $LABELED_DIR: $!";
-safemkdir($GRAMMAR_DIR) or die "Couldn't create output directory $GRAMMAR_DIR: $!";
-if(-e $TOPICS_CONFIG) {
-    copy($TOPICS_CONFIG, $CLUSTER_DIR) or die "Copy failed: $!";
-}
-
-setup_data();
-
-if (lc($MODEL) eq "blagree") {
-    extract_bilingual_context();
-} else {
-    extract_context();
-}
-
-if (lc($MODEL) eq "pyp") {
-    if($HIER_CAT) {
-        $NUM_TOPICS = $NUM_TOPICS_COARSE;
-        $CLUSTER_DIR = $CLUSTER_DIR_C;
-        topic_train();
-        $NUM_TOPICS = $NUM_TOPICS_FINE;
-        $CLUSTER_DIR = $CLUSTER_DIR_F;
-        topic_train();
-    } else {
-        topic_train();
-    }
-} elsif (lc($MODEL) =~ /pr|em|agree/) {
-    prem_train();
-} else { die "Unsupported model type: $MODEL. Must be one of PYP or PREM.\n"; }
-if($HIER_CAT) {
-    $NUM_TOPICS = $NUM_TOPICS_COARSE;
-    $CLUSTER_DIR = $CLUSTER_DIR_C;
-    $LABELED_DIR = $LABELED_DIR_C;
-    label_spans_with_topics();
-    $NUM_TOPICS = $NUM_TOPICS_FINE;
-    $CLUSTER_DIR = $CLUSTER_DIR_F;
-    $LABELED_DIR = $LABELED_DIR_F;
-    label_spans_with_topics();
-    extract_freqs();
-} else {
-    label_spans_with_topics();
-}
-my $res;
-if ($BIDIR) {
-  $res = grammar_extract_bidir();
-} else {
-  $res = grammar_extract();
-}
-print STDERR "\n!!!COMPLETE!!!\n";
-print STDERR "GRAMMAR: $res\nYou should probably run: $SCRIPT_DIR/evaluation-pipeline.pl LANGPAIR giwork/ct1s0.L10.PYP.t4.s20.grammar/grammar.gz -f FEAT1 -f FEAT2\n\n";
-exit 0;
-
-sub setup_data {
-  print STDERR "\n!!!PREPARE CORPORA!!!\n";
-  if (-f $CORPUS_LEX && $CORPUS_CLUSTER) {
-    print STDERR "$CORPUS_LEX and $CORPUS_CLUSTER exist, reusing...\n";
-    return;
-  }
-  copy($CORPUS, $CORPUS_LEX);
-  if ($TAGGED_CORPUS) {
-    die "Can't find $TAGGED_CORPUS" unless -f $TAGGED_CORPUS;
-    my $opt="";
-    $opt = "-s" if ($LANGUAGE eq "source");
-    $opt = $opt . " -a" if ($PRESERVE_PHRASES);
-    my $cmd="$PATCH_CORPUS $opt $TAGGED_CORPUS $CORPUS_LEX > $CORPUS_CLUSTER";
-    safesystem($cmd) or die "Failed to extract contexts.";
-  } else {
-    symlink($LEX_NAME, $CORPUS_CLUSTER);
-  }
-}
-
-sub context_dir {
-  return "ct${CONTEXT_SIZE}s0.L$BASE_PHRASE_MAX_SIZE.l$LANGUAGE";
-}
-
-sub cluster_dir {
-    if (lc($MODEL) eq "pyp") {
-        return context_dir() . ".PYP.t$NUM_TOPICS.s$NUM_SAMPLES";
-    } elsif (lc($MODEL) eq "em") {
-        return context_dir() . ".EM.t$NUM_TOPICS.i$NUM_ITERS";
-    } elsif (lc($MODEL) eq "pr") {
-        return context_dir() . ".PR.t$NUM_TOPICS.i$NUM_ITERS.sp$PR_SCALE_P.sc$PR_SCALE_C";
-    } elsif (lc($MODEL) eq "agree") {
-        return context_dir() . ".AGREE.t$NUM_TOPICS.i$NUM_ITERS";
-    } elsif (lc($MODEL) eq "blagree") {
-        return context_dir() . ".BLAGREE.t$NUM_TOPICS.i$NUM_ITERS";
-    }
-}
-
-sub labeled_dir {
-  if (lc($MODEL) eq "pyp" && $LABEL_THRESHOLD ne "0") {
-    return cluster_dir() . "_lt$LABEL_THRESHOLD";
-  } else {
-    return cluster_dir();
-  }
-}
-
-sub grammar_dir {
-  # TODO add grammar config options -- adjacent NTs, etc
-  if($HIER_CAT) {
-    return cluster_dir() . ".hier$NUM_TOPICS_COARSE-$NUM_TOPICS_FINE.grammar";
-  } else {
-    return labeled_dir() . ".grammar";
-  }
-}
-
-
-
-sub safemkdir {
-  my $dir = shift;
-  if (-d $dir) { return 1; }
-  return mkdir($dir);
-}
-
-sub usage {
-  print <<EOT;
-
-Usage: $0 [OPTIONS] corpus.fr-en-al
-
-Induces a grammar using Pitman-Yor topic modeling or Posterior Regularisation.
-
-EOT
-  exit 1;
-};
-
-sub assert_exec {
-  my @files = @_;
-  for my $file (@files) {
-    die "Can't find $file - did you run make?\n" unless -e $file;
-    die "Can't execute $file" unless -e $file;
-  }
-};
-
-sub extract_context {
- print STDERR "\n!!!CONTEXT EXTRACTION\n"; 
- my $OUT_CONTEXTS = "$CONTEXT_DIR/context.txt.gz";
- if (-e $OUT_CONTEXTS) {
-   print STDERR "$OUT_CONTEXTS exists, reusing...\n";
- } else {
-   my $ccopt = "-c $ITEMS_IN_MEMORY";
-   my $postsort = "| $REDUCER ";
-   if ($COMPLETE_CACHE) {
-     print STDERR "COMPLETE_CACHE is set: removing memory limits on cache.\n";
-     $ccopt = "-c 0";
-     $postsort = "" unless ($PRESERVE_PHRASES);
-   }
-
-   my $presort = ($PRESERVE_PHRASES ? "| $REMOVE_TAGS_CONTEXT --phrase=tok --context=tag " : "");
-
-   if ($MORFMARK ne "") { 
-     $presort = $presort . "| $MORF_DOC_FILTER \"$MORFMARK\" "; 
-   }
-
-   my $cmd = "$EXTRACTOR -i $CORPUS_CLUSTER $ccopt -L $BASE_PHRASE_MAX_SIZE -C -S $CONTEXT_SIZE --phrase_language $LANGUAGE --context_language $LANGUAGE $presort | $SORT_KEYS $postsort | $GZIP > $OUT_CONTEXTS";
-   safesystem($cmd) or die "Failed to extract contexts.";
-  }
-}
-
-sub extract_bilingual_context {
- print STDERR "\n!!!CONTEXT EXTRACTION\n"; 
- my $OUT_SRC_CONTEXTS = "$CONTEXT_DIR/context.source";
- my $OUT_TGT_CONTEXTS = "$CONTEXT_DIR/context.target";
-
- if (-e $OUT_SRC_CONTEXTS . ".gz" and -e $OUT_TGT_CONTEXTS . ".gz") {
-   print STDERR "$OUT_SRC_CONTEXTS.gz and $OUT_TGT_CONTEXTS.gz exist, reusing...\n";
- } else {
-   my $OUT_BI_CONTEXTS = "$CONTEXT_DIR/context.bilingual.txt.gz";
-   my $cmd = "$EXTRACTOR -i $CORPUS_CLUSTER -c $ITEMS_IN_MEMORY -L $BASE_PHRASE_MAX_SIZE -C -S $CONTEXT_SIZE --phrase_language both --context_language both | $SORT_KEYS | $REDUCER | $GZIP > $OUT_BI_CONTEXTS";
-   if ($COMPLETE_CACHE) {
-     print STDERR "COMPLETE_CACHE is set: removing memory limits on cache.\n";
-     $cmd = "$EXTRACTOR -i $CORPUS_CLUSTER -c 0 -L $BASE_PHRASE_MAX_SIZE -C -S $CONTEXT_SIZE  --phrase_language both --context_language both  | $SORT_KEYS | $GZIP > $OUT_BI_CONTEXTS";
-   }
-   safesystem($cmd) or die "Failed to extract contexts.";
-
-   safesystem("$ZCAT $OUT_BI_CONTEXTS | $SPLIT $OUT_SRC_CONTEXTS $OUT_TGT_CONTEXTS") or die "Failed to split contexts.\n";
-   safesystem("$GZIP -f $OUT_SRC_CONTEXTS") or die "Failed to zip output contexts.\n";
-   safesystem("$GZIP -f $OUT_TGT_CONTEXTS") or die "Failed to zip output contexts.\n";
- }
-}
-
-
-sub topic_train {
-  print STDERR "\n!!!TRAIN PYP TOPICS\n";
-  my $IN_CONTEXTS = "$CONTEXT_DIR/context.txt.gz";
-  my $OUT_CLUSTERS = "$CLUSTER_DIR/docs.txt.gz";
-  if (-e $OUT_CLUSTERS) {
-    print STDERR "$OUT_CLUSTERS exists, reusing...\n";
-  } else {
-    safesystem("$TOPIC_TRAIN --data $IN_CONTEXTS --backoff-type simple -t $NUM_TOPICS -s $NUM_SAMPLES -o $OUT_CLUSTERS -c $TOPICS_CONFIG -w /dev/null") or die "Topic training failed.\n";
-  }
-}
-
-sub prem_train {
-  print STDERR "\n!!!TRAIN PR/EM model\n";
-  my $OUT_CLUSTERS = "$CLUSTER_DIR/docs.txt.gz";
-  if (-e $OUT_CLUSTERS) {
-    print STDERR "$OUT_CLUSTERS exists, reusing...\n";
-  } else {
-    my $in = "--in $CONTEXT_DIR/context.txt.gz";
-    my $opts = "";
-    if (lc($MODEL) eq "pr") {
-        $opts = "--scale-phrase $PR_SCALE_P --scale-context $PR_SCALE_C";
-    } elsif (lc($MODEL) eq "agree") {
-        $opts = "--agree-direction";
-    } elsif (lc($MODEL) eq "blagree") {
-        $in = "--in $CONTEXT_DIR/context.source.gz --in1 $CONTEXT_DIR/context.target.gz";
-        $opts = "--agree-language";
-    }
-    safesystem("$PREM_TRAIN $in --topics $NUM_TOPICS --out $OUT_CLUSTERS --iterations $NUM_ITERS $opts $PR_FLAGS") or die "Topic training failed.\n";
-  }
-}
-
-sub label_spans_with_topics {
-  my ($file) = (@_);
-  print STDERR "\n!!!LABEL SPANS\n";
-  my $IN_CLUSTERS = "$CLUSTER_DIR/docs.txt.gz";
-  my $OUT_SPANS = "$LABELED_DIR/labeled_spans.txt";
-  if (-e $OUT_SPANS) {
-    print STDERR "$OUT_SPANS exists, reusing...\n";
-  } else {
-    my $extra = "tt";
-    if ($LANGUAGE eq "source") {
-        $extra = "ss";
-    } elsif ($LANGUAGE eq "both") {
-        $extra = "bb";
-    } else { die "Invalid language specifier $LANGUAGE\n" unless $LANGUAGE eq "target" };
-    $extra = $extra . " tok,tag" if ($PRESERVE_PHRASES);
-    safesystem("$ZCAT $IN_CLUSTERS > $CLUSTER_DIR/clusters.txt") or die "Failed to unzip";
-    safesystem("$EXTRACTOR --base_phrase_spans -i $CORPUS_CLUSTER -c $ITEMS_IN_MEMORY -L $BASE_PHRASE_MAX_SIZE -S $CONTEXT_SIZE | $S2L $CLUSTER_DIR/clusters.txt $CONTEXT_SIZE $LABEL_THRESHOLD $extra > $OUT_SPANS") or die "Failed to label spans";
-    unlink("$CLUSTER_DIR/clusters.txt") or warn "Failed to remove $CLUSTER_DIR/clusters.txt";
-    safesystem("paste -d ' ' $CORPUS_LEX $OUT_SPANS | sed 's/ *||| *\$//'  > $LABELED_DIR/corpus.src_trg_al_label") or die "Couldn't paste";
-  }
-}
-
-sub extract_freqs {
-    print STDERR "\n!!!EXTRACTING FREQUENCIES\n";
-    my $IN_COARSE = "$LABELED_DIR_C/labeled_spans.txt";
-    my $IN_FINE = "$LABELED_DIR_F/labeled_spans.txt";
-    my $OUT_SPANS = "$LABELED_DIR_F/labeled_spans.hier$NUM_TOPICS_COARSE-$NUM_TOPICS_FINE.txt";
-    my $FREQS = "$LABELED_DIR_F/label_freq.hier$NUM_TOPICS_COARSE-$NUM_TOPICS_FINE.txt";
-    my $COARSE_EXPR = "\'s/\\(X[0-9][0-9]*\\)/\\1c/g\'"; #'
-    my $FINE_EXPR = "\'s/\\(X[0-9][0-9]*\\)/\\1f/g\'"; #'
-    my %finehier = ();
-    if (-e $OUT_SPANS) {
-        print STDERR "$OUT_SPANS exists, reusing...\n";
-    } else {
-        safesystem("paste -d ' ' $IN_COARSE $IN_FINE > $OUT_SPANS");
-    }
-    open SPANS, $OUT_SPANS or die $!;
-    while (<SPANS>) {
-        my ($tmp, $coarse, $fine) = split /\|\|\|/;
-        my @coarse_spans = $coarse =~ /\d+-\d+:X(\d+)/g;
-        my @fine_spans = $fine =~ /\d+-\d+:X(\d+)/g;
-        
-        foreach my $i (0..(scalar @coarse_spans)-1) {
-            my $coarse_cat = $coarse_spans[$i];
-            my $fine_cat = $fine_spans[$i];
-            
-            $FREQ_HIER{$coarse_cat}{$fine_cat}++;
-        }
-    }
-    close SPANS;
-    foreach (values %FREQ_HIER) {
-        my $coarse_freq = $_;
-        my $total = 0;
-        $total+=$_ for (values %{ $coarse_freq });
-        $coarse_freq->{$_}=log($coarse_freq->{$_}/$total) for (keys %{ $coarse_freq });
-    }
-    open FREQS, ">", $FREQS or die $!;
-    foreach my $coarse_cat (keys %FREQ_HIER) {
-        print FREQS "$coarse_cat |||";
-        foreach my $fine_cat (keys %{$FREQ_HIER{$coarse_cat}}) {
-            my $res = $FREQ_HIER{$coarse_cat}{$fine_cat};
-            print FREQS " $fine_cat:$res";
-            if(! exists $finehier{$fine_cat} || $finehier{$fine_cat} < $res) {
-               $finehier{$fine_cat} = $coarse_cat;
-            }  
-        }
-        print FREQS "\n";
-    }
-#    foreach my $fine_cat (keys %finehier) {
-#        print FREQS "$fine_cat -> $finehier{$fine_cat}\n";
-#    }
-    close FREQS;
-    $CLUSTER_DIR = $CLUSTER_DIR_F;
-}
-
-sub grammar_extract {
-  my $LABELED = "$LABELED_DIR/corpus.src_trg_al_label";
-  print STDERR "\n!!!EXTRACTING GRAMMAR\n";
-  my $OUTGRAMMAR = "$GRAMMAR_DIR/grammar.gz";
-  if (-e $OUTGRAMMAR) {
-    print STDERR "$OUTGRAMMAR exists, reusing...\n";
-  } else {
-    my $BACKOFF_ARG = ($BACKOFF_GRAMMAR ? "-g" : "");
-    my $DEFAULT_CAT_ARG = ($DEFAULT_CAT ? "-d X" : "");
-    safesystem("$EXTRACTOR -i $LABELED -c $ITEMS_IN_MEMORY -L $BASE_PHRASE_MAX_SIZE -t $NUM_TOPICS $BACKOFF_ARG $DEFAULT_CAT_ARG | $SORT_KEYS | $REDUCER -p | $GZIP > $OUTGRAMMAR") or die "Couldn't extract grammar";
-  }
-  return $OUTGRAMMAR;
-}
-
-sub grammar_extract_bidir {
-#gzcat ex.output.gz | ./mr_stripe_rule_reduce -p -b | sort -t $'\t' -k 1 | ./mr_stripe_rule_reduce | gzip > phrase-table.gz
-  my $LABELED = "$LABELED_DIR/corpus.src_trg_al_label";
-  print STDERR "\n!!!EXTRACTING GRAMMAR\n";
-  my $OUTGRAMMAR = "$GRAMMAR_DIR/grammar.bidir.gz";
-  if (-e $OUTGRAMMAR) {
-    print STDERR "$OUTGRAMMAR exists, reusing...\n";
-  } else {
-    my $BACKOFF_ARG = ($BACKOFF_GRAMMAR ? "-g" : "");
-    safesystem("$EXTRACTOR -i $LABELED -c $ITEMS_IN_MEMORY -L $BASE_PHRASE_MAX_SIZE -b -t $NUM_TOPICS $BACKOFF_ARG | $SORT_KEYS | $REDUCER -p -b | $SORT_KEYS | $REDUCER | $GZIP > $OUTGRAMMAR") or die "Couldn't extract grammar";
-  }
-  return $OUTGRAMMAR;
-}
-
-sub safesystem {
-  print STDERR "Executing: @_\n";
-  system(@_);
-  if ($? == -1) {
-      print STDERR "ERROR: Failed to execute: @_\n  $!\n";
-      exit(1);
-  }
-  elsif ($? & 127) {
-      printf STDERR "ERROR: Execution of: @_\n  died with signal %d, %s coredump\n",
-          ($? & 127),  ($? & 128) ? 'with' : 'without';
-      exit(1);
-  }
-  else {
-    my $exitcode = $? >> 8;
-    print STDERR "Exit code: $exitcode\n" if $exitcode;
-    return ! $exitcode;
-  }
-}
-
diff --git a/gi/pipeline/lticluster.config b/gi/pipeline/lticluster.config
deleted file mode 100644
index 3e23c8cb..00000000
--- a/gi/pipeline/lticluster.config
+++ /dev/null
@@ -1,9 +0,0 @@
-# THIS FILE GIVES THE LOCATIONS OF THE CORPORA USED
-# name path aligned-corpus LM dev dev-refs test1 testt-eval.sh ...
-/home/cdyer/ws10smt-data
-btec /home/cdyer/ws10smt-data/btec/ split.zh-en.al lm/en.3gram.lm.gz devtest/devset1_2.zh devtest/devset1_2.lc.en* devtest/devset3.zh eval-devset3.sh
-zhen /home/cdyer/ws10smt-data/chinese-english corpus.zh-en.al lm/c2e.3gram.lm.gz dev_and_test/mt02.src.txt dev_and_test/mt02.ref.* dev_and_test/mt03.src.txt eval-mt03.sh
-aren /home/cdyer/ws10smt-data/arabic-english corpus.ar-en-al lm/a2e.3gram.lm.gz dev_and_test/dev.src.txt dev_and_test/dev.ref.txt.* dev_and_test/mt05.src.txt eval-mt05.sh
-uren /home/cdyer/ws10smt-data/urdu-english corpus.ur-en.al lm/u2e.en.lm.gz dev/dev.ur dev/dev.en* devtest/devtest.ur eval-devtest.sh
-nlfr /home/cdyer/ws10smt-data/dutch-french corpus.nl-fr.al
-
diff --git a/gi/pipeline/scripts/filter-by-f.pl b/gi/pipeline/scripts/filter-by-f.pl
deleted file mode 100755
index 0cef0606..00000000
--- a/gi/pipeline/scripts/filter-by-f.pl
+++ /dev/null
@@ -1,56 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-
-my $SCRIPT_DIR; BEGIN { use Cwd qw/ abs_path /; use File::Basename; $SCRIPT_DIR = dirname(abs_path($0)); push @INC, $SCRIPT_DIR; }
-
-my $REKEY="$SCRIPT_DIR/rekey.pl";
-my $REFILTER="$SCRIPT_DIR/refilter.pl";
-my $SORT="$SCRIPT_DIR/sort-by-key.sh";
-assert_exec($REKEY, $REFILTER, $SORT);
-
-
-die "Usage: $0 NUM-TRANSLATIONS ingrammar.gz outgrammar.gz\n" unless scalar @ARGV == 3;
-my $translations = shift @ARGV;
-die "Need number: $translations" unless $translations > 0;
-die unless $ARGV[0] =~ /\.gz$/;
-die unless $ARGV[1] =~ /\.gz$/;
-die if $ARGV[0] eq $ARGV[1];
-die "Can't find $ARGV[0]" unless -f $ARGV[0];
-
-my $cmd = "gunzip -c $ARGV[0] | $REKEY | $SORT | $REFILTER $translations | gzip > $ARGV[1]";
-safesystem($ARGV[1], $cmd) or die "Filtering failed";
-exit 0;
-
-sub assert_exec {
-  my @files = @_;
-  for my $file (@files) {
-    die "Can't find $file - did you run make?\n" unless -e $file;
-    die "Can't execute $file" unless -e $file;
-  }
-};
-
-sub safesystem {
-  my $output = shift @_;
-  print STDERR "Executing: @_\n";
-  system(@_);
-  if ($? == -1) {
-      print STDERR "ERROR: Failed to execute: @_\n  $!\n";
-      if (defined $output && -e $output) { printf STDERR "Removing $output\n"; `rm -rf $output`; }
-      exit(1);
-  }
-  elsif ($? & 127) {
-      printf STDERR "ERROR: Execution of: @_\n  died with signal %d, %s coredump\n",
-          ($? & 127),  ($? & 128) ? 'with' : 'without';
-      if (defined $output && -e $output) { printf STDERR "Removing $output\n"; `rm -rf $output`; }
-      exit(1);
-  }
-  else {
-    my $exitcode = $? >> 8;
-    if ($exitcode) {
-      print STDERR "Exit code: $exitcode\n";
-      if (defined $output && -e $output) { printf STDERR "Removing $output\n"; `rm -rf $output`; }
-    }
-    return ! $exitcode;
-  }
-}
-
diff --git a/gi/pipeline/scripts/patch-corpus.pl b/gi/pipeline/scripts/patch-corpus.pl
deleted file mode 100755
index c0eec43e..00000000
--- a/gi/pipeline/scripts/patch-corpus.pl
+++ /dev/null
@@ -1,65 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-
-my $PATCH = shift @ARGV;
-my $TGT = 1;
-my $APPEND;
-while ($PATCH eq "-s" || $PATCH eq "-a") {
-    if ($PATCH eq "-s") {
-        undef $TGT;
-    } else {
-        $APPEND = 1;
-    }
-    $PATCH = shift @ARGV;
-}
-
-die "Usage: $0 [-s] [-a] tagged.en[_fr] < lexical.en_fr_al[_...]\n" unless $PATCH;
-
-open P, "<$PATCH" or die "Can't read tagged corpus $PATCH: $!";
-my $first=<P>; close P;
-my @fields = split / \|\|\| /, $first;
-die "Bad format!" if (scalar @fields > 2);
-
-if (scalar @fields != 1) {
-  # TODO support this
-  die "Patching source and target not supported yet!";
-}
-
-my $line = 0;
-open P, "<$PATCH" or die "Can't read tagged corpus $PATCH: $!";
-while(my $pline = <P>) {
-  chomp $pline;
-  $line++;
-  my $line = <>;
-  die "Too few lines in lexical corpus!" unless $line;
-  chomp $line;
-  @fields = split / \|\|\| /, $line;
-  my @pwords = split /\s+/, $pline;
-  if ($TGT) {
-      my @lwords = split /\s+/, $fields[1];
-      die "Length mismatch in line $line!\n" unless (scalar @pwords == scalar @lwords);
-      if ($APPEND) {
-          foreach my $i (0..(scalar @pwords-1)) {
-              $lwords[$i] = $lwords[$i] . '_' . $pwords[$i];
-          }
-          $fields[1] = join ' ', @lwords;
-      } else {
-          $fields[1] = $pline;
-      }
-  } else { # source side
-      my @lwords = split /\s+/, $fields[0];
-      die "Length mismatch in line $line!\n" unless (scalar @pwords == scalar @lwords);
-      if ($APPEND) {
-          foreach my $i (0..(scalar @pwords-1)) {
-              $lwords[$i] = $lwords[$i] . '_' . $pwords[$i];
-          }
-          $fields[0] = join ' ', @lwords;
-      } else {
-          $fields[0] = $pline;
-      }
-  }
-  print join ' ||| ', @fields;
-  print "\n";
-}
-
-
diff --git a/gi/pipeline/scripts/refilter.pl b/gi/pipeline/scripts/refilter.pl
deleted file mode 100755
index a783eb4e..00000000
--- a/gi/pipeline/scripts/refilter.pl
+++ /dev/null
@@ -1,40 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-
-my $NUM_TRANSLATIONS = shift @ARGV;
-unless ($NUM_TRANSLATIONS) { $NUM_TRANSLATIONS=30; }
-print STDERR "KEEPING $NUM_TRANSLATIONS TRANSLATIONS FOR SOURCE\n";
-
-my $pk = '';
-my %dict;
-while(<>) {
-  s/^(.+)\t//;
-  my $key = $1;
-  if ($key ne $pk) {
-    if ($pk) {
-      emit_dict();
-    }
-    %dict = ();
-    $pk = $key;
-  }
-  my ($lhs, $f, $e, $s) = split / \|\|\| /;
-  my $score = 0;
-  if ($s =~ /XEF=([^ ]+)/) {
-    $score += $1;
-  } else { die; }
-  if ($s =~ /GenerativeProb=([^ ]+)/) {
-    $score += ($1 / 10);
-  } else { die; }
-  $dict{"$lhs ||| $f ||| $e ||| $s"} = $score;
-}
-emit_dict();
-
-sub emit_dict {
-  my $cc = 0;
-  for my $k (sort { $dict{$a} <=> $dict{$b} } keys %dict) {
-    print "$k";
-    $cc++;
-    if ($cc >= $NUM_TRANSLATIONS) { last; }
-  }
-}
-
diff --git a/gi/pipeline/scripts/rekey.pl b/gi/pipeline/scripts/rekey.pl
deleted file mode 100755
index 31eb86b8..00000000
--- a/gi/pipeline/scripts/rekey.pl
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/usr/bin/perl
-
-while(<>) {
-  my ($lhs, $f, $e, $s) = split / \|\|\| /;
-  $f =~ s/\[X[0-9]+\]/\[X\]/g;
-  print "$f\t$_";
-}
-
diff --git a/gi/pipeline/scripts/remove-tags-from-contexts.pl b/gi/pipeline/scripts/remove-tags-from-contexts.pl
deleted file mode 100755
index 20698816..00000000
--- a/gi/pipeline/scripts/remove-tags-from-contexts.pl
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-
-use Getopt::Long "GetOptions";
-
-my $PHRASE = 'tok';
-my $CONTEXT = 'tag';
-
-die "Usage: $0 [--phrase=tok|tag] [--context=tok|tag] < corpus" 
-    unless &GetOptions('phrase=s' => \$PHRASE, 'context=s' => \$CONTEXT);
-
-my $lno = 0;
-while(my $line = <>) {
-    $lno++;
-    chomp $line;
-    my @top = split /\t/, $line;
-    die unless (scalar @top == 2); 
-
-    my @pwords = split /\s+/, $top[0];
-    foreach my $token (@pwords) {
-        #print $token . "\n";
-        my @parts = split /_(?!.*_)/, $token;
-        die unless (scalar @parts == 2); 
-        if ($PHRASE eq "tok") {
-            $token = $parts[0]
-        } elsif ($PHRASE eq "tag") {
-            $token = $parts[1]
-        }
-    }
-
-    my @fields = split / \|\|\| /, $top[1];
-    foreach my $i (0..((scalar @fields) / 2 - 1)) {
-        #print $i . ": " . $fields[2*$i] . " of " . (scalar @fields) . "\n";
-        my @cwords = split /\s+/, $fields[2*$i];
-        foreach my $token (@cwords) {
-            #print $i . ": " . $token . "\n";
-            my @parts = split /_(?!.*_)/, $token;
-            if (scalar @parts == 2) {
-                if ($CONTEXT eq "tok") {
-                    $token = $parts[0]
-                } elsif ($CONTEXT eq "tag") {
-                    $token = $parts[1]
-                }
-            }
-        }
-        $fields[2*$i] = join ' ', @cwords;
-    }
-
-    print join ' ', @pwords;
-    print "\t";
-    print join ' ||| ', @fields;
-    print "\n";
-}
diff --git a/gi/pipeline/scripts/remove-tags-from-corpus.pl b/gi/pipeline/scripts/remove-tags-from-corpus.pl
deleted file mode 100755
index be3e97c0..00000000
--- a/gi/pipeline/scripts/remove-tags-from-corpus.pl
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-
-use Getopt::Long "GetOptions";
-
-my $LANGUAGE = shift @ARGV;
-$LANGUAGE = 'target' unless ($LANGUAGE);
-
-my $lno = 0;
-while(my $line = <>) {
-    $lno++;
-    chomp $line;
-
-    my @fields = split / \|\|\| /, $line;
-
-    if ($LANGUAGE eq "source" or $LANGUAGE eq "both") {
-        my @cwords = split /\s+/, $fields[0];
-        foreach my $token (@cwords) {
-            my @parts = split /_(?!.*_)/, $token;
-            if (scalar @parts == 2) {
-                $token = $parts[0]
-            } else {
-                print STDERR "WARNING: invalid tagged token $token\n";
-            }
-        }
-        $fields[0] = join ' ', @cwords;
-    }
-
-    if ($LANGUAGE eq "target" or $LANGUAGE eq "both") {
-        my @cwords = split /\s+/, $fields[1];
-        foreach my $token (@cwords) {
-            my @parts = split /_(?!.*_)/, $token;
-            if (scalar @parts == 2) {
-                $token = $parts[1]
-            } else {
-                print STDERR "WARNING: invalid tagged token $token\n";
-            }
-        }
-        $fields[0] = join ' ', @cwords;
-    }
-
-    print join ' ||| ', @fields;
-    print "\n";
-}
diff --git a/gi/pipeline/scripts/sort-by-key.sh b/gi/pipeline/scripts/sort-by-key.sh
deleted file mode 100755
index 7ae33e03..00000000
--- a/gi/pipeline/scripts/sort-by-key.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/bash
-
-export LANG=C
-sort -t $'\t' -k 1 -T /tmp -S 6000000000
-
diff --git a/gi/pipeline/scripts/xfeats.pl b/gi/pipeline/scripts/xfeats.pl
deleted file mode 100755
index dc578513..00000000
--- a/gi/pipeline/scripts/xfeats.pl
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-
-die "Usage: $0 x-grammar.scfg[.gz] < cat-grammar.scfg\n" unless scalar @ARGV > 0;
-
-my $xgrammar = shift @ARGV;
-die "Can't find $xgrammar" unless -f $xgrammar;
-my $fh;
-if ($xgrammar =~ /\.gz$/) {
-  open $fh, "gunzip -c $xgrammar|" or die "Can't fork: $!";
-} else {
-  open $fh, "<$xgrammar" or die "Can't read $xgrammar: $!";
-}
-print STDERR "Reading X-feats from $xgrammar...\n";
-my %dict;
-while(<$fh>) {
-  chomp;
-  my ($lhs, $f, $e, $feats) = split / \|\|\| /;
-  my $xfeats;
-  my $cc = 0;
-  my @xfeats = ();
-  while ($feats =~ /(EGivenF|FGivenE|LogRuleCount|LogECount|LogFCount|SingletonRule|SingletonE|SingletonF)=([^ ]+)( |$)/og) {
-    push @xfeats, "X_$1=$2";
-  }
-  #print "$lhs ||| $f ||| $e ||| @xfeats\n";
-  $dict{"$lhs ||| $f ||| $e"} = "@xfeats";
-}
-close $fh;
-
-print STDERR "Add features...\n";
-while(<>) {
-  chomp;
-  my ($lhs, $f, $e) = split / \|\|\| /;
-  $f=~ s/\[[^]]+,([12])\]/\[X,$1\]/g;
-  my $xfeats = $dict{"[X] ||| $f ||| $e"};
-  die "Can't find x features for: $_\n" unless $xfeats;
-  print "$_ $xfeats\n";
-}
-
diff --git a/gi/pipeline/valhalla.config b/gi/pipeline/valhalla.config
deleted file mode 100644
index e00a8485..00000000
--- a/gi/pipeline/valhalla.config
+++ /dev/null
@@ -1,9 +0,0 @@
-# THIS FILE GIVES THE LOCATIONS OF THE CORPORA USED
-# name path aligned-corpus LM dev dev-refs test1 testt-eval.sh ...
-/home/chris/ws10smt/data
-btec /home/chris/ws10smt/data/btec/ split.zh-en.al lm/en.3gram.lm.gz devtest/devset1_2.zh devtest/devset1_2.lc.en* devtest/devset3.zh eval-devset3.sh
-fbis /home/chris/ws10smt/data/chinese-english.fbis corpus.zh-en.al
-zhen /home/chris/ws10smt/data/chinese-english corpus.zh-en.al
-aren /home/chris/ws10smt/data/arabic-english corpus.ar-en.al
-uren /home/chris/ws10smt/data/urdu-english corpus.ur-en.al lm/u2e.en.lm.gz dev/dev.ur dev/dev.en* devtest/devtest.ur eval-devtest.sh
-nlfr /home/chris/ws10smt/data/dutch-french corpus.nl-fr.al
diff --git a/gi/posterior-regularisation/Corpus.java b/gi/posterior-regularisation/Corpus.java
deleted file mode 100644
index 07b27387..00000000
--- a/gi/posterior-regularisation/Corpus.java
+++ /dev/null
@@ -1,167 +0,0 @@
-import gnu.trove.TIntArrayList;
-
-import java.io.*;
-import java.util.*;
-import java.util.regex.Pattern;
-
-public class Corpus
-{
-	private Lexicon<String> tokenLexicon = new Lexicon<String>();
-	private Lexicon<TIntArrayList> phraseLexicon = new Lexicon<TIntArrayList>();
-	private Lexicon<TIntArrayList> contextLexicon = new Lexicon<TIntArrayList>();
-	private List<Edge> edges = new ArrayList<Edge>();
-	private List<List<Edge>> phraseToContext = new ArrayList<List<Edge>>();
-	private List<List<Edge>> contextToPhrase = new ArrayList<List<Edge>>();
-	
-	public class Edge
-	{
-		Edge(int phraseId, int contextId, int count)
-		{
-			this.phraseId = phraseId;
-			this.contextId = contextId;
-			this.count = count;
-		}
-		public int getPhraseId()
-		{
-			return phraseId;
-		}
-		public TIntArrayList getPhrase()
-		{
-			return phraseLexicon.lookup(phraseId);
-		}
-		public String getPhraseString()
-		{
-			StringBuffer b = new StringBuffer();
-			for (int tid: getPhrase().toNativeArray())
-			{
-				if (b.length() > 0)
-					b.append(" ");
-				b.append(tokenLexicon.lookup(tid));
-			}
-			return b.toString();
-		}		
-		public int getContextId()
-		{
-			return contextId;
-		}
-		public TIntArrayList getContext()
-		{
-			return contextLexicon.lookup(contextId);
-		}
-		public String getContextString()
-		{
-			StringBuffer b = new StringBuffer();
-			for (int tid: getContext().toNativeArray())
-			{
-				if (b.length() > 0)
-					b.append(" ");
-				b.append(tokenLexicon.lookup(tid));
-			}
-			return b.toString();
-		}
-		public int getCount()
-		{
-			return count;
-		}
-		private int phraseId;
-		private int contextId;
-		private int count;
-	}
-
-	List<Edge> getEdges()
-	{
-		return edges;
-	}
-	
-	int getNumEdges()
-	{
-		return edges.size();
-	}
-
-	int getNumPhrases()
-	{
-		return phraseLexicon.size();
-	}
-	
-	List<Edge> getEdgesForPhrase(int phraseId)
-	{
-		return phraseToContext.get(phraseId);
-	}
-	
-	int getNumContexts()
-	{
-		return contextLexicon.size();
-	}
-	
-	List<Edge> getEdgesForContext(int contextId)
-	{
-		return contextToPhrase.get(contextId);
-	}
-	
-	int getNumTokens()
-	{
-		return tokenLexicon.size();
-	}
-	
-	static Corpus readFromFile(Reader in) throws IOException
-	{
-		Corpus c = new Corpus();
-		
-		// read in line-by-line
-		BufferedReader bin = new BufferedReader(in);
-		String line;
-		Pattern separator = Pattern.compile(" \\|\\|\\| ");
-
-		while ((line = bin.readLine()) != null)
-		{
-			// split into phrase and contexts
-			StringTokenizer st = new StringTokenizer(line, "\t");
-			assert (st.hasMoreTokens());
-			String phraseToks = st.nextToken();
-			assert (st.hasMoreTokens());
-			String rest = st.nextToken();
-			assert (!st.hasMoreTokens());
-
-			// process phrase	
-			st = new StringTokenizer(phraseToks, " ");
-			TIntArrayList ptoks = new TIntArrayList();
-			while (st.hasMoreTokens())
-				ptoks.add(c.tokenLexicon.insert(st.nextToken()));
-			int phraseId = c.phraseLexicon.insert(ptoks);
-			if (phraseId == c.phraseToContext.size())
-				c.phraseToContext.add(new ArrayList<Edge>());
-			
-			// process contexts
-			String[] parts = separator.split(rest);
-			assert (parts.length % 2 == 0);
-			for (int i = 0; i < parts.length; i += 2)
-			{
-				// process pairs of strings - context and count
-				TIntArrayList ctx = new TIntArrayList();
-				String ctxString = parts[i];
-				String countString = parts[i + 1];
-				StringTokenizer ctxStrtok = new StringTokenizer(ctxString, " ");
-				while (ctxStrtok.hasMoreTokens())
-				{
-					String token = ctxStrtok.nextToken();
-					if (!token.equals("<PHRASE>"))
-						ctx.add(c.tokenLexicon.insert(token));
-				}
-				int contextId = c.contextLexicon.insert(ctx);
-				if (contextId == c.contextToPhrase.size())
-					c.contextToPhrase.add(new ArrayList<Edge>());
-
-				assert (countString.startsWith("C="));
-				Edge e = c.new Edge(phraseId, contextId, 
-						Integer.parseInt(countString.substring(2).trim()));
-				c.edges.add(e);
-				
-				// index the edge for fast phrase, context lookup
-				c.phraseToContext.get(phraseId).add(e);
-				c.contextToPhrase.get(contextId).add(e);
-			}
-		}
-		
-		return c;
-	}	
-}
diff --git a/gi/posterior-regularisation/Lexicon.java b/gi/posterior-regularisation/Lexicon.java
deleted file mode 100644
index 9f0245ee..00000000
--- a/gi/posterior-regularisation/Lexicon.java
+++ /dev/null
@@ -1,32 +0,0 @@
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-
-public class Lexicon<T>
-{
-	public int insert(T word)
-	{
-		Integer i = wordToIndex.get(word);
-		if (i == null)
-		{
-			i = indexToWord.size();
-			wordToIndex.put(word, i);
-			indexToWord.add(word);
-		}
-		return i;
-	}
-
-	public T lookup(int index)
-	{
-		return indexToWord.get(index);
-	}
-
-	public int size()
-	{
-		return indexToWord.size();
-	}
-
-	private Map<T, Integer> wordToIndex = new HashMap<T, Integer>();
-	private List<T> indexToWord = new ArrayList<T>();
-}
\ No newline at end of file
diff --git a/gi/posterior-regularisation/PhraseContextModel.java b/gi/posterior-regularisation/PhraseContextModel.java
deleted file mode 100644
index 85bcfb89..00000000
--- a/gi/posterior-regularisation/PhraseContextModel.java
+++ /dev/null
@@ -1,466 +0,0 @@
-// Input of the form:
-// " the phantom of the opera "    tickets for <PHRASE> tonight ? ||| C=1 ||| seats for <PHRASE> ? </s> ||| C=1 ||| i see <PHRASE> ? </s> ||| C=1
-//                      phrase TAB [context]+
-// where    context =   phrase ||| C=...        which are separated by |||
-
-// Model parameterised as follows:
-// - each phrase, p, is allocated a latent state, t
-// - this is used to generate the contexts, c
-// - each context is generated using 4 independent multinomials, one for each position LL, L, R, RR
-
-// Training with EM:
-// - e-step is estimating q(t) = P(t|p,c) for all x,c
-// - m-step is estimating model parameters P(c,t|p) = P(t) P(c|t)
-// - PR uses alternate e-step, which first optimizes lambda 
-//      min_q KL(q||p) + delta sum_pt max_c E_q[phi_ptc]
-//   where
-//      q(t|p,c) propto p(t,c|p) exp( -phi_ptc )
-//   Then q is used to obtain expectations for vanilla M-step.
-
-// Sexing it up:
-// - learn p-specific conditionals P(t|p)
-// - or generate phrase internals, e.g., generate edge words from
-//   different distribution to central words
-// - agreement between phrase->context model and context->phrase model
-
-import java.io.*;
-import optimization.gradientBasedMethods.*;
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.gradientBasedMethods.stats.ProjectedOptimizerStats;
-import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;
-import optimization.linesearch.GenericPickFirstStep;
-import optimization.linesearch.InterpolationPickFirstStep;
-import optimization.linesearch.LineSearchMethod;
-import optimization.linesearch.WolfRuleLineSearch;
-import optimization.projections.SimplexProjection;
-import optimization.stopCriteria.CompositeStopingCriteria;
-import optimization.stopCriteria.NormalizedProjectedGradientL2Norm;
-import optimization.stopCriteria.NormalizedValueDifference;
-import optimization.stopCriteria.ProjectedGradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.stopCriteria.ValueDifference;
-import optimization.util.MathUtils;
-import java.util.*;
-import java.util.regex.*;
-import gnu.trove.TDoubleArrayList;
-import gnu.trove.TIntArrayList;
-import static java.lang.Math.*;
-
-class PhraseContextModel
-{
-	// model/optimisation configuration parameters
-	int numTags;
-	boolean posteriorRegularisation = true;
-	double constraintScale = 3; // FIXME: make configurable
-	
-	// copied from L1LMax in depparsing code
-	final double c1= 0.0001, c2=0.9, stoppingPrecision = 1e-5, maxStep = 10;
-	final int maxZoomEvals = 10, maxExtrapolationIters = 200;
-	int maxProjectionIterations = 200;
-	int minOccurrencesForProjection = 0;
-
-	// book keeping
-	int numPositions;
-	Random rng = new Random();
-
-	// training set
-	Corpus training;
-
-	// model parameters (learnt)
-	double emissions[][][]; // position in 0 .. 3 x tag x word Pr(word | tag, position)
-	double prior[][]; // phrase x tag Pr(tag | phrase)
-	double lambda[]; // edge = (phrase, context) x tag flattened lagrange multipliers
-
-	PhraseContextModel(Corpus training, int tags)
-	{
-		this.training = training;
-		this.numTags = tags;
-		assert (!training.getEdges().isEmpty());
-		assert (numTags > 1);
-
-		// now initialise emissions
-		numPositions = training.getEdges().get(0).getContext().size();
-		assert (numPositions > 0);
-
-		emissions = new double[numPositions][numTags][training.getNumTokens()];
-		prior = new double[training.getNumEdges()][numTags];
-		if (posteriorRegularisation)
-			lambda = new double[training.getNumEdges() * numTags];
-
-		for (double[][] emissionTW : emissions)
-		{
-			for (double[] emissionW : emissionTW)
-			{
-				randomise(emissionW);
-//				for (int i = 0; i < emissionW.length; ++i)
-//					emissionW[i] = i+1;
-//				normalise(emissionW);
-			}
-		}
-					
-		for (double[] priorTag : prior)
-		{
-			randomise(priorTag);
-//			for (int i = 0; i < priorTag.length; ++i)
-//				priorTag[i] = i+1;
-//			normalise(priorTag);
-		}
-	}
-
-	void expectationMaximisation(int numIterations)
-	{
-		double lastLlh = Double.NEGATIVE_INFINITY;
-
-		for (int iteration = 0; iteration < numIterations; ++iteration)
-		{
-			double emissionsCounts[][][] = new double[numPositions][numTags][training.getNumTokens()];
-			double priorCounts[][] = new double[training.getNumPhrases()][numTags];
-			
-			// E-step
-			double llh = 0;
-			if (posteriorRegularisation)
-			{
-				EStepDualObjective objective = new EStepDualObjective();
-				
-				// copied from x2y2withconstraints
-//				LineSearchMethod ls = new ArmijoLineSearchMinimizationAlongProjectionArc(new InterpolationPickFirstStep(1));				
-//				OptimizerStats stats = new OptimizerStats();
-//				ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);
-//				CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();
-//				compositeStop.add(new ProjectedGradientL2Norm(0.001));
-//				compositeStop.add(new ValueDifference(0.001));
-//				optimizer.setMaxIterations(50);
-//				boolean succeed = optimizer.optimize(objective,stats,compositeStop);
-				
-				// copied from depparser l1lmaxobjective
-				ProjectedOptimizerStats stats = new ProjectedOptimizerStats();
-				GenericPickFirstStep pickFirstStep = new GenericPickFirstStep(1);
-				LineSearchMethod linesearch = new WolfRuleLineSearch(pickFirstStep, c1, c2);
-				ProjectedGradientDescent optimizer = new ProjectedGradientDescent(linesearch);
-				optimizer.setMaxIterations(maxProjectionIterations);
-		        CompositeStopingCriteria stop = new CompositeStopingCriteria();
-		        stop.add(new NormalizedProjectedGradientL2Norm(stoppingPrecision));
-		        stop.add(new NormalizedValueDifference(stoppingPrecision));
-		        boolean succeed = optimizer.optimize(objective, stats, stop);
-
-				System.out.println("Ended optimzation Projected Gradient Descent\n" + stats.prettyPrint(1));
-				//System.out.println("Solution: " + objective.parameters);
-				if (!succeed)
-					System.out.println("Failed to optimize");
-				//System.out.println("Ended optimization in " + optimizer.getCurrentIteration());				
-
-				//lambda = objective.getParameters();
-				llh = objective.primal();
-				
-				for (int i = 0; i < training.getNumPhrases(); ++i)
-				{
-					List<Corpus.Edge> edges = training.getEdgesForPhrase(i);
-					for (int j = 0; j < edges.size(); ++j)
-					{
-						Corpus.Edge e = edges.get(j);
-						for (int t = 0; t < numTags; t++)
-						{
-							double p = objective.q.get(i).get(j).get(t);
-							priorCounts[i][t] += e.getCount() * p;
-							TIntArrayList tokens = e.getContext();
-							for (int k = 0; k < tokens.size(); ++k)
-								emissionsCounts[k][t][tokens.get(k)] += e.getCount() * p;
-						}
-					}
-				}
-			}
-			else
-			{
-				for (int i = 0; i < training.getNumPhrases(); ++i)
-				{
-					List<Corpus.Edge> edges = training.getEdgesForPhrase(i);
-					for (int j = 0; j < edges.size(); ++j)
-					{
-						Corpus.Edge e = edges.get(j);
-						double probs[] = posterior(i, e);			
-						double z = normalise(probs);
-						llh += log(z) * e.getCount();
-						
-						TIntArrayList tokens = e.getContext();
-						for (int t = 0; t < numTags; ++t)
-						{
-							priorCounts[i][t] += e.getCount() * probs[t];
-							for (int k = 0; k < tokens.size(); ++k)
-								emissionsCounts[j][t][tokens.get(k)] += e.getCount() * probs[t];
-						}
-					}
-				}
-			}
-
-			// M-step: normalise
-			for (double[][] emissionTW : emissionsCounts)
-				for (double[] emissionW : emissionTW)
-					normalise(emissionW);
-
-			for (double[] priorTag : priorCounts)
-				normalise(priorTag);
-
-			emissions = emissionsCounts;
-			prior = priorCounts;
-
-			System.out.println("Iteration " + iteration + " llh " + llh);
-
-//			if (llh - lastLlh < 1e-4)
-//				break;
-//			else
-//				lastLlh = llh;
-		}
-	}
-
-	static double normalise(double probs[])
-	{
-		double z = 0;
-		for (double p : probs)
-			z += p;
-		for (int i = 0; i < probs.length; ++i)
-			probs[i] /= z;
-		return z;
-	}
-
-	void randomise(double probs[])
-	{
-		double z = 0;
-		for (int i = 0; i < probs.length; ++i)
-		{
-			probs[i] = 10 + rng.nextDouble();
-			z += probs[i];
-		}
-
-		for (int i = 0; i < probs.length; ++i)
-			probs[i] /= z;
-	}
-
-	static int argmax(double probs[])
-	{
-		double m = Double.NEGATIVE_INFINITY;
-		int mi = -1;
-		for (int i = 0; i < probs.length; ++i)
-		{
-			if (probs[i] > m)
-			{
-				m = probs[i];
-				mi = i;
-			}
-		}
-		return mi;
-	}
-
-	double[] posterior(int phraseId, Corpus.Edge e) // unnormalised
-	{
-		double probs[] = new double[numTags];
-		TIntArrayList tokens = e.getContext();
-		for (int t = 0; t < numTags; ++t)
-		{
-			probs[t] = prior[phraseId][t];
-			for (int k = 0; k < tokens.size(); ++k)
-				probs[t] *= emissions[k][t][tokens.get(k)];
-		}
-		return probs;
-	}
-
-	void displayPosterior()
-	{
-		for (int i = 0; i < training.getNumPhrases(); ++i)
-		{
-			List<Corpus.Edge> edges = training.getEdgesForPhrase(i);
-			for (Corpus.Edge e: edges)
-			{
-				double probs[] = posterior(i, e);
-				normalise(probs);
-
-				// emit phrase
-				System.out.print(e.getPhraseString());
-				System.out.print("\t");
-				System.out.print(e.getContextString());
-				System.out.print("||| C=" + e.getCount() + " |||");
-
-				int t = argmax(probs);
-				System.out.print(" " + t + " ||| " + probs[t]);
-				// for (int t = 0; t < numTags; ++t)
-				// System.out.print(" " + probs[t]);
-				System.out.println();
-			}
-		}
-	}
-
-	public static void main(String[] args)
-	{
-		assert (args.length >= 2);
-		try
-		{
-			Corpus corpus = Corpus.readFromFile(new FileReader(new File(args[0])));
-			PhraseContextModel model = new PhraseContextModel(corpus, Integer.parseInt(args[1]));
-			model.expectationMaximisation(Integer.parseInt(args[2]));
-			model.displayPosterior();
-		} 
-		catch (IOException e)
-		{
-			System.out.println("Failed to read input file: " + args[0]);
-			e.printStackTrace();
-		}
-	}
-
-	class EStepDualObjective extends ProjectedObjective
-	{
-		List<List<TDoubleArrayList>> conditionals; // phrase id x context # x tag - precomputed
-		List<List<TDoubleArrayList>> q; // ditto, but including exp(-lambda) terms
-		double objective = 0; // log(z)
-		// Objective.gradient = d log(z) / d lambda = E_q[phi]
-		double llh = 0;
-
-		public EStepDualObjective()
-		{
-			super();
-			// compute conditionals p(context, tag | phrase) for all training instances
-			conditionals = new ArrayList<List<TDoubleArrayList>>(training.getNumPhrases());
-			q = new ArrayList<List<TDoubleArrayList>>(training.getNumPhrases());
-			for (int i = 0; i < training.getNumPhrases(); ++i)
-			{
-				List<Corpus.Edge> edges = training.getEdgesForPhrase(i);
-
-				conditionals.add(new ArrayList<TDoubleArrayList>(edges.size()));
-				q.add(new ArrayList<TDoubleArrayList>(edges.size()));
-
-				for (int j = 0; j < edges.size(); ++j)
-				{
-					Corpus.Edge e = edges.get(j);
-					double probs[] = posterior(i, e);
-					double z = normalise(probs);
-					llh += log(z) * e.getCount();
-					conditionals.get(i).add(new TDoubleArrayList(probs));
-					q.get(i).add(new TDoubleArrayList(probs));
-				}
-			}
-			
-			gradient = new double[training.getNumEdges()*numTags];
-			setInitialParameters(lambda);
-			computeObjectiveAndGradient();
-		}
-
-		@Override
-		public double[] projectPoint(double[] point)
-		{
-			SimplexProjection p = new SimplexProjection(constraintScale);
-
-			double[] newPoint = point.clone();
-			int edgeIndex = 0;
-			for (int i = 0; i < training.getNumPhrases(); ++i)
-			{
-				List<Corpus.Edge> edges = training.getEdgesForPhrase(i);
-
-				for (int t = 0; t < numTags; t++)
-				{
-					double[] subPoint = new double[edges.size()];
-					for (int j = 0; j < edges.size(); ++j)
-						subPoint[j] = point[edgeIndex+j*numTags+t];
-				
-					p.project(subPoint);
-					for (int j = 0; j < edges.size(); ++j)
-						newPoint[edgeIndex+j*numTags+t] = subPoint[j];
-				}
-				
-				edgeIndex += edges.size() * numTags;
-			}
-//			System.out.println("Proj from: " + Arrays.toString(point)); 
-//			System.out.println("Proj to:   " + Arrays.toString(newPoint)); 
-			return newPoint;
-		}
-
-		@Override
-		public void setParameters(double[] params)
-		{
-			super.setParameters(params);
-			computeObjectiveAndGradient();
-		}
-
-		@Override
-		public double[] getGradient()
-		{
-			gradientCalls += 1;
-			return gradient;
-		}
-
-		@Override
-		public double getValue()
-		{
-			functionCalls += 1;
-			return objective;
-		}
-
-		public void computeObjectiveAndGradient()
-		{
-			int edgeIndex = 0;
-			objective = 0;
-			Arrays.fill(gradient, 0);
-			for (int i = 0; i < training.getNumPhrases(); ++i)
-			{
-				List<Corpus.Edge> edges = training.getEdgesForPhrase(i);
-
-				for (int j = 0; j < edges.size(); ++j)
-				{
-					Corpus.Edge e = edges.get(j);
-					
-					double z = 0;
-					for (int t = 0; t < numTags; t++)
-					{
-						double v = conditionals.get(i).get(j).get(t) * exp(-parameters[edgeIndex+t]);
-						q.get(i).get(j).set(t, v);
-						z += v;
-					}
-					objective += log(z) * e.getCount();
-
-					for (int t = 0; t < numTags; t++)
-					{
-						double v = q.get(i).get(j).get(t) / z; 
-						q.get(i).get(j).set(t, v);
-						gradient[edgeIndex+t] -= e.getCount() * v;
-					}
-					
-					edgeIndex += numTags;
-				}
-			}			
-//			System.out.println("computeObjectiveAndGradient logz=" + objective);
-//			System.out.println("lambda=  " + Arrays.toString(parameters));
-//			System.out.println("gradient=" + Arrays.toString(gradient));
-		}
-
-		public String toString()
-		{
-			StringBuilder sb = new StringBuilder();
-			sb.append(getClass().getCanonicalName()).append(" with ");
-			sb.append(parameters.length).append(" parameters and ");
-			sb.append(training.getNumPhrases() * numTags).append(" constraints");
-			return sb.toString();
-		}
-				
-		double primal()
-		{
-			// primal = llh + KL(q||p) + scale * sum_pt max_c E_q[phi_pct]
-			// kl = sum_Y q(Y) log q(Y) / p(Y|X)
-			//    = sum_Y q(Y) { -lambda . phi(Y) - log Z }
-			//    = -log Z - lambda . E_q[phi]
-			//    = -objective + lambda . gradient
-			
-			double kl = -objective + MathUtils.dotProduct(parameters, gradient);
-			double l1lmax = 0;
-			for (int i = 0; i < training.getNumPhrases(); ++i)
-			{
-				List<Corpus.Edge> edges = training.getEdgesForPhrase(i);
-				for (int t = 0; t < numTags; t++)
-				{
-					double lmax = Double.NEGATIVE_INFINITY;
-					for (int j = 0; j < edges.size(); ++j)
-						lmax = max(lmax, q.get(i).get(j).get(t));
-					l1lmax += lmax;
-				}
-			}
-
-			return llh + kl + constraintScale * l1lmax;
-		}
-	}
-}
diff --git a/gi/posterior-regularisation/README b/gi/posterior-regularisation/README
deleted file mode 100644
index a3d54ffc..00000000
--- a/gi/posterior-regularisation/README
+++ /dev/null
@@ -1,3 +0,0 @@
-  557  ./cdec_extools/extractor -i btec/split.zh-en.al -c 500000 -L 12 -C  | sort -t $'\t' -k 1 | ./cdec_extools/mr_stripe_rule_reduce > btec.concordance
-  559  wc -l btec.concordance 
-  588  cat btec.concordance  | sed  's/.*	//' | awk '{ for (i=1; i < NF; i++) { x=substr($i, 1, 2); if (x == "C=") printf "\n"; else if (x != "||") printf "%s ", $i; }; printf "\n"; }' | sort | uniq | wc -l
diff --git a/gi/posterior-regularisation/alphabet.hh b/gi/posterior-regularisation/alphabet.hh
deleted file mode 100644
index 1db928da..00000000
--- a/gi/posterior-regularisation/alphabet.hh
+++ /dev/null
@@ -1,61 +0,0 @@
-#ifndef _alphabet_hh
-#define _alphabet_hh
-
-#include <cassert>
-#include <iosfwd>
-#include <map>
-#include <string>
-#include <vector>
-
-// Alphabet: indexes a set of types 
-template <typename T>
-class Alphabet: protected std::map<T, int>
-{
-public:
-    Alphabet() {};
-
-    bool empty() const { return std::map<T,int>::empty(); }
-    int size() const { return std::map<T,int>::size(); }
-
-    int operator[](const T &k) const
-    {
-        typename std::map<T,int>::const_iterator cit = find(k);
-        if (cit != std::map<T,int>::end())
-            return cit->second;
-        else
-            return -1;
-    }
-
-    int lookup(const T &k) const { return (*this)[k]; }
-
-    int insert(const T &k) 
-    {
-        int sz = size();
-        assert((unsigned) sz == _items.size());
-
-        std::pair<typename std::map<T,int>::iterator, bool>
-            ins = std::map<T,int>::insert(make_pair(k, sz));
-
-        if (ins.second) 
-            _items.push_back(k);
-
-        return ins.first->second;
-    }
-
-    const T &type(int i) const
-    {
-        assert(i >= 0);
-        assert(i < size());
-        return _items[i];
-    }
-
-    std::ostream &display(std::ostream &out, int i) const
-    {
-        return out << type(i);
-    }
-
-private:
-    std::vector<T> _items;
-};
-
-#endif
diff --git a/gi/posterior-regularisation/canned.concordance b/gi/posterior-regularisation/canned.concordance
deleted file mode 100644
index 710973ff..00000000
--- a/gi/posterior-regularisation/canned.concordance
+++ /dev/null
@@ -1,4 +0,0 @@
-a	0 0 <PHRASE> 0 0 ||| C=1 ||| 1 1 <PHRASE> 1 1 ||| C=1 ||| 2 2 <PHRASE> 2 2 ||| C=1
-b	0 0 <PHRASE> 0 0 ||| C=1 ||| 1 1 <PHRASE> 1 1 ||| C=1 
-c	2 2 <PHRASE> 2 2 ||| C=1 ||| 4 4 <PHRASE> 4 4 ||| C=1 ||| 5 5 <PHRASE> 5 5 ||| C=1
-d	4 4 <PHRASE> 4 4 ||| C=1 ||| 5 5 <PHRASE> 5 5 ||| C=1
diff --git a/gi/posterior-regularisation/em.cc b/gi/posterior-regularisation/em.cc
deleted file mode 100644
index f6c9fd68..00000000
--- a/gi/posterior-regularisation/em.cc
+++ /dev/null
@@ -1,830 +0,0 @@
-// Input of the form:
-// " the phantom of the opera "    tickets for <PHRASE> tonight ? ||| C=1 ||| seats for <PHRASE> ? </s> ||| C=1 ||| i see <PHRASE> ? </s> ||| C=1
-//                      phrase TAB [context]+
-// where    context =   phrase ||| C=...        which are separated by |||
-
-// Model parameterised as follows:
-// - each phrase, p, is allocated a latent state, t
-// - this is used to generate the contexts, c
-// - each context is generated using 4 independent multinomials, one for each position LL, L, R, RR
-
-// Training with EM:
-// - e-step is estimating P(t|p,c) for all x,c
-// - m-step is estimating model parameters P(p,c,t) = P(t) P(p|t) P(c|t)
-
-// Sexing it up:
-// - constrain the posteriors P(t|c) and P(t|p) to have few high-magnitude entries
-// - improve the generation of phrase internals, e.g., generate edge words from
-//   different distribution to central words
-
-#include "alphabet.hh"
-#include "log_add.hh"
-#include <algorithm>
-#include <fstream>
-#include <iostream>
-#include <iterator>
-#include <map>
-#include <sstream>
-#include <stdexcept>
-#include <vector>
-#include <tr1/random>
-#include <tr1/tuple>
-#include <nlopt.h>
-
-using namespace std;
-using namespace std::tr1;
-
-const int numTags = 5;
-const int numIterations = 100;
-const bool posterior_regularisation = true;
-const double PHRASE_VIOLATION_WEIGHT = 10;
-const double CONTEXT_VIOLATION_WEIGHT = 0;
-const bool includePhraseProb = false;
-
-// Data structures:
-Alphabet<string> lexicon;
-typedef vector<int> Phrase;
-typedef tuple<int, int, int, int> Context;
-Alphabet<Phrase> phrases;
-Alphabet<Context> contexts;
-
-typedef map<int, int> ContextCounts;
-typedef map<int, int> PhraseCounts;
-typedef map<int, ContextCounts> PhraseToContextCounts;
-typedef map<int, PhraseCounts> ContextToPhraseCounts;
-
-PhraseToContextCounts concordancePhraseToContexts;
-ContextToPhraseCounts concordanceContextToPhrases;
-
-typedef vector<double> Dist;
-typedef vector<Dist> ConditionalDist;
-Dist prior; // class -> P(class)
-vector<ConditionalDist> probCtx; // word -> class -> P(word | class), for each position of context word
-ConditionalDist probPhrase; // class -> P(word | class)
-Dist probPhraseLength; // class -> P(length | class) expressed as geometric distribution parameter
-
-mt19937 randomGenerator((size_t) time(NULL));
-uniform_real<double> uniDist(0.0, 1e-1);
-variate_generator< mt19937, uniform_real<double> > rng(randomGenerator, uniDist);
-
-void addRandomNoise(Dist &d);
-void normalise(Dist &d);
-void addTo(Dist &d, const Dist &e);
-int argmax(const Dist &d);
-
-map<Phrase, map<Context, int> > lambda_indices;
-
-Dist conditional_probs(const Phrase &phrase, const Context &context, double *normalisation = 0);
-template <typename T>
-Dist
-penalised_conditionals(const Phrase &phrase, const Context &context, 
-                       const T &lambda, double *normalisation);
-//Dist penalised_conditionals(const Phrase &phrase, const Context &context, const double *lambda, double *normalisation = 0);
-double penalised_log_likelihood(int n, const double *lambda, double *gradient, void *data);
-void optimise_lambda(double delta, double gamma, vector<double> &lambda);
-double expected_violation_phrases(const double *lambda);
-double expected_violation_contexts(const double *lambda);
-double primal_kl_divergence(const double *lambda);
-double dual(const double *lambda);
-void print_primal_dual(const double *lambda, double delta, double gamma);
-
-ostream &operator<<(ostream &, const Phrase &);
-ostream &operator<<(ostream &, const Context &);
-ostream &operator<<(ostream &, const Dist &);
-ostream &operator<<(ostream &, const ConditionalDist &);
-
-int
-main(int argc, char *argv[])
-{
-    randomGenerator.seed(time(NULL));
-
-    int edges = 0;
-    istream &input = cin;
-    while (input.good())
-    {
-        // read the phrase
-        string phraseString;
-        Phrase phrase;
-        getline(input, phraseString, '\t');
-        istringstream pinput(phraseString);
-        string token;
-        while (pinput >> token)
-            phrase.push_back(lexicon.insert(token));
-        int phraseId = phrases.insert(phrase);
-
-        // read the rest, storing each context
-        string remainder;
-        getline(input, remainder, '\n');
-        istringstream rinput(remainder);
-        Context context(-1, -1, -1, -1);
-        int index = 0;
-        while (rinput >> token)
-        {
-            if (token != "|||" && token != "<PHRASE>")
-            {
-                if (index < 4)
-                {
-                    // eugh! damn templates
-                    switch (index)
-                    {
-                        case 0: get<0>(context) = lexicon.insert(token); break;
-                        case 1: get<1>(context) = lexicon.insert(token); break;
-                        case 2: get<2>(context) = lexicon.insert(token); break;
-                        case 3: get<3>(context) = lexicon.insert(token); break;
-                        default: assert(false);
-                    }
-                    index += 1;
-                }
-                else if (token.find("C=") == 0)
-                {
-                    int contextId = contexts.insert(context);
-                    int count = atoi(token.substr(strlen("C=")).c_str());
-                    concordancePhraseToContexts[phraseId][contextId] += count;
-                    concordanceContextToPhrases[contextId][phraseId] += count;
-                    index = 0;
-                    context = Context(-1, -1, -1, -1);
-                    edges += 1;
-                }
-            }
-        }
-
-        // trigger EOF
-        input >> ws;
-    }
-
-    cout << "Read in " << phrases.size() << " phrases"
-         << " and " << contexts.size() << " contexts"
-         << " and " << edges << " edges"
-         << " and " << lexicon.size() << " word types\n";
-
-    // FIXME: filter out low count phrases and low count contexts (based on individual words?)
-    // now populate model parameters with uniform + random noise
-    prior.resize(numTags, 1.0);
-    addRandomNoise(prior);
-    normalise(prior);
-
-    probCtx.resize(4, ConditionalDist(numTags, Dist(lexicon.size(), 1.0)));
-    if (includePhraseProb)
-        probPhrase.resize(numTags, Dist(lexicon.size(), 1.0));
-    for (int t = 0; t < numTags; ++t)
-    {
-        for (int j = 0; j < 4; ++j)
-        {
-            addRandomNoise(probCtx[j][t]);
-            normalise(probCtx[j][t]);
-        }
-        if (includePhraseProb)
-        {
-            addRandomNoise(probPhrase[t]);
-            normalise(probPhrase[t]);
-        }
-    }
-    if (includePhraseProb)
-    {
-        probPhraseLength.resize(numTags, 0.5); // geometric distribution p=0.5
-        addRandomNoise(probPhraseLength);
-    }
-
-    cout << "\tprior:     " << prior << "\n";
-    //cout << "\tcontext:   " << probCtx << "\n";
-    //cout << "\tphrase:    " << probPhrase << "\n";
-    //cout << "\tphraseLen: " << probPhraseLength << endl;
-
-    vector<double> lambda;
-
-    // now do EM training
-    for (int iteration = 0; iteration < numIterations; ++iteration)
-    {
-        cout << "EM iteration " << iteration << endl;
-
-        if (posterior_regularisation)
-            optimise_lambda(PHRASE_VIOLATION_WEIGHT, CONTEXT_VIOLATION_WEIGHT, lambda);
-        //cout << "\tlambda " << lambda << endl;
-
-        Dist countsPrior(numTags, 0.0);
-        vector<ConditionalDist> countsCtx(4, ConditionalDist(numTags, Dist(lexicon.size(), 1e-10)));
-        ConditionalDist countsPhrase(numTags, Dist(lexicon.size(), 1e-10));
-        Dist countsPhraseLength(numTags, 0.0);
-        Dist nPhrases(numTags, 0.0);
-
-        double llh = 0;
-        for (PhraseToContextCounts::iterator pcit = concordancePhraseToContexts.begin();
-             pcit != concordancePhraseToContexts.end(); ++pcit)
-        {
-            const Phrase &phrase = phrases.type(pcit->first);
-
-            // e-step: estimate latent class probs; compile (class,word) stats for m-step
-            for (ContextCounts::iterator ccit = pcit->second.begin();
-                 ccit != pcit->second.end(); ++ccit)
-            {
-                const Context &context = contexts.type(ccit->first);
-
-                double z = 0;
-                Dist tagCounts;
-                if (!posterior_regularisation)
-                    tagCounts = conditional_probs(phrase, context, &z);
-                else
-                    tagCounts = penalised_conditionals(phrase, context, lambda, &z);
-
-                llh += log(z) * ccit->second;
-                addTo(countsPrior, tagCounts); // FIXME: times ccit->secon
-
-                for (int t = 0; t < numTags; ++t)
-                {
-                    for (int j = 0; j < 4; ++j)
-                        countsCtx[j][t][get<0>(context)] += tagCounts[t] * ccit->second;
-
-                    if (includePhraseProb)
-                    {
-                        for (Phrase::const_iterator pit = phrase.begin(); pit != phrase.end(); ++pit)
-                            countsPhrase[t][*pit] += tagCounts[t] * ccit->second;
-                        countsPhraseLength[t] += phrase.size() * tagCounts[t] * ccit->second;
-                        nPhrases[t] += tagCounts[t] * ccit->second;
-                    }
-                }
-            }
-        }
-
-        cout << "M-step\n";
-
-        // m-step: normalise prior and (class,word) stats and assign to model parameters
-        normalise(countsPrior);
-        prior = countsPrior;
-        for (int t = 0; t < numTags; ++t)
-        {
-            //cout << "\t\tt " << t << " prior " << countsPrior[t] << "\n";
-            for (int j = 0; j < 4; ++j)
-                normalise(countsCtx[j][t]);
-            if (includePhraseProb)
-            {
-                normalise(countsPhrase[t]);
-                countsPhraseLength[t] = nPhrases[t] / countsPhraseLength[t];
-            }
-        }
-        probCtx = countsCtx;
-        if (includePhraseProb)
-        {
-            probPhrase = countsPhrase;
-            probPhraseLength = countsPhraseLength;
-        }
-
-        double *larray = new double[lambda.size()];
-        copy(lambda.begin(), lambda.end(), larray);
-        print_primal_dual(larray, PHRASE_VIOLATION_WEIGHT, CONTEXT_VIOLATION_WEIGHT);
-        delete [] larray;
-
-        //cout << "\tllh " << llh << endl;
-        //cout << "\tprior:     " << prior << "\n";
-        //cout << "\tcontext:   " << probCtx << "\n";
-        //cout << "\tphrase:    " << probPhrase << "\n";
-        //cout << "\tphraseLen: " << probPhraseLength << "\n";
-    }
-
-    // output class membership
-    for (PhraseToContextCounts::iterator pcit = concordancePhraseToContexts.begin();
-         pcit != concordancePhraseToContexts.end(); ++pcit)
-    {
-        const Phrase &phrase = phrases.type(pcit->first);
-        for (ContextCounts::iterator ccit = pcit->second.begin();
-             ccit != pcit->second.end(); ++ccit)
-        {
-            const Context &context = contexts.type(ccit->first);
-            Dist tagCounts = conditional_probs(phrase, context, 0);
-            cout << phrase << " ||| " << context << " ||| " << argmax(tagCounts) << "\n";
-        }
-    }
-
-    return 0;
-}
-
-void addRandomNoise(Dist &d)
-{
-    for (Dist::iterator dit = d.begin(); dit != d.end(); ++dit)
-        *dit += rng();
-}
-
-void normalise(Dist &d)
-{
-    double z = 0;
-    for (Dist::iterator dit = d.begin(); dit != d.end(); ++dit)
-        z += *dit;
-    for (Dist::iterator dit = d.begin(); dit != d.end(); ++dit)
-        *dit /= z;
-}
-
-void addTo(Dist &d, const Dist &e)
-{
-    assert(d.size() == e.size());
-    for (int i = 0; i < (int) d.size(); ++i)
-        d[i] += e[i];
-}
-
-int argmax(const Dist &d)
-{
-    double best = d[0];
-    int index = 0;
-    for (int i = 1; i < (int) d.size(); ++i)
-    {
-        if (d[i] > best)
-        {
-            best = d[i];
-            index = i;
-        }
-    }
-    return index;
-}
-
-ostream &operator<<(ostream &out, const Phrase &phrase)
-{
-    for (Phrase::const_iterator pit = phrase.begin(); pit != phrase.end(); ++pit)
-        lexicon.display(((pit == phrase.begin()) ? out : out << " "), *pit);
-    return out;
-}
-
-ostream &operator<<(ostream &out, const Context &context)
-{
-    lexicon.display(out, get<0>(context));
-    lexicon.display(out << " ", get<1>(context));
-    lexicon.display(out << " <PHRASE> ", get<2>(context));
-    lexicon.display(out << " ", get<3>(context));
-    return out;
-}
-
-ostream &operator<<(ostream &out, const Dist &dist)
-{
-    for (Dist::const_iterator dit = dist.begin(); dit != dist.end(); ++dit)
-        out << ((dit == dist.begin()) ? "" : " ") << *dit;
-    return out;
-}
-
-ostream &operator<<(ostream &out, const ConditionalDist &dist)
-{
-    for (ConditionalDist::const_iterator dit = dist.begin(); dit != dist.end(); ++dit)
-        out << ((dit == dist.begin()) ? "" : "; ") << *dit;
-    return out;
-}
-
-// FIXME: slow - just use the phrase index, context index to do the mapping
-// (n.b. it's a sparse setup, not just equal to 3d array index)
-int
-lambda_index(const Phrase &phrase, const Context &context, int tag)
-{
-    return lambda_indices[phrase][context] + tag;
-}
-
-template <typename T>
-Dist
-penalised_conditionals(const Phrase &phrase, const Context &context, 
-                       const T &lambda, double *normalisation)
-{
-    Dist d = conditional_probs(phrase, context, 0);
-
-    double z = 0;
-    for (int t = 0; t < numTags; ++t)
-    {
-        d[t] *= exp(-lambda[lambda_index(phrase, context, t)]);
-        z += d[t];
-    }
-
-    if (normalisation)
-        *normalisation = z;
-
-    for (int t = 0; t < numTags; ++t)
-        d[t] /= z;
-
-    return d;
-}
-
-Dist 
-conditional_probs(const Phrase &phrase, const Context &context, double *normalisation)
-{
-    Dist tagCounts(numTags, 0.0);
-    double z = 0;
-    for (int t = 0; t < numTags; ++t)
-    {
-        double prob = prior[t];
-        prob *= (probCtx[0][t][get<0>(context)] * probCtx[1][t][get<1>(context)] *
-                 probCtx[2][t][get<2>(context)] * probCtx[3][t][get<3>(context)]);
-
-        if (includePhraseProb)
-        {
-            prob *= pow(1 - probPhraseLength[t], phrase.size() - 1) * probPhraseLength[t];
-            for (Phrase::const_iterator pit = phrase.begin(); pit != phrase.end(); ++pit)
-                prob *= probPhrase[t][*pit];
-        }
-
-        tagCounts[t] = prob;
-        z += prob;
-    }
-    if (normalisation)
-        *normalisation = z;
-
-    for (int t = 0; t < numTags; ++t)
-        tagCounts[t] /= z;
-
-    return tagCounts;
-}
-
-double 
-penalised_log_likelihood(int n, const double *lambda, double *grad, void *)
-{
-    // return log Z(lambda, theta) over the corpus
-    // where theta are the global parameters (prior, probCtx*, probPhrase*) 
-    // and lambda are lagrange multipliers for the posterior sparsity constraints
-    //
-    // this is formulated as: 
-    // f = log Z(lambda) = sum_i log ( sum_i p_theta(t_i|p_i,c_i) exp [-lambda_{t_i,p_i,c_i}] )
-    // where i indexes the training examples - specifying the (p, c) pair (which may occur with count > 1)
-    //
-    // with derivative:
-    // f'_{tpc} = frac { - count(t,p,c) p_theta(t|p,c) exp (-lambda_{t,p,c}) }
-    //                 { sum_t' p_theta(t'|p,c) exp (-lambda_{t',p,c}) }
-
-    //cout << "penalised_log_likelihood with lambda ";
-    //copy(lambda, lambda+n, ostream_iterator<double>(cout, " "));
-    //cout << "\n";
-
-    double f = 0;
-    if (grad)
-    {
-        for (int i = 0; i < n; ++i)
-            grad[i] = 0.0;
-    }
-
-    for (int p = 0; p < phrases.size(); ++p)
-    {
-        const Phrase &phrase = phrases.type(p);
-        PhraseToContextCounts::const_iterator pcit = concordancePhraseToContexts.find(p);
-        for (ContextCounts::const_iterator ccit = pcit->second.begin();
-             ccit != pcit->second.end(); ++ccit)
-        {
-            const Context &context = contexts.type(ccit->first);
-            double z = 0;
-            Dist scores = penalised_conditionals(phrase, context, lambda, &z);
-
-            f += ccit->second * log(z);
-            //cout << "\tphrase: " << phrase << " context: " << context << " count: " << ccit->second << " z " << z << endl;
-            //cout << "\t\tscores: " << scores << "\n";
-
-            if (grad)
-            {
-                for (int t = 0; t < numTags; ++t)
-                {
-                    int i = lambda_index(phrase, context, t); // FIXME: redundant lookups
-                    assert(grad[i] == 0.0);
-                    grad[i] = - ccit->second * scores[t];
-                }
-            }
-        }
-    }
-
-    //cout << "penalised_log_likelihood returning " << f;
-    //if (grad)
-    //{
-        //cout << "\ngradient: ";
-        //copy(grad, grad+n, ostream_iterator<double>(cout, " "));
-    //}
-    //cout << "\n";
-
-    return f;
-}
-
-typedef struct 
-{
-    // one of p or c should be set to -1, in which case it will be marginalised out 
-    // i.e. sum_p' lambda_{p'ct} <= threshold
-    //   or sum_c' lambda_{pc't} <= threshold
-    int p, c, t, threshold;
-} constraint_data;
-
-double 
-constraint_and_gradient(int n, const double *lambda, double *grad, void *data)
-{
-    constraint_data *d = (constraint_data *) data;
-    assert(d->t >= 0);
-    assert(d->threshold >= 0);
-
-    //cout << "constraint_and_gradient: t " << d->t << " p " << d->p << " c " << d->c << " tau " << d->threshold << endl;
-    //cout << "\tlambda ";
-    //copy(lambda, lambda+n, ostream_iterator<double>(cout, " "));
-    //cout << "\n";
-
-    // FIXME: it's crazy to use a dense gradient here => will only have a handful of non-zero entries
-    if (grad)
-    {
-        for (int i = 0; i < n; ++i)
-            grad[i] = 0.0;
-    }
-
-    //cout << "constraint_and_gradient: " << d->p << "; " << d->c << "; " << d->t << "; " << d->threshold << endl;
-
-    if (d->p >= 0)
-    {
-        assert(d->c < 0);
-        //    sum_c lambda_pct          <= delta [a.k.a. threshold]
-        // => sum_c lambda_pct - delta  <= 0
-        // derivative_pct = { 1, if p and t match; 0, otherwise }
-
-        double val = -d->threshold;
-
-        const Phrase &phrase = phrases.type(d->p);
-        PhraseToContextCounts::const_iterator pcit = concordancePhraseToContexts.find(d->p);
-        assert(pcit != concordancePhraseToContexts.end());
-        for (ContextCounts::const_iterator ccit = pcit->second.begin();
-             ccit != pcit->second.end(); ++ccit)
-        {
-            const Context &context = contexts.type(ccit->first);
-            int i = lambda_index(phrase, context, d->t);
-            val += lambda[i];
-            if (grad) grad[i] = 1;
-        }
-        //cout << "\treturning " << val << endl;
-
-        return val;
-    }
-    else
-    {
-        assert(d->c >= 0);
-        assert(d->p < 0);
-        //    sum_p lambda_pct          <= gamma [a.k.a. threshold]
-        // => sum_p lambda_pct - gamma  <= 0
-        // derivative_pct = { 1, if c and t match; 0, otherwise }
-
-        double val = -d->threshold;
-
-        const Context &context = contexts.type(d->c);
-        ContextToPhraseCounts::iterator cpit = concordanceContextToPhrases.find(d->c);
-        assert(cpit != concordanceContextToPhrases.end());
-        for (PhraseCounts::iterator pcit = cpit->second.begin();
-             pcit != cpit->second.end(); ++pcit)
-        {
-            const Phrase &phrase = phrases.type(pcit->first);
-            int i = lambda_index(phrase, context, d->t);
-            val += lambda[i];
-            if (grad) grad[i] = 1;
-        }
-        //cout << "\treturning " << val << endl;
-
-        return val;
-    }
-}
-
-void
-optimise_lambda(double delta, double gamma, vector<double> &lambdav)
-{
-    int num_lambdas = lambdav.size();
-    if (lambda_indices.empty() || lambdav.empty())
-    {
-        lambda_indices.clear();
-        lambdav.clear();
-
-        int i = 0;
-        for (int p = 0; p < phrases.size(); ++p)
-        {
-            const Phrase &phrase = phrases.type(p);
-            PhraseToContextCounts::iterator pcit = concordancePhraseToContexts.find(p);
-            for (ContextCounts::iterator ccit = pcit->second.begin();
-                 ccit != pcit->second.end(); ++ccit)
-            {
-                const Context &context = contexts.type(ccit->first);
-                lambda_indices[phrase][context] = i;
-                i += numTags;
-            }
-        }
-        num_lambdas = i;
-        lambdav.resize(num_lambdas);
-    }
-    //cout << "optimise_lambda: #langrange multipliers " << num_lambdas << endl;
-
-    // FIXME: better to work with an implicit representation to save memory usage
-    int num_constraints = (((delta > 0) ? phrases.size() : 0) + ((gamma > 0) ? contexts.size() : 0)) * numTags;
-    //cout << "optimise_lambda: #constraints " << num_constraints << endl;
-    constraint_data *data = new constraint_data[num_constraints];
-    int i = 0;
-    if (delta > 0)
-    {
-        for (int p = 0; p < phrases.size(); ++p)
-        {
-            for (int t = 0; t < numTags; ++t, ++i)
-            {
-                constraint_data &d = data[i];
-                d.p = p;
-                d.c = -1;
-                d.t = t;
-                d.threshold = delta;
-            }
-        }
-    }
-
-    if (gamma > 0)
-    {
-        for (int c = 0; c < contexts.size(); ++c)
-        {
-            for (int t = 0; t < numTags; ++t, ++i)
-            {
-                constraint_data &d = data[i];
-                d.p = -1;
-                d.c = c;
-                d.t = t;
-                d.threshold = gamma;
-            }
-        }
-    }
-    assert(i == num_constraints);
-
-    double lambda[num_lambdas];
-    double lb[num_lambdas], ub[num_lambdas];
-    for (i = 0; i < num_lambdas; ++i)
-    {
-        lambda[i] = lambdav[i]; // starting value
-        lb[i] = 0;              // lower bound
-        if (delta <= 0)         // upper bound
-            ub[i] = gamma;      
-        else if (gamma <= 0)
-            ub[i] = delta;
-        else
-            assert(false);
-    }
-
-    //print_primal_dual(lambda, delta, gamma);
-   
-    double minf;
-    int error_code = nlopt_minimize_constrained(NLOPT_LN_COBYLA, num_lambdas, penalised_log_likelihood, NULL,
-                                                num_constraints, constraint_and_gradient, data, sizeof(constraint_data),
-                                                lb, ub, lambda, &minf, -HUGE_VAL, 0.0, 0.0, 1e-4, NULL, 0, 0.0);
-    //cout << "optimise error code " << error_code << endl;
-
-    //print_primal_dual(lambda, delta, gamma);
-
-    delete [] data;
-
-    if (error_code < 0)
-        cout << "WARNING: optimisation failed with error code: " << error_code << endl;
-    //else
-    //{
-        //cout << "success; minf " << minf << endl;
-        //print_primal_dual(lambda, delta, gamma);
-    //}
-
-    lambdav = vector<double>(&lambda[0], &lambda[0] + num_lambdas);
-}
-
-// FIXME: inefficient - cache the scores
-double
-expected_violation_phrases(const double *lambda)
-{
-    // sum_pt max_c E_q[phi_pct]
-    double violation = 0;
-
-    for (int p = 0; p < phrases.size(); ++p)
-    {
-        const Phrase &phrase = phrases.type(p);
-        PhraseToContextCounts::const_iterator pcit = concordancePhraseToContexts.find(p);
-
-        for (int t = 0; t < numTags; ++t)
-        {
-            double best = 0;
-            for (ContextCounts::const_iterator ccit = pcit->second.begin();
-                 ccit != pcit->second.end(); ++ccit)
-            {
-                const Context &context = contexts.type(ccit->first);
-                Dist scores = penalised_conditionals(phrase, context, lambda, 0);
-                best = max(best, scores[t]);
-            }
-            violation += best;
-        }
-    }
-
-    return violation;
-}
-
-// FIXME: inefficient - cache the scores
-double
-expected_violation_contexts(const double *lambda)
-{
-    // sum_ct max_p E_q[phi_pct]
-    double violation = 0;
-
-    for (int c = 0; c < contexts.size(); ++c)
-    {
-        const Context &context = contexts.type(c);
-        ContextToPhraseCounts::iterator cpit = concordanceContextToPhrases.find(c);
-
-        for (int t = 0; t < numTags; ++t)
-        {
-            double best = 0;
-            for (PhraseCounts::iterator pit = cpit->second.begin();
-                 pit != cpit->second.end(); ++pit)
-            {
-                const Phrase &phrase = phrases.type(pit->first);
-                Dist scores = penalised_conditionals(phrase, context, lambda, 0);
-                best = max(best, scores[t]);
-            }
-            violation += best;
-        }
-    }
-
-    return violation;
-}
-
-// FIXME: possibly inefficient
-double 
-primal_likelihood() // FIXME: primal evaluation needs to use lambda and calculate l1linf terms
-{
-    double llh = 0;
-    for (int p = 0; p < phrases.size(); ++p)
-    {
-        const Phrase &phrase = phrases.type(p);
-        PhraseToContextCounts::const_iterator pcit = concordancePhraseToContexts.find(p);
-        for (ContextCounts::const_iterator ccit = pcit->second.begin();
-             ccit != pcit->second.end(); ++ccit)
-        {
-            const Context &context = contexts.type(ccit->first);
-            double z = 0;
-            Dist scores = conditional_probs(phrase, context, &z);
-            llh += ccit->second * log(z);
-        }
-    }
-    return llh;
-}
-
-// FIXME: inefficient - cache the scores
-double 
-primal_kl_divergence(const double *lambda)
-{
-    // return KL(q || p) = sum_y q(y) { log q(y) - log p(y | x) }
-    //                   = sum_y q(y) { log p(y | x) - lambda . phi(x, y) - log Z - log p(y | x) }
-    //                   = sum_y q(y) { - lambda . phi(x, y) } - log Z
-    // and q(y) factors with each edge, ditto for Z
-    
-    double feature_sum = 0, log_z = 0;
-    for (int p = 0; p < phrases.size(); ++p)
-    {
-        const Phrase &phrase = phrases.type(p);
-        PhraseToContextCounts::const_iterator pcit = concordancePhraseToContexts.find(p);
-        for (ContextCounts::const_iterator ccit = pcit->second.begin();
-             ccit != pcit->second.end(); ++ccit)
-        {
-            const Context &context = contexts.type(ccit->first);
-
-            double local_z = 0;
-            double local_f = 0;
-            Dist d = conditional_probs(phrase, context, 0);
-            for (int t = 0; t < numTags; ++t)
-            {
-                int i = lambda_index(phrase, context, t);
-                double s = d[t] * exp(-lambda[i]);
-                local_f += lambda[i] * s;
-                local_z += s;
-            }
-
-            log_z += ccit->second * log(local_z);
-            feature_sum += ccit->second * (local_f / local_z);
-        }
-    }
-
-    return -feature_sum - log_z;
-}
-
-// FIXME: inefficient - cache the scores
-double 
-dual(const double *lambda)
-{
-    // return log(Z) = - log { sum_y p(y | x) exp( - lambda . phi(x, y) }
-    // n.b. have flipped the sign as we're minimising
-    
-    double z = 0;
-    for (int p = 0; p < phrases.size(); ++p)
-    {
-        const Phrase &phrase = phrases.type(p);
-        PhraseToContextCounts::const_iterator pcit = concordancePhraseToContexts.find(p);
-        for (ContextCounts::const_iterator ccit = pcit->second.begin();
-             ccit != pcit->second.end(); ++ccit)
-        {
-            const Context &context = contexts.type(ccit->first);
-            double lz = 0;
-            Dist scores = penalised_conditionals(phrase, context, lambda, &z);
-            z += lz * ccit->second;
-        }
-    }
-    return log(z);
-}
-
-void
-print_primal_dual(const double *lambda, double delta, double gamma)
-{
-    double likelihood = primal_likelihood();
-    double kl = primal_kl_divergence(lambda);
-    double sum_pt = expected_violation_phrases(lambda);
-    double sum_ct = expected_violation_contexts(lambda);
-    //double d = dual(lambda);
-
-    cout << "\tllh=" << likelihood
-         << " kl=" << kl
-         << " violations phrases=" << sum_pt
-         << " contexts=" << sum_ct
-         //<< " primal=" << (kl + delta * sum_pt + gamma * sum_ct) 
-         //<< " dual=" << d
-         << " objective=" << (likelihood - kl + delta * sum_pt + gamma * sum_ct) 
-         << endl;
-}
diff --git a/gi/posterior-regularisation/invert.hh b/gi/posterior-regularisation/invert.hh
deleted file mode 100644
index d06356e9..00000000
--- a/gi/posterior-regularisation/invert.hh
+++ /dev/null
@@ -1,45 +0,0 @@
-// The following code inverts the matrix input using LU-decomposition with
-// backsubstitution of unit vectors. Reference: Numerical Recipies in C, 2nd
-// ed., by Press, Teukolsky, Vetterling & Flannery. 
-// Code written by Fredrik Orderud.
-// http://www.crystalclearsoftware.com/cgi-bin/boost_wiki/wiki.pl?LU_Matrix_Inversion
-
-#ifndef INVERT_MATRIX_HPP
-#define INVERT_MATRIX_HPP
-
-// REMEMBER to update "lu.hpp" header includes from boost-CVS
-#include <boost/numeric/ublas/vector.hpp>
-#include <boost/numeric/ublas/vector_proxy.hpp>
-#include <boost/numeric/ublas/matrix.hpp>
-#include <boost/numeric/ublas/triangular.hpp>
-#include <boost/numeric/ublas/lu.hpp>
-#include <boost/numeric/ublas/io.hpp>
-
-namespace ublas = boost::numeric::ublas;
-
-/* Matrix inversion routine.
-   Uses lu_factorize and lu_substitute in uBLAS to invert a matrix */
-template<class T>
-bool invert_matrix(const ublas::matrix<T>& input, ublas::matrix<T>& inverse) 
-{
-    using namespace boost::numeric::ublas;
-    typedef permutation_matrix<std::size_t> pmatrix;
-    // create a working copy of the input
-    matrix<T> A(input);
-    // create a permutation matrix for the LU-factorization
-    pmatrix pm(A.size1());
-
-    // perform LU-factorization
-    int res = lu_factorize(A,pm);
-    if( res != 0 ) return false;
-
-    // create identity matrix of "inverse"
-    inverse.assign(ublas::identity_matrix<T>(A.size1()));
-
-    // backsubstitute to get the inverse
-    lu_substitute(A, pm, inverse);
-    
-    return true;
-}
-
-#endif //INVERT_MATRIX_HPP
diff --git a/gi/posterior-regularisation/linesearch.py b/gi/posterior-regularisation/linesearch.py
deleted file mode 100644
index 5a3f2e9c..00000000
--- a/gi/posterior-regularisation/linesearch.py
+++ /dev/null
@@ -1,58 +0,0 @@
-## Automatically adapted for scipy Oct 07, 2005 by convertcode.py
-
-from scipy.optimize import minpack2
-import numpy
-
-import __builtin__
-pymin = __builtin__.min
-
-def line_search(f, myfprime, xk, pk, gfk, old_fval, old_old_fval,
-                args=(), c1=1e-4, c2=0.9, amax=50):
-
-    fc = 0
-    gc = 0
-    phi0 = old_fval
-    derphi0 = numpy.dot(gfk,pk)
-    alpha1 = pymin(1.0,1.01*2*(phi0-old_old_fval)/derphi0)
-    # trevor: added this test
-    alpha1 = pymin(alpha1,amax)
-
-    if isinstance(myfprime,type(())):
-        eps = myfprime[1]
-        fprime = myfprime[0]
-        newargs = (f,eps) + args
-        gradient = False
-    else:
-        fprime = myfprime
-        newargs = args
-        gradient = True
-
-    xtol = 1e-14
-    amin = 1e-8
-    isave = numpy.zeros((2,), numpy.intc)
-    dsave = numpy.zeros((13,), float)
-    task = 'START'
-    fval = old_fval
-    gval = gfk
-
-    while 1:
-        stp,fval,derphi,task = minpack2.dcsrch(alpha1, phi0, derphi0, c1, c2,
-                                               xtol, task, amin, amax,isave,dsave)
-        #print 'minpack2.dcsrch', alpha1, phi0, derphi0, c1, c2, xtol, task, amin, amax,isave,dsave
-        #print 'returns', stp,fval,derphi,task
-
-        if task[:2] == 'FG':
-            alpha1 = stp
-            fval = f(xk+stp*pk,*args)
-            fc += 1
-            gval = fprime(xk+stp*pk,*newargs)
-            if gradient: gc += 1
-            else: fc += len(xk) + 1
-            phi0 = fval
-            derphi0 = numpy.dot(gval,pk)
-        else:
-            break
-
-    if task[:5] == 'ERROR' or task[1:4] == 'WARN':
-        stp = None  # failed
-    return stp, fc, gc, fval, old_fval, gval
diff --git a/gi/posterior-regularisation/log_add.hh b/gi/posterior-regularisation/log_add.hh
deleted file mode 100644
index e0620c5a..00000000
--- a/gi/posterior-regularisation/log_add.hh
+++ /dev/null
@@ -1,30 +0,0 @@
-#ifndef log_add_hh
-#define log_add_hh
-
-#include <limits>
-#include <iostream>
-#include <cassert>
-#include <cmath>
-
-template <typename T>
-struct Log
-{
-    static T zero() { return -std::numeric_limits<T>::infinity(); } 
-
-    static T add(T l1, T l2)
-    {
-        if (l1 == zero()) return l2;
-        if (l1 > l2) 
-            return l1 + std::log(1 + exp(l2 - l1));
-        else
-            return l2 + std::log(1 + exp(l1 - l2));
-    }
-
-    static T subtract(T l1, T l2)
-    {
-        //std::assert(l1 >= l2);
-        return l1 + log(1 - exp(l2 - l1));
-    }
-};
-
-#endif
diff --git a/gi/posterior-regularisation/prjava.jar b/gi/posterior-regularisation/prjava.jar
deleted file mode 120000
index da8bf761..00000000
--- a/gi/posterior-regularisation/prjava.jar
+++ /dev/null
@@ -1 +0,0 @@
-prjava/prjava-20100708.jar
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/Makefile b/gi/posterior-regularisation/prjava/Makefile
deleted file mode 100755
index bd3bfca0..00000000
--- a/gi/posterior-regularisation/prjava/Makefile
+++ /dev/null
@@ -1,8 +0,0 @@
-all:
-	ant dist
-
-check:
-	echo no tests
-
-clean:
-	ant clean
diff --git a/gi/posterior-regularisation/prjava/build.xml b/gi/posterior-regularisation/prjava/build.xml
deleted file mode 100644
index 7222b3c8..00000000
--- a/gi/posterior-regularisation/prjava/build.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<project name="prjava" default="dist" basedir=".">
-  <!-- set global properties for this build -->
-  <property name="src" location="src"/>
-  <property name="build" location="build"/>
-  <property name="dist" location="lib"/>
-  <path id="classpath">
-      <pathelement location="lib/trove-2.0.2.jar"/>
-      <pathelement location="lib/optimization.jar"/>
-      <pathelement location="lib/jopt-simple-3.2.jar"/>
-      <pathelement location="lib/commons-math-2.1.jar"/>
-  </path>
-
-  <target name="init">
-    <!-- Create the time stamp -->
-    <tstamp/>
-    <!-- Create the build directory structure used by compile -->
-    <mkdir dir="${build}"/>
-  </target>
-
-  <target name="compile" depends="init"
-        description="compile the source " >
-    <!-- Compile the java code from ${src} into ${build} -->
-    <javac srcdir="${src}" destdir="${build}" includeantruntime="false">
-            <classpath refid="classpath"/>
-    </javac>
-  </target>
-
-  <target name="dist" depends="compile"
-        description="generate the distribution" >
-    <jar jarfile="${dist}/prjava-${DSTAMP}.jar" basedir="${build}"/>
-    <symlink link="./prjava.jar" resource="${dist}/prjava-${DSTAMP}.jar" overwrite="true"/>
-  </target>
-
-  <target name="clean"
-        description="clean up" >
-    <delete dir="${build}"/>
-  </target>
-</project>
diff --git a/gi/posterior-regularisation/prjava/lib/commons-math-2.1.jar b/gi/posterior-regularisation/prjava/lib/commons-math-2.1.jar
deleted file mode 100644
index 43b4b369..00000000
--- a/gi/posterior-regularisation/prjava/lib/commons-math-2.1.jar
+++ /dev/null
diff --git a/gi/posterior-regularisation/prjava/lib/jopt-simple-3.2.jar b/gi/posterior-regularisation/prjava/lib/jopt-simple-3.2.jar
deleted file mode 100644
index 56373621..00000000
--- a/gi/posterior-regularisation/prjava/lib/jopt-simple-3.2.jar
+++ /dev/null
diff --git a/gi/posterior-regularisation/prjava/lib/trove-2.0.2.jar b/gi/posterior-regularisation/prjava/lib/trove-2.0.2.jar
deleted file mode 100644
index 3e59fbf3..00000000
--- a/gi/posterior-regularisation/prjava/lib/trove-2.0.2.jar
+++ /dev/null
diff --git a/gi/posterior-regularisation/prjava/src/arr/F.java b/gi/posterior-regularisation/prjava/src/arr/F.java
deleted file mode 100644
index be0a6ed6..00000000
--- a/gi/posterior-regularisation/prjava/src/arr/F.java
+++ /dev/null
@@ -1,99 +0,0 @@
-package arr;

-

-import java.util.Arrays;

-import java.util.Random;

-

-public class F {

-	public static Random rng = new Random();

-

-	public static void randomise(double probs[])

-	{

-		randomise(probs, true);

-	}

-

-	public static void randomise(double probs[], boolean normalise)

-	{

-		double z = 0;

-		for (int i = 0; i < probs.length; ++i)

-		{

-			probs[i] = 10 + rng.nextDouble();

-			if (normalise)

-				z += probs[i];

-		}

-

-		if (normalise)

-			for (int i = 0; i < probs.length; ++i)

-				probs[i] /= z;

-	}

-	

-	public static void uniform(double probs[])

-	{

-		for (int i = 0; i < probs.length; ++i)

-			probs[i] = 1.0 / probs.length;

-	}

-	

-	public static void l1normalize(double [] a){

-		double sum=0;

-		for(int i=0;i<a.length;i++){

-			sum+=a[i];

-		}

-		if(sum==0)

-			Arrays.fill(a, 1.0/a.length);

-		else

-		{

-			for(int i=0;i<a.length;i++){

-				a[i]/=sum;

-			}

-		}

-	}

-	

-	public  static void l1normalize(double [][] a){

-		double sum=0;

-		for(int i=0;i<a.length;i++){

-			for(int j=0;j<a[i].length;j++){

-				sum+=a[i][j];

-			}

-		}

-		if(sum==0){

-			return;

-		}

-		for(int i=0;i<a.length;i++){

-			for(int j=0;j<a[i].length;j++){

-				a[i][j]/=sum;

-			}

-		}

-	}

-	

-	public static double l1norm(double a[]){

-		// FIXME: this isn't the l1 norm for a < 0

-		double norm=0;

-		for(int i=0;i<a.length;i++){

-			norm += a[i];

-		}

-		return norm;

-	}

-	

-	public static double l2norm(double a[]){

-		double norm=0;

-		for(int i=0;i<a.length;i++){

-			norm += a[i]*a[i];

-		}

-		return Math.sqrt(norm);

-	}

-	

-	public static int argmax(double probs[])

-	{

-		double m = Double.NEGATIVE_INFINITY;

-		int mi = -1;

-		for (int i = 0; i < probs.length; ++i)

-		{

-			if (probs[i] > m)

-			{

-				m = probs[i];

-				mi = i;

-			}

-		}

-		return mi;

-	}

-	

-}

diff --git a/gi/posterior-regularisation/prjava/src/data/Corpus.java b/gi/posterior-regularisation/prjava/src/data/Corpus.java
deleted file mode 100644
index 425ede11..00000000
--- a/gi/posterior-regularisation/prjava/src/data/Corpus.java
+++ /dev/null
@@ -1,233 +0,0 @@
-package data;

-

-import java.util.ArrayList;

-import java.util.Arrays;

-import java.util.HashMap;

-import java.util.Scanner;

-

-public class Corpus {

-

-	public static final String alphaFilename="../posdata/corpus.alphabet";

-	public static final String tagalphaFilename="../posdata/corpus.tag.alphabet";

-	

-//	public static final String START_SYM="<s>";

-	public static final String END_SYM="<e>";

-	public static final String NUM_TOK="<NUM>";

-	

-	public static final String UNK_TOK="<unk>";

-	

-	private ArrayList<String[]>sent;

-	private ArrayList<int[]>data;

-	

-	public ArrayList<String[]>tag;

-	public  ArrayList<int[]>tagData;

-	

-	public static boolean convertNumTok=true;

-	

-	private HashMap<String,Integer>freq;

-	public HashMap<String,Integer>vocab;

-	

-	public HashMap<String,Integer>tagVocab;

-	private int tagV;

-	

-	private int V;

-	

-	public static void main(String[] args) {

-		Corpus c=new Corpus("../posdata/en_test.conll");

-		System.out.println(

-			Arrays.toString(c.get(0))	

-		);

-		System.out.println(

-				Arrays.toString(c.getInt(0))	

-			);

-		

-		System.out.println(

-				Arrays.toString(c.get(1))	

-			);

-			System.out.println(

-					Arrays.toString(c.getInt(1))	

-				);

-	}

-

-	public Corpus(String filename,HashMap<String,Integer>dict){

-		V=0;

-		tagV=0;

-		freq=new HashMap<String,Integer>();

-		tagVocab=new HashMap<String,Integer>();

-		vocab=dict;

-		

-		sent=new ArrayList<String[]>();

-		tag=new ArrayList<String[]>();

-		

-		Scanner sc=io.FileUtil.openInFile(filename);

-		ArrayList<String>s=new ArrayList<String>();

-	//	s.add(START_SYM);

-		while(sc.hasNextLine()){

-			String line=sc.nextLine();

-			String toks[]=line.split("\t");

-			if(toks.length<2){

-				s.add(END_SYM);

-				sent.add(s.toArray(new String[0]));

-				s=new ArrayList<String>();

-		//		s.add(START_SYM);

-				continue;

-			}

-			String tok=toks[1].toLowerCase();

-			s.add(tok);

-		}

-		sc.close();

-

-		buildData();

-	}

-	

-	public Corpus(String filename){

-		V=0;

-		freq=new HashMap<String,Integer>();

-		vocab=new HashMap<String,Integer>();

-		tagVocab=new HashMap<String,Integer>();

-		

-		sent=new ArrayList<String[]>();

-		tag=new ArrayList<String[]>();

-		

-		System.out.println("Reading:"+filename);

-		

-		Scanner sc=io.FileUtil.openInFile(filename);

-		ArrayList<String>s=new ArrayList<String>();

-		ArrayList<String>tags=new ArrayList<String>();

-		//s.add(START_SYM);

-		while(sc.hasNextLine()){

-			String line=sc.nextLine();

-			String toks[]=line.split("\t");

-			if(toks.length<2){

-				s.add(END_SYM);

-				tags.add(END_SYM);

-				if(s.size()>2){

-					sent.add(s.toArray(new String[0]));

-					tag.add(tags.toArray(new String [0]));

-				}

-				s=new ArrayList<String>();

-				tags=new ArrayList<String>();

-			//	s.add(START_SYM);

-				continue;

-			}

-			

-			String tok=toks[1].toLowerCase();

-			if(convertNumTok && tok.matches(".*\\d.*")){

-				tok=NUM_TOK;

-			}

-			s.add(tok);

-			

-			if(toks.length>3){

-				tok=toks[3].toLowerCase();

-			}else{

-				tok="_";

-			}

-			tags.add(tok);

-			

-		}

-		sc.close();

-		

-		for(int i=0;i<sent.size();i++){

-			String[]toks=sent.get(i);

-			for(int j=0;j<toks.length;j++){

-				addVocab(toks[j]);

-				addTag(tag.get(i)[j]);

-			}

-		}

-		

-		buildVocab();

-		buildData();

-		System.out.println(data.size()+"sentences, "+vocab.keySet().size()+" word types");

-	}

-

-	public String[] get(int idx){

-		return sent.get(idx);

-	}

-	

-	private void addVocab(String s){

-		Integer integer=freq.get(s);

-		if(integer==null){

-			integer=0;

-		}

-		freq.put(s, integer+1);

-	}

-	

-	public int tokIdx(String tok){

-		Integer integer=vocab.get(tok);

-		if(integer==null){

-			return V;

-		}

-		return integer;

-	}

-	

-	public int tagIdx(String tok){

-		Integer integer=tagVocab.get(tok);

-		if(integer==null){

-			return tagV;

-		}

-		return integer;

-	}

-	

-	private void buildData(){

-		data=new ArrayList<int[]>();

-		for(int i=0;i<sent.size();i++){

-			String s[]=sent.get(i);

-			data.add(new int [s.length]);

-			for(int j=0;j<s.length;j++){

-				data.get(i)[j]=tokIdx(s[j]);

-			}

-		}

-		

-		tagData=new ArrayList<int[]>();

-		for(int i=0;i<tag.size();i++){

-			String s[]=tag.get(i);

-			tagData.add(new int [s.length]);

-			for(int j=0;j<s.length;j++){

-				tagData.get(i)[j]=tagIdx(s[j]);

-			}

-		}

-		sent=null;

-		tag=null;

-		System.gc();

-	}

-	

-	public int [] getInt(int idx){

-		return data.get(idx);

-	}

-	

-	/**

-	 * 

-	 * @return size of vocabulary 

-	 */

-	public int getVocabSize(){

-		return V;

-	}

-	

-	public int [][]getAllData(){

-		return data.toArray(new int [0][]);

-	}

-	

-	public int [][]getTagData(){

-		return tagData.toArray(new int [0][]);

-	}

-	

-	private void buildVocab(){

-		for (String key:freq.keySet()){

-			if(freq.get(key)>2){

-				vocab.put(key, V);

-				V++;

-			}

-		}

-		io.SerializedObjects.writeSerializedObject(vocab, alphaFilename);

-		io.SerializedObjects.writeSerializedObject(tagVocab,tagalphaFilename);

-	}

-

-	private void addTag(String tag){

-		Integer i=tagVocab.get(tag);

-		if(i==null){

-			tagVocab.put(tag, tagV);

-			tagV++;

-		}

-	}

-	

-}

diff --git a/gi/posterior-regularisation/prjava/src/hmm/HMM.java b/gi/posterior-regularisation/prjava/src/hmm/HMM.java
deleted file mode 100644
index 17a4679f..00000000
--- a/gi/posterior-regularisation/prjava/src/hmm/HMM.java
+++ /dev/null
@@ -1,579 +0,0 @@
-package hmm;

-

-import java.io.File;

-import java.io.FileNotFoundException;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.ArrayList;

-import java.util.Scanner;

-

-public class HMM {

-

-	

-	//trans[i][j]=prob of going FROM i to j

-	double [][]trans;

-	double [][]emit;

-	double []pi;

-	int  [][]data;

-	int [][]tagdata;

-	

-	double logtrans[][];

-	

-	public HMMObjective o;

-	

-	public static void main(String[] args) {

-	

-	}

-	

-	public HMM(int n_state,int n_emit,int [][]data){

-		trans=new double [n_state][n_state];

-		emit=new double[n_state][n_emit];

-		pi=new double [n_state];

-		System.out.println(" random initial parameters");

-		fillRand(trans);

-		fillRand(emit);

-		fillRand(pi);

-

-		this.data=data;

-		

-	}

-	

-	private void fillRand(double [][] a){

-		for(int i=0;i<a.length;i++){

-			for(int j=0;j<a[i].length;j++){

-				a[i][j]=Math.random();

-			}

-			l1normalize(a[i]);

-		}

-	}

-	private void fillRand(double []a){

-		for(int i=0;i<a.length;i++){

-				a[i]=Math.random();

-		}

-		l1normalize(a);

-	}

-	

-	private double loglikely=0;

-	

-	public void EM(){

-		double trans_exp_cnt[][]=new double [trans.length][trans.length];

-		double emit_exp_cnt[][]=new double[trans.length][emit[0].length];

-		double start_exp_cnt[]=new double[trans.length];

-		loglikely=0;

-		

-		//E

-		for(int i=0;i<data.length;i++){

-			

-			double [][][] post=forwardBackward(data[i]);

-			incrementExpCnt(post, data[i], 

-					trans_exp_cnt,

-					emit_exp_cnt,

-					start_exp_cnt);

-			

-			

-			if(i%100==0){

-				System.out.print(".");

-			}

-			if(i%1000==0){

-				System.out.println(i);

-			}

-			

-		}

-		System.out.println("Log likelihood: "+loglikely);

-		

-		//M

-		addOneSmooth(emit_exp_cnt);

-		for(int i=0;i<trans.length;i++){

-		

-			//transition probs

-			double sum=0;

-			for(int j=0;j<trans.length;j++){

-				sum+=trans_exp_cnt[i][j];

-			}

-			//avoid NAN

-			if(sum==0){

-				sum=1;

-			}

-			for(int j=0;j<trans[i].length;j++){

-				trans[i][j]=trans_exp_cnt[i][j]/sum;

-			}

-			

-			//emission probs

-			

-			sum=0;

-			for(int j=0;j<emit[i].length;j++){

-				sum+=emit_exp_cnt[i][j];

-			}

-			//avoid NAN

-			if(sum==0){

-				sum=1;

-			}

-			for(int j=0;j<emit[i].length;j++){

-				emit[i][j]=emit_exp_cnt[i][j]/sum;

-			}

-			

-			

-			//initial probs

-			for(int j=0;j<pi.length;j++){

-				pi[j]=start_exp_cnt[j];

-			}

-			l1normalize(pi);

-		}

-	}

-	

-	private double [][][]forwardBackward(int [] seq){

-		double a[][]=new double [seq.length][trans.length];

-		double b[][]=new double [seq.length][trans.length];

-		

-		int len=seq.length;

-		//initialize the first step

-		for(int i=0;i<trans.length;i++){

-			a[0][i]=emit[i][seq[0]]*pi[i];

-			b[len-1][i]=1;

-		}

-		

-		//log of denominator for likelyhood

-		double c=Math.log(l1norm(a[0]));

-		

-		l1normalize(a[0]);

-		l1normalize(b[len-1]);

-		

-		

-		

-		//forward

-		for(int n=1;n<len;n++){

-			for(int i=0;i<trans.length;i++){

-				for(int j=0;j<trans.length;j++){

-					a[n][i]+=trans[j][i]*a[n-1][j];

-				}

-				a[n][i]*=emit[i][seq[n]];

-			}

-			c+=Math.log(l1norm(a[n]));

-			l1normalize(a[n]);

-		}

-		

-		loglikely+=c;

-		

-		//backward

-		for(int n=len-2;n>=0;n--){

-			for(int i=0;i<trans.length;i++){

-				for(int j=0;j<trans.length;j++){

-					b[n][i]+=trans[i][j]*b[n+1][j]*emit[j][seq[n+1]];

-				}

-			}

-			l1normalize(b[n]);

-		}

-		

-		

-		//expected transition 

-		double p[][][]=new double [seq.length][trans.length][trans.length];

-		for(int n=0;n<len-1;n++){

-			for(int i=0;i<trans.length;i++){

-				for(int j=0;j<trans.length;j++){

-					p[n][i][j]=a[n][i]*trans[i][j]*emit[j][seq[n+1]]*b[n+1][j];

-					

-				}

-			}

-

-			l1normalize(p[n]);

-		}

-		return p;

-	}

-	

-	private void incrementExpCnt(

-			double post[][][],int [] seq, 

-			double trans_exp_cnt[][],

-			double emit_exp_cnt[][],

-			double start_exp_cnt[])

-	{

-		

-		for(int n=0;n<post.length;n++){

-			for(int i=0;i<trans.length;i++){

-				double py=0;

-				for(int j=0;j<trans.length;j++){

-					py+=post[n][i][j];

-					trans_exp_cnt[i][j]+=post[n][i][j];

-				}

-

-				emit_exp_cnt[i][seq[n]]+=py;				

-				

-			}

-		}

-		

-		//the first state

-		for(int i=0;i<trans.length;i++){

-			double py=0;

-			for(int j=0;j<trans.length;j++){

-				py+=post[0][i][j];

-			}

-			start_exp_cnt[i]+=py;	

-		}

-		

-		

-		//the last state

-		int len=post.length;

-		for(int i=0;i<trans.length;i++){

-			double py=0;

-			for(int j=0;j<trans.length;j++){

-				py+=post[len-2][j][i];

-			}

-			emit_exp_cnt[i][seq[len-1]]+=py;	

-		}

-	}

-	

-	public void l1normalize(double [] a){

-		double sum=0;

-		for(int i=0;i<a.length;i++){

-			sum+=a[i];

-		}

-		if(sum==0){

-			return ;

-		}

-		for(int i=0;i<a.length;i++){

-			a[i]/=sum;

-		}

-	}

-	

-	public  void l1normalize(double [][] a){

-		double sum=0;

-		for(int i=0;i<a.length;i++){

-			for(int j=0;j<a[i].length;j++){

-				sum+=a[i][j];

-			}

-		}

-		if(sum==0){

-			return;

-		}

-		for(int i=0;i<a.length;i++){

-			for(int j=0;j<a[i].length;j++){

-				a[i][j]/=sum;

-			}

-		}

-	}

-	

-	public void writeModel(String modelFilename) throws FileNotFoundException, IOException{

-		PrintStream ps=io.FileUtil.printstream(new File(modelFilename));

-		ps.println(trans.length);

-		ps.println("Initial Probabilities:");

-		for(int i=0;i<pi.length;i++){

-			ps.print(pi[i]+"\t");

-		}

-		ps.println();

-		ps.println("Transition Probabilities:");

-		for(int i=0;i<trans.length;i++){

-			for(int j=0;j<trans[i].length;j++){

-				ps.print(trans[i][j]+"\t");

-			}

-			ps.println();

-		}

-		ps.println("Emission Probabilities:");

-		ps.println(emit[0].length);

-		for(int i=0;i<trans.length;i++){

-			for(int j=0;j<emit[i].length;j++){

-				ps.println(emit[i][j]);

-			}

-			ps.println();

-		}

-		ps.close();

-	}

-	

-	public HMM(){

-	

-	}

-	

-	public void readModel(String modelFilename){

-		Scanner sc=io.FileUtil.openInFile(modelFilename);

-	

-		int n_state=sc.nextInt();

-		sc.nextLine();

-		sc.nextLine();

-		pi=new double [n_state];

-		for(int i=0;i<n_state;i++){

-			pi[i]=sc.nextDouble();

-		}

-		sc.nextLine();

-		sc.nextLine();

-		trans=new double[n_state][n_state];

-		for(int i=0;i<trans.length;i++){

-			for(int j=0;j<trans[i].length;j++){

-				trans[i][j]=sc.nextDouble();

-			}

-		}

-		sc.nextLine();

-		sc.nextLine();

-		

-		int n_obs=sc.nextInt();

-		emit=new double[n_state][n_obs];

-		for(int i=0;i<trans.length;i++){

-			for(int j=0;j<emit[i].length;j++){

-				emit[i][j]=sc.nextDouble();

-			}

-		}

-		sc.close();

-	}

-	

-	public int []viterbi(int [] seq){

-		double [][]p=new double [seq.length][trans.length];

-		int backp[][]=new int [seq.length][trans.length];

-		

-		for(int i=0;i<trans.length;i++){

-			p[0][i]=Math.log(emit[i][seq[0]]*pi[i]);

-		}

-		

-		double a[][]=logtrans;

-		if(logtrans==null){

-			a=new double [trans.length][trans.length];

-			for(int i=0;i<trans.length;i++){

-				for(int j=0;j<trans.length;j++){

-					a[i][j]=Math.log(trans[i][j]);

-				}

-			}

-			logtrans=a;

-		}

-		

-		double maxprob=0;

-		for(int n=1;n<seq.length;n++){

-			for(int i=0;i<trans.length;i++){

-				maxprob=p[n-1][0]+a[0][i];

-				backp[n][i]=0;

-				for(int j=1;j<trans.length;j++){

-					double prob=p[n-1][j]+a[j][i];

-					if(maxprob<prob){

-						backp[n][i]=j;

-						maxprob=prob;

-					}

-				}

-				p[n][i]=maxprob+Math.log(emit[i][seq[n]]);

-			}

-		}

-		

-		maxprob=p[seq.length-1][0];

-		int maxIdx=0;

-		for(int i=1;i<trans.length;i++){

-			if(p[seq.length-1][i]>maxprob){

-				maxprob=p[seq.length-1][i];

-				maxIdx=i;

-			}

-		}

-		int ans[]=new int [seq.length];

-		ans[seq.length-1]=maxIdx;

-		for(int i=seq.length-2;i>=0;i--){

-			ans[i]=backp[i+1][ans[i+1]];

-		}

-		return ans;

-	}

-	

-	public double l1norm(double a[]){

-		double norm=0;

-		for(int i=0;i<a.length;i++){

-			norm += a[i];

-		}

-		return norm;

-	}

-	

-	public double [][]getEmitProb(){

-		return emit;

-	}

-	

-	public int [] sample(int terminalSym){

-		ArrayList<Integer > s=new ArrayList<Integer>();

-		int state=sample(pi);

-		int sym=sample(emit[state]);

-		while(sym!=terminalSym){

-			s.add(sym);

-			state=sample(trans[state]);

-			sym=sample(emit[state]);

-		}

-		

-		int ans[]=new int [s.size()];

-		for(int i=0;i<ans.length;i++){

-			ans[i]=s.get(i);

-		}

-		return ans;

-	}

-	

-	public int sample(double p[]){

-		double r=Math.random();

-		double sum=0;

-		for(int i=0;i<p.length;i++){

-			sum+=p[i];

-			if(sum>=r){

-				return i;

-			}

-		}

-		return p.length-1;

-	}

-	

-	public void train(int tagdata[][]){

-		double trans_exp_cnt[][]=new double [trans.length][trans.length];

-		double emit_exp_cnt[][]=new double[trans.length][emit[0].length];

-		double start_exp_cnt[]=new double[trans.length];

-		

-		for(int i=0;i<tagdata.length;i++){

-			start_exp_cnt[tagdata[i][0]]++;

-			

-			for(int j=0;j<tagdata[i].length;j++){

-				if(j+1<tagdata[i].length){

-					trans_exp_cnt[ tagdata[i][j] ] [ tagdata[i][j+1] ]++;

-				}

-				emit_exp_cnt[tagdata[i][j]][data[i][j]]++;

-			}

-			

-		}

-		

-		//M

-		addOneSmooth(emit_exp_cnt);

-		for(int i=0;i<trans.length;i++){

-		

-			//transition probs

-			double sum=0;

-			for(int j=0;j<trans.length;j++){

-				sum+=trans_exp_cnt[i][j];

-			}

-			if(sum==0){

-				sum=1;

-			}

-			for(int j=0;j<trans[i].length;j++){

-				trans[i][j]=trans_exp_cnt[i][j]/sum;

-			}

-			

-			//emission probs

-

-			sum=0;

-			for(int j=0;j<emit[i].length;j++){

-				sum+=emit_exp_cnt[i][j];

-			}

-			if(sum==0){

-				sum=1;

-			}

-			for(int j=0;j<emit[i].length;j++){

-				emit[i][j]=emit_exp_cnt[i][j]/sum;

-			}

-

-			

-			//initial probs

-			for(int j=0;j<pi.length;j++){

-				pi[j]=start_exp_cnt[j];

-			}

-			l1normalize(pi);

-		}

-	}

-	

-	private void addOneSmooth(double a[][]){

-		for(int i=0;i<a.length;i++){

-			for(int j=0;j<a[i].length;j++){

-				a[i][j]+=0.01;

-			}

-			//l1normalize(a[i]);

-		}

-	}

-	

-	public void PREM(){

-		

-		o.optimizeWithProjectedGradientDescent();

-		

-		double trans_exp_cnt[][]=new double [trans.length][trans.length];

-		double emit_exp_cnt[][]=new double[trans.length][emit[0].length];

-		double start_exp_cnt[]=new double[trans.length];

-		

-		o.loglikelihood=0;

-		//E

-		for(int sentNum=0;sentNum<data.length;sentNum++){

-			

-			double [][][] post=o.forwardBackward(sentNum);

-			incrementExpCnt(post, data[sentNum], 

-					trans_exp_cnt,

-					emit_exp_cnt,

-					start_exp_cnt);

-			

-			

-			if(sentNum%100==0){

-				System.out.print(".");

-			}

-			if(sentNum%1000==0){

-				System.out.println(sentNum);

-			}

-			

-		}

-		

-		System.out.println("Log likelihood: "+o.getValue());

-		

-		//M

-		addOneSmooth(emit_exp_cnt);

-		for(int i=0;i<trans.length;i++){

-		

-			//transition probs

-			double sum=0;

-			for(int j=0;j<trans.length;j++){

-				sum+=trans_exp_cnt[i][j];

-			}

-			//avoid NAN

-			if(sum==0){

-				sum=1;

-			}

-			for(int j=0;j<trans[i].length;j++){

-				trans[i][j]=trans_exp_cnt[i][j]/sum;

-			}

-			

-			//emission probs

-			

-			sum=0;

-			for(int j=0;j<emit[i].length;j++){

-				sum+=emit_exp_cnt[i][j];

-			}

-			//avoid NAN

-			if(sum==0){

-				sum=1;

-			}

-			for(int j=0;j<emit[i].length;j++){

-				emit[i][j]=emit_exp_cnt[i][j]/sum;

-			}

-			

-			

-			//initial probs

-			for(int j=0;j<pi.length;j++){

-				pi[j]=start_exp_cnt[j];

-			}

-			l1normalize(pi);

-		}

-		

-	}

-	

-	public void computeMaxwt(double[][]maxwt, int[][] d){

-

-		for(int sentNum=0;sentNum<d.length;sentNum++){

-			double post[][][]=forwardBackward(d[sentNum]);

-			

-			for(int n=0;n<post.length;n++){

-				for(int i=0;i<trans.length;i++){

-					double py=0;

-					for(int j=0;j<trans.length;j++){

-						py+=post[n][i][j];

-					}

-

-					if(py>maxwt[i][d[sentNum][n]]){

-						maxwt[i][d[sentNum][n]]=py;

-					}

-					

-				}

-			}

-			

-			//the last state

-			int len=post.length;

-			for(int i=0;i<trans.length;i++){

-				double py=0;

-				for(int j=0;j<trans.length;j++){

-					py+=post[len-2][j][i];

-				}

-				

-				if(py>maxwt[i][d[sentNum][len-1]]){

-					maxwt[i][d[sentNum][len-1]]=py;

-				}

-				

-			}

-			

-		}

-	

-	}

-	

-}//end of class

diff --git a/gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java b/gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java
deleted file mode 100644
index 70b6c966..00000000
--- a/gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java
+++ /dev/null
@@ -1,351 +0,0 @@
-package hmm;

-

-import gnu.trove.TIntArrayList;

-import optimization.gradientBasedMethods.ProjectedGradientDescent;

-import optimization.gradientBasedMethods.ProjectedObjective;

-import optimization.gradientBasedMethods.stats.OptimizerStats;

-import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;

-import optimization.linesearch.InterpolationPickFirstStep;

-import optimization.linesearch.LineSearchMethod;

-import optimization.projections.SimplexProjection;

-import optimization.stopCriteria.CompositeStopingCriteria;

-import optimization.stopCriteria.ProjectedGradientL2Norm;

-import optimization.stopCriteria.StopingCriteria;

-import optimization.stopCriteria.ValueDifference;

-

-public class HMMObjective extends ProjectedObjective{

-

-	

-	private static final double GRAD_DIFF = 3;

-	public static double INIT_STEP_SIZE=10;

-	public static double VAL_DIFF=1000;

-	

-	private HMM hmm;

-	double[] newPoint  ;

-	

-	//posterior[sent num][tok num][tag]=index into lambda

-	private int posteriorMap[][][];

-	//projection[word][tag].get(occurence)=index into lambda

-	private TIntArrayList projectionMap[][];

-

-	//Size of the simplex

-	public double scale=10;

-	private SimplexProjection projection;

-	

-	private int wordFreq[];

-	private static int MIN_FREQ=10;

-	private int numWordsToProject=0;

-	

-	private int n_param;

-	

-	public  double loglikelihood;

-	

-	public HMMObjective(HMM h){

-		hmm=h;

-		

-		countWords();

-		buildMap();

-

-		gradient=new double [n_param];

-		projection = new SimplexProjection(scale);

-		newPoint  = new double[n_param];

-		setInitialParameters(new double[n_param]);

-		

-	}

-	

-	/**@brief counts word frequency in the corpus

-	 * 

-	 */

-	private void countWords(){

-		wordFreq=new int [hmm.emit[0].length];

-		for(int i=0;i<hmm.data.length;i++){

-			for(int j=0;j<hmm.data[i].length;j++){

-				wordFreq[hmm.data[i][j]]++;

-			}

-		}

-	}

-	

-	/**@brief build posterior and projection indices

-	 * 

-	 */

-	private void buildMap(){

-		//number of sentences hidden states and words

-		int n_states=hmm.trans.length;

-		int n_words=hmm.emit[0].length;

-		int n_sents=hmm.data.length;

-		

-		n_param=0;

-		posteriorMap=new int[n_sents][][];

-		projectionMap=new TIntArrayList[n_words][];

-		for(int sentNum=0;sentNum<n_sents;sentNum++){

-			int [] data=hmm.data[sentNum];

-			posteriorMap[sentNum]=new int[data.length][n_states];

-			numWordsToProject=0;

-			for(int i=0;i<data.length;i++){

-				int word=data[i];

-				for(int state=0;state<n_states;state++){

-					if(wordFreq[word]>MIN_FREQ){

-						if(projectionMap[word]==null){

-							projectionMap[word]=new TIntArrayList[n_states];

-						}

-			//			if(posteriorMap[sentNum][i]==null){

-			//				posteriorMap[sentNum][i]=new int[n_states];

-			//			}

-						

-						posteriorMap[sentNum][i][state]=n_param;

-						if(projectionMap[word][state]==null){

-							projectionMap[word][state]=new TIntArrayList();

-							numWordsToProject++;

-						}

-						projectionMap[word][state].add(n_param);

-						n_param++;

-					}

-					else{

-						posteriorMap[sentNum][i][state]=-1;

-					}

-				}

-			}

-		}

-	}

-	

-	@Override

-	public double[] projectPoint(double[] point) {

-		// TODO Auto-generated method stub

-		for(int i=0;i<projectionMap.length;i++){

-			

-			if(projectionMap[i]==null){

-				//this word is not constrained

-				continue;

-			}

-			

-			for(int j=0;j<projectionMap[i].length;j++){

-				TIntArrayList instances=projectionMap[i][j];

-				double[] toProject = new double[instances.size()];

-				

-				for (int k = 0; k < toProject.length; k++) {

-					//	System.out.print(instances.get(k) + " ");

-						toProject[k] = point[instances.get(k)];

-				}

-				

-				projection.project(toProject);

-				for (int k = 0; k < toProject.length; k++) {

-					newPoint[instances.get(k)]=toProject[k];

-				}

-			}

-		}

-		return newPoint;

-	}

-

-	@Override

-	public double[] getGradient() {

-		// TODO Auto-generated method stub

-		gradientCalls++;

-		return gradient;

-	}

-

-	@Override

-	public double getValue() {

-		// TODO Auto-generated method stub

-		functionCalls++;

-		return loglikelihood;

-	}

-	

-

-	@Override

-	public String toString() {

-		// TODO Auto-generated method stub

-		StringBuffer sb = new StringBuffer();

-		for (int i = 0; i < parameters.length; i++) {

-			sb.append(parameters[i]+" ");

-			if(i%100==0){

-				sb.append("\n");

-			}

-		}

-		sb.append("\n");

-		/*

-		for (int i = 0; i < gradient.length; i++) {

-			sb.append(gradient[i]+" ");

-			if(i%100==0){

-				sb.append("\n");

-			}

-		}

-		sb.append("\n");

-		*/

-		return sb.toString();

-	}

-

-	

-	/**

-	 * @param seq

-	 * @return posterior probability of each transition

-	 */

-	public double [][][]forwardBackward(int sentNum){

-		int [] seq=hmm.data[sentNum];

-		int n_states=hmm.trans.length;

-		double a[][]=new double [seq.length][n_states];

-		double b[][]=new double [seq.length][n_states];

-		

-		int len=seq.length;

-		

-		boolean  constrained=

-			(projectionMap[seq[0]]!=null);

-

-		//initialize the first step

-		for(int i=0;i<n_states;i++){

-			a[0][i]=hmm.emit[i][seq[0]]*hmm.pi[i];

-			if(constrained){

-				a[0][i]*=

-					Math.exp(- parameters[ posteriorMap[sentNum][0][i] ] );

-			}

-			b[len-1][i]=1;

-		}

-		

-		loglikelihood+=Math.log(hmm.l1norm(a[0]));		

-		hmm.l1normalize(a[0]);

-		hmm.l1normalize(b[len-1]);

-		

-		//forward

-		for(int n=1;n<len;n++){

-			

-			constrained=

-				(projectionMap[seq[n]]!=null);

-			

-			for(int i=0;i<n_states;i++){

-				for(int j=0;j<n_states;j++){

-					a[n][i]+=hmm.trans[j][i]*a[n-1][j];

-				}

-				a[n][i]*=hmm.emit[i][seq[n]];

-				

-				if(constrained){

-					a[n][i]*=

-						Math.exp(- parameters[ posteriorMap[sentNum][n][i] ] );

-				}

-				

-			}

-			loglikelihood+=Math.log(hmm.l1norm(a[n]));

-			hmm.l1normalize(a[n]);

-		}

-		

-		//temp variable for e^{-\lambda}

-		double factor=1;

-		//backward

-		for(int n=len-2;n>=0;n--){

-			

-			constrained=

-				(projectionMap[seq[n+1]]!=null);

-			

-			for(int i=0;i<n_states;i++){

-				for(int j=0;j<n_states;j++){

-					

-					if(constrained){

-						factor=

-							Math.exp(- parameters[ posteriorMap[sentNum][n+1][j] ] );

-					}else{

-						factor=1;

-					}

-					

-					b[n][i]+=hmm.trans[i][j]*b[n+1][j]*hmm.emit[j][seq[n+1]]*factor;

-					

-				}

-			}

-			hmm.l1normalize(b[n]);

-		}

-		

-		//expected transition 

-		double p[][][]=new double [seq.length][n_states][n_states];

-		for(int n=0;n<len-1;n++){

-			

-			constrained=

-				(projectionMap[seq[n+1]]!=null);

-			

-			for(int i=0;i<n_states;i++){

-				for(int j=0;j<n_states;j++){

-					

-					if(constrained){

-						factor=

-							Math.exp(- parameters[ posteriorMap[sentNum][n+1][j] ] );

-					}else{

-						factor=1;

-					}

-					

-					p[n][i][j]=a[n][i]*hmm.trans[i][j]*

-						hmm.emit[j][seq[n+1]]*b[n+1][j]*factor;

-					

-				}

-			}

-

-			hmm.l1normalize(p[n]);

-		}

-		return p;

-	}

-

-	public void optimizeWithProjectedGradientDescent(){

-		LineSearchMethod ls =

-			new ArmijoLineSearchMinimizationAlongProjectionArc

-				(new InterpolationPickFirstStep(INIT_STEP_SIZE));

-		

-		OptimizerStats stats = new OptimizerStats();

-		

-		

-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);

-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(GRAD_DIFF);

-		StopingCriteria stopValue = new ValueDifference(VAL_DIFF);

-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();

-		compositeStop.add(stopGrad);

-		compositeStop.add(stopValue);

-		

-		optimizer.setMaxIterations(10);

-		updateFunction();

-		boolean succed = optimizer.optimize(this,stats,compositeStop);

-		System.out.println("Ended optimzation Projected Gradient Descent\n" + stats.prettyPrint(1));

-		if(succed){

-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());

-		}else{

-			System.out.println("Failed to optimize");

-		}

-	}

-	

-	@Override

-	public void setParameters(double[] params) {

-		super.setParameters(params);

-		updateFunction();

-	}

-	

-	private void updateFunction(){

-		

-		updateCalls++;

-		loglikelihood=0;

-	

-		for(int sentNum=0;sentNum<hmm.data.length;sentNum++){

-			double [][][]p=forwardBackward(sentNum);

-			

-			for(int n=0;n<p.length-1;n++){

-				for(int i=0;i<p[n].length;i++){

-					if(projectionMap[hmm.data[sentNum][n]]!=null){

-						double posterior=0;

-						for(int j=0;j<p[n][i].length;j++){

-							posterior+=p[n][i][j];

-						}

-						gradient[posteriorMap[sentNum][n][i]]=-posterior;

-					}

-				}

-			}

-			

-			//the last state

-			int n=p.length-2;

-			for(int i=0;i<p[n].length;i++){

-				if(projectionMap[hmm.data[sentNum][n+1]]!=null){

-					

-					double posterior=0;

-					for(int j=0;j<p[n].length;j++){

-						posterior+=p[n][j][i];

-					}

-					gradient[posteriorMap[sentNum][n+1][i]]=-posterior;

-				

-				}

-			}	

-		}

-		

-	}

-	

-}

diff --git a/gi/posterior-regularisation/prjava/src/hmm/POS.java b/gi/posterior-regularisation/prjava/src/hmm/POS.java
deleted file mode 100644
index bdcbc683..00000000
--- a/gi/posterior-regularisation/prjava/src/hmm/POS.java
+++ /dev/null
@@ -1,120 +0,0 @@
-package hmm;

-

-import java.io.File;

-import java.io.FileNotFoundException;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.HashMap;

-

-import data.Corpus;

-

-public class POS {

-

-	//public String trainFilename="../posdata/en_train.conll";

-	public static String trainFilename="../posdata/small_train.txt";

-//	public static String trainFilename="../posdata/en_test.conll";

-//	public static String trainFilename="../posdata/trial1.txt";

-	

-	public static String testFilename="../posdata/en_test.conll";

-	//public static String testFilename="../posdata/trial1.txt";

-	

-	public static String predFilename="../posdata/en_test.predict.conll";

-	public static String modelFilename="../posdata/posModel.out";

-	public static final int ITER=20;

-	public static final int N_STATE=30;

-	

-	public static void main(String[] args) {

-		//POS p=new POS();

-		//POS p=new POS(true);

-		try {

-			PRPOS();

-		} catch (FileNotFoundException e) {

-			e.printStackTrace();

-		} catch (IOException e) {

-			e.printStackTrace();

-		}

-	}

-

-	

-	public POS() throws FileNotFoundException, IOException{

-		Corpus c= new Corpus(trainFilename);

-		//size of vocabulary +1 for unknown tokens

-		HMM hmm =new HMM(N_STATE, c.getVocabSize()+1,c.getAllData());

-		for(int i=0;i<ITER;i++){

-			System.out.println("Iter"+i);

-			hmm.EM();

-			if((i+1)%10==0){

-				hmm.writeModel(modelFilename+i);

-			}

-		}

-

-		hmm.writeModel(modelFilename);

-		

-		Corpus test=new Corpus(testFilename,c.vocab);

-		

-		PrintStream ps= io.FileUtil.printstream(new File(predFilename));

-		

-		int [][]data=test.getAllData();

-		for(int i=0;i<data.length;i++){

-			int []tag=hmm.viterbi(data[i]);

-			String sent[]=test.get(i);

-			for(int j=0;j<data[i].length;j++){

-				ps.println(sent[j]+"\t"+tag[j]);

-			}

-			ps.println();

-		}

-		ps.close();

-	}

-	

-	//POS induction with L1/Linf constraints

-	public static void PRPOS() throws FileNotFoundException, IOException{

-		Corpus c= new Corpus(trainFilename);

-		//size of vocabulary +1 for unknown tokens

-		HMM hmm =new HMM(N_STATE, c.getVocabSize()+1,c.getAllData());

-		hmm.o=new HMMObjective(hmm);

-		for(int i=0;i<ITER;i++){

-			System.out.println("Iter: "+i);

-			hmm.PREM();

-			if((i+1)%10==0){

-				hmm.writeModel(modelFilename+i);

-			}

-		}

-

-		hmm.writeModel(modelFilename);

-	}

-	

-	

-	public POS(boolean supervised) throws FileNotFoundException, IOException{

-		Corpus c= new Corpus(trainFilename);

-		//size of vocabulary +1 for unknown tokens

-		HMM hmm =new HMM(c.tagVocab.size() , c.getVocabSize()+1,c.getAllData());

-		hmm.train(c.getTagData());

-

-		hmm.writeModel(modelFilename);

-		

-		Corpus test=new Corpus(testFilename,c.vocab);

-		

-		HashMap<String, Integer>tagVocab=

-			(HashMap<String, Integer>) io.SerializedObjects.readSerializedObject(Corpus.tagalphaFilename);

-		String [] tagdict=new String [tagVocab.size()+1];

-		for(String key:tagVocab.keySet()){

-			tagdict[tagVocab.get(key)]=key;

-		}

-		tagdict[tagdict.length-1]=Corpus.UNK_TOK;

-		

-		System.out.println(c.vocab.get("<e>"));

-		

-		PrintStream ps= io.FileUtil.printstream(new File(predFilename));

-		

-		int [][]data=test.getAllData();

-		for(int i=0;i<data.length;i++){

-			int []tag=hmm.viterbi(data[i]);

-			String sent[]=test.get(i);

-			for(int j=0;j<data[i].length;j++){

-				ps.println(sent[j]+"\t"+tagdict[tag[j]]);

-			}

-			ps.println();

-		}

-		ps.close();

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/io/FileUtil.java b/gi/posterior-regularisation/prjava/src/io/FileUtil.java
deleted file mode 100644
index 6720d087..00000000
--- a/gi/posterior-regularisation/prjava/src/io/FileUtil.java
+++ /dev/null
@@ -1,48 +0,0 @@
-package io;

-import java.util.*;

-import java.util.zip.GZIPInputStream;

-import java.util.zip.GZIPOutputStream;

-import java.io.*;

-public class FileUtil 

-{

-	public static BufferedReader reader(File file) throws FileNotFoundException, IOException

-	{

-		if (file.getName().endsWith(".gz"))

-			return new BufferedReader(new InputStreamReader(new GZIPInputStream(new FileInputStream(file)), "UTF8"));

-		else

-			return new BufferedReader(new InputStreamReader(new FileInputStream(file), "UTF8"));

-	}

-	

-	public static PrintStream printstream(File file) throws FileNotFoundException, IOException

-	{

-		if (file.getName().endsWith(".gz"))

-			return new PrintStream(new GZIPOutputStream(new FileOutputStream(file)), true, "UTF8");

-		else

-			return new PrintStream(new FileOutputStream(file), true, "UTF8");

-	}

-

-	public static Scanner openInFile(String filename)

-	{

-		Scanner localsc=null;

-		try

-		{

-			localsc=new Scanner(new FileInputStream(filename), "UTF8");

-

-		}catch(IOException ioe){

-			System.out.println(ioe.getMessage());

-		}

-		return localsc;

-	}

-	

-	public static FileInputStream openInputStream(String infilename)

-	{

-		FileInputStream fis=null;

-		try {

-			fis = new FileInputStream(infilename);

-			

-		} catch (IOException ioe) {

-			System.out.println(ioe.getMessage());

-		}

-		return fis;

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/io/SerializedObjects.java b/gi/posterior-regularisation/prjava/src/io/SerializedObjects.java
deleted file mode 100644
index d1631b51..00000000
--- a/gi/posterior-regularisation/prjava/src/io/SerializedObjects.java
+++ /dev/null
@@ -1,83 +0,0 @@
-package io;
-
-
-
-import java.io.BufferedInputStream;
-import java.io.BufferedOutputStream;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.ObjectInput;
-import java.io.ObjectInputStream;
-import java.io.ObjectOutput;
-import java.io.ObjectOutputStream;
-import java.io.OutputStream;
-
-public class SerializedObjects
-{
-	public static void writeSerializedObject(Object object, String outFile)
-	{
-		ObjectOutput output = null;
-	    try{
-	      //use buffering
-	      OutputStream file = new FileOutputStream(outFile);
-	      OutputStream buffer = new BufferedOutputStream( file );
-	      output = new ObjectOutputStream( buffer );
-	      output.writeObject(object);
-	      buffer.close();
-	      file.close();
-	    }
-	    catch(IOException ex){
-	    	ex.printStackTrace();
-	    }
-	    finally{
-	      try {
-	        if (output != null) {
-	          //flush and close "output" and its underlying streams
-	          output.close();
-	        }
-	      }
-	      catch (IOException ex ){
-	    	  ex.printStackTrace();
-	      }
-	    }
-	}
-	
-	public static Object readSerializedObject(String inputFile)
-	{
-		ObjectInput input = null;
-	    Object recoveredObject=null;
-		try{
-	      //use buffering
-	      InputStream file = new FileInputStream(inputFile);
-	      InputStream buffer = new BufferedInputStream(file);
-	      input = new ObjectInputStream(buffer);
-	      //deserialize the List
-	      recoveredObject = input.readObject();
-	    }
-	    catch(IOException ex){
-	    	ex.printStackTrace();
-	    }
-	    catch (ClassNotFoundException ex){
-	    	ex.printStackTrace();
-	    }
-	    catch(Exception ex)
-	    {
-	    	ex.printStackTrace();
-	    }
-	    finally{
-	      try {
-	        if ( input != null ) {
-	          //close "input" and its underlying streams
-	          input.close();
-	        }
-	      }
-	      catch (IOException ex){
-	    	  ex.printStackTrace();
-	      }
-	    }
-	    return recoveredObject;
-	 }
-	
-}
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java b/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java
deleted file mode 100644
index 25fa7f09..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java
+++ /dev/null
@@ -1,110 +0,0 @@
-package optimization.examples;
-
-
-import optimization.gradientBasedMethods.ConjugateGradient;
-import optimization.gradientBasedMethods.GradientDescent;
-import optimization.gradientBasedMethods.LBFGS;
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.Optimizer;
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.ArmijoLineSearchMinimization;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.GradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-/**
- * 
- * @author javg
- * f(x) = \sum_{i=1}^{N-1} \left[ (1-x_i)^2+ 100 (x_{i+1} - x_i^2 )^2 \right] \quad \forall x\in\mathbb{R}^N.
- */
-public class GeneralizedRosenbrock extends Objective{
-
-	
-	
-	public GeneralizedRosenbrock(int dimensions){
-		parameters = new double[dimensions];		
-		java.util.Arrays.fill(parameters, 0);
-		gradient = new double[dimensions];
-		
-	}
-	
-	public GeneralizedRosenbrock(int dimensions, double[] params){
-		parameters = params;	
-		gradient = new double[dimensions];
-	}
-	
-	
-	public double getValue() {
-		functionCalls++;
-		double value = 0;
-		for(int i = 0; i < parameters.length-1; i++){
-			value += MathUtils.square(1-parameters[i]) + 100*MathUtils.square(parameters[i+1] - MathUtils.square(parameters[i]));
-		}
-		
-		return value;
-	}
-
-	/**
-	 * gx = -2(1-x) -2x200(y-x^2)
-	 * gy = 200(y-x^2)
-	 */
-	public double[] getGradient() {
-		gradientCalls++;
-		java.util.Arrays.fill(gradient,0);
-		for(int i = 0; i < parameters.length-1; i++){
-			gradient[i]+=-2*(1-parameters[i]) - 400*parameters[i]*(parameters[i+1] - MathUtils.square(parameters[i]));
-			gradient[i+1]+=200*(parameters[i+1] - MathUtils.square(parameters[i]));
-		}	
-		return gradient;
-	}
-
-	
-
-	
-
-	
-	
-	public String toString(){
-		String  res ="";
-		for(int i = 0; i < parameters.length; i++){
-			res += "P" + i+ " " + parameters[i];
-		}
-		res += " Value " + getValue();
-		return res;
-	}
-	
-	public static void main(String[] args) {
-		
-		GeneralizedRosenbrock o = new GeneralizedRosenbrock(2);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		;
-
-		System.out.println("Doing Gradient descent");
-		//LineSearchMethod wolfe = new WolfRuleLineSearch(new InterpolationPickFirstStep(1),100,0.001,0.1);
-		StopingCriteria stop = new GradientL2Norm(0.001);		
-		LineSearchMethod ls = new ArmijoLineSearchMinimization();
-		Optimizer optimizer = new GradientDescent(ls);		
-		OptimizerStats stats = new OptimizerStats();
-		optimizer.setMaxIterations(1000);
-		boolean succed = optimizer.optimize(o,stats, stop);
-		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
-		System.out.println("Doing Conjugate Gradient descent");
-		o = new GeneralizedRosenbrock(2);
-	//	wolfe = new WolfRuleLineSearch(new InterpolationPickFirstStep(1),100,0.001,0.1);
-		optimizer = new ConjugateGradient(ls);
-		stats = new OptimizerStats();
-		optimizer.setMaxIterations(1000);
-		succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
-		System.out.println("Doing Quasi newton descent");
-		o = new GeneralizedRosenbrock(2);
-		optimizer = new LBFGS(ls,10);
-		stats = new OptimizerStats();
-		optimizer.setMaxIterations(1000);
-		succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
-
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java
deleted file mode 100644
index f087681e..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java
+++ /dev/null
@@ -1,128 +0,0 @@
-package optimization.examples;
-
-
-import optimization.gradientBasedMethods.ConjugateGradient;
-
-import optimization.gradientBasedMethods.GradientDescent;
-import optimization.gradientBasedMethods.LBFGS;
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.GenericPickFirstStep;
-import optimization.linesearch.LineSearchMethod;
-import optimization.linesearch.WolfRuleLineSearch;
-import optimization.stopCriteria.GradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-
-
-/**
- * @author javg
- *
- */
-public class x2y2 extends Objective{
-
-	
-	//Implements function ax2+ by2 
-	double a, b;
-	public x2y2(double a, double b){
-		this.a = a;
-		this.b = b;
-		parameters = new double[2];
-		parameters[0] = 4;
-		parameters[1] = 4;
-		gradient = new double[2];
-	}
-	
-	public double getValue() {
-		functionCalls++;
-		return a*parameters[0]*parameters[0]+b*parameters[1]*parameters[1];
-	}
-
-	public double[] getGradient() {
-		gradientCalls++;
-		gradient[0]=2*a*parameters[0];
-		gradient[1]=2*b*parameters[1];
-		return gradient;
-//		if(debugLevel >=2){
-//			double[] numericalGradient = DebugHelpers.getNumericalGradient(this, parameters, 0.000001);
-//			for(int i = 0; i < parameters.length; i++){
-//				double diff = Math.abs(gradient[i]-numericalGradient[i]);
-//				if(diff > 0.00001){
-//					System.out.println("Numerical Gradient does not match");
-//					System.exit(1);
-//				}
-//			}
-//		}
-	}
-
-	
-	
-	public void optimizeWithGradientDescent(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
-		GradientDescent optimizer = new GradientDescent(ls);
-		StopingCriteria stop = new GradientL2Norm(0.001);
-//		optimizer.setGradientConvergenceValue(0.001);
-		optimizer.setMaxIterations(100);
-		boolean succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Ended optimzation Gradient Descent\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	public void optimizeWithConjugateGradient(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
-		ConjugateGradient optimizer = new ConjugateGradient(ls);
-		StopingCriteria stop = new GradientL2Norm(0.001);
-
-		optimizer.setMaxIterations(10);
-		boolean succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Ended optimzation Conjugate Gradient\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	public void optimizeWithLBFGS(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
-		LBFGS optimizer = new LBFGS(ls,10);
-		StopingCriteria stop = new GradientL2Norm(0.001);
-		optimizer.setMaxIterations(10);
-		boolean succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Ended optimzation LBFGS\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	public static void main(String[] args) {
-		x2y2 o = new x2y2(1,10);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		o.setDebugLevel(4);
-		LineSearchMethod wolfe = new WolfRuleLineSearch(new GenericPickFirstStep(1),0.001,0.9);;
-//		LineSearchMethod ls = new ArmijoLineSearchMinimization();
-		OptimizerStats stats = new OptimizerStats();
-		o.optimizeWithGradientDescent(wolfe, stats, o);
-		o = new x2y2(1,10);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-//		ls = new ArmijoLineSearchMinimization();
-		stats = new OptimizerStats();
-		o.optimizeWithConjugateGradient(wolfe, stats, o);
-		o = new x2y2(1,10);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-//		ls = new ArmijoLineSearchMinimization();
-		stats = new OptimizerStats();
-		o.optimizeWithLBFGS(wolfe, stats, o);	
-	}
-	
-	public String toString(){
-		return "P1: " + parameters[0] + " P2: " + parameters[1] + " value " + getValue();
-	}
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java
deleted file mode 100644
index 391775b7..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java
+++ /dev/null
@@ -1,127 +0,0 @@
-package optimization.examples;
-
-
-import optimization.gradientBasedMethods.ProjectedGradientDescent;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;
-import optimization.linesearch.InterpolationPickFirstStep;
-import optimization.linesearch.LineSearchMethod;
-import optimization.projections.BoundsProjection;
-import optimization.projections.Projection;
-import optimization.projections.SimplexProjection;
-import optimization.stopCriteria.CompositeStopingCriteria;
-import optimization.stopCriteria.GradientL2Norm;
-import optimization.stopCriteria.ProjectedGradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.stopCriteria.ValueDifference;
-
-
-/**
- * @author javg
- * 
- * 
- *ax2+ b(y2 -displacement)
- */
-public class x2y2WithConstraints extends ProjectedObjective{
-
-
-	double a, b;
-	double dx;
-	double dy;
-	Projection projection;
-	
-	
-	public x2y2WithConstraints(double a, double b, double[] params, double dx, double dy, Projection proj){
-		//projection = new BoundsProjection(0.2,Double.MAX_VALUE);
-		super();
-		projection = proj;	
-		this.a = a;
-		this.b = b;
-		this.dx = dx;
-		this.dy = dy;
-		setInitialParameters(params);
-		System.out.println("Function " +a+"(x-"+dx+")^2 + "+b+"(y-"+dy+")^2");
-		System.out.println("Gradient " +(2*a)+"(x-"+dx+") ; "+(b*2)+"(y-"+dy+")");
-		printParameters();
-		projection.project(parameters);
-		printParameters();
-		gradient = new double[2];
-	}
-	
-	public double getValue() {
-		functionCalls++;
-		return a*(parameters[0]-dx)*(parameters[0]-dx)+b*((parameters[1]-dy)*(parameters[1]-dy));
-	}
-
-	public double[] getGradient() {
-		if(gradient == null){
-			gradient = new double[2];
-		}
-		gradientCalls++;
-		gradient[0]=2*a*(parameters[0]-dx);
-		gradient[1]=2*b*(parameters[1]-dy);
-		return gradient;
-	}
-	
-	
-	public double[] projectPoint(double[] point) {
-		double[] newPoint = point.clone();
-		projection.project(newPoint);
-		return newPoint;
-	}	
-	
-	public void optimizeWithProjectedGradientDescent(LineSearchMethod ls, OptimizerStats stats, x2y2WithConstraints o){
-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);
-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(0.001);
-		StopingCriteria stopValue = new ValueDifference(0.001);
-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();
-		compositeStop.add(stopGrad);
-		compositeStop.add(stopValue);
-		
-		optimizer.setMaxIterations(5);
-		boolean succed = optimizer.optimize(o,stats,compositeStop);
-		System.out.println("Ended optimzation Projected Gradient Descent\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	
-	
-	public String toString(){
-		
-		return "P1: " + parameters[0] + " P2: " + parameters[1] + " value " + getValue() + " grad (" + getGradient()[0] + ":" + getGradient()[1]+")";
-	}
-	
-	public static void main(String[] args) {
-		double a = 1;
-		double b=1;
-		double x0 = 0;
-		double y0  =1;
-		double dx = 0.5;
-		double dy = 0.5	;
-		double [] parameters = new double[2];
-		parameters[0] = x0;
-		parameters[1] = y0;
-		x2y2WithConstraints o = new x2y2WithConstraints(a,b,parameters,dx,dy, new SimplexProjection(0.5));
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1] + " a " + a + " b "+b );
-		o.setDebugLevel(4);
-		
-		LineSearchMethod ls = new ArmijoLineSearchMinimizationAlongProjectionArc(new InterpolationPickFirstStep(1));
-		
-		OptimizerStats stats = new OptimizerStats();
-		o.optimizeWithProjectedGradientDescent(ls, stats, o);
-		
-//		o = new x2y2WithConstraints(a,b,x0,y0,dx,dy);
-//		stats = new OptimizerStats();
-//		o.optimizeWithSpectralProjectedGradientDescent(stats, o);
-	}
-	
-	
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java
deleted file mode 100644
index 2fcb7990..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java
+++ /dev/null
@@ -1,120 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-/**
- * 
- * @author javg
- *
- */
-public abstract class AbstractGradientBaseMethod implements Optimizer{
-	
-	protected int maxNumberOfIterations=10000;
-	
-	
-	
-	protected int currentProjectionIteration;
-	protected double currValue;	
-	protected double previousValue = Double.MAX_VALUE;;
-	protected double step;
-	protected double[] gradient;
-	public double[] direction;
-	
-	//Original values
-	protected double originalGradientL2Norm;
-	
-	protected LineSearchMethod lineSearch;
-	DifferentiableLineSearchObjective lso;
-	
-	
-	public void reset(){
-		direction = null;
-		gradient = null;
-		previousValue = Double.MAX_VALUE;
-		currentProjectionIteration = 0;
-		originalGradientL2Norm = 0;
-		step = 0;
-		currValue = 0;
-	}
-	
-	public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){
-		lso =   new DifferentiableLineSearchObjective(o);
-	}
-	public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){
-	}
-	
-	public void updateStructuresAfterStep(Objective o,OptimizerStats stats, StopingCriteria stop){
-	}
-	
-	public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){
-		//Initialize structures
-			
-		stats.collectInitStats(this, o);
-		direction = new double[o.getNumParameters()];
-		initializeStructures(o, stats, stop);
-		for (currentProjectionIteration = 1; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){
-			//System.out.println("\tgradient descent iteration " + currentProjectionIteration);
-			//System.out.print("\tparameters:" );
-			//o.printParameters();
-			previousValue = currValue;
-			currValue = o.getValue();
-			gradient = o.getGradient();
-			if(stop.stopOptimization(o)){
-				stats.collectFinalStats(this, o);
-				return true;
-			}	
-			
-			getDirection();
-			if(MathUtils.dotProduct(gradient, direction) > 0){
-				System.out.println("Not a descent direction");
-				System.out.println(" current stats " + stats.prettyPrint(1));
-				System.exit(-1);
-			}
-			updateStructuresBeforeStep(o, stats, stop);
-			lso.reset(direction);
-			step = lineSearch.getStepSize(lso);
-			//System.out.println("\t\tLeave with step: " + step);
-			if(step==-1){
-				System.out.println("Failed to find step");
-				stats.collectFinalStats(this, o);
-				return false;		
-			}
-			updateStructuresAfterStep( o, stats,  stop);
-//			previousValue = currValue;
-//			currValue = o.getValue();
-//			gradient = o.getGradient();
-			stats.collectIterationStats(this, o);
-		}
-		stats.collectFinalStats(this, o);
-		return false;
-	}
-	
-	
-	public int getCurrentIteration() {
-		return currentProjectionIteration;
-	}
-
-	
-	/**
-	 * Method specific
-	 */
-	public abstract double[] getDirection();
-
-	public double getCurrentStep() {
-		return step;
-	}
-
-
-
-	public void setMaxIterations(int max) {
-		maxNumberOfIterations = max;
-	}
-
-	public double getCurrentValue() {
-		return currValue;
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java
deleted file mode 100644
index 28295729..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java
+++ /dev/null
@@ -1,92 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-
-
-public class ConjugateGradient extends AbstractGradientBaseMethod{
-	
-	
-	double[] previousGradient;
-	double[] previousDirection;
-
-	public ConjugateGradient(LineSearchMethod lineSearch) {
-		this.lineSearch = lineSearch;
-	}
-	
-	public void reset(){
-		super.reset();
-		java.util.Arrays.fill(previousDirection, 0);
-		java.util.Arrays.fill(previousGradient, 0);
-	}
-	
-	public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){
-		super.initializeStructures(o, stats, stop);
-		previousGradient = new double[o.getNumParameters()];
-		previousDirection = new double[o.getNumParameters()];
-	}
-	public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){
-		System.arraycopy(gradient, 0, previousGradient, 0, gradient.length);
-		System.arraycopy(direction, 0, previousDirection, 0, direction.length);	
-	}
-	
-//	public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){
-//		DifferentiableLineSearchObjective lso = new DifferentiableLineSearchObjective(o);
-//		stats.collectInitStats(this, o);
-//		direction = new double[o.getNumParameters()];
-//		initializeStructures(o, stats, stop);
-//		for (currentProjectionIteration = 0; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){
-//			previousValue = currValue;
-//			currValue = o.getValue();
-//			gradient =o.getGradient();
-//			if(stop.stopOptimization(gradient)){
-//				stats.collectFinalStats(this, o);
-//				return true;
-//			}
-//			getDirection();
-//			updateStructures(o, stats, stop);
-//			lso.reset(direction);
-//			step = lineSearch.getStepSize(lso);	
-//			if(step==-1){
-//				System.out.println("Failed to find a step size");
-//				System.out.println("Failed to find step");
-//				stats.collectFinalStats(this, o);
-//				return false;	
-//			}
-//			
-//			stats.collectIterationStats(this, o);
-//		}
-//		stats.collectFinalStats(this, o);
-//		return false;
-//	}
-	
-	public double[] getDirection(){
-		direction = MathUtils.negation(gradient);
-		if(currentProjectionIteration != 1){
-			//Using Polak-Ribiere method (book equation 5.45)
-			double b = MathUtils.dotProduct(gradient, MathUtils.arrayMinus(gradient, previousGradient))
-			/MathUtils.dotProduct(previousGradient, previousGradient);
-			if(b<0){
-				System.out.println("Defaulting to gradient descent");
-				b = Math.max(b, 0);
-			}
-			MathUtils.plusEquals(direction, previousDirection, b);
-			//Debug code
-			if(MathUtils.dotProduct(direction, gradient) > 0){
-				System.out.println("Not an descent direction reseting to gradien");
-				direction = MathUtils.negation(gradient);
-			}
-		}
-		return direction;
-	}
-	
-	
-	
-
-
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java
deleted file mode 100644
index 6dc4ef6c..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java
+++ /dev/null
@@ -1,65 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import java.util.ArrayList;
-
-import optimization.util.MathUtils;
-
-
-
-public class DebugHelpers {
-	public static void getLineSearchGraph(Objective o, double[] direction, 
-			double[] parameters, double originalObj,
-			double originalDot, double c1, double c2){
-		ArrayList<Double> stepS = new ArrayList<Double>();
-		ArrayList<Double> obj = new ArrayList<Double>();
-		ArrayList<Double> norm = new ArrayList<Double>();
-		double[] gradient = new double[o.getNumParameters()];
-		double[] newParameters = parameters.clone();
-		MathUtils.plusEquals(newParameters,direction,0);
-		o.setParameters(newParameters);
-		double minValue = o.getValue();
-		int valuesBiggerThanMax = 0;
-		for(double step = 0; step < 2; step +=0.01 ){
-			newParameters = parameters.clone();
-			MathUtils.plusEquals(newParameters,direction,step);
-			o.setParameters(newParameters);
-			double newValue = o.getValue();
-			gradient = o.getGradient();
-			double newgradDirectionDot = MathUtils.dotProduct(gradient,direction);
-			stepS.add(step);
-			obj.add(newValue);
-			norm.add(newgradDirectionDot);
-			if(newValue <= minValue){
-				minValue = newValue;
-			}else{
-				valuesBiggerThanMax++;
-			}
-			
-			if(valuesBiggerThanMax > 10){
-				break;
-			}
-			
-		}
-		System.out.println("step\torigObj\tobj\tsuffdec\tnorm\tcurvature1");
-		for(int i = 0; i < stepS.size(); i++){
-			double cnorm= norm.get(i); 
-			System.out.println(stepS.get(i)+"\t"+originalObj +"\t"+obj.get(i) + "\t" + 
-					(originalObj + originalDot*((Double)stepS.get(i))*c1) +"\t"+Math.abs(cnorm) +"\t"+c2*Math.abs(originalDot));
-		}
-	}
-	
-	public static double[] getNumericalGradient(Objective o, double[] parameters, double epsilon){
-		int nrParameters = o.getNumParameters();
-		double[] gradient = new double[nrParameters];
-		double[] newParameters;
-		double originalValue = o.getValue();
-		for(int parameter = 0; parameter < nrParameters; parameter++){
-			newParameters = parameters.clone();
-			newParameters[parameter]+=epsilon;
-			o.setParameters(newParameters);
-			double newValue = o.getValue();
-			gradient[parameter]=(newValue-originalValue)/epsilon;
-		}	
-		return gradient;
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java
deleted file mode 100644
index 9a53cef4..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java
+++ /dev/null
@@ -1,19 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.linesearch.LineSearchMethod;
-
-
-
-public class GradientDescent extends AbstractGradientBaseMethod{
-	
-	public GradientDescent(LineSearchMethod lineSearch) {
-		this.lineSearch = lineSearch;
-	}
-		
-	public double[] getDirection(){
-		for(int i = 0; i< gradient.length; i++){
-			direction[i] = -gradient[i];
-		}
-		return direction;
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java
deleted file mode 100644
index dedbc942..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java
+++ /dev/null
@@ -1,234 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-public class LBFGS extends AbstractGradientBaseMethod{
-
-	//How many previous values are being saved
-	int history;
-	double[][] skList;
-	double[][] ykList;
-	double initialHessianParameters;
-	double[] previousGradient;
-	double[] previousParameters;
-	
-	//auxiliar structures
-	double q[];
-	double[] roi;
-	double[] alphai;
-	
-	public LBFGS(LineSearchMethod ls, int history) {
-		lineSearch = ls;
-		this.history = history;
-		skList = new double[history][];
-		ykList = new double[history][];
-
-	}
-	
-	public void reset(){
-		super.reset();
-		initialHessianParameters = 0;
-		previousParameters = null;
-		previousGradient = null;
-		skList = new double[history][];
-		ykList = new double[history][];
-		q = null;
-		roi = null;
-		alphai = null;
-	}
-	
-	public double[] LBFGSTwoLoopRecursion(double hessianConst){
-		//Only create array once
-		if(q == null){
-			 q = new double[gradient.length];
-		}
-		System.arraycopy(gradient, 0, q, 0, gradient.length);
-		//Only create array once
-		if(roi == null){
-			roi = new double[history]; 
-		}
-		//Only create array once
-		if(alphai == null){
-			alphai = new double[history];
-		}
-		
-		for(int i = history-1; i >=0 && skList[i]!= null && ykList[i]!=null; i-- ){			
-		//	System.out.println("New to Old proj " + currentProjectionIteration + " history "+history + " index " + i);
-			double[] si =  skList[i];
-			double[] yi = ykList[i];
-			roi[i]= 1.0/MathUtils.dotProduct(yi,si);
-			alphai[i] = MathUtils.dotProduct(si, q)*roi[i];
-			MathUtils.plusEquals(q, yi, -alphai[i]);
-		}
-		//Initial Hessian is just a constant
-		MathUtils.scalarMultiplication(q, hessianConst);
-		for(int i = 0; i <history && skList[i]!= null && ykList[i]!=null; i++ ){
-		//	System.out.println("Old to New proj " + currentProjectionIteration + " history "+history + " index " + i);
-			double beta = MathUtils.dotProduct(ykList[i], q)*roi[i];
-			MathUtils.plusEquals(q, skList[i], (alphai[i]-beta));
-		}
-		return q;
-	}
-	
-	
-	
-	
-	@Override
-	public double[] getDirection() {
-		
-		calculateInitialHessianParameter();
-//		System.out.println("Initial hessian " + initialHessianParameters);
-		return direction = MathUtils.negation(LBFGSTwoLoopRecursion(initialHessianParameters));		
-	}
-	
-	public void calculateInitialHessianParameter(){
-		if(currentProjectionIteration == 1){
-			//Use gradient
-			initialHessianParameters = 1;
-		}else if(currentProjectionIteration <= history){
-			double[] sk = skList[currentProjectionIteration-2];
-			double[] yk = ykList[currentProjectionIteration-2];
-			initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
-		}else{
-			//get the last one
-			double[] sk = skList[history-1];
-			double[] yk = ykList[history-1];
-			initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
-		}
-	}
-	
-	//TODO if structures exit just reset them to zero
-	public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){
-		super.initializeStructures(o, stats, stop);
-		previousParameters = new double[o.getNumParameters()];
-		previousGradient = new double[o.getNumParameters()];
-	}
-	public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){	
-		super.initializeStructures(o, stats, stop);
-		System.arraycopy(o.getParameters(), 0, previousParameters, 0, previousParameters.length);
-		System.arraycopy(gradient, 0, previousGradient, 0, gradient.length);
-	}
-
-	public void 	updateStructuresAfterStep( Objective o,OptimizerStats stats, StopingCriteria stop){
-		double[] diffX = MathUtils.arrayMinus(o.getParameters(), previousParameters);
-		double[] diffGrad = MathUtils.arrayMinus(gradient, previousGradient);
-		//Save new values and discard new ones
-		if(currentProjectionIteration > history){
-			for(int i = 0; i < history-1;i++){
-				skList[i]=skList[i+1];
-				ykList[i]=ykList[i+1];
-			}
-			skList[history-1]=diffX;
-			ykList[history-1]=diffGrad;
-		}else{
-			skList[currentProjectionIteration-1]=diffX;
-			ykList[currentProjectionIteration-1]=diffGrad;
-		}	
-	}
-	
-//	public boolean optimize(Objective o, OptimizerStats stats, StopingCriteria stop) {		
-//		DifferentiableLineSearchObjective lso = new DifferentiableLineSearchObjective(o);		
-//		gradient = o.getGradient();
-//		direction = new double[o.getNumParameters()];
-//		previousGradient = new double[o.getNumParameters()];
-//		
-//		previousParameters = new double[o.getNumParameters()];
-//	
-//		stats.collectInitStats(this, o);
-//		previousValue = Double.MAX_VALUE;
-//		currValue= o.getValue();
-//		//Used for stopping criteria
-//		double[] originalGradient = o.getGradient();
-//		
-//		originalGradientL2Norm = MathUtils.L2Norm(originalGradient);
-//		if(stop.stopOptimization(originalGradient)){
-//			stats.collectFinalStats(this, o);
-//			return true;
-//		}
-//		for (currentProjectionIteration = 1; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){
-//			
-//			
-//			currValue = o.getValue();
-//			gradient  = o.getGradient();
-//			currParameters = o.getParameters();
-//			
-//			
-//			if(currentProjectionIteration == 1){
-//				//Use gradient
-//				initialHessianParameters = 1;
-//			}else if(currentProjectionIteration <= history){
-//				double[] sk = skList[currentProjectionIteration-2];
-//				double[] yk = ykList[currentProjectionIteration-2];
-//				initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
-//			}else{
-//				//get the last one
-//				double[] sk = skList[history-1];
-//				double[] yk = ykList[history-1];
-//				initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
-//			}
-//			
-//			getDirection();
-//			
-//			//MatrixOutput.printDoubleArray(direction, "direction");
-//			double dot = MathUtils.dotProduct(direction, gradient);
-//			if(dot > 0){				
-//				throw new RuntimeException("Not a descent direction");
-//			} if (Double.isNaN(dot)){
-//				throw new RuntimeException("dot is not a number!!");
-//			}
-//			System.arraycopy(currParameters, 0, previousParameters, 0, currParameters.length);
-//			System.arraycopy(gradient, 0, previousGradient, 0, gradient.length);
-//			lso.reset(direction);
-//			step = lineSearch.getStepSize(lso);
-//			if(step==-1){
-//				System.out.println("Failed to find a step size");
-////				lso.printLineSearchSteps();
-////				System.out.println(stats.prettyPrint(1));
-//				stats.collectFinalStats(this, o);
-//				return false;	
-//			}
-//			stats.collectIterationStats(this, o);
-//			
-//			//We are not updating the alpha since it is done in line search already
-//			currParameters = o.getParameters();
-//			gradient = o.getGradient();
-//			
-//			if(stop.stopOptimization(gradient)){
-//				stats.collectFinalStats(this, o);
-//				return true;
-//			}
-//			double[] diffX = MathUtils.arrayMinus(currParameters, previousParameters);
-//			double[] diffGrad = MathUtils.arrayMinus(gradient, previousGradient);
-//			//Save new values and discard new ones
-//			if(currentProjectionIteration > history){
-//				for(int i = 0; i < history-1;i++){
-//					skList[i]=skList[i+1];
-//					ykList[i]=ykList[i+1];
-//				}
-//				skList[history-1]=diffX;
-//				ykList[history-1]=diffGrad;
-//			}else{
-//				skList[currentProjectionIteration-1]=diffX;
-//				ykList[currentProjectionIteration-1]=diffGrad;
-//			}		
-//			previousValue = currValue;
-//		}
-//		stats.collectFinalStats(this, o);
-//		return false;	
-//	}
-	
-
-
-	
-
-	
-
-	
-	
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java
deleted file mode 100644
index 6be01bf9..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java
+++ /dev/null
@@ -1,87 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-/**
- * Defines an optimization objective:
- * 
- * 
- * @author javg
- *
- */
-public abstract  class Objective {
-
-	protected int functionCalls = 0;
-	protected int gradientCalls = 0;
-	protected int updateCalls = 0;
-	
-	protected double[] parameters;
-	
-	//Contains a cache with the gradient
-	public double[] gradient;
-	int debugLevel = 0;
-	
-	public void setDebugLevel(int level){
-		debugLevel = level;
-	}
-	
-	public int getNumParameters() {
-		return parameters.length;
-	}
-
-	public double getParameter(int index) {
-		return parameters[index];
-	}
-
-	public double[] getParameters() {
-		return parameters;
-	}
-
-	public abstract double[] getGradient( );
-	
-	public void setParameter(int index, double value) {
-		parameters[index]=value;
-	}
-
-	public void setParameters(double[] params) {
-		if(parameters == null){
-			parameters = new double[params.length];
-		}
-		updateCalls++;
-		System.arraycopy(params, 0, parameters, 0, params.length);
-	}
-
-	
-	public int getNumberFunctionCalls() {
-		return functionCalls;
-	}
-
-	public int getNumberGradientCalls() {
-		return gradientCalls;
-	}
-	
-	public int getNumberUpdateCalls() {
-		return updateCalls;
-	}
-	
-	public String finalInfoString() {
-		return "FE: " + functionCalls + " GE " + gradientCalls + " Params updates" +
-		updateCalls;
-	}
-	public void printParameters() {
-		System.out.println(toString());
-	}	
-	
-	public abstract String toString();	
-	public abstract double getValue ();
-	
-	/**
-	 * Sets the initial objective parameters
-	 * For unconstrained models this just sets the objective params = argument no copying
-	 * For a constrained objective project the parameters and then set
-	 * @param params
-	 */
-	public  void setInitialParameters(double[] params){
-		parameters = params;
-	}
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java
deleted file mode 100644
index 96fce5b0..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java
+++ /dev/null
@@ -1,19 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.stopCriteria.StopingCriteria;
-
-public interface Optimizer {
-	public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stoping);
-	
-	
-	public double[] getDirection();
-	public double getCurrentStep();
-	public double getCurrentValue();
-	public int getCurrentIteration();
-	public void reset();
-	
-	public void setMaxIterations(int max);
-	
-		
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java
deleted file mode 100644
index afb29d04..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java
+++ /dev/null
@@ -1,11 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-/**
- * 
- * @author javg
- *
- */
-public abstract class ProjectedAbstractGradientBaseMethod extends AbstractGradientBaseMethod implements ProjectedOptimizer{
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java
deleted file mode 100644
index 0186e945..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java
+++ /dev/null
@@ -1,154 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import java.io.IOException;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.linesearch.ProjectedDifferentiableLineSearchObjective;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-
-/**
- * This class implements the projected gradiend
- * as described in Bertsekas "Non Linear Programming"
- * section 2.3.
- * 
- * The update is given by:
- * x_k+1 = x_k + alpha^k(xbar_k-x_k)
- * Where xbar is:
- * xbar = [x_k -s_k grad(f(x_k))]+
- * where []+ is the projection into the feasibility set
- * 
- * alpha is the step size 
- * s_k - is a positive scalar which can be view as a step size as well, by 
- * setting alpha to 1, then x_k+1 = [x_k -s_k grad(f(x_k))]+
- * This is called taking a step size along the projection arc (Bertsekas) which
- * we will use by default.
- * 
- * Note that the only place where we actually take a step size is on pick a step size
- * so this is going to be just like a normal gradient descent but use a different 
- * armijo line search where we project after taking a step.
- * 
- * 
- * @author javg
- *
- */
-public class ProjectedGradientDescent extends ProjectedAbstractGradientBaseMethod{
-	
-
-	
-	
-	public ProjectedGradientDescent(LineSearchMethod lineSearch) {
-		this.lineSearch = lineSearch;
-	}
-	
-	//Use projected differential objective instead
-	public void initializeStructures(Objective o, OptimizerStats stats, StopingCriteria stop) {
-		lso = new ProjectedDifferentiableLineSearchObjective(o);
-	};
-	
-	
-	ProjectedObjective obj;
-	public boolean optimize(ProjectedObjective o,OptimizerStats stats, StopingCriteria stop){
-		obj = o;
-		return super.optimize(o, stats, stop);
-	}
-	
-	public double[] getDirection(){
-		for(int i = 0; i< gradient.length; i++){
-			direction[i] = -gradient[i];
-		}
-		return direction;
-	}
-	
-	
-
-		
-}
-
-
-
-
-
-
-
-///OLD CODE
-
-//Use projected gradient norm
-//public boolean stopCriteria(double[] gradient){
-//	if(originalDirenctionL2Norm == 0){
-//		System.out.println("Leaving original direction norm is zero");
-//		return true;	
-//	}
-//	if(MathUtils.L2Norm(direction)/originalDirenctionL2Norm < gradientConvergenceValue){
-//		System.out.println("Leaving projected gradient Norm smaller than epsilon");
-//		return true;	
-//	}
-//	if((previousValue - currValue)/Math.abs(previousValue) < valueConvergenceValue) {
-//		System.out.println("Leaving value change below treshold " + previousValue + " - " + currValue);
-//		System.out.println(previousValue/currValue + " - " + currValue/currValue 
-//				+ " = " + (previousValue - currValue)/Math.abs(previousValue));
-//		return true;
-//	}
-//	return false;
-//}
-//
-
-//public boolean optimize(ProjectedObjective o,OptimizerStats stats, StopingCriteria stop){
-//		stats.collectInitStats(this, o);
-//		obj = o;
-//		step = 0;
-//		currValue = o.getValue();
-//		previousValue = Double.MAX_VALUE;
-//		gradient = o.getGradient();
-//		originalGradientL2Norm = MathUtils.L2Norm(gradient);
-//		parameterChange = new double[gradient.length];
-//		getDirection();
-//		ProjectedDifferentiableLineSearchObjective lso = new ProjectedDifferentiableLineSearchObjective(o,direction);
-//		
-//		originalDirenctionL2Norm = MathUtils.L2Norm(direction);
-//		//MatrixOutput.printDoubleArray(currParameters, "parameters");
-//		for (currentProjectionIteration = 0; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){		
-//		//	System.out.println("Iter " + currentProjectionIteration);
-//			//o.printParameters();
-//			
-//			
-//			
-//			if(stop.stopOptimization(gradient)){
-//				stats.collectFinalStats(this, o);
-//				lastStepUsed = step;
-//				return true;
-//			}			
-//			lso.reset(direction);
-//			step = lineSearch.getStepSize(lso);
-//			if(step==-1){
-//				System.out.println("Failed to find step");
-//				stats.collectFinalStats(this, o);
-//				return false;	
-//				
-//			}
-//			
-//			//Update the direction for stopping criteria
-//			previousValue = currValue;
-//			currValue = o.getValue();
-//			gradient = o.getGradient();
-//			direction = getDirection();		
-//			if(MathUtils.dotProduct(gradient, direction) > 0){
-//				System.out.println("Not a descent direction");
-//				System.out.println(" current stats " + stats.prettyPrint(1));
-//				System.exit(-1);
-//			}
-//			stats.collectIterationStats(this, o);
-//		}
-//		lastStepUsed = step;
-//		stats.collectFinalStats(this, o);
-//		return false;
-//	}
-
-//public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){
-//	System.out.println("Objective is not a projected objective");
-//	throw new RuntimeException();
-//}
-
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java
deleted file mode 100644
index c3d21393..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java
+++ /dev/null
@@ -1,29 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.util.MathUtils;
-
-
-/**
- * Computes a projected objective
- * When we tell it to set some parameters it automatically projects the parameters back into the simplex:
- * 
- * 
- * When we tell it to get the gradient in automatically returns the projected gradient:
- * @author javg
- *
- */
-public abstract class ProjectedObjective extends Objective{
-	
-	public abstract double[] projectPoint (double[] point);
-	
-	public double[] auxParameters;
-	
-	
-	public  void setInitialParameters(double[] params){
-		setParameters(projectPoint(params));
-	}
-	
-	
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java
deleted file mode 100644
index 81d8403e..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java
+++ /dev/null
@@ -1,10 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-
-public interface ProjectedOptimizer extends Optimizer{
-	
-	
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java
deleted file mode 100644
index 6340ef73..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java
+++ /dev/null
@@ -1,86 +0,0 @@
-package optimization.gradientBasedMethods.stats;
-
-import java.util.ArrayList;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.Optimizer;
-import optimization.util.MathUtils;
-import optimization.util.StaticTools;
-
-
-public class OptimizerStats {
-	
-	double start = 0;
-	double totalTime = 0;
-	
-	String objectiveFinalStats;
-	
-	ArrayList<Double> gradientNorms = new ArrayList<Double>();
-	ArrayList<Double> steps = new ArrayList<Double>();
-	ArrayList<Double> value = new ArrayList<Double>();
-	ArrayList<Integer> iterations = new ArrayList<Integer>();
-	double prevValue =0;
-	
-	public void reset(){
-		start = 0;
-		totalTime = 0;
-		
-		objectiveFinalStats="";
-		
-		gradientNorms.clear();
-		steps.clear();
-		value.clear();
-		iterations.clear();
-		prevValue =0;
-	}
-	
-	public void startTime() {
-		start = System.currentTimeMillis();
-	}
-	public void stopTime() {
-		totalTime += System.currentTimeMillis() - start;
-	}
-	
-	public String prettyPrint(int level){
-		StringBuffer res = new StringBuffer();
-		res.append("Total time " + totalTime/1000 + " seconds \n" + "Iterations " + iterations.size() + "\n");
-		res.append(objectiveFinalStats+"\n");
-		if(level > 0){
-			if(iterations.size() > 0){
-			res.append("\tIteration"+iterations.get(0)+"\tstep: "+StaticTools.prettyPrint(steps.get(0), "0.00E00", 6)+ "\tgradientNorm "+ 
-					StaticTools.prettyPrint(gradientNorms.get(0), "0.00000E00", 10)+ "\tvalue "+ StaticTools.prettyPrint(value.get(0), "0.000000E00",11)+"\n");
-			}
-			for(int i = 1; i < iterations.size(); i++){
-			res.append("\tIteration:\t"+iterations.get(i)+"\tstep:"+StaticTools.prettyPrint(steps.get(i), "0.00E00", 6)+ "\tgradientNorm "+ 
-					StaticTools.prettyPrint(gradientNorms.get(i), "0.00000E00", 10)+ 
-					"\tvalue:\t"+ StaticTools.prettyPrint(value.get(i), "0.000000E00",11)+
-					"\tvalueDiff:\t"+ StaticTools.prettyPrint((value.get(i-1)-value.get(i)), "0.000000E00",11)+
-					"\n");
-			}
-		}
-		return res.toString();
-	}
-	
-	
-	public void collectInitStats(Optimizer optimizer, Objective objective){
-		startTime();
-		iterations.add(-1);
-		gradientNorms.add(MathUtils.L2Norm(objective.getGradient()));
-		steps.add(0.0);
-		value.add(objective.getValue());
-	}
-	
-	public void collectIterationStats(Optimizer optimizer, Objective objective){
-		iterations.add(optimizer.getCurrentIteration());
-		gradientNorms.add(MathUtils.L2Norm(objective.getGradient()));
-		steps.add(optimizer.getCurrentStep());
-		value.add(optimizer.getCurrentValue());
-	}
-	
-	
-	public void collectFinalStats(Optimizer optimizer, Objective objective){
-		stopTime();
-		objectiveFinalStats = objective.finalInfoString();
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java
deleted file mode 100644
index d65a1267..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java
+++ /dev/null
@@ -1,70 +0,0 @@
-package optimization.gradientBasedMethods.stats;
-
-import java.util.ArrayList;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.Optimizer;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.gradientBasedMethods.ProjectedOptimizer;
-import optimization.util.MathUtils;
-import optimization.util.StaticTools;
-
-
-public class ProjectedOptimizerStats extends OptimizerStats{
-	
-	
-	
-	public void reset(){
-		super.reset();
-		projectedGradientNorms.clear();
-	}
-	
-	ArrayList<Double> projectedGradientNorms = new ArrayList<Double>();
-
-	public String prettyPrint(int level){
-		StringBuffer res = new StringBuffer();
-		res.append("Total time " + totalTime/1000 + " seconds \n" + "Iterations " + iterations.size() + "\n");
-		res.append(objectiveFinalStats+"\n");
-		if(level > 0){
-			if(iterations.size() > 0){
-			res.append("\tIteration"+iterations.get(0)+"\tstep: "+
-					StaticTools.prettyPrint(steps.get(0), "0.00E00", 6)+ "\tgradientNorm "+ 
-					StaticTools.prettyPrint(gradientNorms.get(0), "0.00000E00", 10)
-					+ "\tdirection"+
-					StaticTools.prettyPrint(projectedGradientNorms.get(0), "0.00000E00", 10)+
-					"\tvalue "+ StaticTools.prettyPrint(value.get(0), "0.000000E00",11)+"\n");
-			}
-			for(int i = 1; i < iterations.size(); i++){
-			res.append("\tIteration"+iterations.get(i)+"\tstep: "+StaticTools.prettyPrint(steps.get(i), "0.00E00", 6)+ "\tgradientNorm "+ 
-					StaticTools.prettyPrint(gradientNorms.get(i), "0.00000E00", 10)+ 
-					"\t direction "+
-					StaticTools.prettyPrint(projectedGradientNorms.get(i), "0.00000E00", 10)+
-					"\tvalue "+ StaticTools.prettyPrint(value.get(i), "0.000000E00",11)+
-					"\tvalueDiff "+ StaticTools.prettyPrint((value.get(i-1)-value.get(i)), "0.000000E00",11)+
-					"\n");
-			}
-		}
-		return res.toString();
-	}
-	
-	
-	public void collectInitStats(Optimizer optimizer, Objective objective){
-		startTime();
-	}
-	
-	public void collectIterationStats(Optimizer optimizer, Objective objective){		
-		iterations.add(optimizer.getCurrentIteration());
-		gradientNorms.add(MathUtils.L2Norm(objective.getGradient()));
-		projectedGradientNorms.add(MathUtils.L2Norm(optimizer.getDirection()));
-		steps.add(optimizer.getCurrentStep());
-		value.add(optimizer.getCurrentValue());
-	}
-	
-	
-	
-	public void collectFinalStats(Optimizer optimizer, Objective objective){
-		stopTime();
-		objectiveFinalStats = objective.finalInfoString();
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
deleted file mode 100644
index c9f9b8df..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
+++ /dev/null
@@ -1,102 +0,0 @@
-package optimization.linesearch;
-
-import optimization.util.Interpolation;
-
-
-/**
- * Implements Back Tracking Line Search as described on page 37 of Numerical Optimization.
- * Also known as armijo rule
- * @author javg
- *
- */
-public class ArmijoLineSearchMinimization implements LineSearchMethod{
-
-	/**
-	 * How much should the step size decrease at each iteration.
-	 */
-	double contractionFactor = 0.5;
-	double c1 = 0.0001;
-	
-	double sigma1 = 0.1;
-	double sigma2 = 0.9;
-
-
-	
-	double initialStep;
-	int maxIterations = 10;
-	
-			
-	public ArmijoLineSearchMinimization(){
-		this.initialStep = 1;
-	}
-	
-	//Experiment
-	double previousStepPicked = -1;;
-	double previousInitGradientDot = -1;
-	double currentInitGradientDot = -1;
-	
-	
-	public void reset(){
-		previousStepPicked = -1;;
-		previousInitGradientDot = -1;
-		currentInitGradientDot = -1;
-	}
-	
-	public void setInitialStep(double initial){
-		initialStep = initial;
-	}
-	
-	/**
-	 * 
-	 */
-	
-	public double getStepSize(DifferentiableLineSearchObjective o) {	
-		currentInitGradientDot = o.getInitialGradient();
-		//Should update all in the objective
-		o.updateAlpha(initialStep);	
-		int nrIterations = 0;
-		//System.out.println("tried alpha" + initialStep + " value " + o.getCurrentValue());
-		while(!WolfeConditions.suficientDecrease(o,c1)){			
-			if(nrIterations >= maxIterations){
-				o.printLineSearchSteps();	
-				return -1;
-			}
-			double alpha=o.getAlpha();
-			double alphaTemp = 
-				Interpolation.quadraticInterpolation(o.getOriginalValue(), o.getInitialGradient(), alpha, o.getCurrentValue());
-			if(alphaTemp >= sigma1 || alphaTemp <= sigma2*o.getAlpha()){
-//				System.out.println("using alpha temp " + alphaTemp);
-				alpha = alphaTemp;
-			}else{
-//				System.out.println("Discarding alpha temp " + alphaTemp);
-				alpha = alpha*contractionFactor;
-			}
-//			double alpha =o.getAlpha()*contractionFactor;
-
-			o.updateAlpha(alpha);
-			//System.out.println("tried alpha" + alpha+ " value " + o.getCurrentValue());
-			nrIterations++;			
-		}
-		
-		//System.out.println("Leavning line search used:");
-		//o.printLineSearchSteps();	
-		
-		previousInitGradientDot = currentInitGradientDot;
-		previousStepPicked = o.getAlpha();
-		return o.getAlpha();
-	}
-
-	public double getInitialGradient() {
-		return currentInitGradientDot;
-		
-	}
-
-	public double getPreviousInitialGradient() {
-		return previousInitGradientDot;
-	}
-
-	public double getPreviousStepUsed() {
-		return previousStepPicked;
-	}
-		
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java
deleted file mode 100644
index e153f2da..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java
+++ /dev/null
@@ -1,141 +0,0 @@
-package optimization.linesearch;
-
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.Interpolation;
-import optimization.util.MathUtils;
-
-
-
-
-
-/**
- * Implements Armijo Rule Line search along the projection arc (Non-Linear Programming page 230)
- * To be used with Projected gradient Methods.
- * 
- * Recall that armijo tries successive step sizes alpha until the sufficient decrease is satisfied:
- * f(x+alpha*direction) < f(x) + alpha*c1*grad(f)*direction
- * 
- * In this case we are optimizing over a convex set X so we must guarantee that the new point stays inside the 
- * constraints.
- * First the direction as to be feasible (inside constraints) and will be define as:
- * d = (x_k_f - x_k) where x_k_f is a feasible point.
- * so the armijo condition can be rewritten as:
- * f(x+alpha(x_k_f - x_k)) < f(x) + c1*grad(f)*(x_k_f - x_k)
- * and x_k_f is defined as:
- * [x_k-alpha*grad(f)]+
- * where []+ mean a projection to the feasibility set.
- * So this means that we take a step on the negative gradient (gradient descent) and then obtain then project
- * that point to the feasibility set. 
- * Note that if the point is already feasible then we are back to the normal armijo rule.
- * 
- * @author javg
- *
- */
-public class ArmijoLineSearchMinimizationAlongProjectionArc implements LineSearchMethod{
-
-	/**
-	 * How much should the step size decrease at each iteration.
-	 */
-	double contractionFactor = 0.5;
-	double c1 = 0.0001;
-	
-	
-	double initialStep;
-	int maxIterations = 100;
-			
-	
-	double sigma1 = 0.1;
-	double sigma2 = 0.9;
-	
-	//Experiment
-	double previousStepPicked = -1;;
-	double previousInitGradientDot = -1;
-	double currentInitGradientDot = -1;
-	
-	GenericPickFirstStep strategy;
-	
-	
-	public void reset(){
-		previousStepPicked = -1;;
-		previousInitGradientDot = -1;
-		currentInitGradientDot = -1;
-	}
-
-	
-	public ArmijoLineSearchMinimizationAlongProjectionArc(){
-		this.initialStep = 1;
-	}
-	
-	public ArmijoLineSearchMinimizationAlongProjectionArc(GenericPickFirstStep strategy){
-		this.strategy = strategy;
-		this.initialStep = strategy.getFirstStep(this);
-	}
-	
-	
-	public void setInitialStep(double initial){
-		this.initialStep = initial;
-	}
-	
-	/**
-	 * 
-	 */
-	
-	public double getStepSize(DifferentiableLineSearchObjective o) {	
-
-		
-		//Should update all in the objective
-		initialStep = strategy.getFirstStep(this);
-		o.updateAlpha(initialStep);	
-		previousInitGradientDot=currentInitGradientDot;
-		currentInitGradientDot=o.getCurrentGradient();
-		int nrIterations = 0;
-	
-		//Armijo rule, the current value has to be smaller than the original value plus a small step of the gradient
-		while(o.getCurrentValue()  >
-			o.getOriginalValue() + c1*(o.getCurrentGradient())){			
-//			System.out.println("curr value "+o.getCurrentValue());
-//			System.out.println("original value "+o.getOriginalValue());
-//			System.out.println("GRADIENT decrease" +(MathUtils.dotProduct(o.o.gradient,
-//					MathUtils.arrayMinus(o.originalParameters,((ProjectedObjective)o.o).auxParameters))));
-//			System.out.println("GRADIENT SAVED" + o.getCurrentGradient());
-			if(nrIterations >= maxIterations){
-				System.out.println("Could not find a step leaving line search with -1");
-				o.printLineSearchSteps();
-				return -1;
-			}
-			double alpha=o.getAlpha();
-			double alphaTemp = 
-				Interpolation.quadraticInterpolation(o.getOriginalValue(), o.getInitialGradient(), alpha, o.getCurrentValue());
-			if(alphaTemp >= sigma1 || alphaTemp <= sigma2*o.getAlpha()){
-				alpha = alphaTemp;
-			}else{
-				alpha = alpha*contractionFactor;
-			}
-//			double alpha =obj.getAlpha()*contractionFactor;
-			o.updateAlpha(alpha);
-			nrIterations++;			
-		}
-//		System.out.println("curr value "+o.getCurrentValue());
-//		System.out.println("original value "+o.getOriginalValue());
-//		System.out.println("sufficient decrease" +c1*o.getCurrentGradient());
-//		System.out.println("Leavning line search used:");
-//		o.printSmallLineSearchSteps();	
-		
-		previousStepPicked = o.getAlpha();
-		return o.getAlpha();
-	}
-	
-	public double getInitialGradient() {
-		return currentInitGradientDot;
-		
-	}
-
-	public double getPreviousInitialGradient() {
-		return previousInitGradientDot;
-	}
-
-	public double getPreviousStepUsed() {
-		return previousStepPicked;
-	}
-		
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java
deleted file mode 100644
index a5bc958e..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java
+++ /dev/null
@@ -1,185 +0,0 @@
-package optimization.linesearch;
-
-import gnu.trove.TDoubleArrayList;
-import gnu.trove.TIntArrayList;
-
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.Comparator;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-import optimization.util.StaticTools;
-
-
-
-import util.MathUtil;
-import util.Printing;
-
-
-/**
- * A wrapper class for the actual objective in order to perform 
- * line search.  The optimization code assumes that this does a lot 
- * of caching in order to simplify legibility.  For the applications 
- * we use it for, caching the entire history of evaluations should be 
- * a win. 
- * 
- * Note: the lastEvaluatedAt value is very important, since we will use
- * it to avoid doing an evaluation of the gradient after the line search.  
- * 
- * The differentiable line search objective defines a search along the ray
- * given by a direction of the main objective.
- * It defines the following function, 
- * where f is the original objective function:
- * g(alpha) = f(x_0 + alpha*direction)
- * g'(alpha) = f'(x_0 + alpha*direction)*direction
- * 
- * @author joao
- *
- */
-public class DifferentiableLineSearchObjective {
-
-	
-	
-	Objective o;
-	int nrIterations;
-	TDoubleArrayList steps;
-	TDoubleArrayList values;
-	TDoubleArrayList gradients;
-	
-	//This variables cannot change
-	public double[] originalParameters;
-	public double[] searchDirection;
-
-	
-	/**
-	 * Defines a line search objective:
-	 * Receives:
-	 * Objective to each we are performing the line search, is used to calculate values and gradients
-	 * Direction where to do the ray search, note that the direction does not depend of the 
-	 * objective but depends from the method.
-	 * @param o
-	 * @param direction
-	 */
-	public DifferentiableLineSearchObjective(Objective o) {
-		this.o = o;
-		originalParameters = new double[o.getNumParameters()];
-		searchDirection = new double[o.getNumParameters()];
-		steps = new TDoubleArrayList();
-		values = new TDoubleArrayList();
-		gradients = new TDoubleArrayList();
-	}
-	/**
-	 * Called whenever we start a new iteration. 
-	 * Receives the ray where we are searching for and resets all values
-	 * 
-	 */
-	public void reset(double[] direction){
-		//Copy initial values
-		System.arraycopy(o.getParameters(), 0, originalParameters, 0, o.getNumParameters());
-		System.arraycopy(direction, 0, searchDirection, 0, o.getNumParameters());
-		
-		//Initialize variables
-		nrIterations = 0;
-		steps.clear();
-		values.clear();
-		gradients.clear();
-	
-		values.add(o.getValue());
-		gradients.add(MathUtils.dotProduct(o.getGradient(),direction));	
-		steps.add(0);
-	}
-	
-	
-	/**
-	 * update the current value of alpha.
-	 * Takes a step with that alpha in direction
-	 * Get the real objective value and gradient and calculate all required information.
-	 */
-	public void updateAlpha(double alpha){
-		if(alpha < 0){
-			System.out.println("alpha may not be smaller that zero");
-			throw new RuntimeException();
-		}
-		nrIterations++;
-		steps.add(alpha);
-		//x_t+1 = x_t + alpha*direction
-		System.arraycopy(originalParameters,0, o.getParameters(), 0, originalParameters.length);
-		MathUtils.plusEquals(o.getParameters(), searchDirection, alpha);
-		o.setParameters(o.getParameters());
-//		System.out.println("Took a step of " + alpha + " new value " + o.getValue());
-		values.add(o.getValue());
-		gradients.add(MathUtils.dotProduct(o.getGradient(),searchDirection));		
-	}
-
-	
-	
-	public int getNrIterations(){
-		return nrIterations;
-	}
-	
-	/**
-	 * return g(alpha) for the current value of alpha
-	 * @param iter
-	 * @return
-	 */
-	public double getValue(int iter){
-		return values.get(iter);
-	}
-	
-	public double getCurrentValue(){
-		return values.get(nrIterations);
-	}
-	
-	public double getOriginalValue(){
-		return values.get(0);
-	}
-
-	/**
-	 * return g'(alpha) for the current value of alpha
-	 * @param iter
-	 * @return
-	 */
-	public double getGradient(int iter){
-		return gradients.get(iter);
-	}
-	
-	public double getCurrentGradient(){
-		return gradients.get(nrIterations);
-	}
-	
-	public double getInitialGradient(){
-		return gradients.get(0);
-	}
-	
-	
-	
-	
-	public double getAlpha(){
-		return steps.get(nrIterations);
-	}
-	
-	public void printLineSearchSteps(){
-		System.out.println(
-				" Steps size "+steps.size() + 
-				"Values size "+values.size() +
-				"Gradeients size "+gradients.size());
-		for(int i =0; i < steps.size();i++){
-			System.out.println("Iter " + i + " step " + steps.get(i) +
-					" value " + values.get(i) + " grad "  + gradients.get(i));
-		}
-	}
-	
-	public void printSmallLineSearchSteps(){
-		for(int i =0; i < steps.size();i++){
-			System.out.print(StaticTools.prettyPrint(steps.get(i), "0.0000E00",8) + " ");
-		}
-		System.out.println();
-	}
-	
-	public static void main(String[] args) {
-		
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java
deleted file mode 100644
index a33eb311..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java
+++ /dev/null
@@ -1,20 +0,0 @@
-package optimization.linesearch;
-
-
-public class GenericPickFirstStep{
-	double _initValue;
-	public GenericPickFirstStep(double initValue) {
-		_initValue = initValue;
-	}
-	
-	public double getFirstStep(LineSearchMethod ls){
-		return _initValue;
-	}
-	public void collectInitValues(LineSearchMethod ls){
-		
-	}
-	
-	public void collectFinalValues(LineSearchMethod ls){
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java
deleted file mode 100644
index 0deebcdb..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java
+++ /dev/null
@@ -1,25 +0,0 @@
-package optimization.linesearch;
-
-
-public class InterpolationPickFirstStep extends GenericPickFirstStep{
-	public InterpolationPickFirstStep(double initValue) {
-		super(initValue);
-	}
-	
-	public double getFirstStep(LineSearchMethod ls){
-		if(ls.getPreviousStepUsed() != -1 && ls.getPreviousInitialGradient()!=0){
-			double newStep = Math.min(300, 1.02*ls.getPreviousInitialGradient()*ls.getPreviousStepUsed()/ls.getInitialGradient());
-		//	System.out.println("proposing " + newStep);
-			return newStep;
-			
-		}
-		return _initValue;
-	}
-	public void collectInitValues(WolfRuleLineSearch ls){
-		
-	}
-	
-	public void collectFinalValues(WolfRuleLineSearch ls){
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java
deleted file mode 100644
index 80cd7f39..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java
+++ /dev/null
@@ -1,14 +0,0 @@
-package optimization.linesearch;
-
-
-public interface LineSearchMethod {
-	
-	double getStepSize(DifferentiableLineSearchObjective o);
-	
-	public double getInitialGradient();
-	public double getPreviousInitialGradient();
-	public double getPreviousStepUsed();
-	
-	public void setInitialStep(double initial);
-	public void reset();
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java
deleted file mode 100644
index 4b354fd9..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java
+++ /dev/null
@@ -1,33 +0,0 @@
-package optimization.linesearch;
-
-/**
- * Non newtwon since we don't always try 1...
- * Not sure if that is even usefull for newton
- * @author javg
- *
- */
-public class NonNewtonInterpolationPickFirstStep extends GenericPickFirstStep{
-	public NonNewtonInterpolationPickFirstStep(double initValue) {
-		super(initValue);
-	}
-	
-	public double getFirstStep(LineSearchMethod ls){
-//		System.out.println("Previous step used " + ls.getPreviousStepUsed());
-//		System.out.println("PreviousGradinebt " + ls.getPreviousInitialGradient());
-//		System.out.println("CurrentGradinebt " + ls.getInitialGradient());
-		if(ls.getPreviousStepUsed() != -1 && ls.getPreviousInitialGradient()!=0){
-			double newStep = 1.01*ls.getPreviousInitialGradient()*ls.getPreviousStepUsed()/ls.getInitialGradient();
-			//System.out.println("Suggesting " + newStep);
-			return newStep;
-			
-		}
-		return _initValue;
-	}
-	public void collectInitValues(WolfRuleLineSearch ls){
-		
-	}
-	
-	public void collectFinalValues(WolfRuleLineSearch ls){
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java
deleted file mode 100644
index 29ccbc32..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java
+++ /dev/null
@@ -1,137 +0,0 @@
-package optimization.linesearch;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-
-
-/**
- * See ArmijoLineSearchMinimizationAlongProjectionArc for description
- * @author javg
- *
- */
-public class ProjectedDifferentiableLineSearchObjective extends DifferentiableLineSearchObjective{
-
-	
-	
-	ProjectedObjective obj;
-	public ProjectedDifferentiableLineSearchObjective(Objective o) {
-		super(o);
-		if(!(o instanceof ProjectedObjective)){
-			System.out.println("Must receive a projected objective");
-			throw new RuntimeException();
-		}
-		obj = (ProjectedObjective) o;
-	}
-
-	
-	
-	public double[] projectPoint (double[] point){
-		return ((ProjectedObjective)o).projectPoint(point);
-	}
-	public void updateAlpha(double alpha){
-		if(alpha < 0){
-			System.out.println("alpha may not be smaller that zero");
-			throw new RuntimeException();
-		}
-		
-		if(obj.auxParameters == null){
-			obj.auxParameters = new double[obj.getParameters().length];
-		}
-		
-		nrIterations++;
-		
-		steps.add(alpha);		
-		System.arraycopy(originalParameters, 0, obj.auxParameters, 0, obj.auxParameters.length);
-		
-		//Take a step into the search direction
-		
-//		MatrixOutput.printDoubleArray(obj.getGradient(), "gradient");
-		
-//		alpha=gradients.get(0)*alpha/(gradients.get(gradients.size()-1));
-	
-		//x_t+1 = x_t - alpha*gradient = x_t + alpha*direction
-		MathUtils.plusEquals(obj.auxParameters, searchDirection, alpha);
-//		MatrixOutput.printDoubleArray(obj.auxParameters, "before projection");
-		obj.auxParameters = projectPoint(obj.auxParameters);
-//		MatrixOutput.printDoubleArray(obj.auxParameters, "after projection");
-		o.setParameters(obj.auxParameters);
-//		System.out.println("new parameters");
-//		o.printParameters();
-		values.add(o.getValue());
-		//Computes the new gradient x_k-[x_k-alpha*Gradient(x_k)]+ 
-		MathUtils.minusEqualsInverse(originalParameters,obj.auxParameters,1);
-//		MatrixOutput.printDoubleArray(obj.auxParameters, "new gradient");
-		//Dot product between the new direction and the new gradient
-		double gradient = MathUtils.dotProduct(obj.auxParameters,searchDirection);
-		gradients.add(gradient);	
-		if(gradient > 0){
-			System.out.println("Gradient on line search has to be smaller than zero");
-			System.out.println("Iter: " + nrIterations);
-			MatrixOutput.printDoubleArray(obj.auxParameters, "new direction");
-			MatrixOutput.printDoubleArray(searchDirection, "search direction");
-			throw new RuntimeException();
-			
-		}
-		
-	}
-	
-	/**
-	 * 
-	 */
-//	public void updateAlpha(double alpha){
-//		
-//		if(alpha < 0){
-//			System.out.println("alpha may not be smaller that zero");
-//			throw new RuntimeException();
-//		}
-//		
-//		nrIterations++;
-//		steps.add(alpha);
-//		//x_t+1 = x_t - alpha*direction
-//		System.arraycopy(originalParameters, 0, parametersChange, 0, parametersChange.length);
-////		MatrixOutput.printDoubleArray(parametersChange, "parameters before step");
-////		System.out.println("Step" + alpha);
-//		MatrixOutput.printDoubleArray(originalGradient, "gradient + " + alpha);
-//
-//		MathUtils.minusEquals(parametersChange, originalGradient, alpha);
-//		
-//		//Project the points into the feasibility set
-////		MatrixOutput.printDoubleArray(parametersChange, "before projection");
-//		//x_k(alpha) = [x_k - alpha*grad f(x_k)]+
-//		parametersChange = projectPoint(parametersChange);
-////		MatrixOutput.printDoubleArray(parametersChange, "after projection");
-//		o.setParameters(parametersChange);
-//		values.add(o.getValue());
-//		//Computes the new direction x_k-[x_k-alpha*Gradient(x_k)]+
-//		
-//		direction=MathUtils.arrayMinus(parametersChange,originalParameters);
-////		MatrixOutput.printDoubleArray(direction, "new direction");
-//		
-//		double gradient = MathUtils.dotProduct(originalGradient,direction);
-//		gradients.add(gradient);		
-//		if(gradient > 1E-10){
-//			System.out.println("cosine " + gradient/(MathUtils.L2Norm(originalGradient)*MathUtils.L2Norm(direction)));
-//			
-//			
-//			System.out.println("not a descent direction for alpha " + alpha);
-//			System.arraycopy(originalParameters, 0, parametersChange, 0, parametersChange.length);
-//			MathUtils.minusEquals(parametersChange, originalGradient, 1E-20);
-//			
-//			parametersChange = projectPoint(parametersChange);
-//			direction=MathUtils.arrayMinus(parametersChange,originalParameters);
-//			gradient = MathUtils.dotProduct(originalGradient,direction);
-//			if(gradient > 0){
-//				System.out.println("Direction is really non-descent evern for small alphas:" + gradient);
-//			}
-//			System.out.println("ProjecteLineSearchObjective: Should be a descent direction at " + nrIterations + ": "+ gradient);
-////			System.out.println(Printing.doubleArrayToString(originalGradient, null,"Original gradient"));
-////			System.out.println(Printing.doubleArrayToString(originalParameters, null,"Original parameters"));
-////			System.out.println(Printing.doubleArrayToString(parametersChange, null,"Projected parameters"));
-////			System.out.println(Printing.doubleArrayToString(direction, null,"Direction"));
-//			throw new RuntimeException();
-//		}
-//	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java
deleted file mode 100644
index 5489f2d0..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java
+++ /dev/null
@@ -1,300 +0,0 @@
-package optimization.linesearch;
-
-import java.io.PrintStream;
-import java.util.ArrayList;
-
-import optimization.util.Interpolation;
-
-
-
-
-/**
- * 
- * @author javg
- *
- */
-public class WolfRuleLineSearch implements LineSearchMethod{
-
-	GenericPickFirstStep pickFirstStep;
-	
-	double c1 = 1.0E-4;
-	double c2 = 0.9;
-	
-	//Application dependent
-	double maxStep=100;
-	
-	int extrapolationIteration;
-	int maxExtrapolationIteration = 1000;
-	
-	
-	double minZoomDiffTresh = 10E-10;
-
-	
-	ArrayList<Double> steps;
-	ArrayList<Double> gradientDots;
-	ArrayList<Double> functionVals;
-	
-	int debugLevel = 0;
-	boolean foudStep = false;
-	
-	public WolfRuleLineSearch(GenericPickFirstStep pickFirstStep){
-		this.pickFirstStep = pickFirstStep;
-		
-	}
-	
-	
-
-	
-	public WolfRuleLineSearch(GenericPickFirstStep pickFirstStep,  double c1, double c2){
-		this.pickFirstStep = pickFirstStep;
-		initialStep = pickFirstStep.getFirstStep(this);
-		this.c1 = c1;
-		this.c2 = c2;
-	}
-	
-	public void setDebugLevel(int level){
-		debugLevel = level;
-	}
-	
-	//Experiment
-	double previousStepPicked = -1;;
-	double previousInitGradientDot = -1;
-	double currentInitGradientDot = -1;
-	
-	double initialStep;
-
-	
-	public void reset(){
-		previousStepPicked = -1;;
-		previousInitGradientDot = -1;
-		currentInitGradientDot = -1;
-		if(steps != null)
-			steps.clear();
-		if(gradientDots != null)
-			gradientDots.clear();
-		if(functionVals != null)
-			functionVals.clear();
-	}
-	
-	public void setInitialStep(double initial){
-		initialStep = pickFirstStep.getFirstStep(this);
-	}
-	
-	
-	
-	/**
-	 * Implements Wolf Line search as described in nocetal.
-	 * This process consists in two stages. The first stage we try to satisfy the
-	 * biggest step size that still satisfies the curvature condition. We keep increasing
-	 * the initial step size until we find a step satisfying the curvature condition, we return 
-	 * success, we failed the sufficient increase so we cannot increase more and we can call zoom with 
-	 * that maximum step, or we pass the minimum in which case we can call zoom the same way. 
-	 * 
-	 */
-	public double getStepSize(DifferentiableLineSearchObjective objective){
-		//System.out.println("entering line search");
-		
-		foudStep = false;
-		if(debugLevel >= 1){
-			steps = new ArrayList<Double>();
-			gradientDots = new ArrayList<Double>();
-			functionVals  =new ArrayList<Double>();
-		}
-		
-		//test
-		currentInitGradientDot = objective.getInitialGradient();
-		
-		
-		double previousValue = objective.getCurrentValue();
-		double previousStep = 0;
-		double currentStep =pickFirstStep.getFirstStep(this);
-		for(extrapolationIteration = 0; 
-		extrapolationIteration < maxExtrapolationIteration; extrapolationIteration++){	
-			
-			objective.updateAlpha(currentStep);
-			double currentValue = objective.getCurrentValue();
-			if(debugLevel >= 1){
-				steps.add(currentStep);
-				functionVals.add(currentValue);
-				gradientDots.add(objective.getCurrentGradient());
-			}
-			
-			
-			//The current step does not satisfy the sufficient decrease condition anymore
-			// so we cannot get bigger than that calling zoom.
-			if(!WolfeConditions.suficientDecrease(objective,c1)||					
-					(extrapolationIteration > 0 && currentValue >= previousValue)){
-				currentStep = zoom(objective,previousStep,currentStep,objective.nrIterations-1,objective.nrIterations);
-				break;
-			}
-			
-			//Satisfying both conditions ready to leave
-			if(WolfeConditions.sufficientCurvature(objective,c1,c2)){
-				//Found step
-				foudStep = true;
-				break;
-			}
-			
-			/**
-			 * This means that we passed the minimum already since the dot product that should be 
-			 * negative (descent direction) is now positive. So we cannot increase more. On the other hand
-			 * since we know the direction is a descent direction the value the objective at the current step
-			 * is for sure smaller than the preivous step so we change the order.
-			 */
-			if(objective.getCurrentGradient() >= 0){
-				currentStep =  zoom(objective,currentStep,previousStep,objective.nrIterations,objective.nrIterations-1);
-				break;
-			}
-			
-			
-			//Ok, so we can still get a bigger step, 
-			double aux = currentStep;
-			//currentStep = currentStep*2;
-			if(Math.abs(currentStep-maxStep)>1.1e-2){
-				currentStep = (currentStep+maxStep)/2;
-			}else{
-				currentStep = currentStep*2;
-			}
-			previousStep = aux;
-			previousValue = currentValue;
-			//Could be done better
-			if(currentStep >= maxStep){
-				System.out.println("Excedded max step...calling zoom with maxStepSize");
-				currentStep = zoom(objective,previousStep,currentStep,objective.nrIterations-1,objective.nrIterations);
-			}
-		}
-		if(!foudStep){
-			System.out.println("Wolfe Rule exceed number of iterations");
-			if(debugLevel >= 1){
-				printSmallWolfeStats(System.out);
-//				System.out.println("Line search values");
-//				DebugHelpers.getLineSearchGraph(o,  direction, originalParameters,origValue, origGradDirectionDot,c1,c2);			
-			}
-			return -1;
-		}
-		if(debugLevel >= 1){
-			printSmallWolfeStats(System.out);
-		}
-
-		previousStepPicked = currentStep;
-		previousInitGradientDot = currentInitGradientDot;
-//		objective.printLineSearchSteps();
-		return currentStep;
-	}
-	
-	
-	
-	
-	
-	public void printWolfeStats(PrintStream out){
-		for(int i = 0; i < steps.size(); i++){		
-			out.println("Step " + steps.get(i) + " value " + functionVals.get(i) + " dot " + gradientDots.get(i));
-		}
-	}
-	
-	public void printSmallWolfeStats(PrintStream out){
-		for(int i = 0; i < steps.size(); i++){		
-			out.print(steps.get(i) + ":"+functionVals.get(i)+":"+gradientDots.get(i)+" ");
-		}
-		System.out.println();
-	}
-	
-	
-	
-	/**
-	 * Pick a step satisfying the strong wolfe condition from an given from lowerStep and higherStep
-	 * picked on the routine above.
-	 * 
-	 * Both lowerStep and higherStep have been evaluated, so we only need to pass the iteration where they have
-	 * been evaluated and save extra evaluations.
-	 * 
-	 * We know that lowerStepValue as to be smaller than higherStepValue, and that a point 
-	 * satisfying both conditions exists in such interval.
-	 * 
-	 * LowerStep always satisfies at least the sufficient decrease
-	 * @return
-	 */
-	public double zoom(DifferentiableLineSearchObjective o, double lowerStep, double higherStep,
-			int lowerStepIter, int higherStepIter){
-		
-		if(debugLevel >=2){
-			System.out.println("Entering zoom with " + lowerStep+"-"+higherStep);
-		}
-		
-		double currentStep=-1;
-		
-		int zoomIter = 0;
-		while(zoomIter < 1000){		
-			if(Math.abs(lowerStep-higherStep) < minZoomDiffTresh){
-				o.updateAlpha(lowerStep);
-				if(debugLevel >= 1){
-					steps.add(lowerStep);
-					functionVals.add(o.getCurrentValue());
-					gradientDots.add(o.getCurrentGradient());
-				}
-				foudStep = true;
-				return lowerStep;
-			}	
-	
-			//Cubic interpolation
-			currentStep = 
-				Interpolation.cubicInterpolation(lowerStep, o.getValue(lowerStepIter), o.getGradient(lowerStepIter), 
-						higherStep, o.getValue(higherStepIter), o.getGradient(higherStepIter));
-			
-			//Safeguard.... should not be required check in what condtions it is required
-			if(currentStep < 0 ){
-				currentStep = (lowerStep+higherStep)/2;
-			}
-			if(Double.isNaN(currentStep) || Double.isInfinite(currentStep)){
-				currentStep = (lowerStep+higherStep)/2;
-			}
-//			currentStep = (lowerStep+higherStep)/2;
-//			System.out.println("Trying "+currentStep);
-			o.updateAlpha(currentStep);
-			if(debugLevel >=1){
-				steps.add(currentStep);
-				functionVals.add(o.getCurrentValue());
-				gradientDots.add(o.getCurrentGradient());
-			}
-			if(!WolfeConditions.suficientDecrease(o,c1)
-					|| o.getCurrentValue() >= o.getValue(lowerStepIter)){
-				higherStepIter = o.nrIterations;
-				higherStep = currentStep;
-			}
-			//Note when entering here the new step satisfies the sufficent decrease and
-			// or as a function value that is better than the previous best (lowerStepFunctionValues)
-			// so we either leave or change the value of the alpha low.
-			else{
-				if(WolfeConditions.sufficientCurvature(o,c1,c2)){
-					//Satisfies the both wolf conditions
-					foudStep = true;
-					break;
-				}
-				//If does not satisfy curvature 
-				if(o.getCurrentGradient()*(higherStep-lowerStep) >= 0){
-					higherStep = lowerStep;
-					higherStepIter = lowerStepIter;
-				}
-				lowerStep = currentStep;
-				lowerStepIter = o.nrIterations;
-			}
-			zoomIter++;
-		}
-		return currentStep;
-	}
-
-	public double getInitialGradient() {
-		return currentInitGradientDot;
-		
-	}
-
-	public double getPreviousInitialGradient() {
-		return previousInitGradientDot;
-	}
-
-	public double getPreviousStepUsed() {
-		return previousStepPicked;
-	}
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java
deleted file mode 100644
index dcc704eb..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java
+++ /dev/null
@@ -1,45 +0,0 @@
-package optimization.linesearch;
-
-
-public class WolfeConditions {
-	
-	/**
-	 * Sufficient Increase number. Default constant
-	 */
-	
-	
-	/**
-	 * Value for suficient curvature:
-	 * 0.9 - For newton and quase netwon methods
-	 * 0.1 - Non linear conhugate gradient
-	 */
-	
-	int debugLevel = 0;
-	public void setDebugLevel(int level){
-		debugLevel = level;
-	}
-	
-	public  static boolean suficientDecrease(DifferentiableLineSearchObjective o, double c1){	
-		double value = o.getOriginalValue()+c1*o.getAlpha()*o.getInitialGradient();
-//		System.out.println("Sufficient Decrease original "+value+" new "+  o.getCurrentValue());
-		return o.getCurrentValue() <= value;
-	}
-	
-	
-
-
-	public static boolean sufficientCurvature(DifferentiableLineSearchObjective o, double c1, double c2){
-//		if(debugLevel >= 2){
-//			double current = Math.abs(o.getCurrentGradient());
-//			double orig = -c2*o.getInitialGradient();
-//			if(current <= orig){
-//				return true;
-//			}else{
-//				System.out.println("Not satistfying curvature condition curvature " + current + " wants " + orig);
-//				return false;
-//			}
-//		}
-		return Math.abs(o.getCurrentGradient()) <= -c2*o.getInitialGradient();
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java b/gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java
deleted file mode 100644
index 0429d531..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java
+++ /dev/null
@@ -1,104 +0,0 @@
-package optimization.projections;
-
-
-import java.util.Random;
-
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-
-/**
- * Implements a projection into a box set defined by a and b.
- * If either a or b are infinity then that bound is ignored.
- * @author javg
- *
- */
-public class BoundsProjection extends Projection{
-
-	double a,b;
-	boolean ignoreA = false;
-	boolean ignoreB = false;
-	public BoundsProjection(double lowerBound, double upperBound) {
-		if(Double.isInfinite(lowerBound)){
-			this.ignoreA = true;
-		}else{
-			this.a =lowerBound;
-		}
-		if(Double.isInfinite(upperBound)){
-			this.ignoreB = true;
-		}else{
-			this.b =upperBound;
-		}
-	}
-	
-	
-	
-	/**
-	* Projects into the bounds
-	* a <= x_i <=b
-	 */
-	public void project(double[] original){
-		for (int i = 0; i < original.length; i++) {
-			if(!ignoreA && original[i] < a){
-				original[i] = a;
-			}else if(!ignoreB && original[i]>b){
-				original[i]=b;
-			}
-		}
-	}
-	
-	/**
-	 * Generates a random number between a and b.
-	 */
-
-	Random r = new Random();
-	
-	public double[] samplePoint(int numParams) {
-		double[] point = new double[numParams];
-		for (int i = 0; i < point.length; i++) {
-			double rand = r.nextDouble();
-			if(ignoreA && ignoreB){
-				//Use const to avoid number near overflow
-				point[i] = rand*(1.E100+1.E100)-1.E100;
-			}else if(ignoreA){
-				point[i] = rand*(b-1.E100)-1.E100;
-			}else if(ignoreB){
-				point[i] = rand*(1.E100-a)-a;
-			}else{
-				point[i] = rand*(b-a)-a;
-			}
-		}
-		return point;
-	}
-	
-	public static void main(String[] args) {
-		BoundsProjection sp = new BoundsProjection(0,Double.POSITIVE_INFINITY);
-		
-		
-		MatrixOutput.printDoubleArray(sp.samplePoint(3), "random 1");
-		MatrixOutput.printDoubleArray(sp.samplePoint(3), "random 2");
-		MatrixOutput.printDoubleArray(sp.samplePoint(3), "random 3");
-		
-		double[] d = {-1.1,1.2,1.4};
-		double[] original = d.clone();
-		MatrixOutput.printDoubleArray(d, "before");
-		
-		sp.project(d);
-		MatrixOutput.printDoubleArray(d, "after");
-		System.out.println("Test projection: " + sp.testProjection(original, d));
-	}
-	
-	double epsilon = 1.E-10;
-	public double[] perturbePoint(double[] point, int parameter){
-		double[] newPoint = point.clone();
-		if(!ignoreA && MathUtils.almost(point[parameter], a)){
-			newPoint[parameter]+=epsilon;
-		}else if(!ignoreB && MathUtils.almost(point[parameter], b)){
-			newPoint[parameter]-=epsilon;
-		}else{
-			newPoint[parameter]-=epsilon;
-		}
-		return newPoint;
-	}
-
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java b/gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java
deleted file mode 100644
index b5a9f92f..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java
+++ /dev/null
@@ -1,72 +0,0 @@
-package optimization.projections;
-
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-import util.ArrayMath;
-import util.Printing;
-
-
-
-public abstract class Projection {
-
-	
-	public abstract void project(double[] original);
-	
-	
-	/**
-	 *  From the projection theorem "Non-Linear Programming" page
-	 *  201 fact 2.
-	 *  
-	 *  Given some z in R, and a vector x* in X;
-	 *  x* = z+ iif for all x in X 
-	 *  (z-x*)'(x-x*) <= 0 where 0 is when x*=x
-	 *  See figure 2.16 in book
-	 *  
-	 * @param original
-	 * @param projected
-	 * @return
-	 */
-	public boolean testProjection(double[] original, double[] projected){
-		double[] original1 = original.clone();
-		//System.out.println(Printing.doubleArrayToString(original1, null, "original"));
-		//System.out.println(Printing.doubleArrayToString(projected, null, "projected"));
-		MathUtils.minusEquals(original1, projected, 1);
-		//System.out.println(Printing.doubleArrayToString(original1, null, "minus1"));
-		for(int i = 0; i < 10; i++){
-			double[] x = samplePoint(original.length);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "sample"));
-			//If the same this returns zero so we are there.	
-			MathUtils.minusEquals(x, projected, 1);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "minus2"));
-			double dotProd = MathUtils.dotProduct(original1, x);
-			
-		//	System.out.println("dot " + dotProd);
-			if(dotProd > 0) return false;
-		}
-		
-		//Perturbs the point a bit in all possible directions
-		for(int i = 0; i < original.length; i++){
-			double[] x = perturbePoint(projected,i);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "perturbed"));
-			//If the same this returns zero so we are there.	
-			MathUtils.minusEquals(x, projected, 1);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "minus2"));
-			double dotProd = MathUtils.dotProduct(original1, x);
-			
-		//	System.out.println("dot " + dotProd);
-			if(dotProd > 0) return false;
-		}
-		
-		
-		
-		return true;
-	}
-
-	//Samples a point from the constrained set
-	public abstract double[] samplePoint(int dimensions);
-	
-	//Perturbs a point a bit still leaving it at the constraints set
-	public abstract double[] perturbePoint(double[] point, int parameter);
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java b/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java
deleted file mode 100644
index f22afcaf..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java
+++ /dev/null
@@ -1,127 +0,0 @@
-package optimization.projections;
-
-
-
-import java.util.Random;
-
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-
-public class SimplexProjection extends Projection{
-
-	double scale;
-	public SimplexProjection(double scale) {
-		this.scale = scale;
-	}
-	
-	/**
-	 * projects the numbers of the array 
-	 * into a simplex of size. 
-	 * We follow the description of the paper
-	 * "Efficient Projetions onto the l1-Ball
-	 * for learning in high dimensions"
-	 */
-	public void project(double[] original){
-		double[] ds = new double[original.length];
-		System.arraycopy(original, 0, ds, 0, ds.length);
-		//If sum is smaller then zero then its ok
-		for (int i = 0; i < ds.length; i++) ds[i] = ds[i]>0? ds[i]:0;
-		double sum = MathUtils.sum(ds);
-		if (scale - sum >=  -1.E-10 ){
-			System.arraycopy(ds, 0, original, 0, ds.length);
-			//System.out.println("Not projecting");
-			return;
-		}
-		//System.out.println("projecting " + sum + " scontraints " + scale);	
-		util.Array.sortDescending(ds);
-		double currentSum = 0;
-		double previousTheta = 0;
-		double theta = 0;
-		for (int i = 0; i < ds.length; i++) {
-			currentSum+=ds[i];
-			theta = (currentSum-scale)/(i+1);
-			if(ds[i]-theta < -1e-10){
-				break;
-			}
-			previousTheta = theta;
-		}
-		//DEBUG
-		if(previousTheta < 0){
-			System.out.println("Simple Projection: Theta is smaller than zero: " + previousTheta);
-			System.exit(-1);
-		}
-		for (int i = 0; i < original.length; i++) {
-			original[i] = Math.max(original[i]-previousTheta, 0);
-		}
-	}
-	
-	
-	
-	
-	
-
-	/**
-	 * Samples a point from the simplex of scale. Just sample
-	 * random number from 0-scale and then if
-	 * their sum is bigger then sum make them normalize.
-	 * This is probably not sampling uniformly from the simplex but it is
-	 * enough for our goals in here.
-	 */
-	Random r = new Random();
-	public double[] samplePoint(int dimensions) {
-		double[] newPoint = new double[dimensions];
-		double sum =0;
-		for (int i = 0; i < newPoint.length; i++) {
-			double rand = r.nextDouble()*scale;
-			sum+=rand;
-			newPoint[i]=rand;
-		}
-		//Normalize
-		if(sum > scale){
-			for (int i = 0; i < newPoint.length; i++) {
-				newPoint[i]=scale*newPoint[i]/sum;
-			}
-		}
-		return newPoint;
-	}
-	
-	public static void main(String[] args) {
-		SimplexProjection sp = new SimplexProjection(1);
-		
-		
-		double[] point = sp.samplePoint(3);
-		MatrixOutput.printDoubleArray(point , "random 1 sum:" + MathUtils.sum(point));
-		point = sp.samplePoint(3);
-		MatrixOutput.printDoubleArray(point , "random 2 sum:" + MathUtils.sum(point));
-		point = sp.samplePoint(3);
-		MatrixOutput.printDoubleArray(point , "random 3 sum:" + MathUtils.sum(point));
-		
-		double[] d = {0,1.1,-10};
-		double[] original = d.clone();
-		MatrixOutput.printDoubleArray(d, "before");
-		
-		sp.project(d);
-		MatrixOutput.printDoubleArray(d, "after");
-		System.out.println("Test projection: " + sp.testProjection(original, d));
-		
-	}
-	
-	
-	double epsilon = 1.E-10;
-	public double[] perturbePoint(double[] point, int parameter){
-		double[] newPoint = point.clone();
-		if(MathUtils.almost(MathUtils.sum(point), scale)){
-			newPoint[parameter]-=epsilon;
-		}
-		else if(point[parameter]==0){
-			newPoint[parameter]+=epsilon;
-		}else if(MathUtils.almost(point[parameter], scale)){
-			newPoint[parameter]-=epsilon;
-		}
-		else{
-			newPoint[parameter]-=epsilon;
-		}
-		return newPoint;
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java
deleted file mode 100644
index 15760f18..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java
+++ /dev/null
@@ -1,33 +0,0 @@
-package optimization.stopCriteria;
-
-import java.util.ArrayList;
-
-import optimization.gradientBasedMethods.Objective;
-
-public class CompositeStopingCriteria implements StopingCriteria {
-	
-	ArrayList<StopingCriteria> criterias;
-	
-	public CompositeStopingCriteria() {
-		criterias = new ArrayList<StopingCriteria>();
-	}
-	
-	public void add(StopingCriteria criteria){
-		criterias.add(criteria);
-	}
-	
-	public boolean stopOptimization(Objective obj){
-		for(StopingCriteria criteria: criterias){
-			if(criteria.stopOptimization(obj)){
-				return true;
-			}
-		}
-		return false;
-	}
-	
-	public void reset(){
-		for(StopingCriteria criteria: criterias){
-			criteria.reset();
-		}
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java
deleted file mode 100644
index 534ff833..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java
+++ /dev/null
@@ -1,30 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-
-public class GradientL2Norm implements StopingCriteria{
-	
-	/**
-	 * Stop if gradientNorm/(originalGradientNorm) smaller
-	 * than gradientConvergenceValue
-	 */
-	protected double gradientConvergenceValue;
-	
-	
-	public GradientL2Norm(double gradientConvergenceValue){
-		this.gradientConvergenceValue = gradientConvergenceValue;
-	}
-	
-	public void reset(){}
-	
-	public boolean stopOptimization(Objective obj){
-		double norm = MathUtils.L2Norm(obj.gradient);
-		if(norm < gradientConvergenceValue){
-			System.out.println("Gradient norm below treshold");
-			return true;
-		}
-		return false;
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java
deleted file mode 100644
index 4a489641..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java
+++ /dev/null
@@ -1,48 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-
-/**
- * Divides the norm by the norm at the begining of the iteration
- * @author javg
- *
- */
-public class NormalizedGradientL2Norm extends GradientL2Norm{
-	
-	/**
-	 * Stop if gradientNorm/(originalGradientNorm) smaller
-	 * than gradientConvergenceValue
-	 */
-	protected double originalGradientNorm = -1;
-	
-	public void reset(){
-		originalGradientNorm = -1;
-	}
-	public NormalizedGradientL2Norm(double gradientConvergenceValue){
-		super(gradientConvergenceValue);
-	}
-	
-	
-	 
-	
-	public boolean stopOptimization(Objective obj){
-			double norm = MathUtils.L2Norm(obj.gradient);
-			if(originalGradientNorm == -1){
-				originalGradientNorm = norm;
-			}
-			if(originalGradientNorm < 1E-10){
-				System.out.println("Gradient norm is zero " +  originalGradientNorm);
-				return true;
-			}
-			double normalizedNorm = 1.0*norm/originalGradientNorm;
-			if( normalizedNorm < gradientConvergenceValue){
-				System.out.println("Gradient norm below normalized normtreshold: " + norm + " original: " + originalGradientNorm + " normalized norm: " + normalizedNorm);
-				return true;
-			}else{
-//				System.out.println("projected gradient norm: " + norm);
-				return false;
-			}
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java
deleted file mode 100644
index 5ae554c2..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java
+++ /dev/null
@@ -1,60 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-
-/**
- * Divides the norm by the norm at the begining of the iteration
- * @author javg
- *
- */
-public class NormalizedProjectedGradientL2Norm extends ProjectedGradientL2Norm{
-	
-	/**
-	 * Stop if gradientNorm/(originalGradientNorm) smaller
-	 * than gradientConvergenceValue
-	 */
-	double originalProjectedNorm = -1;
-	
-	public NormalizedProjectedGradientL2Norm(double gradientConvergenceValue){
-		super(gradientConvergenceValue);
-	}
-	
-	public void reset(){
-		originalProjectedNorm = -1;
-	}
-	
-	
-	 double[] projectGradient(ProjectedObjective obj){
-		
-		if(obj.auxParameters == null){
-			obj.auxParameters = new double[obj.getNumParameters()];
-		}
-		System.arraycopy(obj.getParameters(), 0, obj.auxParameters, 0, obj.getNumParameters());
-		MathUtils.minusEquals(obj.auxParameters, obj.gradient, 1);
-		obj.auxParameters = obj.projectPoint(obj.auxParameters);
-		MathUtils.minusEquals(obj.auxParameters,obj.getParameters(),1);
-		return obj.auxParameters;
-	}
-	
-	public boolean stopOptimization(Objective obj){
-		if(obj instanceof ProjectedObjective) {
-			ProjectedObjective o = (ProjectedObjective) obj;
-			double norm = MathUtils.L2Norm(projectGradient(o));
-			if(originalProjectedNorm == -1){
-				originalProjectedNorm = norm;
-			}
-			double normalizedNorm = 1.0*norm/originalProjectedNorm;
-			if( normalizedNorm < gradientConvergenceValue){
-				System.out.println("Gradient norm below normalized normtreshold: " + norm + " original: " + originalProjectedNorm + " normalized norm: " + normalizedNorm);
-				return true;
-			}else{
-//				System.out.println("projected gradient norm: " + norm);
-				return false;
-			}
-		}
-		System.out.println("Not a projected objective");
-		throw new RuntimeException();
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java
deleted file mode 100644
index 6dbbc50d..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java
+++ /dev/null
@@ -1,54 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-
-public class NormalizedValueDifference implements StopingCriteria{
-	
-	/**
-	 * Stop if the different between values is smaller than a treshold
-	 */
-	protected double valueConvergenceValue=0.01;
-	protected double previousValue = Double.NaN;
-	protected double currentValue = Double.NaN;
-	
-	public NormalizedValueDifference(double valueConvergenceValue){
-		this.valueConvergenceValue = valueConvergenceValue;
-	}
-	
-	public void reset(){
-		previousValue = Double.NaN;
-		currentValue = Double.NaN;
-	}
-
-	
-	public boolean stopOptimization(Objective obj){
-		if(Double.isNaN(currentValue)){
-			currentValue = obj.getValue();
-			return false;
-		}else {
-			previousValue = currentValue;
-			currentValue = obj.getValue();
-			if(previousValue != 0){
-				double valueDiff = Math.abs(previousValue - currentValue)/Math.abs(previousValue);
-				if( valueDiff  < valueConvergenceValue){
-					System.out.println("Leaving different in values is to small: Prev " 
-							+ (previousValue/previousValue) + " Curr: " + (currentValue/previousValue) 
-							+ " diff: " + valueDiff);
-					return true;
-				}
-			}else{
-				double valueDiff = Math.abs(previousValue - currentValue);
-				if( valueDiff  < valueConvergenceValue){
-					System.out.println("Leaving different in values is to small: Prev " 
-							+ (previousValue) + " Curr: " + (currentValue) 
-							+ " diff: " + valueDiff);
-					return true;
-				}
-			}
-
-			return false;
-		}
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java
deleted file mode 100644
index aadf1fd5..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java
+++ /dev/null
@@ -1,51 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-
-public class ProjectedGradientL2Norm implements StopingCriteria{
-	
-	/**
-	 * Stop if gradientNorm/(originalGradientNorm) smaller
-	 * than gradientConvergenceValue
-	 */
-	protected double gradientConvergenceValue;
-	
-	
-	public ProjectedGradientL2Norm(double gradientConvergenceValue){
-		this.gradientConvergenceValue = gradientConvergenceValue;
-	}
-	
-	public void reset(){
-		
-	}
-	
-	 double[] projectGradient(ProjectedObjective obj){
-		
-		if(obj.auxParameters == null){
-			obj.auxParameters = new double[obj.getNumParameters()];
-		}
-		System.arraycopy(obj.getParameters(), 0, obj.auxParameters, 0, obj.getNumParameters());
-		MathUtils.minusEquals(obj.auxParameters, obj.gradient, 1);
-		obj.auxParameters = obj.projectPoint(obj.auxParameters);
-		MathUtils.minusEquals(obj.auxParameters,obj.getParameters(),1);
-		return obj.auxParameters;
-	}
-	
-	public boolean stopOptimization(Objective obj){
-		if(obj instanceof ProjectedObjective) {
-			ProjectedObjective o = (ProjectedObjective) obj;
-			double norm = MathUtils.L2Norm(projectGradient(o));
-			if(norm < gradientConvergenceValue){
-	//			System.out.println("Gradient norm below treshold: " + norm);
-				return true;
-			}else{
-//				System.out.println("projected gradient norm: " + norm);
-				return false;
-			}
-		}
-		System.out.println("Not a projected objective");
-		throw new RuntimeException();
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java
deleted file mode 100644
index 10cf0522..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java
+++ /dev/null
@@ -1,8 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-
-public interface StopingCriteria {
-	public boolean stopOptimization(Objective obj);
-	public void reset();
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java
deleted file mode 100644
index e5d07229..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java
+++ /dev/null
@@ -1,41 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-
-public class ValueDifference implements StopingCriteria{
-	
-	/**
-	 * Stop if the different between values is smaller than a treshold
-	 */
-	protected double valueConvergenceValue=0.01;
-	protected double previousValue = Double.NaN;
-	protected double currentValue = Double.NaN;
-	
-	public ValueDifference(double valueConvergenceValue){
-		this.valueConvergenceValue = valueConvergenceValue;
-	}
-	
-	public void reset(){
-		previousValue = Double.NaN;
-		currentValue = Double.NaN;
-	}
-	
-	public boolean stopOptimization(Objective obj){
-		if(Double.isNaN(currentValue)){
-			currentValue = obj.getValue();
-			return false;
-		}else {
-			previousValue = currentValue;
-			currentValue = obj.getValue();
-			if(previousValue - currentValue   < valueConvergenceValue){
-//				System.out.println("Leaving different in values is to small: Prev " 
-//						+ previousValue + " Curr: " + currentValue 
-//						+ " diff: " + (previousValue - currentValue));
-				return true;
-			}
-			return false;
-		}
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java b/gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java
deleted file mode 100644
index cdbdefc6..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java
+++ /dev/null
@@ -1,37 +0,0 @@
-package optimization.util;
-
-public class Interpolation {
-
-	/**
-	 * Fits a cubic polinomyal to a function given two points,
-	 * such that either gradB is bigger than zero or funcB >= funcA
-	 * 
-	 * NonLinear Programming appendix C
-	 * @param funcA
-	 * @param gradA
-	 * @param funcB
-	 * @param gradB
-	 */
-	public final static double cubicInterpolation(double a, 
-			double funcA, double gradA, double b,double funcB, double gradB ){
-		if(gradB < 0 && funcA > funcB){
-			System.out.println("Cannot call cubic interpolation");
-			return -1;
-		}
-		
-		double z = 3*(funcA-funcB)/(b-a) + gradA + gradB;
-		double w = Math.sqrt(z*z - gradA*gradB);
-		double min = b -(gradB+w-z)*(b-a)/(gradB-gradA+2*w);
-		return min;
-	}
-	
-	public final static double quadraticInterpolation(double initFValue, 
-			double initGrad, double point,double pointFValue){
-				double min = -1*initGrad*point*point/(2*(pointFValue-initGrad*point-initFValue));
-		return min;
-	}
-	
-	public static void main(String[] args) {
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/Logger.java b/gi/posterior-regularisation/prjava/src/optimization/util/Logger.java
deleted file mode 100644
index 5343a39b..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/Logger.java
+++ /dev/null
@@ -1,7 +0,0 @@
-package optimization.util;
-
-public class Logger {
-	
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java b/gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java
deleted file mode 100644
index af66f82c..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java
+++ /dev/null
@@ -1,339 +0,0 @@
-package optimization.util;
-
-import java.util.Arrays;
-
-
-
-public class MathUtils {
-	
-	/**
-	 * 
-	 * @param vector
-	 * @return
-	 */
-	public static double L2Norm(double[] vector){
-		double value = 0;
-		for(int i = 0; i < vector.length; i++){
-			double v = vector[i];
-			value+=v*v;
-		}
-		return Math.sqrt(value);
-	}
-	
-	public static double sum(double[] v){
-		double sum = 0;
-		for (int i = 0; i < v.length; i++) {
-			sum+=v[i];
-		}
-		return sum;
-	}
-	
-	
-	
-	
-	/**
-	 * w = w + v
-	 * @param w
-	 * @param v
-	 */
-	public static void plusEquals(double[] w, double[] v) {
-		for(int i=0; i<w.length;i++){
-			w[i] += w[i] + v[i];
-		}		
-	}
-	
-	/**
-	 * w[i] = w[i] + v
-	 * @param w
-	 * @param v
-	 */
-	public static void plusEquals(double[] w, double v) {
-		for(int i=0; i<w.length;i++){
-			w[i] += w[i] + v;
-		}		
-	}
-	
-	/**
-	 * w[i] = w[i] - v
-	 * @param w
-	 * @param v
-	 */
-	public static void minusEquals(double[] w, double v) {
-		for(int i=0; i<w.length;i++){
-			w[i] -= w[i] + v;
-		}		
-	}
-	
-	/**
-	 * w = w + a*v
-	 * @param w
-	 * @param v
-	 * @param a
-	 */
-	public static void plusEquals(double[] w, double[] v, double a) {
-		for(int i=0; i<w.length;i++){
-			w[i] += a*v[i];
-		}		
-	}
-	
-	/**
-	 * w = w - a*v
-	 * @param w
-	 * @param v
-	 * @param a
-	 */
-	public static void minusEquals(double[] w, double[] v, double a) {
-		for(int i=0; i<w.length;i++){
-			w[i] -= a*v[i];
-		}		
-	}
-	/**
-	 * v = w - a*v
-	 * @param w
-	 * @param v
-	 * @param a
-	 */
-	public static void minusEqualsInverse(double[] w, double[] v, double a) {
-		for(int i=0; i<w.length;i++){
-			v[i] = w[i] - a*v[i];
-		}		
-	}
-	
-	public static double dotProduct(double[] w, double[] v){
-		double accum = 0;
-		for(int i=0; i<w.length;i++){
-			accum += w[i]*v[i];
-		}
-		return accum;
-	}
-	
-	public static double[] arrayMinus(double[]w, double[]v){
-		double result[] = w.clone();
-		for(int i=0; i<w.length;i++){
-			result[i] -= v[i];
-		}
-		return result;
-	}
-	
-	public static double[] arrayMinus(double[] result , double[]w, double[]v){
-		for(int i=0; i<w.length;i++){
-			result[i] = w[i]-v[i];
-		}
-		return result;
-	}
-	
-	public static double[] negation(double[]w){
-		double result[]  = new double[w.length];
-		for(int i=0; i<w.length;i++){
-			result[i] = -w[i];
-		}
-		return result;
-	}
-	
-	public static double square(double value){
-		return value*value;
-	}
-	public static double[][] outerProduct(double[] w, double[] v){
-		double[][] result = new double[w.length][v.length];
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < v.length; j++){
-				result[i][j] = w[i]*v[j];
-			}
-		}
-		return result;
-	}
-	/**
-	 * results = a*W*V
-	 * @param w
-	 * @param v
-	 * @param a
-	 * @return
-	 */
-	public static double[][] weightedouterProduct(double[] w, double[] v, double a){
-		double[][] result = new double[w.length][v.length];
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < v.length; j++){
-				result[i][j] = a*w[i]*v[j];
-			}
-		}
-		return result;
-	}
-	
-	public static double[][] identity(int size){
-		double[][] result = new double[size][size];
-		for(int i = 0; i < size; i++){
-			result[i][i] = 1;
-		}
-		return result;
-	}
-	
-	/**
-	 * v -= w
-	 * @param v
-	 * @param w
-	 */
-	public static void minusEquals(double[][] w, double[][] v){
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < w[0].length; j++){
-				w[i][j] -= v[i][j];
-			}
-		}
-	}
-	
-	/**
-	 * v[i][j] -= a*w[i][j]
-	 * @param v
-	 * @param w
-	 */
-	public static void minusEquals(double[][] w, double[][] v, double a){
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < w[0].length; j++){
-				w[i][j] -= a*v[i][j];
-			}
-		}
-	}
-	
-	/**
-	 * v += w
-	 * @param v
-	 * @param w
-	 */
-	public static void plusEquals(double[][] w, double[][] v){
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < w[0].length; j++){
-				w[i][j] += v[i][j];
-			}
-		}
-	}
-	
-	/**
-	 * v[i][j] += a*w[i][j]
-	 * @param v
-	 * @param w
-	 */
-	public static void plusEquals(double[][] w, double[][] v, double a){
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < w[0].length; j++){
-				w[i][j] += a*v[i][j];
-			}
-		}
-	}
-	
-	
-	/**
-	 * results = w*v
-	 * @param w
-	 * @param v
-	 * @return
-	 */
-	public static  double[][] matrixMultiplication(double[][] w,double[][] v){
-		int w1 = w.length;
-		int w2 = w[0].length;
-		int v1 = v.length;
-		int v2 = v[0].length;
-		
-		if(w2 != v1){
-			System.out.println("Matrix dimensions do not agree...");
-			System.exit(-1);
-		}
-		
-		double[][] result = new double[w1][v2];
-		for(int w_i1 = 0; w_i1 < w1; w_i1++){
-			for(int v_i2 = 0; v_i2 < v2; v_i2++){
-				double sum = 0;
-				for(int w_i2 = 0; w_i2 < w2; w_i2++){
-						sum += w[w_i1 ][w_i2]*v[w_i2][v_i2];	
-				}
-				result[w_i1][v_i2] = sum;
-			}
-		}
-		return result;
-	}
-	
-	/**
-	 * w = w.*v
-	 * @param w
-	 * @param v
-	 */
-	public static  void matrixScalarMultiplication(double[][] w,double v){
-		int w1 = w.length;
-		int w2 = w[0].length;	
-		for(int w_i1 = 0; w_i1 < w1; w_i1++){
-				for(int w_i2 = 0; w_i2 < w2; w_i2++){
-						w[w_i1 ][w_i2] *= v;	
-				}
-		}
-	}
-	
-	public static  void scalarMultiplication(double[] w,double v){
-		int w1 = w.length;
-		for(int w_i1 = 0; w_i1 < w1; w_i1++){
-			w[w_i1 ] *= v;	
-		}
-		
-	}
-	
-	public static  double[] matrixVector(double[][] w,double[] v){
-		int w1 = w.length;
-		int w2 = w[0].length;
-		int v1 = v.length;
-		
-		if(w2 != v1){
-			System.out.println("Matrix dimensions do not agree...");
-			System.exit(-1);
-		}
-		
-		double[] result = new double[w1];
-		for(int w_i1 = 0; w_i1 < w1; w_i1++){
-				double sum = 0;
-				for(int w_i2 = 0; w_i2 < w2; w_i2++){
-						sum += w[w_i1 ][w_i2]*v[w_i2];	
-				}
-				result[w_i1] = sum;
-		}
-		return result;
-	}
-	
-	public static boolean allPositive(double[] array){
-		for (int i = 0; i < array.length; i++) {
-			if(array[i] < 0) return false;
-		}
-		return true;
-	}
-	
-	
-	
-	
-	
-		public static void main(String[] args) {
-			double[][] m1 = new double[2][2];
-			m1[0][0]=2;
-			m1[1][0]=2;
-			m1[0][1]=2;
-			m1[1][1]=2;
-			MatrixOutput.printDoubleArray(m1, "m1");
-			double[][] m2 = new double[2][2];
-			m2[0][0]=3;
-			m2[1][0]=3;
-			m2[0][1]=3;
-			m2[1][1]=3;
-			MatrixOutput.printDoubleArray(m2, "m2");
-			double[][] result = matrixMultiplication(m1, m2);
-			MatrixOutput.printDoubleArray(result, "result");
-			matrixScalarMultiplication(result, 3);
-			MatrixOutput.printDoubleArray(result, "result after multiply by 3");
-		}
-	
-		public static boolean almost(double a, double b, double prec){
-			return Math.abs(a-b)/Math.abs(a+b) <= prec || (almostZero(a) && almostZero(b));
-		}
-
-		public static boolean almost(double a, double b){
-			return Math.abs(a-b)/Math.abs(a+b) <= 1e-10 || (almostZero(a) && almostZero(b));
-		}
-
-		public static boolean almostZero(double a) {
-			return Math.abs(a) <= 1e-30;
-		}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java b/gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java
deleted file mode 100644
index 9fbdf955..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java
+++ /dev/null
@@ -1,28 +0,0 @@
-package optimization.util;
-
-
-public class MatrixOutput {
-	public static void printDoubleArray(double[][] array, String arrayName) {
-		int size1 = array.length;
-		int size2 = array[0].length;
-		System.out.println(arrayName);
-		for (int i = 0; i < size1; i++) {
-			for (int j = 0; j < size2; j++) {
-				System.out.print(" " + StaticTools.prettyPrint(array[i][j],
-						"00.00E00", 4) + " ");
-
-			}
-			System.out.println();
-		}
-		System.out.println();
-	}
-	
-	public static void printDoubleArray(double[] array, String arrayName) {
-		System.out.println(arrayName);
-		for (int i = 0; i < array.length; i++) {
-				System.out.print(" " + StaticTools.prettyPrint(array[i],
-						"00.00E00", 4) + " ");
-		}
-		System.out.println();
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java b/gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java
deleted file mode 100644
index bcabee06..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java
+++ /dev/null
@@ -1,180 +0,0 @@
-package optimization.util;
-
-
-import java.io.File;
-import java.io.PrintStream;
-
-public class StaticTools {
-
-	static java.text.DecimalFormat fmt = new java.text.DecimalFormat();
-
-	public static void createDir(String directory) {
-
-		File dir = new File(directory);
-		if (!dir.isDirectory()) {
-			boolean success = dir.mkdirs();
-			if (!success) {
-				System.out.println("Unable to create directory " + directory);
-				System.exit(0);
-			}
-			System.out.println("Created directory " + directory);
-		} else {
-			System.out.println("Reusing directory " + directory);
-		}
-	}
-
-	/*
-	 * q and p are indexed by source/foreign Sum_S(q) = 1 the same for p KL(q,p) =
-	 * Eq*q/p
-	 */
-	public static double KLDistance(double[][] p, double[][] q, int sourceSize,
-			int foreignSize) {
-		double totalKL = 0;
-		// common.StaticTools.printMatrix(q, sourceSize, foreignSize, "q",
-		// System.out);
-		// common.StaticTools.printMatrix(p, sourceSize, foreignSize, "p",
-		// System.out);
-		for (int i = 0; i < sourceSize; i++) {
-			double kl = 0;
-			for (int j = 0; j < foreignSize; j++) {
-				assert !Double.isNaN(q[i][j]) : "KLDistance q:  prob is NaN";
-				assert !Double.isNaN(p[i][j]) : "KLDistance p:  prob is NaN";
-				if (p[i][j] == 0 || q[i][j] == 0) {
-					continue;
-				} else {
-					kl += q[i][j] * Math.log(q[i][j] / p[i][j]);
-				}
-
-			}
-			totalKL += kl;
-		}
-		assert !Double.isNaN(totalKL) : "KLDistance: prob is NaN";
-		if (totalKL < -1.0E-10) {
-			System.out.println("KL Smaller than zero " + totalKL);
-			System.out.println("Source Size" + sourceSize);
-			System.out.println("Foreign Size" + foreignSize);
-			StaticTools.printMatrix(q, sourceSize, foreignSize, "q",
-					System.out);
-			StaticTools.printMatrix(p, sourceSize, foreignSize, "p",
-					System.out);
-			System.exit(-1);
-		}
-		return totalKL / sourceSize;
-	}
-
-	/*
-	 * indexed the by [fi][si]
-	 */
-	public static double KLDistancePrime(double[][] p, double[][] q,
-			int sourceSize, int foreignSize) {
-		double totalKL = 0;
-		for (int i = 0; i < sourceSize; i++) {
-			double kl = 0;
-			for (int j = 0; j < foreignSize; j++) {
-				assert !Double.isNaN(q[j][i]) : "KLDistance q:  prob is NaN";
-				assert !Double.isNaN(p[j][i]) : "KLDistance p:  prob is NaN";
-				if (p[j][i] == 0 || q[j][i] == 0) {
-					continue;
-				} else {
-					kl += q[j][i] * Math.log(q[j][i] / p[j][i]);
-				}
-
-			}
-			totalKL += kl;
-		}
-		assert !Double.isNaN(totalKL) : "KLDistance: prob is NaN";
-		return totalKL / sourceSize;
-	}
-
-	public static double Entropy(double[][] p, int sourceSize, int foreignSize) {
-		double totalE = 0;
-		for (int i = 0; i < foreignSize; i++) {
-			double e = 0;
-			for (int j = 0; j < sourceSize; j++) {
-				e += p[i][j] * Math.log(p[i][j]);
-			}
-			totalE += e;
-		}
-		return totalE / sourceSize;
-	}
-
-	public static double[][] copyMatrix(double[][] original, int sourceSize,
-			int foreignSize) {
-		double[][] result = new double[sourceSize][foreignSize];
-		for (int i = 0; i < sourceSize; i++) {
-			for (int j = 0; j < foreignSize; j++) {
-				result[i][j] = original[i][j];
-			}
-		}
-		return result;
-	}
-
-	public static void printMatrix(double[][] matrix, int sourceSize,
-			int foreignSize, String info, PrintStream out) {
-
-		java.text.DecimalFormat fmt = new java.text.DecimalFormat();
-		fmt.setMaximumFractionDigits(3);
-		fmt.setMaximumIntegerDigits(3);
-		fmt.setMinimumFractionDigits(3);
-		fmt.setMinimumIntegerDigits(3);
-
-		out.println(info);
-
-		for (int i = 0; i < foreignSize; i++) {
-			for (int j = 0; j < sourceSize; j++) {
-				out.print(prettyPrint(matrix[j][i], ".00E00", 6) + " ");
-			}
-			out.println();
-		}
-		out.println();
-		out.println();
-	}
-
-	public static void printMatrix(int[][] matrix, int sourceSize,
-			int foreignSize, String info, PrintStream out) {
-
-		out.println(info);
-		for (int i = 0; i < foreignSize; i++) {
-			for (int j = 0; j < sourceSize; j++) {
-				out.print(matrix[j][i] + " ");
-			}
-			out.println();
-		}
-		out.println();
-		out.println();
-	}
-
-	public static String formatTime(long duration) {
-		StringBuilder sb = new StringBuilder();
-		double d = duration / 1000;
-		fmt.applyPattern("00");
-		sb.append(fmt.format((int) (d / (60 * 60))) + ":");
-		d -= ((int) d / (60 * 60)) * 60 * 60;
-		sb.append(fmt.format((int) (d / 60)) + ":");
-		d -= ((int) d / 60) * 60;
-		fmt.applyPattern("00.0");
-		sb.append(fmt.format(d));
-		return sb.toString();
-	}
-
-	public static String prettyPrint(double d, String patt, int len) {
-		fmt.applyPattern(patt);
-		String s = fmt.format(d);
-		while (s.length() < len) {
-			s = " " + s;
-		}
-		return s;
-	}
-	
-	
-	public static long getUsedMemory(){
-		System.gc();
-		return (Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory())/ (1024 * 1024);
-	}
-	
-	public final static boolean compareDoubles(double d1, double d2){
-		return Math.abs(d1-d2) <= 1.E-10;
-	}
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/phrase/Agree.java b/gi/posterior-regularisation/prjava/src/phrase/Agree.java
deleted file mode 100644
index 8f7b499e..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/Agree.java
+++ /dev/null
@@ -1,204 +0,0 @@
-package phrase;

-

-import gnu.trove.TIntArrayList;

-

-import io.FileUtil;

-

-import java.io.File;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.List;

-

-import phrase.Corpus.Edge;

-

-public class Agree {

-	PhraseCluster model1;

-	C2F model2;

-	Corpus c;

-	private int K,n_phrases, n_words, n_contexts, n_positions1,n_positions2;

-	

-	/**@brief sum of loglikelihood of two

-	 * individual models

-	 */

-	public double llh;

-	/**@brief Bhattacharyya distance

-	 * 

-	 */

-	public double bdist; 

-	/**

-	 * 

-	 * @param numCluster

-	 * @param corpus

-	 */

-	public Agree(int numCluster, Corpus corpus){

-		

-		model1=new PhraseCluster(numCluster, corpus);

-		model2=new C2F(numCluster,corpus);

-		c=corpus;

-		n_words=c.getNumWords();

-		n_phrases=c.getNumPhrases();

-		n_contexts=c.getNumContexts();

-		n_positions1=c.getNumContextPositions();

-		n_positions2=2;

-		K=numCluster;

-		

-	}

-	

-	/**@brief test

-	 * 

-	 */

-	public static void main(String args[]){

-		//String in="../pdata/canned.con";

-		String in="../pdata/btec.con";

-		String out="../pdata/posterior.out";

-		int numCluster=25;

-		Corpus corpus = null;

-		File infile = new File(in);

-		try {

-			System.out.println("Reading concordance from " + infile);

-			corpus = Corpus.readFromFile(FileUtil.reader(infile));

-			corpus.printStats(System.out);

-		} catch (IOException e) {

-			System.err.println("Failed to open input file: " + infile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-		Agree agree=new Agree(numCluster, corpus);

-		int iter=20;

-		for(int i=0;i<iter;i++){

-			agree.EM();

-			System.out.println("Iter"+i+", llh: "+agree.llh+

-					", divergence:"+agree.bdist+

-							" sum: "+(agree.llh+agree.bdist));

-		}

-		

-		File outfile = new File (out);

-		try {

-			PrintStream ps = FileUtil.printstream(outfile);

-			agree.displayPosterior(ps);

-		//	ps.println();

-		//	c2f.displayModelParam(ps);

-			ps.close();

-		} catch (IOException e) {

-			System.err.println("Failed to open output file: " + outfile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-	}

-	

-	public double EM(){

-		

-		double [][][]exp_emit1=new double [K][n_positions1][n_words];

-		double [][]exp_pi1=new double[n_phrases][K];

-		

-		double [][][]exp_emit2=new double [K][n_positions2][n_words];

-		double [][]exp_pi2=new double[n_contexts][K];

-		

-		llh=0;

-		bdist=0;

-		//E

-		for(int context=0; context< n_contexts; context++){

-			

-			List<Edge> contexts = c.getEdgesForContext(context);

-

-			for (int ctx=0; ctx<contexts.size(); ctx++){

-				Edge edge = contexts.get(ctx);

-				int phrase=edge.getPhraseId();

-				double p[]=posterior(edge);

-				double z = arr.F.l1norm(p);

-				assert z > 0;

-				bdist += edge.getCount() * Math.log(z);

-				arr.F.l1normalize(p);

-				

-				double count = edge.getCount();

-				//increment expected count

-				TIntArrayList phraseToks = edge.getPhrase();

-				TIntArrayList contextToks = edge.getContext();

-				for(int tag=0;tag<K;tag++){

-

-					for(int position=0;position<n_positions1;position++){

-						exp_emit1[tag][position][contextToks.get(position)]+=p[tag]*count;

-					}

-					

-					exp_emit2[tag][0][phraseToks.get(0)]+=p[tag]*count;

-					exp_emit2[tag][1][phraseToks.get(phraseToks.size()-1)]+=p[tag]*count;

-					

-					exp_pi1[phrase][tag]+=p[tag]*count;

-					exp_pi2[context][tag]+=p[tag]*count;

-				}

-			}

-		}

-		

-		//System.out.println("Log likelihood: "+loglikelihood);

-		

-		//M

-		for(double [][]i:exp_emit1){

-			for(double []j:i){

-				arr.F.l1normalize(j);

-			}

-		}

-		

-		for(double []j:exp_pi1){

-			arr.F.l1normalize(j);

-		}

-		

-		for(double [][]i:exp_emit2){

-			for(double []j:i){

-				arr.F.l1normalize(j);

-			}

-		}

-		

-		for(double []j:exp_pi2){

-			arr.F.l1normalize(j);

-		}

-		

-		model1.emit=exp_emit1;

-		model1.pi=exp_pi1;

-		model2.emit=exp_emit2;

-		model2.pi=exp_pi2;

-		

-		return llh;

-	}

-

-	public double[] posterior(Corpus.Edge edge) 

-	{

-		double[] prob1=model1.posterior(edge);

-		double[] prob2=model2.posterior(edge);

-		

-		llh+=edge.getCount()*Math.log(arr.F.l1norm(prob1));

-		llh+=edge.getCount()*Math.log(arr.F.l1norm(prob2));

-		arr.F.l1normalize(prob1);

-		arr.F.l1normalize(prob2);

-		

-		for(int i=0;i<prob1.length;i++){

-			prob1[i]*=prob2[i];

-			prob1[i]=Math.sqrt(prob1[i]);

-		}

-		

-		return prob1;

-	}

-	

-	public void displayPosterior(PrintStream ps)

-	{	

-		displayPosterior(ps, c.getEdges());

-	}

-	

-	public void displayPosterior(PrintStream ps, List<Edge> test)

-	{	

-		for (Edge edge : test)

-		{

-			double probs[] = posterior(edge);

-			arr.F.l1normalize(probs);

-

-			// emit phrase

-			ps.print(edge.getPhraseString());

-			ps.print("\t");

-			ps.print(edge.getContextString(true));

-			int t=arr.F.argmax(probs);

-			ps.println(" ||| C=" + t);

-		}

-	}

-	

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Agree2Sides.java b/gi/posterior-regularisation/prjava/src/phrase/Agree2Sides.java
deleted file mode 100644
index 031f887f..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/Agree2Sides.java
+++ /dev/null
@@ -1,197 +0,0 @@
-package phrase;

-

-import gnu.trove.TIntArrayList;

-

-import io.FileUtil;

-

-import java.io.File;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.List;

-

-import phrase.Corpus.Edge;

-

-public class Agree2Sides {

-	PhraseCluster model1,model2;

-	Corpus c1,c2;

-	private int K;

-	

-	/**@brief sum of loglikelihood of two

-	 * individual models

-	 */

-	public double llh;

-	/**@brief Bhattacharyya distance

-	 * 

-	 */

-	public double bdist; 

-	/**

-	 * 

-	 * @param numCluster

-	 * @param corpus

-	 */

-	public Agree2Sides(int numCluster, Corpus corpus1 , Corpus corpus2 ){

-		

-		model1=new PhraseCluster(numCluster, corpus1);

-		model2=new PhraseCluster(numCluster,corpus2);

-		c1=corpus1;

-		c2=corpus2;

-		K=numCluster;

-		

-	}

-	

-	/**@brief test

-	 * 

-	 */

-	public static void main(String args[]){

-		//String in="../pdata/canned.con";

-	//	String in="../pdata/btec.con";

-		String in1="../pdata/source.txt";

-		String in2="../pdata/target.txt";

-		String out="../pdata/posterior.out";

-		int numCluster=25;

-		Corpus corpus1 = null,corpus2=null;

-		File infile1 = new File(in1),infile2=new File(in2);

-		try {

-			System.out.println("Reading concordance from " + infile1);

-			corpus1 = Corpus.readFromFile(FileUtil.reader(infile1));

-			System.out.println("Reading concordance from " + infile2);

-			corpus2 = Corpus.readFromFile(FileUtil.reader(infile2));

-			corpus1.printStats(System.out);

-		} catch (IOException e) {

-			System.err.println("Failed to open input file: " + infile1);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-		Agree2Sides agree=new Agree2Sides(numCluster, corpus1,corpus2);

-		int iter=20;

-		for(int i=0;i<iter;i++){

-			agree.EM();

-			System.out.println("Iter"+i+", llh: "+agree.llh+

-					", divergence:"+agree.bdist+

-							" sum: "+(agree.llh+agree.bdist));

-		}

-		

-		File outfile = new File (out);

-		try {

-			PrintStream ps = FileUtil.printstream(outfile);

-			agree.displayPosterior(ps);

-		//	ps.println();

-		//	c2f.displayModelParam(ps);

-			ps.close();

-		} catch (IOException e) {

-			System.err.println("Failed to open output file: " + outfile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-	}

-	

-	public double EM(){

-		

-		double [][][]exp_emit1=new double [K][c1.getNumContextPositions()][c1.getNumWords()];

-		double [][]exp_pi1=new double[c1.getNumPhrases()][K];

-		

-		double [][][]exp_emit2=new double [K][c2.getNumContextPositions()][c2.getNumWords()];

-		double [][]exp_pi2=new double[c2.getNumPhrases()][K];

-		

-		llh=0;

-		bdist=0;

-		//E

-		for(int i=0;i<c1.getEdges().size();i++){

-			Edge edge1=c1.getEdges().get(i);

-			Edge edge2=c2.getEdges().get(i);

-			double p[]=posterior(i);

-			double z = arr.F.l1norm(p);

-			assert z > 0;

-			bdist += edge1.getCount() * Math.log(z);

-			arr.F.l1normalize(p);

-			double count = edge1.getCount();

-				//increment expected count

-			TIntArrayList contextToks1 = edge1.getContext();

-			TIntArrayList contextToks2 = edge2.getContext();

-			int phrase1=edge1.getPhraseId();

-			int phrase2=edge2.getPhraseId();

-			for(int tag=0;tag<K;tag++){

-				for(int position=0;position<c1.getNumContextPositions();position++){

-					exp_emit1[tag][position][contextToks1.get(position)]+=p[tag]*count;

-				}

-				for(int position=0;position<c2.getNumContextPositions();position++){

-					exp_emit2[tag][position][contextToks2.get(position)]+=p[tag]*count;

-				}

-				exp_pi1[phrase1][tag]+=p[tag]*count;

-				exp_pi2[phrase2][tag]+=p[tag]*count;

-			}

-		}

-		

-		//System.out.println("Log likelihood: "+loglikelihood);

-		

-		//M

-		for(double [][]i:exp_emit1){

-			for(double []j:i){

-				arr.F.l1normalize(j);

-			}

-		}

-		

-		for(double []j:exp_pi1){

-			arr.F.l1normalize(j);

-		}

-		

-		for(double [][]i:exp_emit2){

-			for(double []j:i){

-				arr.F.l1normalize(j);

-			}

-		}

-		

-		for(double []j:exp_pi2){

-			arr.F.l1normalize(j);

-		}

-		

-		model1.emit=exp_emit1;

-		model1.pi=exp_pi1;

-		model2.emit=exp_emit2;

-		model2.pi=exp_pi2;

-		

-		return llh;

-	}

-

-	public double[] posterior(int edgeIdx) 

-	{

-		return posterior(c1.getEdges().get(edgeIdx), c2.getEdges().get(edgeIdx));

-	}

-	

-	public double[] posterior(Edge e1, Edge e2) 

-	{

-		double[] prob1=model1.posterior(e1);

-		double[] prob2=model2.posterior(e2);

-		

-		llh+=e1.getCount()*Math.log(arr.F.l1norm(prob1));

-		llh+=e2.getCount()*Math.log(arr.F.l1norm(prob2));

-		arr.F.l1normalize(prob1);

-		arr.F.l1normalize(prob2);

-		

-		for(int i=0;i<prob1.length;i++){

-			prob1[i]*=prob2[i];

-			prob1[i]=Math.sqrt(prob1[i]);

-		}

-		

-		return prob1;

-	}

-	

-	public void displayPosterior(PrintStream ps)

-	{	

-		for (int i=0;i<c1.getEdges().size();i++)

-		{

-			Edge edge=c1.getEdges().get(i);

-			double probs[] = posterior(i);

-			arr.F.l1normalize(probs);

-

-			// emit phrase

-			ps.print(edge.getPhraseString());

-			ps.print("\t");

-			ps.print(edge.getContextString(true));

-			int t=arr.F.argmax(probs);

-			ps.println(" ||| C=" + t);

-		}

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/C2F.java b/gi/posterior-regularisation/prjava/src/phrase/C2F.java
deleted file mode 100644
index e8783950..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/C2F.java
+++ /dev/null
@@ -1,216 +0,0 @@
-package phrase;

-

-import gnu.trove.TIntArrayList;

-

-import io.FileUtil;

-

-import java.io.File;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.Arrays;

-import java.util.List;

-

-import phrase.Corpus.Edge;

-

-/**

- * @brief context generates phrase

- * @author desaic

- *

- */

-public class C2F {

-	public int K;

-	private int n_words, n_contexts, n_positions;

-	public Corpus c;

-	

-	/**@brief

-	 *  emit[tag][position][word] = p(word | tag, position in phrase)

-	 */

-	public double emit[][][];

-	/**@brief

-	 *  pi[context][tag] = p(tag | context)

-	 */

-	public double pi[][];

-	

-	public C2F(int numCluster, Corpus corpus){

-		K=numCluster;

-		c=corpus;

-		n_words=c.getNumWords();

-		n_contexts=c.getNumContexts();

-		

-		//number of words in a phrase to be considered

-		//currently the first and last word in source and target

-		//if the phrase has length 1 in either dimension then

-		//we use the same word for two positions

-		n_positions=c.phraseEdges(c.getEdges().get(0).getPhrase()).size();

-		

-		emit=new double [K][n_positions][n_words];

-		pi=new double[n_contexts][K];

-		

-		for(double [][]i:emit){

-			for(double []j:i){

-				arr.F.randomise(j);

-			}

-		}

-		

-		for(double []j:pi){

-			arr.F.randomise(j);

-		}

-	}

-	

-	/**@brief test

-	 * 

-	 */

-	public static void main(String args[]){

-		String in="../pdata/canned.con";

-		String out="../pdata/posterior.out";

-		int numCluster=25;

-		Corpus corpus = null;

-		File infile = new File(in);

-		try {

-			System.out.println("Reading concordance from " + infile);

-			corpus = Corpus.readFromFile(FileUtil.reader(infile));

-			corpus.printStats(System.out);

-		} catch (IOException e) {

-			System.err.println("Failed to open input file: " + infile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-		C2F c2f=new C2F(numCluster,corpus);

-		int iter=20;

-		double llh=0;

-		for(int i=0;i<iter;i++){

-			llh=c2f.EM();

-			System.out.println("Iter"+i+", llh: "+llh);

-		}

-		

-		File outfile = new File (out);

-		try {

-			PrintStream ps = FileUtil.printstream(outfile);

-			c2f.displayPosterior(ps);

-		//	ps.println();

-		//	c2f.displayModelParam(ps);

-			ps.close();

-		} catch (IOException e) {

-			System.err.println("Failed to open output file: " + outfile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-	}

-	

-	public double EM(){

-		double [][][]exp_emit=new double [K][n_positions][n_words];

-		double [][]exp_pi=new double[n_contexts][K];

-		

-		double loglikelihood=0;

-		

-		//E

-		for(int context=0; context< n_contexts; context++){

-			

-			List<Edge> contexts = c.getEdgesForContext(context);

-

-			for (int ctx=0; ctx<contexts.size(); ctx++){

-				Edge edge = contexts.get(ctx);

-				double p[]=posterior(edge);

-				double z = arr.F.l1norm(p);

-				assert z > 0;

-				loglikelihood += edge.getCount() * Math.log(z);

-				arr.F.l1normalize(p);

-				

-				double count = edge.getCount();

-				//increment expected count

-				TIntArrayList phrase= edge.getPhrase();

-				for(int tag=0;tag<K;tag++){

-

-					exp_emit[tag][0][phrase.get(0)]+=p[tag]*count;

-					exp_emit[tag][1][phrase.get(phrase.size()-1)]+=p[tag]*count;

-					

-					exp_pi[context][tag]+=p[tag]*count;

-				}

-			}

-		}

-		

-		//System.out.println("Log likelihood: "+loglikelihood);

-		

-		//M

-		for(double [][]i:exp_emit){

-			for(double []j:i){

-				arr.F.l1normalize(j);

-			}

-		}

-		

-		emit=exp_emit;

-		

-		for(double []j:exp_pi){

-			arr.F.l1normalize(j);

-		}

-		

-		pi=exp_pi;

-		

-		return loglikelihood;

-	}

-

-	public double[] posterior(Corpus.Edge edge) 

-	{

-		double[] prob=Arrays.copyOf(pi[edge.getContextId()], K);

-		

-		TIntArrayList phrase = edge.getPhrase();

-		TIntArrayList offsets = c.phraseEdges(phrase);

-		for(int tag=0;tag<K;tag++)

-		{

-			for (int i=0; i < offsets.size(); ++i)

-				prob[tag]*=emit[tag][i][phrase.get(offsets.get(i))];

-		}

-			

-		return prob;

-	}

-

-	public void displayPosterior(PrintStream ps)

-	{	

-		for (Edge edge : c.getEdges())

-		{

-			double probs[] = posterior(edge);

-			arr.F.l1normalize(probs);

-

-			// emit phrase

-			ps.print(edge.getPhraseString());

-			ps.print("\t");

-			ps.print(edge.getContextString(true));

-			int t=arr.F.argmax(probs);

-			ps.println(" ||| C=" + t);

-		}

-	}

-	

-	public void displayModelParam(PrintStream ps)

-	{

-		final double EPS = 1e-6;

-		

-		ps.println("P(tag|context)");

-		for (int i = 0; i < n_contexts; ++i)

-		{

-			ps.print(c.getContext(i));

-			for(int j=0;j<pi[i].length;j++){

-				if (pi[i][j] > EPS)

-					ps.print("\t" + j + ": " + pi[i][j]);

-			}

-			ps.println();

-		}

-		

-		ps.println("P(word|tag,position)");

-		for (int i = 0; i < K; ++i)

-		{

-			for(int position=0;position<n_positions;position++){

-				ps.println("tag " + i + " position " + position);

-				for(int word=0;word<emit[i][position].length;word++){

-					if (emit[i][position][word] > EPS)

-						ps.print(c.getWord(word)+"="+emit[i][position][word]+"\t");

-				}

-				ps.println();

-			}

-			ps.println();

-		}

-		

-	}

-	

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Corpus.java b/gi/posterior-regularisation/prjava/src/phrase/Corpus.java
deleted file mode 100644
index 4b1939cd..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/Corpus.java
+++ /dev/null
@@ -1,288 +0,0 @@
-package phrase;
-
-import gnu.trove.TIntArrayList;
-
-import java.io.*;
-import java.util.*;
-import java.util.regex.Pattern;
-
-
-public class Corpus
-{
-	private Lexicon<String> wordLexicon = new Lexicon<String>();
-	private Lexicon<TIntArrayList> phraseLexicon = new Lexicon<TIntArrayList>();
-	private Lexicon<TIntArrayList> contextLexicon = new Lexicon<TIntArrayList>();
-	private List<Edge> edges = new ArrayList<Edge>();
-	private List<List<Edge>> phraseToContext = new ArrayList<List<Edge>>();
-	private List<List<Edge>> contextToPhrase = new ArrayList<List<Edge>>();
-	public int splitSentinel;
-	public int phraseSentinel;
-	public int rareSentinel;
-
-	public Corpus()
-	{
-		splitSentinel = wordLexicon.insert("<SPLIT>");
-		phraseSentinel = wordLexicon.insert("<PHRASE>");		
-		rareSentinel = wordLexicon.insert("<RARE>");
-	}
-	
-	public class Edge
-	{
-		
-		Edge(int phraseId, int contextId, double count,int tag)
-		{
-			this.phraseId = phraseId;
-			this.contextId = contextId;
-			this.count = count;
-			fixTag=tag;
-		}
-		
-		Edge(int phraseId, int contextId, double count)
-		{
-			this.phraseId = phraseId;
-			this.contextId = contextId;
-			this.count = count;
-			fixTag=-1;
-		}
-		public int getTag(){
-			return fixTag;
-		}
-		
-		public int getPhraseId()
-		{
-			return phraseId;
-		}
-		public TIntArrayList getPhrase()
-		{
-			return Corpus.this.getPhrase(phraseId);
-		}
-		public String getPhraseString()
-		{
-			return Corpus.this.getPhraseString(phraseId);
-		}		
-		public int getContextId()
-		{
-			return contextId;
-		}
-		public TIntArrayList getContext()
-		{
-			return Corpus.this.getContext(contextId);
-		}
-		public String getContextString(boolean insertPhraseSentinel)
-		{
-			return Corpus.this.getContextString(contextId, insertPhraseSentinel);
-		}
-		public double getCount()
-		{
-			return count;
-		}
-		public boolean equals(Object other)
-		{
-			if (other instanceof Edge) 
-			{
-				Edge oe = (Edge) other;
-				return oe.phraseId == phraseId && oe.contextId == contextId; 
-			}
-			else return false;
-		}
-		public int hashCode()
-		{   // this is how boost's hash_combine does it
-			int seed = phraseId;
-			seed ^= contextId + 0x9e3779b9 + (seed << 6) + (seed >> 2);
-			return seed;
-		}
-		public String toString()
-		{
-			return getPhraseString() + "\t" + getContextString(true);
-		}
-		
-		private int phraseId;
-		private int contextId;
-		private double count;
-		private int fixTag;
-	}
-
-	List<Edge> getEdges()
-	{
-		return edges;
-	}
-	
-	int getNumEdges()
-	{
-		return edges.size();
-	}
-
-	int getNumPhrases()
-	{
-		return phraseLexicon.size();
-	}
-	
-	int getNumContextPositions()
-	{
-		return contextLexicon.lookup(0).size();
-	}
-	
-	List<Edge> getEdgesForPhrase(int phraseId)
-	{
-		return phraseToContext.get(phraseId);
-	}
-	
-	int getNumContexts()
-	{
-		return contextLexicon.size();
-	}
-	
-	List<Edge> getEdgesForContext(int contextId)
-	{
-		return contextToPhrase.get(contextId);
-	}
-	
-	int getNumWords()
-	{
-		return wordLexicon.size();
-	}
-	
-	String getWord(int wordId)
-	{
-		return wordLexicon.lookup(wordId);
-	}
-	
-	public TIntArrayList getPhrase(int phraseId)
-	{
-		return phraseLexicon.lookup(phraseId);
-	}
-	
-	public String getPhraseString(int phraseId)
-	{
-		StringBuffer b = new StringBuffer();
-		for (int tid: getPhrase(phraseId).toNativeArray())
-		{
-			if (b.length() > 0)
-				b.append(" ");
-			b.append(wordLexicon.lookup(tid));
-		}
-		return b.toString();
-	}		
-	
-	public TIntArrayList getContext(int contextId)
-	{
-		return contextLexicon.lookup(contextId);
-	}
-	
-	public String getContextString(int contextId, boolean insertPhraseSentinel)
-	{
-		StringBuffer b = new StringBuffer();
-		TIntArrayList c = getContext(contextId);
-		for (int i = 0; i < c.size(); ++i)
-		{
-			if (i > 0) b.append(" ");
-			//if (i == c.size() / 2) b.append("<PHRASE> ");
-			b.append(wordLexicon.lookup(c.get(i)));
-		}
-		return b.toString();
-	}
-	
-	public boolean isSentinel(int wordId)
-	{
-		return wordId == splitSentinel || wordId == phraseSentinel;
-	}
-	
-	List<Edge> readEdges(Reader in) throws IOException
-	{	
-		// read in line-by-line
-		BufferedReader bin = new BufferedReader(in);
-		String line;
-		Pattern separator = Pattern.compile(" \\|\\|\\| ");
-		
-		List<Edge> edges = new ArrayList<Edge>();
-		while ((line = bin.readLine()) != null)
-		{
-			// split into phrase and contexts
-			StringTokenizer st = new StringTokenizer(line, "\t");
-			assert (st.hasMoreTokens());
-			String phraseToks = st.nextToken();
-			assert (st.hasMoreTokens());
-			String rest = st.nextToken();
-			assert (!st.hasMoreTokens());
-
-			// process phrase	
-			st = new StringTokenizer(phraseToks, " ");
-			TIntArrayList ptoks = new TIntArrayList();
-			while (st.hasMoreTokens())
-				ptoks.add(wordLexicon.insert(st.nextToken()));
-			int phraseId = phraseLexicon.insert(ptoks);
-			
-			// process contexts
-			String[] parts = separator.split(rest);
-			assert (parts.length % 2 == 0);
-			for (int i = 0; i < parts.length; i += 2)
-			{
-				// process pairs of strings - context and count
-				String ctxString = parts[i];
-				String countString = parts[i + 1];
-
-				assert (countString.startsWith("C="));
-
-				String []countToks=countString.split(" ");
-				
-				double count = Double.parseDouble(countToks[0].substring(2).trim());
-				
-				TIntArrayList ctx = new TIntArrayList();
-				StringTokenizer ctxStrtok = new StringTokenizer(ctxString, " ");
-				while (ctxStrtok.hasMoreTokens())
-				{
-					String token = ctxStrtok.nextToken();
-					ctx.add(wordLexicon.insert(token));
-				}
-				int contextId = contextLexicon.insert(ctx);
-
-
-				if(countToks.length<2){
-					edges.add(new Edge(phraseId, contextId, count));
-				}
-				else{
-					int tag=Integer.parseInt(countToks[1].substring(2));
-					edges.add(new Edge(phraseId, contextId, count,tag));
-				}
-			}
-		}
-		return edges;
-	}
-	
-	static Corpus readFromFile(Reader in) throws IOException
-	{	
-		Corpus c = new Corpus();
-		c.edges = c.readEdges(in);
-		for (Edge edge: c.edges)
-		{
-			while (edge.getPhraseId() >= c.phraseToContext.size())
-				c.phraseToContext.add(new ArrayList<Edge>());
-			while (edge.getContextId() >= c.contextToPhrase.size())
-				c.contextToPhrase.add(new ArrayList<Edge>());
-			
-			// index the edge for fast phrase, context lookup
-			c.phraseToContext.get(edge.getPhraseId()).add(edge);
-			c.contextToPhrase.get(edge.getContextId()).add(edge);
-		}
-		return c;
-	}
-		
-	TIntArrayList phraseEdges(TIntArrayList phrase)
-	{
-		TIntArrayList r = new TIntArrayList(4);
-		for (int p = 0; p < phrase.size(); ++p)
-		{
-			if (p == 0 || phrase.get(p-1) == splitSentinel) 				
-				r.add(p);
-			if (p == phrase.size() - 1 || phrase.get(p+1) == splitSentinel) 
-				r.add(p);
-		}
-		return r;
-	}
-
-	public void printStats(PrintStream out) 
-	{
-		out.println("Corpus has " + edges.size() + " edges " + phraseLexicon.size() + " phrases " 
-				+ contextLexicon.size() + " contexts and " + wordLexicon.size() + " word types");
-	}
-}
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/src/phrase/Lexicon.java b/gi/posterior-regularisation/prjava/src/phrase/Lexicon.java
deleted file mode 100644
index a386e4a3..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/Lexicon.java
+++ /dev/null
@@ -1,34 +0,0 @@
-package phrase;
-
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-
-public class Lexicon<T>
-{
-	public int insert(T word)
-	{
-		Integer i = wordToIndex.get(word);
-		if (i == null)
-		{
-			i = indexToWord.size();
-			wordToIndex.put(word, i);
-			indexToWord.add(word);
-		}
-		return i;
-	}
-
-	public T lookup(int index)
-	{
-		return indexToWord.get(index);
-	}
-
-	public int size()
-	{
-		return indexToWord.size();
-	}
-
-	private Map<T, Integer> wordToIndex = new HashMap<T, Integer>();
-	private List<T> indexToWord = new ArrayList<T>();
-}
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
deleted file mode 100644
index c032bb2b..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
+++ /dev/null
@@ -1,540 +0,0 @@
-package phrase;

-

-import gnu.trove.TIntArrayList;

-import org.apache.commons.math.special.Gamma;

-

-import java.io.BufferedReader;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.ArrayList;

-import java.util.Arrays;

-import java.util.List;

-import java.util.concurrent.Callable;

-import java.util.concurrent.ExecutionException;

-import java.util.concurrent.ExecutorService;

-import java.util.concurrent.Executors;

-import java.util.concurrent.Future;

-import java.util.concurrent.LinkedBlockingQueue;

-import java.util.concurrent.atomic.AtomicInteger;

-import java.util.concurrent.atomic.AtomicLong;

-import java.util.regex.Pattern;

-

-import phrase.Corpus.Edge;

-

-

-public class PhraseCluster {

-	

-	public int K;

-	private int n_phrases, n_words, n_contexts, n_positions;

-	public Corpus c;

-	public ExecutorService pool; 

-

-	double[] lambdaPTCT;

-	double[][] lambdaPT;

-	boolean cacheLambda = true;

-

-	// emit[tag][position][word] = p(word | tag, position in context)

-	double emit[][][];

-	// pi[phrase][tag] = p(tag | phrase)

-	double pi[][];

-	

-	public PhraseCluster(int numCluster, Corpus corpus)

-	{

-		K=numCluster;

-		c=corpus;

-		n_words=c.getNumWords();

-		n_phrases=c.getNumPhrases();

-		n_contexts=c.getNumContexts();

-		n_positions=c.getNumContextPositions();

-

-		emit=new double [K][n_positions][n_words];

-		pi=new double[n_phrases][K];

-		

-		for(double [][]i:emit)

-			for(double []j:i)

-				arr.F.randomise(j, true);

-

-		for(double []j:pi)

-			arr.F.randomise(j, true);

-	}

-	

-	void useThreadPool(ExecutorService pool)

-	{

-		this.pool = pool;

-	}

-

-	public double EM(int phraseSizeLimit)

-	{

-		double [][][]exp_emit=new double [K][n_positions][n_words];

-		double []exp_pi=new double[K];

-		

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				Arrays.fill(j, 1e-10);

-		

-		double loglikelihood=0;

-		

-		//E

-		for(int phrase=0; phrase < n_phrases; phrase++)

-		{

-			if (phraseSizeLimit >= 1 && c.getPhrase(phrase).size() > phraseSizeLimit)

-				continue;

-

-			Arrays.fill(exp_pi, 1e-10);

-			

-			List<Edge> contexts = c.getEdgesForPhrase(phrase);

-

-			for (int ctx=0; ctx<contexts.size(); ctx++)

-			{

-				Edge edge = contexts.get(ctx);

-				

-				double p[]=posterior(edge);

-				double z = arr.F.l1norm(p);

-				assert z > 0;

-				loglikelihood += edge.getCount() * Math.log(z);

-				arr.F.l1normalize(p);

-				

-				double count = edge.getCount();

-				//increment expected count

-				TIntArrayList context = edge.getContext();

-				for(int tag=0;tag<K;tag++)

-				{

-					for(int pos=0;pos<n_positions;pos++){

-						exp_emit[tag][pos][context.get(pos)]+=p[tag]*count;

-					}

-					exp_pi[tag]+=p[tag]*count;

-				}

-			}

-			arr.F.l1normalize(exp_pi);

-			System.arraycopy(exp_pi, 0, pi[phrase], 0, K);

-		}

-

-		//M

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				arr.F.l1normalize(j);

-			

-		emit=exp_emit;

-

-		return loglikelihood;

-	}

-	

-	public double PREM(double scalePT, double scaleCT, int phraseSizeLimit)

-	{

-		if (scaleCT == 0)

-		{

-			if (pool != null)

-				return PREM_phrase_constraints_parallel(scalePT, phraseSizeLimit);

-			else

-				return PREM_phrase_constraints(scalePT, phraseSizeLimit);

-		}

-		else // FIXME: ignores phraseSizeLimit

-			return this.PREM_phrase_context_constraints(scalePT, scaleCT);

-	}

-

-	

-	public double PREM_phrase_constraints(double scalePT, int phraseSizeLimit)

-	{

-		double [][][]exp_emit=new double[K][n_positions][n_words];

-		double []exp_pi=new double[K];

-		

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				Arrays.fill(j, 1e-10);

-		

-		if (lambdaPT == null && cacheLambda)

-			lambdaPT = new double[n_phrases][];

-		

-		double loglikelihood=0, kl=0, l1lmax=0, primal=0;

-		int failures=0, iterations=0;

-		long start = System.currentTimeMillis();

-		//E

-		for(int phrase=0; phrase<n_phrases; phrase++)

-		{

-			if (phraseSizeLimit >= 1 && c.getPhrase(phrase).size() > phraseSizeLimit)

-			{

-				//System.arraycopy(pi[phrase], 0, exp_pi[phrase], 0, K);

-				continue;

-			}

-			

-			Arrays.fill(exp_pi, 1e-10);

-			

-			// FIXME: add rare edge check to phrase objective & posterior processing

-			PhraseObjective po = new PhraseObjective(this, phrase, scalePT, (cacheLambda) ? lambdaPT[phrase] : null);

-			boolean ok = po.optimizeWithProjectedGradientDescent();

-			if (!ok) ++failures;

-			if (cacheLambda) lambdaPT[phrase] = po.getParameters();

-			iterations += po.getNumberUpdateCalls();

-			double [][] q=po.posterior();

-			loglikelihood += po.loglikelihood();

-			kl += po.KL_divergence();

-			l1lmax += po.l1lmax();

-			primal += po.primal(scalePT);

-			List<Edge> edges = c.getEdgesForPhrase(phrase);

-

-			for(int edge=0;edge<q.length;edge++){

-				Edge e = edges.get(edge);

-				TIntArrayList context = e.getContext();

-				double contextCnt = e.getCount();

-				//increment expected count

-				for(int tag=0;tag<K;tag++){

-					for(int pos=0;pos<n_positions;pos++){

-						exp_emit[tag][pos][context.get(pos)]+=q[edge][tag]*contextCnt;

-					}

-					

-					exp_pi[tag]+=q[edge][tag]*contextCnt;

-					

-				}

-			}

-			arr.F.l1normalize(exp_pi);

-			System.arraycopy(exp_pi, 0, pi[phrase], 0, K);

-		}

-		

-		long end = System.currentTimeMillis();

-		if (failures > 0)

-			System.out.println("WARNING: failed to converge in " + failures + "/" + n_phrases + " cases");

-		System.out.println("\tmean iters:     " + iterations/(double)n_phrases + " elapsed time " + (end - start) / 1000.0);

-		System.out.println("\tllh:            " + loglikelihood);

-		System.out.println("\tKL:             " + kl);

-		System.out.println("\tphrase l1lmax:  " + l1lmax);

-		

-		//M

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				arr.F.l1normalize(j);

-		emit=exp_emit;

-		

-		return primal;

-	}

-

-	public double PREM_phrase_constraints_parallel(final double scalePT, int phraseSizeLimit)

-	{

-		assert(pool != null);

-		

-		final LinkedBlockingQueue<PhraseObjective> expectations 

-			= new LinkedBlockingQueue<PhraseObjective>();

-		

-		double [][][]exp_emit=new double [K][n_positions][n_words];

-		double [][]exp_pi=new double[n_phrases][K];

-		

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				Arrays.fill(j, 1e-10);

-		for(double []j:exp_pi)

-			Arrays.fill(j, 1e-10);

-		

-		double loglikelihood=0, kl=0, l1lmax=0, primal=0;

-		final AtomicInteger failures = new AtomicInteger(0);

-		final AtomicLong elapsed = new AtomicLong(0l);

-		int iterations=0;

-		long start = System.currentTimeMillis();

-		List<Future<PhraseObjective>> results = new ArrayList<Future<PhraseObjective>>();

-		

-		if (lambdaPT == null && cacheLambda)

-			lambdaPT = new double[n_phrases][];

-

-		//E

-		for(int phrase=0;phrase<n_phrases;phrase++) {

-			if (phraseSizeLimit >= 1 && c.getPhrase(phrase).size() > phraseSizeLimit) {

-				System.arraycopy(pi[phrase], 0, exp_pi[phrase], 0, K);

-				continue;

-			}

-

-			final int p=phrase;

-			results.add(pool.submit(new Callable<PhraseObjective>() {

-				public PhraseObjective call() {

-					//System.out.println("" + Thread.currentThread().getId() + " optimising lambda for " + p);

-					long start = System.currentTimeMillis();

-					PhraseObjective po = new PhraseObjective(PhraseCluster.this, p, scalePT, (cacheLambda) ? lambdaPT[p] : null);

-					boolean ok = po.optimizeWithProjectedGradientDescent();

-					if (!ok) failures.incrementAndGet();

-					long end = System.currentTimeMillis();

-					elapsed.addAndGet(end - start);

-					//System.out.println("" + Thread.currentThread().getId() + " done optimising lambda for " + p);

-					return po;

-				}

-			}));

-		}

-		

-		// aggregate the expectations as they become available

-		for (Future<PhraseObjective> fpo : results)

-		{

-			try {

-				//System.out.println("" + Thread.currentThread().getId() + " reading queue #" + count);

-

-				// wait (blocking) until something is ready

-				PhraseObjective po = fpo.get();

-				// process

-				int phrase = po.phrase;

-				if (cacheLambda) lambdaPT[phrase] = po.getParameters();

-				//System.out.println("" + Thread.currentThread().getId() + " taken phrase " + phrase);

-				double [][] q=po.posterior();

-				loglikelihood += po.loglikelihood();

-				kl += po.KL_divergence();

-				l1lmax += po.l1lmax();

-				primal += po.primal(scalePT);

-				iterations += po.getNumberUpdateCalls();

-

-				List<Edge> edges = c.getEdgesForPhrase(phrase);

-				for(int edge=0;edge<q.length;edge++){

-					Edge e = edges.get(edge);

-					TIntArrayList context = e.getContext();

-					double contextCnt = e.getCount();

-					//increment expected count

-					for(int tag=0;tag<K;tag++){

-						for(int pos=0;pos<n_positions;pos++){

-							exp_emit[tag][pos][context.get(pos)]+=q[edge][tag]*contextCnt;

-						}

-						exp_pi[phrase][tag]+=q[edge][tag]*contextCnt;

-					}

-				}

-			} catch (InterruptedException e) {

-				System.err.println("M-step thread interrupted. Probably fatal!");

-				throw new RuntimeException(e);

-			} catch (ExecutionException e) {

-				System.err.println("M-step thread execution died. Probably fatal!");

-				throw new RuntimeException(e);

-			}

-		}

-		

-		long end = System.currentTimeMillis();

-		

-		if (failures.get() > 0)

-			System.out.println("WARNING: failed to converge in " + failures.get() + "/" + n_phrases + " cases");

-		System.out.println("\tmean iters:     " + iterations/(double)n_phrases + " walltime " + (end-start)/1000.0 + " threads " + elapsed.get() / 1000.0);

-		System.out.println("\tllh:            " + loglikelihood);

-		System.out.println("\tKL:             " + kl);

-		System.out.println("\tphrase l1lmax:  " + l1lmax);

-		

-		//M

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				arr.F.l1normalize(j);

-		emit=exp_emit;

-		

-		for(double []j:exp_pi)

-			arr.F.l1normalize(j);

-		pi=exp_pi;

-		

-		return primal;

-	}

-	

-	public double PREM_phrase_context_constraints(double scalePT, double scaleCT)

-	{	

-		double[][][] exp_emit = new double [K][n_positions][n_words];

-		double[][] exp_pi = new double[n_phrases][K];

-

-		//E step

-		PhraseContextObjective pco = new PhraseContextObjective(this, lambdaPTCT, pool, scalePT, scaleCT);

-		boolean ok = pco.optimizeWithProjectedGradientDescent();

-		if (cacheLambda) lambdaPTCT = pco.getParameters();

-

-		//now extract expectations

-		List<Corpus.Edge> edges = c.getEdges();

-		for(int e = 0; e < edges.size(); ++e)

-		{

-			double [] q = pco.posterior(e);

-			Corpus.Edge edge = edges.get(e);

-

-			TIntArrayList context = edge.getContext();

-			double contextCnt = edge.getCount();

-			//increment expected count

-			for(int tag=0;tag<K;tag++)

-			{

-				for(int pos=0;pos<n_positions;pos++)

-					exp_emit[tag][pos][context.get(pos)]+=q[tag]*contextCnt;

-				exp_pi[edge.getPhraseId()][tag]+=q[tag]*contextCnt;

-			}

-		}

-		

-		System.out.println("\tllh:            " + pco.loglikelihood());

-		System.out.println("\tKL:             " + pco.KL_divergence());

-		System.out.println("\tphrase l1lmax:  " + pco.phrase_l1lmax());

-		System.out.println("\tcontext l1lmax: " + pco.context_l1lmax());

-		

-		//M step

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				arr.F.l1normalize(j);

-		emit=exp_emit;

-		

-		for(double []j:exp_pi)

-			arr.F.l1normalize(j);

-		pi=exp_pi;

-		

-		return pco.primal();

-	}	

-		

-	/**

-	 * @param phrase index of phrase

-	 * @param ctx array of context

-	 * @return unnormalized posterior

-	 */

-	public double[] posterior(Corpus.Edge edge) 

-	{

-		double[] prob;

-		

-		if(edge.getTag()>=0){

-			prob=new double[K];

-			prob[edge.getTag()]=1;

-			return prob;

-		}

-		

-		if (edge.getPhraseId() < n_phrases)

-			prob = Arrays.copyOf(pi[edge.getPhraseId()], K);

-		else

-		{

-			prob = new double[K];

-			Arrays.fill(prob, 1.0);

-		}

-		

-		TIntArrayList ctx = edge.getContext();

-		for(int tag=0;tag<K;tag++)

-		{

-			for(int c=0;c<n_positions;c++)

-			{

-				int word = ctx.get(c);

-				if (!this.c.isSentinel(word) && word < n_words)

-					prob[tag]*=emit[tag][c][word];

-			}

-		}

-		

-		return prob;

-	}

-	

-	public void displayPosterior(PrintStream ps, List<Edge> testing)

-	{	

-		for (Edge edge : testing)

-		{

-			double probs[] = posterior(edge);

-			arr.F.l1normalize(probs);

-

-			// emit phrase

-			ps.print(edge.getPhraseString());

-			ps.print("\t");

-			ps.print(edge.getContextString(true));

-			int t=arr.F.argmax(probs);

-			ps.println(" ||| C=" + t + " T=" + edge.getCount() + " P=" + probs[t]);

-			//ps.println("# probs " + Arrays.toString(probs));

-		}

-	}

-	

-	public void displayModelParam(PrintStream ps)

-	{

-		final double EPS = 1e-6;

-		ps.println("phrases " + n_phrases + " tags " + K + " positions " + n_positions);

-		

-		for (int i = 0; i < n_phrases; ++i)

-			for(int j=0;j<pi[i].length;j++)

-				if (pi[i][j] > EPS)

-					ps.println(i + " " + j + " " + pi[i][j]);

-

-		ps.println();

-		for (int i = 0; i < K; ++i)

-		{

-			for(int position=0;position<n_positions;position++)

-			{

-				for(int word=0;word<emit[i][position].length;word++)

-				{

-					if (emit[i][position][word] > EPS)

-						ps.println(i + " " + position + " " + word + " " + emit[i][position][word]);

-				}

-			}

-		}

-	}

-	

-	double phrase_l1lmax()

-	{

-		double sum=0;

-		for(int phrase=0; phrase<n_phrases; phrase++)

-		{

-			double [] maxes = new double[K];

-			for (Edge edge : c.getEdgesForPhrase(phrase))

-			{

-				double p[] = posterior(edge);

-				arr.F.l1normalize(p);

-				for(int tag=0;tag<K;tag++)

-					maxes[tag] = Math.max(maxes[tag], p[tag]);

-			}

-			for(int tag=0;tag<K;tag++)

-				sum += maxes[tag];

-		}

-		return sum;

-	}

-

-	double context_l1lmax()

-	{

-		double sum=0;

-		for(int context=0; context<n_contexts; context++)

-		{

-			double [] maxes = new double[K];

-			for (Edge edge : c.getEdgesForContext(context))

-			{

-				double p[] = posterior(edge);

-				arr.F.l1normalize(p);

-				for(int tag=0;tag<K;tag++)

-					maxes[tag] = Math.max(maxes[tag], p[tag]);

-			}

-			for(int tag=0;tag<K;tag++)

-				sum += maxes[tag];

-		}

-		return sum;

-	}

-

-	public void loadParameters(BufferedReader input) throws IOException

-	{	

-		final double EPS = 1e-50;

-		

-		// overwrite pi, emit with ~zeros

-		for(double [][]i:emit)

-			for(double []j:i)

-				Arrays.fill(j, EPS);

-

-		for(double []j:pi)

-			Arrays.fill(j, EPS);

-

-		String line = input.readLine();

-		assert line != null;

-

-		Pattern space = Pattern.compile(" +");

-		String[] parts = space.split(line);

-		assert parts.length == 6;

-

-		assert parts[0].equals("phrases");

-		int phrases = Integer.parseInt(parts[1]);

-		int tags = Integer.parseInt(parts[3]);

-		int positions = Integer.parseInt(parts[5]);

-		

-		assert phrases == n_phrases;

-		assert tags == K;

-		assert positions == n_positions;

-

-		// read in pi

-		while ((line = input.readLine()) != null)

-		{

-			line = line.trim();

-			if (line.isEmpty()) break;

-			

-			String[] tokens = space.split(line);

-			assert tokens.length == 3;

-			int p = Integer.parseInt(tokens[0]);

-			int t = Integer.parseInt(tokens[1]);

-			double v = Double.parseDouble(tokens[2]);

-

-			pi[p][t] = v;

-		}

-		

-		// read in emissions

-		while ((line = input.readLine()) != null)

-		{

-			String[] tokens = space.split(line);

-			assert tokens.length == 4;

-			int t = Integer.parseInt(tokens[0]);

-			int p = Integer.parseInt(tokens[1]);

-			int w = Integer.parseInt(tokens[2]);

-			double v = Double.parseDouble(tokens[3]);

-

-			emit[t][p][w] = v;

-		}

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
deleted file mode 100644
index 646ff392..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
+++ /dev/null
@@ -1,436 +0,0 @@
-package phrase;

-

-import java.util.ArrayList;

-import java.util.Arrays;

-import java.util.HashMap;

-import java.util.List;

-import java.util.Map;

-import java.util.concurrent.ExecutionException;

-import java.util.concurrent.ExecutorService;

-import java.util.concurrent.Future;

-

-import optimization.gradientBasedMethods.ProjectedGradientDescent;

-import optimization.gradientBasedMethods.ProjectedObjective;

-import optimization.gradientBasedMethods.stats.OptimizerStats;

-import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;

-import optimization.linesearch.InterpolationPickFirstStep;

-import optimization.linesearch.LineSearchMethod;

-import optimization.projections.SimplexProjection;

-import optimization.stopCriteria.CompositeStopingCriteria;

-import optimization.stopCriteria.ProjectedGradientL2Norm;

-import optimization.stopCriteria.StopingCriteria;

-import optimization.stopCriteria.ValueDifference;

-import optimization.util.MathUtils;

-import phrase.Corpus.Edge;

-

-public class PhraseContextObjective extends ProjectedObjective

-{

-	private static final double GRAD_DIFF = 0.00002;

-	private static double INIT_STEP_SIZE = 300;

-	private static double VAL_DIFF = 1e-8;

-	private static int ITERATIONS = 20;

-	boolean debug = false;

-	

-	private PhraseCluster c;

-	

-	// un-regularized unnormalized posterior, p[edge][tag]

-	// P(tag|edge) \propto P(tag|phrase)P(context|tag)

-	private double p[][];

-

-	// regularized unnormalized posterior 

-	// q[edge][tag] propto p[edge][tag]*exp(-lambda)

-	private double q[][];

-	private List<Corpus.Edge> data;

-	

-	// log likelihood under q

-	private double loglikelihood;

-	private SimplexProjection projectionPhrase;

-	private SimplexProjection projectionContext;

-	

-	double[] newPoint;

-	private int n_param;

-	

-	// likelihood under p

-	public double llh;

-	

-	private static Map<Corpus.Edge, Integer> edgeIndex;

-	

-	private long projectionTime;

-	private long objectiveTime;

-	private long actualProjectionTime;

-	private ExecutorService pool;

-	

-	double scalePT;

-	double scaleCT;

-	

-	public PhraseContextObjective(PhraseCluster cluster, double[] startingParameters, ExecutorService pool,

-			double scalePT, double scaleCT)

-	{

-		c=cluster;

-		data=c.c.getEdges();

-		n_param=data.size()*c.K*2;

-		this.pool=pool;

-		this.scalePT = scalePT;

-		this.scaleCT = scaleCT;

-		

-		parameters = startingParameters;

-		if (parameters == null)

-			parameters = new double[n_param];

-		

-		System.out.println("Num parameters " + n_param);

-		newPoint = new double[n_param];

-		gradient = new double[n_param];

-		initP();

-		projectionPhrase = new SimplexProjection(scalePT);

-		projectionContext = new SimplexProjection(scaleCT);

-		q=new double [data.size()][c.K];

-		

-		if (edgeIndex == null) {

-			edgeIndex = new HashMap<Edge, Integer>();

-			for (int e=0; e<data.size(); e++)

-			{

-				edgeIndex.put(data.get(e), e);

-				//if (debug) System.out.println("Edge " + data.get(e) + " index " + e);

-			}

-		}

-		

-		setParameters(parameters);

-	}

-

-	private void initP(){

-		p=new double[data.size()][];

-		for(int edge=0;edge<data.size();edge++)

-		{

-			p[edge]=c.posterior(data.get(edge));

-			llh += data.get(edge).getCount() * Math.log(arr.F.l1norm(p[edge]));

-			arr.F.l1normalize(p[edge]);

-		}

-	}

-	

-	@Override

-	public void setParameters(double[] params) {

-		//System.out.println("setParameters " + Arrays.toString(parameters));

-		// TODO: test if params have changed and skip update otherwise

-		super.setParameters(params);

-		updateFunction();

-	}

-	

-	private void updateFunction()

-	{

-		updateCalls++;

-		loglikelihood=0;

-

-		System.out.print(".");

-		System.out.flush();

-

-		long begin = System.currentTimeMillis();

-		for (int e=0; e<data.size(); e++) 

-		{

-			Edge edge = data.get(e);

-			for(int tag=0; tag<c.K; tag++)

-			{

-				int ip = index(e, tag, true);

-				int ic = index(e, tag, false);

-				q[e][tag] = p[e][tag]*

-					Math.exp((-parameters[ip]-parameters[ic]) / edge.getCount());

-				//if (debug)

-					//System.out.println("\tposterior " + edge + " with tag " + tag + " p " + p[e][tag] + " params " + parameters[ip] + " and " + parameters[ic] + " q " + q[e][tag]);

-			}

-		}

-	

-		for(int edge=0;edge<data.size();edge++) {

-			loglikelihood+=data.get(edge).getCount() * Math.log(arr.F.l1norm(q[edge]));

-			arr.F.l1normalize(q[edge]);

-		}

-		

-		for (int e=0; e<data.size(); e++) 

-		{

-			for(int tag=0; tag<c.K; tag++)

-			{

-				int ip = index(e, tag, true);

-				int ic = index(e, tag, false);

-				gradient[ip]=-q[e][tag];

-				gradient[ic]=-q[e][tag];

-			}

-		}

-		//if (debug) {

-			//System.out.println("objective " + loglikelihood + " ||gradient||_2: " + arr.F.l2norm(gradient));		

-			//System.out.println("gradient " + Arrays.toString(gradient));

-		//}

-		objectiveTime += System.currentTimeMillis() - begin;

-	}

-	

-	@Override

-	public double[] projectPoint(double[] point) 

-	{

-		long begin = System.currentTimeMillis();

-		List<Future<?>> tasks = new ArrayList<Future<?>>();

-		

-		System.out.print(",");

-		System.out.flush();

-

-		Arrays.fill(newPoint, 0, newPoint.length, 0);

-		

-		// first project using the phrase-tag constraints,

-		// for all p,t: sum_c lambda_ptc < scaleP 

-		if (pool == null)

-		{

-			for (int p = 0; p < c.c.getNumPhrases(); ++p)

-			{

-				List<Edge> edges = c.c.getEdgesForPhrase(p);

-				double[] toProject = new double[edges.size()];

-				for(int tag=0;tag<c.K;tag++)

-				{

-					// FIXME: slow hash lookup for e (twice)

-					for(int e=0; e<edges.size(); e++) 						

-						toProject[e] = point[index(edges.get(e), tag, true)];

-					long lbegin = System.currentTimeMillis();

-					projectionPhrase.project(toProject);

-					actualProjectionTime += System.currentTimeMillis() - lbegin;

-					for(int e=0; e<edges.size(); e++)

-						newPoint[index(edges.get(e), tag, true)] = toProject[e];

-				}

-			}

-		}

-		else // do above in parallel using thread pool

-		{	

-			for (int p = 0; p < c.c.getNumPhrases(); ++p)

-			{

-				final int phrase = p;

-				final double[] inPoint = point;

-				Runnable task = new Runnable()

-				{

-					public void run()

-					{

-						List<Edge> edges = c.c.getEdgesForPhrase(phrase);

-						double toProject[] = new double[edges.size()];

-						for(int tag=0;tag<c.K;tag++)

-						{

-							// FIXME: slow hash lookup for e

-							for(int e=0; e<edges.size(); e++)

-								toProject[e] = inPoint[index(edges.get(e), tag, true)];

-							projectionPhrase.project(toProject);

-							for(int e=0; e<edges.size(); e++)

-								newPoint[index(edges.get(e), tag, true)] = toProject[e];

-						}

-					}		

-				};

-				tasks.add(pool.submit(task));

-			}

-		}

-		//System.out.println("after PT " + Arrays.toString(newPoint));

-	

-		// now project using the context-tag constraints,

-		// for all c,t: sum_p omega_pct < scaleC

-		if (pool == null)

-		{

-			for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)

-			{

-				List<Edge> edges = c.c.getEdgesForContext(ctx);

-				double toProject[] = new double[edges.size()];

-				for(int tag=0;tag<c.K;tag++)

-				{

-					// FIXME: slow hash lookup for e

-					for(int e=0; e<edges.size(); e++)

-						toProject[e] = point[index(edges.get(e), tag, false)];

-					long lbegin = System.currentTimeMillis();

-					projectionContext.project(toProject);

-					actualProjectionTime += System.currentTimeMillis() - lbegin;

-					for(int e=0; e<edges.size(); e++)

-						newPoint[index(edges.get(e), tag, false)] = toProject[e];

-				}

-			}

-		}

-		else

-		{

-			// do above in parallel using thread pool

-			for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)

-			{

-				final int context = ctx;

-				final double[] inPoint = point;

-				Runnable task = new Runnable()

-				{

-					public void run()

-					{

-						List<Edge> edges = c.c.getEdgesForContext(context);

-						double toProject[] = new double[edges.size()];

-						for(int tag=0;tag<c.K;tag++)

-						{

-							// FIXME: slow hash lookup for e

-							for(int e=0; e<edges.size(); e++)

-								toProject[e] = inPoint[index(edges.get(e), tag, false)];

-							projectionContext.project(toProject);

-							for(int e=0; e<edges.size(); e++)

-								newPoint[index(edges.get(e), tag, false)] = toProject[e];

-						}

-					}

-				};

-				tasks.add(pool.submit(task));

-			}

-		}

-		

-		if (pool != null)

-		{

-			// wait for all the jobs to complete

-			Exception failure = null;

-			for (Future<?> task: tasks)

-			{

-				try {

-					task.get();

-				} catch (InterruptedException e) {

-					System.err.println("ERROR: Projection thread interrupted");

-					e.printStackTrace();

-					failure = e;

-				} catch (ExecutionException e) {

-					System.err.println("ERROR: Projection thread died");

-					e.printStackTrace();

-					failure = e;

-				}

-			}

-			// rethrow the exception

-			if (failure != null)

-			{

-				pool.shutdownNow();

-				throw new RuntimeException(failure);

-			}

-		}

-		

-		double[] tmp = newPoint;

-		newPoint = point;

-		projectionTime += System.currentTimeMillis() - begin;

-		

-		//if (debug)

-			//System.out.println("\t\treturning " + Arrays.toString(tmp));

-		return tmp;

-	}

-	

-	private int index(Edge edge, int tag, boolean phrase)

-	{

-		// NB if indexing changes must also change code in updateFunction and constructor

-		if (phrase)

-			return tag * edgeIndex.size() + edgeIndex.get(edge);

-		else

-			return (c.K + tag) * edgeIndex.size() + edgeIndex.get(edge);

-	}

-

-	private int index(int e, int tag, boolean phrase)

-	{

-		// NB if indexing changes must also change code in updateFunction and constructor

-		if (phrase)

-			return tag * edgeIndex.size() + e;

-		else

-			return (c.K + tag) * edgeIndex.size() + e;

-	}

-	

-	@Override

-	public double[] getGradient() {

-		gradientCalls++;

-		return gradient;

-	}

-

-	@Override

-	public double getValue() {

-		functionCalls++;

-		return loglikelihood;

-	}

-

-	@Override

-	public String toString() {

-		return "No need for pointless toString";

-	}

-

-	public double []posterior(int edgeIndex){

-		return q[edgeIndex];

-	}

-	

-	public boolean optimizeWithProjectedGradientDescent()

-	{

-		projectionTime = 0;

-		actualProjectionTime = 0;

-		objectiveTime = 0;

-		long start = System.currentTimeMillis();

-

-		LineSearchMethod ls =

-			new ArmijoLineSearchMinimizationAlongProjectionArc

-				(new InterpolationPickFirstStep(INIT_STEP_SIZE));

-		//LineSearchMethod  ls = new WolfRuleLineSearch(

-		//		(new InterpolationPickFirstStep(INIT_STEP_SIZE)), c1, c2);

-		OptimizerStats stats = new OptimizerStats();

-		

-		

-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);

-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(GRAD_DIFF);

-		StopingCriteria stopValue = new ValueDifference(VAL_DIFF*(-llh));

-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();

-		compositeStop.add(stopGrad);

-		compositeStop.add(stopValue);

-		optimizer.setMaxIterations(ITERATIONS);

-		updateFunction();

-		boolean success = optimizer.optimize(this,stats,compositeStop);

-

-		System.out.println();

-		System.out.println(stats.prettyPrint(1));

-		

-		if (success)

-			System.out.print("\toptimization took " + optimizer.getCurrentIteration() + " iterations");

-	 	else

-			System.out.print("\toptimization failed to converge");

-		long total = System.currentTimeMillis() - start;

-		System.out.println(" and " + total + " ms: projection " + projectionTime + 

-				" actual " + actualProjectionTime + " objective " + objectiveTime);

-

-		return success;

-	}

-	

-	double loglikelihood()

-	{

-		return llh;

-	}

-	

-	double KL_divergence()

-	{

-		return -loglikelihood + MathUtils.dotProduct(parameters, gradient);

-	}

-	

-	double phrase_l1lmax()

-	{

-		// \sum_{tag,phrase} max_{context} P(tag|context,phrase)

-		double sum=0;

-		for (int p = 0; p < c.c.getNumPhrases(); ++p)

-		{

-			List<Edge> edges = c.c.getEdgesForPhrase(p);

-			for(int tag=0;tag<c.K;tag++)

-			{

-				double max=0;

-				for (Edge edge: edges)

-					max = Math.max(max, q[edgeIndex.get(edge)][tag]);

-				sum+=max;

-			}	

-		}

-		return sum;

-	}

-	

-	double context_l1lmax()

-	{

-		// \sum_{tag,context} max_{phrase} P(tag|context,phrase)

-		double sum=0;

-		for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)

-		{

-			List<Edge> edges = c.c.getEdgesForContext(ctx);

-			for(int tag=0; tag<c.K; tag++)

-			{

-				double max=0;

-				for (Edge edge: edges)

-					max = Math.max(max, q[edgeIndex.get(edge)][tag]);

-				sum+=max;

-			}	

-		}

-		return sum;

-	}

-	

-	// L - KL(q||p) - scalePT * l1lmax_phrase - scaleCT * l1lmax_context

-	public double primal()

-	{

-		return loglikelihood() - KL_divergence() - scalePT * phrase_l1lmax() - scaleCT * context_l1lmax();

-	}

-}
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCorpus.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCorpus.java
deleted file mode 100644
index 0cf31c1c..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCorpus.java
+++ /dev/null
@@ -1,193 +0,0 @@
-package phrase;

-

-import io.FileUtil;

-

-import java.io.BufferedInputStream;

-import java.io.BufferedReader;

-import java.io.File;

-import java.io.FileNotFoundException;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.ArrayList;

-import java.util.HashMap;

-import java.util.Scanner;

-

-public class PhraseCorpus 

-{

-	public HashMap<String,Integer>wordLex;

-	public HashMap<String,Integer>phraseLex;

-	

-	public String wordList[];

-	public String phraseList[];

-	

-	//data[phrase][num context][position]

-	public int data[][][];

-	public int numContexts;	

-

-	public PhraseCorpus(String filename) throws FileNotFoundException, IOException

-	{

-		BufferedReader r = FileUtil.reader(new File(filename));

-		

-		phraseLex=new HashMap<String,Integer>();

-		wordLex=new HashMap<String,Integer>();

-		

-		ArrayList<int[][]>dataList=new ArrayList<int[][]>();

-		String line=null;

-		numContexts = 0;

-		

-		while((line=readLine(r))!=null){

-			

-			String toks[]=line.split("\t");

-			String phrase=toks[0];

-			addLex(phrase,phraseLex);

-			

-			toks=toks[1].split(" \\|\\|\\| ");

-			

-			ArrayList <int[]>ctxList=new ArrayList<int[]>();

-			

-			for(int i=0;i<toks.length;i+=2){

-				String ctx=toks[i];

-				String words[]=ctx.split(" ");

-				if (numContexts == 0)

-					numContexts = words.length - 1;

-				else

-					assert numContexts == words.length - 1;

-				

-				int []context=new int [numContexts+1];

-				int idx=0;

-				for(String word:words){

-					if(word.equals("<PHRASE>")){

-						continue;

-					}

-					addLex(word,wordLex);

-					context[idx]=wordLex.get(word);

-					idx++;

-				}

-				

-				String count=toks[i+1];

-				context[idx]=Integer.parseInt(count.trim().substring(2));

-				

-				ctxList.add(context);

-			}

-			

-			dataList.add(ctxList.toArray(new int [0][]));

-			

-		}

-		try{

-			r.close();

-		}catch(IOException ioe){

-			ioe.printStackTrace();

-		}

-		data=dataList.toArray(new int[0][][]);

-	}

-

-	private void addLex(String key, HashMap<String,Integer>lex){

-		Integer i=lex.get(key);

-		if(i==null){

-			lex.put(key, lex.size());

-		}

-	}

-	

-	//for debugging

-	public void saveLex(String lexFilename) throws FileNotFoundException, IOException

-	{

-		PrintStream ps = FileUtil.printstream(new File(lexFilename));

-		ps.println("Phrase Lexicon");

-		ps.println(phraseLex.size());

-		printDict(phraseLex,ps);

-		

-		ps.println("Word Lexicon");

-		ps.println(wordLex.size());

-		printDict(wordLex,ps);

-		ps.close();

-	}

-	

-	private static void printDict(HashMap<String,Integer>lex,PrintStream ps){

-		String []dict=buildList(lex);

-		for(int i=0;i<dict.length;i++){

-			ps.println(dict[i]);

-		}

-	}

-	

-	public void loadLex(String lexFilename){

-		Scanner sc=io.FileUtil.openInFile(lexFilename);

-		

-		sc.nextLine();

-		int size=sc.nextInt();

-		sc.nextLine();

-		String[]dict=new String[size];

-		for(int i=0;i<size;i++){

-			dict[i]=sc.nextLine();

-		}

-		phraseLex=buildMap(dict);

-

-		sc.nextLine();

-		size=sc.nextInt();

-		sc.nextLine();

-		dict=new String[size];

-		for(int i=0;i<size;i++){

-			dict[i]=sc.nextLine();

-		}

-		wordLex=buildMap(dict);

-		sc.close();

-	}

-	

-	private HashMap<String, Integer> buildMap(String[]dict){

-		HashMap<String,Integer> map=new HashMap<String,Integer>();

-		for(int i=0;i<dict.length;i++){

-			map.put(dict[i], i);

-		}

-		return map;

-	}

-	

-	public void buildList(){

-		if(wordList==null){

-			wordList=buildList(wordLex);

-			phraseList=buildList(phraseLex);

-		}

-	}

-	

-	private static String[]buildList(HashMap<String,Integer>lex){

-		String dict[]=new String [lex.size()];

-		for(String key:lex.keySet()){

-			dict[lex.get(key)]=key;

-		}

-		return dict;

-	}

-	

-	public String getContextString(int context[], boolean addPhraseMarker)

-	{

-		StringBuffer b = new StringBuffer();

-		for (int i=0;i<context.length-1;i++)

-		{

-			if (b.length() > 0)

-				b.append(" ");

-

-			if (i == context.length/2)

-				b.append("<PHRASE> ");

-			

-			b.append(wordList[context[i]]);

-		}

-		return b.toString();

-	}

-	

-	public static String readLine(BufferedReader r){

-		try{

-			return r.readLine();

-		}

-		catch(IOException ioe){

-			ioe.printStackTrace();

-		}

-		return null;

-	}

-

-	public static void main(String[] args) throws Exception 

-	{

-		String LEX_FILENAME="../pdata/lex.out";

-		String DATA_FILENAME="../pdata/btec.con";

-		PhraseCorpus c=new PhraseCorpus(DATA_FILENAME);

-		c.saveLex(LEX_FILENAME);

-		c.loadLex(LEX_FILENAME);

-		c.saveLex(LEX_FILENAME);

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
deleted file mode 100644
index ac73a075..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
+++ /dev/null
@@ -1,224 +0,0 @@
-package phrase;

-

-import java.util.Arrays;

-import java.util.List;

-

-import optimization.gradientBasedMethods.ProjectedGradientDescent;

-import optimization.gradientBasedMethods.ProjectedObjective;

-import optimization.gradientBasedMethods.stats.OptimizerStats;

-import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;

-import optimization.linesearch.InterpolationPickFirstStep;

-import optimization.linesearch.LineSearchMethod;

-import optimization.linesearch.WolfRuleLineSearch;

-import optimization.projections.SimplexProjection;

-import optimization.stopCriteria.CompositeStopingCriteria;

-import optimization.stopCriteria.ProjectedGradientL2Norm;

-import optimization.stopCriteria.StopingCriteria;

-import optimization.stopCriteria.ValueDifference;

-import optimization.util.MathUtils;

-

-public class PhraseObjective extends ProjectedObjective

-{

-	static final double GRAD_DIFF = 0.00002;

-	static double INIT_STEP_SIZE = 300;

-	static double VAL_DIFF = 1e-8; // tuned to BTEC subsample

-	static int ITERATIONS = 100;

-	private PhraseCluster c;

-	

-	/**@brief

-	 *  for debugging purposes

-	 */

-	//public static PrintStream ps;

-	

-	/**@brief current phrase being optimzed*/

-	public int phrase;

-

-	/**@brief un-regularized posterior

-	 * unnormalized

-	 * p[edge][tag]

-	*  P(tag|edge) \propto P(tag|phrase)P(context|tag)

-	 */

-	private double[][]p;

-

-	/**@brief regularized posterior

-	 * q[edge][tag] propto p[edge][tag]*exp(-lambda)

-	 */

-	private double q[][];

-	private List<Corpus.Edge> data;

-	

-	/**@brief log likelihood of the associated phrase

-	 * 

-	 */

-	private double loglikelihood;

-	private SimplexProjection projection;

-	

-	double[] newPoint  ;

-	

-	private int n_param;

-	

-	/**@brief likelihood under p

-	 * 

-	 */

-	public double llh;

-	

-	public PhraseObjective(PhraseCluster cluster, int phraseIdx, double scale, double[] lambda){

-		phrase=phraseIdx;

-		c=cluster;

-		data=c.c.getEdgesForPhrase(phrase);

-		n_param=data.size()*c.K;

-		//System.out.println("Num parameters " + n_param + " for phrase #" + phraseIdx);

-		

-		if (lambda==null) 

-			lambda=new double[n_param];

-		

-		parameters = lambda;

-		newPoint = new double[n_param];

-		gradient = new double[n_param];

-		initP();

-		projection=new SimplexProjection(scale);

-		q=new double [data.size()][c.K];

-

-		setParameters(parameters);

-	}

-

-	private void initP(){

-		p=new double[data.size()][];

-		for(int edge=0;edge<data.size();edge++){

-			p[edge]=c.posterior(data.get(edge));

-			llh += data.get(edge).getCount() * Math.log(arr.F.l1norm(p[edge])); // Was bug here - count inside log!

-			arr.F.l1normalize(p[edge]);

-		}

-	}

-	

-	@Override

-	public void setParameters(double[] params) {

-		super.setParameters(params);

-		updateFunction();

-	}

-	

-	private void updateFunction(){

-		updateCalls++;

-		loglikelihood=0;

-

-		for(int tag=0;tag<c.K;tag++){

-			for(int edge=0;edge<data.size();edge++){

-				q[edge][tag]=p[edge][tag]*

-					Math.exp(-parameters[tag*data.size()+edge]/data.get(edge).getCount());

-			}

-		}

-	

-		for(int edge=0;edge<data.size();edge++){

-			loglikelihood+=data.get(edge).getCount() * Math.log(arr.F.l1norm(q[edge]));

-			arr.F.l1normalize(q[edge]);

-		}

-		

-		for(int tag=0;tag<c.K;tag++){

-			for(int edge=0;edge<data.size();edge++){

-				gradient[tag*data.size()+edge]=-q[edge][tag];

-			}

-		}

-	}

-	

-	@Override

-	public double[] projectPoint(double[] point) 

-	{

-		double toProject[]=new double[data.size()];

-		for(int tag=0;tag<c.K;tag++){

-			for(int edge=0;edge<data.size();edge++){

-				toProject[edge]=point[tag*data.size()+edge];

-			}

-			projection.project(toProject);

-			for(int edge=0;edge<data.size();edge++){

-				newPoint[tag*data.size()+edge]=toProject[edge];

-			}

-		}

-		return newPoint;

-	}

-

-	@Override

-	public double[] getGradient() {

-		gradientCalls++;

-		return gradient;

-	}

-

-	@Override

-	public double getValue() {

-		functionCalls++;

-		return loglikelihood;

-	}

-

-	@Override

-	public String toString() {

-		return Arrays.toString(parameters);

-	}

-

-	public double [][]posterior(){

-		return q;

-	}

-	

-	long optimizationTime;

-	

-	public boolean optimizeWithProjectedGradientDescent(){

-		long start = System.currentTimeMillis();

-		

-		LineSearchMethod ls =

-			new ArmijoLineSearchMinimizationAlongProjectionArc

-				(new InterpolationPickFirstStep(INIT_STEP_SIZE));

-		//LineSearchMethod  ls = new WolfRuleLineSearch(

-		//		(new InterpolationPickFirstStep(INIT_STEP_SIZE)), c1, c2);

-		OptimizerStats stats = new OptimizerStats();

-		

-		

-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);

-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(GRAD_DIFF);

-		StopingCriteria stopValue = new ValueDifference(VAL_DIFF*(-llh));

-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();

-		compositeStop.add(stopGrad);

-		compositeStop.add(stopValue);

-		optimizer.setMaxIterations(ITERATIONS);

-		updateFunction();

-		boolean success = optimizer.optimize(this,stats,compositeStop);

-		//System.out.println("Ended optimzation Projected Gradient Descent\n" + stats.prettyPrint(1));

-		//if(succed){

-			//System.out.println("Ended optimization in " + optimizer.getCurrentIteration());

-		//}else{

-//			System.out.println("Failed to optimize");

-		//}

-		//System.out.println(Arrays.toString(parameters));

-		

-		//	for(int edge=0;edge<data.getSize();edge++){

-		//	ps.println(Arrays.toString(q[edge]));

-		//	}

-

-		return success;

-	}

-	

-	public double KL_divergence()

-	{

-		return -loglikelihood + MathUtils.dotProduct(parameters, gradient);

-	}

-	

-	public double loglikelihood()

-	{

-		return llh;

-	}

-	

-	public double l1lmax()

-	{

-		double sum=0;

-		for(int tag=0;tag<c.K;tag++){

-			double max=0;

-			for(int edge=0;edge<data.size();edge++){

-				if(q[edge][tag]>max)

-					max=q[edge][tag];

-			}

-			sum+=max;

-		}

-		return sum;

-	}

-

-	public double primal(double scale)

-	{

-		return loglikelihood() - KL_divergence() - scale * l1lmax();	

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Trainer.java b/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
deleted file mode 100644
index 6f302b20..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
+++ /dev/null
@@ -1,257 +0,0 @@
-package phrase;
-
-import io.FileUtil;
-import joptsimple.OptionParser;
-import joptsimple.OptionSet;
-import java.io.File;
-import java.io.IOException;
-import java.io.PrintStream;
-import java.util.List;
-import java.util.Random;
-import java.util.concurrent.ExecutorService;
-import java.util.concurrent.Executors;
-
-import phrase.Corpus.Edge;
-
-import arr.F;
-
-public class Trainer 
-{
-	public static void main(String[] args) 
-	{
-        OptionParser parser = new OptionParser();
-        parser.accepts("help");
-        parser.accepts("in").withRequiredArg().ofType(File.class);
-        parser.accepts("in1").withRequiredArg().ofType(File.class);
-        parser.accepts("test").withRequiredArg().ofType(File.class);
-        parser.accepts("out").withRequiredArg().ofType(File.class);
-        parser.accepts("start").withRequiredArg().ofType(File.class);
-        parser.accepts("parameters").withRequiredArg().ofType(File.class);
-        parser.accepts("topics").withRequiredArg().ofType(Integer.class).defaultsTo(5);
-        parser.accepts("iterations").withRequiredArg().ofType(Integer.class).defaultsTo(10);
-        parser.accepts("threads").withRequiredArg().ofType(Integer.class).defaultsTo(0);
-        parser.accepts("scale-phrase").withRequiredArg().ofType(Double.class).defaultsTo(0.0);
-        parser.accepts("scale-context").withRequiredArg().ofType(Double.class).defaultsTo(0.0);
-        parser.accepts("seed").withRequiredArg().ofType(Long.class).defaultsTo(0l);
-        parser.accepts("convergence-threshold").withRequiredArg().ofType(Double.class).defaultsTo(1e-6);
-        parser.accepts("variational-bayes");
-        parser.accepts("alpha-emit").withRequiredArg().ofType(Double.class).defaultsTo(0.1);
-        parser.accepts("alpha-pi").withRequiredArg().ofType(Double.class).defaultsTo(0.0001);
-        parser.accepts("agree-direction");
-        parser.accepts("agree-language");
-        parser.accepts("no-parameter-cache");
-        parser.accepts("skip-large-phrases").withRequiredArg().ofType(Integer.class).defaultsTo(5);
-        OptionSet options = parser.parse(args);
-
-        if (options.has("help") || !options.has("in"))
-        {
-        	try {
-				parser.printHelpOn(System.err);
-			} catch (IOException e) {
-				System.err.println("This should never happen.");
-				e.printStackTrace();
-			}
-        	System.exit(1);     
-        }
-		
-		int tags = (Integer) options.valueOf("topics");
-		int iterations = (Integer) options.valueOf("iterations");
-		double scale_phrase = (Double) options.valueOf("scale-phrase");
-		double scale_context = (Double) options.valueOf("scale-context");
-		int threads = (Integer) options.valueOf("threads");
-		double threshold = (Double) options.valueOf("convergence-threshold");
-		boolean vb = options.has("variational-bayes");
-		double alphaEmit = (vb) ? (Double) options.valueOf("alpha-emit") : 0;
-		double alphaPi = (vb) ? (Double) options.valueOf("alpha-pi") : 0;
-		int skip = (Integer) options.valueOf("skip-large-phrases");
-		
-		if (options.has("seed"))
-			F.rng = new Random((Long) options.valueOf("seed"));
-		
-		ExecutorService threadPool = null;
-		if (threads > 0)
-			threadPool = Executors.newFixedThreadPool(threads);			
-		
-		if (tags <= 1 || scale_phrase < 0 || scale_context < 0 || threshold < 0)
-		{
-			System.err.println("Invalid arguments. Try again!");
-			System.exit(1);
-		}
-		
-		Corpus corpus = null;
-		File infile = (File) options.valueOf("in");
-		Corpus corpus1 = null;
-		File infile1 = (File) options.valueOf("in1");
-		try {
-			System.out.println("Reading concordance from " + infile);
-			corpus = Corpus.readFromFile(FileUtil.reader(infile));
-			corpus.printStats(System.out);
-			if(options.has("in1")){
-				corpus1 = Corpus.readFromFile(FileUtil.reader(infile1));
-				corpus1.printStats(System.out);
-			}
-		} catch (IOException e) {
-			System.err.println("Failed to open input file: " + infile);
-			e.printStackTrace();
-			System.exit(1);
-		}
-				
-		if (!(options.has("agree-direction")||options.has("agree-language")))
-			System.out.println("Running with " + tags + " tags " +
-					"for " + iterations + " iterations " +
-					((skip > 0) ? "skipping large phrases for first " + skip + " iterations " : "") +
-					"with scale " + scale_phrase + " phrase and " + scale_context + " context " +
-					"and " + threads + " threads");
-		else
-			System.out.println("Running agreement model with " + tags + " tags " +
-	 				"for " + iterations);
-
-	 	System.out.println();
-		
- 		PhraseCluster cluster = null;
- 		Agree2Sides agree2sides = null;
- 		Agree agree= null;
- 		VB vbModel=null;
- 		if (options.has("agree-language"))
- 			agree2sides = new Agree2Sides(tags, corpus,corpus1);
- 		else if (options.has("agree-direction"))
- 			agree = new Agree(tags, corpus);
- 		else
- 		{
- 			if (vb)	
- 			{
- 				vbModel=new VB(tags,corpus);
- 				vbModel.alpha=alphaPi;
- 				vbModel.lambda=alphaEmit;
- 	 			if (threadPool != null) vbModel.useThreadPool(threadPool);
- 			} 
- 			else 
- 			{
- 				cluster = new PhraseCluster(tags, corpus);
- 	 			if (threadPool != null) cluster.useThreadPool(threadPool);
- 				
-	 			if (options.has("no-parameter-cache")) 
-	 				cluster.cacheLambda = false;
-	 			if (options.has("start"))
-	 			{
-	 				try {
-						System.err.println("Reading starting parameters from " + options.valueOf("start"));
-						cluster.loadParameters(FileUtil.reader((File)options.valueOf("start")));
-					} catch (IOException e) {
-						System.err.println("Failed to open input file: " + options.valueOf("start"));
-						e.printStackTrace();
-					}
-	 			}
- 			}
- 		}
-				
-		double last = 0;
-		for (int i=0; i < iterations; i++)
-		{
-			double o;
-			if (agree != null)
-				o = agree.EM();
-			else if(agree2sides!=null)
-				o = agree2sides.EM();
-			else
-			{
-				if (i < skip)
-					System.out.println("Skipping phrases of length > " + (i+1));
-				
-				if (scale_phrase <= 0 && scale_context <= 0)
-				{
-					if (!vb)
-						o = cluster.EM((i < skip) ? i+1 : 0);
-					else
-						o = vbModel.EM();	
-				}
-				else
-					o = cluster.PREM(scale_phrase, scale_context, (i < skip) ? i+1 : 0);
-			}
-			
-			System.out.println("ITER: "+i+" objective: " + o);
-			
-			// sometimes takes a few iterations to break the ties
-			if (i > 5 && Math.abs((o - last) / o) < threshold)
-			{
-				last = o;
-				break;
-			}
-			last = o;
-		}
-		
-		double pl1lmax = 0, cl1lmax = 0;
-		if (cluster != null)
-		{
-			pl1lmax = cluster.phrase_l1lmax();
-			cl1lmax = cluster.context_l1lmax();
-		}
-		else if (agree != null)
-		{
-			// fairly arbitrary choice of model1 cf model2
-			pl1lmax = agree.model1.phrase_l1lmax();
-			cl1lmax = agree.model1.context_l1lmax();
-		}
-		else if (agree2sides != null)
-		{
-			// fairly arbitrary choice of model1 cf model2
-			pl1lmax = agree2sides.model1.phrase_l1lmax();
-			cl1lmax = agree2sides.model1.context_l1lmax();
-		}
-
-		System.out.println("\nFinal posterior phrase l1lmax " + pl1lmax + " context l1lmax " + cl1lmax);
-		
-		if (options.has("out"))
-		{
-			File outfile = (File) options.valueOf("out");
-			try {
-				PrintStream ps = FileUtil.printstream(outfile);
-				List<Edge> test;
-				if (!options.has("test")) // just use the training
-					test = corpus.getEdges();
-				else
-				{	// if --test supplied, load up the file
-					infile = (File) options.valueOf("test");
-					System.out.println("Reading testing concordance from " + infile);
-					test = corpus.readEdges(FileUtil.reader(infile));
-				}
-				if(vb) {
-					assert !options.has("test");
-					vbModel.displayPosterior(ps);
-				} else if (cluster != null) 
-					cluster.displayPosterior(ps, test);
-				else if (agree != null) 
-					agree.displayPosterior(ps, test);
-				else if (agree2sides != null) {
-					assert !options.has("test");
-					agree2sides.displayPosterior(ps);
-				}
-				
-				ps.close();
-			} catch (IOException e) {
-				System.err.println("Failed to open either testing file or output file");
-				e.printStackTrace();
-				System.exit(1);
-			}
-		}
-
-		if (options.has("parameters"))
-		{
-			assert !vb;
-			File outfile = (File) options.valueOf("parameters");
-			PrintStream ps;
-			try {
-				ps = FileUtil.printstream(outfile);
-				cluster.displayModelParam(ps);
-				ps.close();
-			} catch (IOException e) {
-				System.err.println("Failed to open output parameters file: " + outfile);
-				e.printStackTrace();
-				System.exit(1);
-			}
-		}
-		
-		if (cluster != null && cluster.pool != null)
-			cluster.pool.shutdown();
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/phrase/VB.java b/gi/posterior-regularisation/prjava/src/phrase/VB.java
deleted file mode 100644
index cd3f4966..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/VB.java
+++ /dev/null
@@ -1,419 +0,0 @@
-package phrase;

-

-import gnu.trove.TIntArrayList;

-

-import io.FileUtil;

-

-import java.io.File;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.ArrayList;

-import java.util.Arrays;

-import java.util.List;

-import java.util.concurrent.Callable;

-import java.util.concurrent.ExecutionException;

-import java.util.concurrent.ExecutorService;

-import java.util.concurrent.Future;

-

-import org.apache.commons.math.special.Gamma;

-

-import phrase.Corpus.Edge;

-

-public class VB {

-

-	public static int MAX_ITER=400;

-	

-	/**@brief

-	 * hyper param for beta

-	 * where beta is multinomial

-	 * for generating words from a topic

-	 */

-	public double lambda=0.1;

-	/**@brief

-	 * hyper param for theta

-	 * where theta is dirichlet for z

-	 */

-	public double alpha=0.0001;

-	/**@brief

-	 * variational param for beta

-	 */

-	private double rho[][][];

-	private double digamma_rho[][][];

-	private double rho_sum[][];

-	/**@brief

-	 * variational param for z

-	 */

-	//private double phi[][];

-	/**@brief

-	 * variational param for theta

-	 */

-	private double gamma[];

-	private static double VAL_DIFF_RATIO=0.005;

-	

-	private int n_positions;

-	private int n_words;

-	private int K;

-	private ExecutorService pool;

-	

-	private Corpus c;

-	public static void main(String[] args) {

-	//	String in="../pdata/canned.con";

-		String in="../pdata/btec.con";

-		String out="../pdata/vb.out";

-		int numCluster=25;

-		Corpus corpus = null;

-		File infile = new File(in);

-		try {

-			System.out.println("Reading concordance from " + infile);

-			corpus = Corpus.readFromFile(FileUtil.reader(infile));

-			corpus.printStats(System.out);

-		} catch (IOException e) {

-			System.err.println("Failed to open input file: " + infile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-		VB vb=new VB(numCluster, corpus);

-		int iter=20;

-		for(int i=0;i<iter;i++){

-			double obj=vb.EM();

-			System.out.println("Iter "+i+": "+obj);

-		}

-		

-		File outfile = new File (out);

-		try {

-			PrintStream ps = FileUtil.printstream(outfile);

-			vb.displayPosterior(ps);

-		//	ps.println();

-		//	c2f.displayModelParam(ps);

-			ps.close();

-		} catch (IOException e) {

-			System.err.println("Failed to open output file: " + outfile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-	}

-

-	public VB(int numCluster, Corpus corpus){

-		c=corpus;

-		K=numCluster;

-		n_positions=c.getNumContextPositions();

-		n_words=c.getNumWords();

-		rho=new double[K][n_positions][n_words];

-		//to init rho

-		//loop through data and count up words

-		double[] phi_tmp=new double[K];

-		for(int i=0;i<K;i++){

-			for(int pos=0;pos<n_positions;pos++){

-				Arrays.fill(rho[i][pos], lambda);

-			}

-		}

-		for(int d=0;d<c.getNumPhrases();d++){

-			List<Edge>doc=c.getEdgesForPhrase(d);

-			for(int n=0;n<doc.size();n++){

-				TIntArrayList context=doc.get(n).getContext();

-				arr.F.randomise(phi_tmp);

-				for(int i=0;i<K;i++){

-					for(int pos=0;pos<n_positions;pos++){

-						rho[i][pos][context.get(pos)]+=phi_tmp[i];

-					}

-				}

-			}

-		}

-		

-	}

-	

-	private double inference(int phraseID, double[][] phi, double[] gamma)

-	{

-		List<Edge > doc=c.getEdgesForPhrase(phraseID);

-		for(int i=0;i<phi.length;i++){

-			for(int j=0;j<phi[i].length;j++){

-				phi[i][j]=1.0/K;

-			}

-		}

-		Arrays.fill(gamma,alpha+1.0/K);

-		

-		double digamma_gamma[]=new double[K];

-		

-		double gamma_sum=digamma(arr.F.l1norm(gamma));

-		for(int i=0;i<K;i++){

-			digamma_gamma[i]=digamma(gamma[i]);

-		}

-		double gammaSum[]=new double [K];

-		double prev_val=0;

-		double obj=0;

-		

-		for(int iter=0;iter<MAX_ITER;iter++){

-			prev_val=obj;

-			obj=0;

-			Arrays.fill(gammaSum,0.0);

-			for(int n=0;n<doc.size();n++){

-				TIntArrayList context=doc.get(n).getContext();

-				double phisum=0;

-				for(int i=0;i<K;i++){

-					double sum=0;

-					for(int pos=0;pos<n_positions;pos++){

-						int word=context.get(pos);

-						sum+=digamma_rho[i][pos][word]-rho_sum[i][pos];

-					}

-					sum+= digamma_gamma[i]-gamma_sum;

-					phi[n][i]=sum;

-					

-					if (i > 0){

-	                    phisum = log_sum(phisum, phi[n][i]);

-					}

-	                else{

-	                    phisum = phi[n][i];

-	                }

-					

-				}//end of  a word

-				

-				for(int i=0;i<K;i++){

-					phi[n][i]=Math.exp(phi[n][i]-phisum);

-					gammaSum[i]+=phi[n][i];

-				}

-				

-			}//end of doc

-			

-			for(int i=0;i<K;i++){

-				gamma[i]=alpha+gammaSum[i];

-			}

-			gamma_sum=digamma(arr.F.l1norm(gamma));

-			for(int i=0;i<K;i++){

-				digamma_gamma[i]=digamma(gamma[i]);

-			}

-			//compute objective for reporting

-

-			obj=0;

-			

-			for(int i=0;i<K;i++){

-				obj+=(alpha-1)*(digamma_gamma[i]-gamma_sum);

-			}

-			

-			

-			for(int n=0;n<doc.size();n++){

-				TIntArrayList context=doc.get(n).getContext();

-				

-				for(int i=0;i<K;i++){

-					//entropy of phi + expected log likelihood of z

-					obj+=phi[n][i]*(digamma_gamma[i]-gamma_sum);

-					

-					if(phi[n][i]>1e-10){

-						obj+=phi[n][i]*Math.log(phi[n][i]);

-					}

-					

-					double beta_sum=0;

-					for(int pos=0;pos<n_positions;pos++){

-						int word=context.get(pos);

-						beta_sum+=(digamma(rho[i][pos][word])-rho_sum[i][pos]);

-					}

-					obj+=phi[n][i]*beta_sum;

-				}

-			}

-			

-			obj-=log_gamma(arr.F.l1norm(gamma));

-			for(int i=0;i<K;i++){

-				obj+=Gamma.logGamma(gamma[i]);

-				obj-=(gamma[i]-1)*(digamma_gamma[i]-gamma_sum);

-			}

-			

-//			System.out.println(phraseID+": "+obj);

-			if(iter>0 && (obj-prev_val)/Math.abs(obj)<VAL_DIFF_RATIO){

-				break;

-			}

-		}//end of inference loop

-		

-		return obj;

-	}//end of inference

-	

-	/**

-	 * @return objective of this iteration

-	 */

-	public double EM(){

-		double emObj=0;

-		if(digamma_rho==null){

-			digamma_rho=new double[K][n_positions][n_words];

-		}

-		for(int i=0;i<K;i++){

-			for (int pos=0;pos<n_positions;pos++){

-				for(int j=0;j<n_words;j++){

-					digamma_rho[i][pos][j]= digamma(rho[i][pos][j]);

-				}

-			}

-		}

-		

-		if(rho_sum==null){

-			rho_sum=new double [K][n_positions];

-		}

-		for(int i=0;i<K;i++){

-			for(int pos=0;pos<n_positions;pos++){

-				rho_sum[i][pos]=digamma(arr.F.l1norm(rho[i][pos]));

-			}

-		}

-

-		//E

-		double exp_rho[][][]=new double[K][n_positions][n_words];

-		if (pool == null)

-		{

-			for (int d=0;d<c.getNumPhrases();d++)

-			{		

-				List<Edge > doc=c.getEdgesForPhrase(d);

-				double[][] phi = new double[doc.size()][K];

-				double[] gamma = new double[K];

-				

-				emObj += inference(d, phi, gamma);

-				

-				for(int n=0;n<doc.size();n++){

-					TIntArrayList context=doc.get(n).getContext();

-					for(int pos=0;pos<n_positions;pos++){

-						int word=context.get(pos);

-						for(int i=0;i<K;i++){	

-							exp_rho[i][pos][word]+=phi[n][i];

-						}

-					}

-				}

-				//if(d!=0 && d%100==0)  System.out.print(".");

-				//if(d!=0 && d%1000==0) System.out.println(d);

-			}

-		}

-		else // multi-threaded version of above loop

-		{

-			class PartialEStep implements Callable<PartialEStep>

-			{

-				double[][] phi;

-				double[] gamma;

-				double obj;

-				int d;

-				PartialEStep(int d) { this.d = d; }

-

-				public PartialEStep call()

-				{

-					phi = new double[c.getEdgesForPhrase(d).size()][K];

-					gamma = new double[K];

-					obj = inference(d, phi, gamma);

-					return this;

-				}			

-			}

-

-			List<Future<PartialEStep>> jobs = new ArrayList<Future<PartialEStep>>();

-			for (int d=0;d<c.getNumPhrases();d++)

-				jobs.add(pool.submit(new PartialEStep(d)));

-		

-			for (Future<PartialEStep> job: jobs)

-			{

-				try {

-					PartialEStep e = job.get();

-					

-					emObj += e.obj;				

-					List<Edge> doc = c.getEdgesForPhrase(e.d);

-					for(int n=0;n<doc.size();n++){

-						TIntArrayList context=doc.get(n).getContext();

-						for(int pos=0;pos<n_positions;pos++){

-							int word=context.get(pos);

-							for(int i=0;i<K;i++){	

-								exp_rho[i][pos][word]+=e.phi[n][i];

-							}

-						}

-					}

-				} catch (ExecutionException e) {

-					System.err.println("ERROR: E-step thread execution failed.");

-					throw new RuntimeException(e);

-				} catch (InterruptedException e) {

-					System.err.println("ERROR: Failed to join E-step thread.");

-					throw new RuntimeException(e);

-				}

-			}

-		}	

-	//	System.out.println("EM Objective:"+emObj);

-		

-		//M

-		for(int i=0;i<K;i++){

-			for(int pos=0;pos<n_positions;pos++){

-				for(int j=0;j<n_words;j++){

-					rho[i][pos][j]=lambda+exp_rho[i][pos][j];

-				}

-			}

-		}

-		

-		//E[\log p(\beta|\lambda)] - E[\log q(\beta)]

-		for(int i=0;i<K;i++){

-			double rhoSum=0;

-			for(int pos=0;pos<n_positions;pos++){

-				for(int j=0;j<n_words;j++){

-					rhoSum+=rho[i][pos][j];

-				}

-				double digamma_rhoSum=Gamma.digamma(rhoSum);

-				emObj-=Gamma.logGamma(rhoSum);

-				for(int j=0;j<n_words;j++){

-					emObj+=(lambda-rho[i][pos][j])*(Gamma.digamma(rho[i][pos][j])-digamma_rhoSum);

-					emObj+=Gamma.logGamma(rho[i][pos][j]);

-				}

-			}

-		}

-		

-		return emObj;

-	}//end of EM

-	

-	public void displayPosterior(PrintStream ps)

-	{	

-		for(int d=0;d<c.getNumPhrases();d++){

-			List<Edge > doc=c.getEdgesForPhrase(d);

-			double[][] phi = new double[doc.size()][K];

-			for(int i=0;i<phi.length;i++)

-				for(int j=0;j<phi[i].length;j++)

-					phi[i][j]=1.0/K;

-			double[] gamma = new double[K];

-

-			inference(d, phi, gamma);

-

-			for(int n=0;n<doc.size();n++){

-				Edge edge=doc.get(n);

-				int tag=arr.F.argmax(phi[n]);

-				ps.print(edge.getPhraseString());

-				ps.print("\t");

-				ps.print(edge.getContextString(true));

-

-				ps.println(" ||| C=" + tag);

-			}

-		}

-	}

-

-	double log_sum(double log_a, double log_b)

-	{

-	  double v;

-

-	  if (log_a < log_b)

-	      v = log_b+Math.log(1 + Math.exp(log_a-log_b));

-	  else

-	      v = log_a+Math.log(1 + Math.exp(log_b-log_a));

-	  return(v);

-	}

-		

-	double digamma(double x)

-	{

-	    double p;

-	    x=x+6;

-	    p=1/(x*x);

-	    p=(((0.004166666666667*p-0.003968253986254)*p+

-		0.008333333333333)*p-0.083333333333333)*p;

-	    p=p+Math.log(x)-0.5/x-1/(x-1)-1/(x-2)-1/(x-3)-1/(x-4)-1/(x-5)-1/(x-6);

-	    return p;

-	}

-	

-	double log_gamma(double x)

-	{

-	     double z=1/(x*x);

-

-	    x=x+6;

-	    z=(((-0.000595238095238*z+0.000793650793651)

-		*z-0.002777777777778)*z+0.083333333333333)/x;

-	    z=(x-0.5)*Math.log(x)-x+0.918938533204673+z-Math.log(x-1)-

-	    Math.log(x-2)-Math.log(x-3)-Math.log(x-4)-Math.log(x-5)-Math.log(x-6);

-	    return z;

-	}

-

-	public void useThreadPool(ExecutorService threadPool) 

-	{

-		pool = threadPool;

-	}

-}//End of  class

diff --git a/gi/posterior-regularisation/prjava/src/test/CorpusTest.java b/gi/posterior-regularisation/prjava/src/test/CorpusTest.java
deleted file mode 100644
index b4c3041f..00000000
--- a/gi/posterior-regularisation/prjava/src/test/CorpusTest.java
+++ /dev/null
@@ -1,60 +0,0 @@
-package test;

-

-import java.util.Arrays;

-import java.util.HashMap;

-

-import data.Corpus;

-import hmm.POS;

-

-public class CorpusTest {

-

-	public static void main(String[] args) {

-		Corpus c=new Corpus(POS.trainFilename);

-

-		

-		int idx=30;

-		

-		

-		HashMap<String, Integer>vocab=

-			(HashMap<String, Integer>) io.SerializedObjects.readSerializedObject(Corpus.alphaFilename);

-		

-		HashMap<String, Integer>tagVocab=

-			(HashMap<String, Integer>) io.SerializedObjects.readSerializedObject(Corpus.tagalphaFilename);

-		

-		

-		String [] dict=new String [vocab.size()+1];

-		for(String key:vocab.keySet()){

-			dict[vocab.get(key)]=key;

-		}

-		dict[dict.length-1]=Corpus.UNK_TOK;

-		

-		String [] tagdict=new String [tagVocab.size()+1];

-		for(String key:tagVocab.keySet()){

-			tagdict[tagVocab.get(key)]=key;

-		}

-		tagdict[tagdict.length-1]=Corpus.UNK_TOK;

-		

-		String[] sent=c.get(idx);

-		int []data=c.getInt(idx);

-		

-		

-		String []roundtrip=new String [sent.length];

-		for(int i=0;i<sent.length;i++){

-			roundtrip[i]=dict[data[i]];

-		}

-		System.out.println(Arrays.toString(sent));

-		System.out.println(Arrays.toString(roundtrip));

-		

-		sent=c.tag.get(idx);

-		data=c.tagData.get(idx);

-		

-		

-		roundtrip=new String [sent.length];

-		for(int i=0;i<sent.length;i++){

-			roundtrip[i]=tagdict[data[i]];

-		}

-		System.out.println(Arrays.toString(sent));

-		System.out.println(Arrays.toString(roundtrip));

-	}

-

-}

diff --git a/gi/posterior-regularisation/prjava/src/test/HMMModelStats.java b/gi/posterior-regularisation/prjava/src/test/HMMModelStats.java
deleted file mode 100644
index d54525c8..00000000
--- a/gi/posterior-regularisation/prjava/src/test/HMMModelStats.java
+++ /dev/null
@@ -1,105 +0,0 @@
-package test;

-

-import hmm.HMM;

-import hmm.POS;

-

-import java.io.File;

-import java.io.FileNotFoundException;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.ArrayList;

-import java.util.Collections;

-import java.util.HashMap;

-

-import data.Corpus;

-

-public class HMMModelStats {

-

-	public static String modelFilename="../posdata/posModel.out";

-	public static String alphaFilename="../posdata/corpus.alphabet";

-	public static String statsFilename="../posdata/model.stats";

-

-	public static final int NUM_WORD=50;

-	

-	public static String testFilename="../posdata/en_test.conll";

-	

-	public static double [][]maxwt;

-	

-	public static void main(String[] args) {

-		HashMap<String, Integer>vocab=

-			(HashMap<String, Integer>) io.SerializedObjects.readSerializedObject(alphaFilename);

-		

-		Corpus test=new Corpus(testFilename,vocab);

-		

-		String [] dict=new String [vocab.size()+1];

-		for(String key:vocab.keySet()){

-			dict[vocab.get(key)]=key;

-		}

-		dict[dict.length-1]=Corpus.UNK_TOK;

-		

-		HMM hmm=new HMM();

-		hmm.readModel(modelFilename);

-

-		

-		

-		PrintStream ps = null;

-		try {

-			ps = io.FileUtil.printstream(new File(statsFilename));

-		} catch (IOException e) {

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-		double [][] emit=hmm.getEmitProb();

-		for(int i=0;i<emit.length;i++){

-			ArrayList<IntDoublePair>l=new ArrayList<IntDoublePair>();

-			for(int j=0;j<emit[i].length;j++){

-				l.add(new IntDoublePair(j,emit[i][j]));

-			}

-			Collections.sort(l);

-			ps.println(i);

-			for(int j=0;j<NUM_WORD;j++){

-				if(j>=dict.length){

-					break;

-				}

-				ps.print(dict[l.get(j).idx]+"\t");

-				if((1+j)%10==0){

-					ps.println();

-				}

-			}

-			ps.println("\n");

-		}

-		

-		checkMaxwt(hmm,ps,test.getAllData());

-		

-		int terminalSym=vocab.get(Corpus .END_SYM);

-		//sample 10 sentences

-		for(int i=0;i<10;i++){

-			int []sent=hmm.sample(terminalSym);

-			for(int j=0;j<sent.length;j++){

-				ps.print(dict[sent[j]]+"\t");

-			}

-			ps.println();

-		}

-		

-		ps.close();

-		

-	}

-	

-	public static void checkMaxwt(HMM hmm,PrintStream ps,int [][]data){

-		double [][]emit=hmm.getEmitProb();

-		maxwt=new double[emit.length][emit[0].length];

-		

-		hmm.computeMaxwt(maxwt,data);

-		double sum=0;

-		for(int i=0;i<maxwt.length;i++){

-			for(int j=0;j<maxwt.length;j++){

-				sum+=maxwt[i][j];

-			}

-		}

-		

-		ps.println("max w t P(w_i|t): "+sum);

-		

-	}

-	

-}

diff --git a/gi/posterior-regularisation/prjava/src/test/IntDoublePair.java b/gi/posterior-regularisation/prjava/src/test/IntDoublePair.java
deleted file mode 100644
index 3f9f0ad7..00000000
--- a/gi/posterior-regularisation/prjava/src/test/IntDoublePair.java
+++ /dev/null
@@ -1,23 +0,0 @@
-package test;

-

-public class IntDoublePair implements Comparable{

-	double val;

-	int idx;

-	public int compareTo(Object o){

-		if(o instanceof IntDoublePair){

-			IntDoublePair pair=(IntDoublePair)o;

-			if(pair.val>val){

-				return 1;

-			}

-			if(pair.val<val){

-				return -1;

-			}

-			return 0;

-		}

-		return -1;

-	}

-	public IntDoublePair(int i,double v){

-		val=v;

-		idx=i;

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/test/X2y2WithConstraints.java b/gi/posterior-regularisation/prjava/src/test/X2y2WithConstraints.java
deleted file mode 100644
index 9059a59e..00000000
--- a/gi/posterior-regularisation/prjava/src/test/X2y2WithConstraints.java
+++ /dev/null
@@ -1,131 +0,0 @@
-package test;

-

-

-

-import optimization.gradientBasedMethods.ProjectedGradientDescent;

-import optimization.gradientBasedMethods.ProjectedObjective;

-import optimization.gradientBasedMethods.stats.OptimizerStats;

-import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;

-import optimization.linesearch.InterpolationPickFirstStep;

-import optimization.linesearch.LineSearchMethod;

-import optimization.projections.BoundsProjection;

-import optimization.projections.Projection;

-import optimization.projections.SimplexProjection;

-import optimization.stopCriteria.CompositeStopingCriteria;

-import optimization.stopCriteria.GradientL2Norm;

-import optimization.stopCriteria.ProjectedGradientL2Norm;

-import optimization.stopCriteria.StopingCriteria;

-import optimization.stopCriteria.ValueDifference;

-

-

-/**

- * @author javg

- * 

- * 

- *ax2+ b(y2 -displacement)

- */

-public class X2y2WithConstraints extends ProjectedObjective{

-

-

-	double a, b;

-	double dx;

-	double dy;

-	Projection projection;

-	

-	

-	public X2y2WithConstraints(double a, double b, double[] params, double dx, double dy, Projection proj){

-		//projection = new BoundsProjection(0.2,Double.MAX_VALUE);

-		super();

-		projection = proj;	

-		this.a = a;

-		this.b = b;

-		this.dx = dx;

-		this.dy = dy;

-		setInitialParameters(params);

-		System.out.println("Function " +a+"(x-"+dx+")^2 + "+b+"(y-"+dy+")^2");

-		System.out.println("Gradient " +(2*a)+"(x-"+dx+") ; "+(b*2)+"(y-"+dy+")");

-		printParameters();

-		projection.project(parameters);

-		printParameters();

-		gradient = new double[2];

-	}

-	

-	public double getValue() {

-		functionCalls++;

-		return a*(parameters[0]-dx)*(parameters[0]-dx)+b*((parameters[1]-dy)*(parameters[1]-dy));

-	}

-

-	public double[] getGradient() {

-		if(gradient == null){

-			gradient = new double[2];

-		}

-		gradientCalls++;

-		gradient[0]=2*a*(parameters[0]-dx);

-		gradient[1]=2*b*(parameters[1]-dy);

-		return gradient;

-	}

-	

-	

-	public double[] projectPoint(double[] point) {

-		double[] newPoint = point.clone();

-		projection.project(newPoint);

-		return newPoint;

-	}	

-	

-	public void optimizeWithProjectedGradientDescent(LineSearchMethod ls, OptimizerStats stats, X2y2WithConstraints o){

-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);

-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(0.001);

-		StopingCriteria stopValue = new ValueDifference(0.001);

-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();

-		compositeStop.add(stopGrad);

-		compositeStop.add(stopValue);

-		

-		optimizer.setMaxIterations(5);

-		boolean succed = optimizer.optimize(o,stats,compositeStop);

-		System.out.println("Ended optimzation Projected Gradient Descent\n" + stats.prettyPrint(1));

-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);

-		if(succed){

-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());

-		}else{

-			System.out.println("Failed to optimize");

-		}

-	}

-	

-	

-	

-	public String toString(){

-		

-		return "P1: " + parameters[0] + " P2: " + parameters[1] + " value " + getValue() + " grad (" + getGradient()[0] + ":" + getGradient()[1]+")";

-	}

-	

-	public static void main(String[] args) {

-		double a = 1;

-		double b=1;

-		double x0 = 0;

-		double y0  =1;

-		double dx = 0.5;

-		double dy = 0.2	;

-		double [] parameters = new double[2];

-		parameters[0] = x0;

-		parameters[1] = y0;

-		X2y2WithConstraints o = new X2y2WithConstraints(a,b,parameters,dx,dy, 

-				new SimplexProjection(0.5)

-				//new BoundsProjection(0.0,0.4)

-		);

-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1] + " a " + a + " b "+b );

-		o.setDebugLevel(4);

-		

-		LineSearchMethod ls = new ArmijoLineSearchMinimizationAlongProjectionArc(new InterpolationPickFirstStep(1));

-		

-		OptimizerStats stats = new OptimizerStats();

-		o.optimizeWithProjectedGradientDescent(ls, stats, o);

-		

-//		o = new x2y2WithConstraints(a,b,x0,y0,dx,dy);

-//		stats = new OptimizerStats();

-//		o.optimizeWithSpectralProjectedGradientDescent(stats, o);

-	}

-	

-	

-	

-	

-}

diff --git a/gi/posterior-regularisation/prjava/src/util/Array.java b/gi/posterior-regularisation/prjava/src/util/Array.java
deleted file mode 100644
index cc4725af..00000000
--- a/gi/posterior-regularisation/prjava/src/util/Array.java
+++ /dev/null
@@ -1,41 +0,0 @@
-package util;
-
-import java.util.Arrays;
-
-public class Array {
-
-	
-	
-	public  static void sortDescending(double[] ds){
-		for (int i = 0; i < ds.length; i++) ds[i] = -ds[i];
-		Arrays.sort(ds);
-		for (int i = 0; i < ds.length; i++) ds[i] = -ds[i];
-	}
-	
-	/** 
-	 * Return a new reversed array
-	 * @param array
-	 * @return
-	 */
-	public static int[] reverseIntArray(int[] array){
-		int[] reversed = new int[array.length];
-		for (int i = 0; i < reversed.length; i++) {
-			reversed[i] = array[reversed.length-1-i];
-		}
-		return reversed;
-	}
-	
-	public static String[] sumArray(String[] in, int from){
-		String[] res = new String[in.length-from];
-		for (int i = from; i < in.length; i++) {
-			res[i-from] = in[i];
-		}
-		return res;
-	}
-	
-	public static void main(String[] args) {
-		int[] i = {1,2,3,4};
-		util.Printing.printIntArray(i, null, "original");
-		util.Printing.printIntArray(reverseIntArray(i), null, "reversed");
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/ArrayMath.java b/gi/posterior-regularisation/prjava/src/util/ArrayMath.java
deleted file mode 100644
index 398a13a2..00000000
--- a/gi/posterior-regularisation/prjava/src/util/ArrayMath.java
+++ /dev/null
@@ -1,186 +0,0 @@
-package util;
-
-import java.util.Arrays;
-
-public class ArrayMath {
-
-	public static double dotProduct(double[] v1, double[] v2) {
-		assert(v1.length == v2.length);
-		double result = 0;
-		for(int i = 0; i < v1.length; i++)
-			result += v1[i]*v2[i];
-		return result;
-	}
-
-	public static double twoNormSquared(double[] v) {
-		double result = 0;
-		for(double d : v)
-			result += d*d;
-		return result;
-	}
-
-	public static boolean containsInvalid(double[] v) {
-		for(int i = 0; i < v.length; i++)
-			if(Double.isNaN(v[i]) || Double.isInfinite(v[i]))
-				return true;
-		return false;
-	}
-
-
-	
-	public static double safeAdd(double[] toAdd) {
-		// Make sure there are no positive infinities
-		double sum = 0;
-		for(int i = 0; i < toAdd.length; i++) {
-			assert(!(Double.isInfinite(toAdd[i]) && toAdd[i] > 0));
-			assert(!Double.isNaN(toAdd[i]));
-			sum += toAdd[i];
-		}
-		
-		return sum;
-	}
-
-	/* Methods for filling integer and double arrays (of up to four dimensions) with the given value. */
-	
-	public static void set(int[][][][] array, int value) {
-		for(int i = 0; i < array.length; i++) {
-			set(array[i], value);
-		}
-	}
-	
-	public static void set(int[][][] array, int value) {
-		for(int i = 0; i < array.length; i++) {
-			set(array[i], value);
-		}
-	}
-	
-	public static void set(int[][] array, int value) {
-		for(int i = 0; i < array.length; i++) {
-			set(array[i], value);
-		}
-	}
-	
-	public static void set(int[] array, int value) {
-		Arrays.fill(array, value);
-	}
-	
-	
-	public static void set(double[][][][] array, double value) {
-		for(int i = 0; i < array.length; i++) {
-			set(array[i], value);
-		}
-	}
-	
-	public static void set(double[][][] array, double value) {
-		for(int i = 0; i < array.length; i++) {
-			set(array[i], value);
-		}
-	}
-	
-	public static void set(double[][] array, double value) {
-		for(int i = 0; i < array.length; i++) {
-			set(array[i], value);
-		}
-	}
-	
-	public static void set(double[] array, double value) {
-		Arrays.fill(array, value);
-	}
-
-	public static void setEqual(double[][][][] dest, double[][][][] source){
-		for (int i = 0; i < source.length; i++) {
-			setEqual(dest[i],source[i]);
-		}
-	}
-
-	
-	public static void setEqual(double[][][] dest, double[][][] source){
-		for (int i = 0; i < source.length; i++) {
-			set(dest[i],source[i]);
-		}
-	}
-
-	
-	public static void set(double[][] dest, double[][] source){
-		for (int i = 0; i < source.length; i++) {
-			setEqual(dest[i],source[i]);
-		}
-	}
-
-	public static void setEqual(double[] dest, double[] source){
-		System.arraycopy(source, 0, dest, 0, source.length);
-	}
-
-	public static void plusEquals(double[][][][] array, double val){
-		for (int i = 0; i < array.length; i++) {
-			plusEquals(array[i], val);
-		}
-	}	
-	
-	public static void plusEquals(double[][][] array, double val){
-		for (int i = 0; i < array.length; i++) {
-			plusEquals(array[i], val);
-		}
-	}	
-	
-	public static void plusEquals(double[][] array, double val){
-		for (int i = 0; i < array.length; i++) {
-			plusEquals(array[i], val);
-		}
-	}	
-	
-	public static void plusEquals(double[] array, double val){
-		for (int i = 0; i < array.length; i++) {
-			array[i] += val;
-		}
-	}
-
-	
-	public static double sum(double[] array) {
-		double res = 0;
-		for (int i = 0; i < array.length; i++) res += array[i];
-		return res;
-	}
-
-
-	
-	public static  double[][] deepclone(double[][] in){
-		double[][] res = new double[in.length][];
-		for (int i = 0; i < res.length; i++) {
-			res[i] = in[i].clone();
-		}
-		return res;
-	}
-
-	
-	public static  double[][][] deepclone(double[][][] in){
-		double[][][] res = new double[in.length][][];
-		for (int i = 0; i < res.length; i++) {
-			res[i] = deepclone(in[i]);
-		}
-		return res;
-	}
-
-	public static double cosine(double[] a,
-			double[] b) {
-		return (dotProduct(a, b)+1e-5)/(Math.sqrt(dotProduct(a, a)+1e-5)*Math.sqrt(dotProduct(b, b)+1e-5));
-	}
-
-	public static double max(double[] ds) {
-		double max = Double.NEGATIVE_INFINITY;
-		for(double d:ds) max = Math.max(d,max);
-		return max;
-	}
-
-	public static void exponentiate(double[] a) {
-		for (int i = 0; i < a.length; i++) {
-			a[i] = Math.exp(a[i]);
-		}
-	}
-
-	public static int sum(int[] array) {
-		int res = 0;
-		for (int i = 0; i < array.length; i++) res += array[i];
-		return res;
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/DifferentiableObjective.java b/gi/posterior-regularisation/prjava/src/util/DifferentiableObjective.java
deleted file mode 100644
index 1ff1ae4a..00000000
--- a/gi/posterior-regularisation/prjava/src/util/DifferentiableObjective.java
+++ /dev/null
@@ -1,14 +0,0 @@
-package util;
-
-public interface DifferentiableObjective {
-
-	public double getValue();
-
-	public void getGradient(double[] gradient);
-
-	public void getParameters(double[] params);
-
-	public void setParameters(double[] newParameters);
-
-	public int getNumParameters();
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/DigammaFunction.java b/gi/posterior-regularisation/prjava/src/util/DigammaFunction.java
deleted file mode 100644
index ff1478ad..00000000
--- a/gi/posterior-regularisation/prjava/src/util/DigammaFunction.java
+++ /dev/null
@@ -1,21 +0,0 @@
-package util;
-
-public class DigammaFunction {
-	public static double expDigamma(double number){
-		if(number==0)return number;
-		return Math.exp(digamma(number));
-	}
-	
-	public static double digamma(double number){
-		if(number > 7){
-			return digammApprox(number-0.5);
-		}else{
-			return digamma(number+1) - 1.0/number;
-		}
-	}
-	
-	private static double digammApprox(double value){
-		return Math.log(value) + 0.04167*Math.pow(value, -2) - 0.00729*Math.pow(value, -4) 
-		+  0.00384*Math.pow(value, -6) - 0.00413*Math.pow(value, -8);
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/FileSystem.java b/gi/posterior-regularisation/prjava/src/util/FileSystem.java
deleted file mode 100644
index d7812e40..00000000
--- a/gi/posterior-regularisation/prjava/src/util/FileSystem.java
+++ /dev/null
@@ -1,21 +0,0 @@
-package util;
-
-import java.io.File;
-
-public class FileSystem {
-	public static boolean createDir(String directory) {
-
-		File dir = new File(directory);
-		if (!dir.isDirectory()) {
-			boolean success = dir.mkdirs();
-			if (!success) {
-				System.out.println("Unable to create directory " + directory);
-				return false;
-			}
-			System.out.println("Created directory " + directory);
-		} else {
-			System.out.println("Reusing directory " + directory);
-		}
-		return true;
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/InputOutput.java b/gi/posterior-regularisation/prjava/src/util/InputOutput.java
deleted file mode 100644
index da7f71bf..00000000
--- a/gi/posterior-regularisation/prjava/src/util/InputOutput.java
+++ /dev/null
@@ -1,67 +0,0 @@
-package util;
-
-import java.io.BufferedReader;
-import java.io.FileInputStream;
-import java.io.FileNotFoundException;
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.io.InputStreamReader;
-import java.io.OutputStream;
-import java.io.PrintStream;
-import java.io.UnsupportedEncodingException;
-import java.util.Properties;
-import java.util.zip.GZIPInputStream;
-import java.util.zip.GZIPOutputStream;
-
-public class InputOutput {
-
-	/**
-	 * Opens a file either compress with gzip or not compressed.
-	 */
-	public static BufferedReader openReader(String fileName) throws UnsupportedEncodingException, FileNotFoundException, IOException{
-		System.out.println("Reading: " + fileName);
-		BufferedReader reader;
-		fileName = fileName.trim();
-		if(fileName.endsWith("gz")){
-			reader = new BufferedReader(
-			new InputStreamReader(new GZIPInputStream(new FileInputStream(fileName)),"UTF8"));
-		}else{
-			reader = new BufferedReader(new InputStreamReader(
-					new FileInputStream(fileName), "UTF8"));
-		}
-		
-		return reader;
-	}
-	
-	
-	public static PrintStream openWriter(String fileName) 
-	throws UnsupportedEncodingException, FileNotFoundException, IOException{
-		System.out.println("Writting to file: " + fileName);
-		PrintStream writter;
-		fileName = fileName.trim();
-		if(fileName.endsWith("gz")){
-			writter = new PrintStream(new GZIPOutputStream(new FileOutputStream(fileName)),
-					true, "UTF-8");
-
-		}else{
-			writter = new PrintStream(new FileOutputStream(fileName),
-					true, "UTF-8");
-
-		}
-		
-		return writter;
-	}
-	
-	public static Properties readPropertiesFile(String fileName) {
-		Properties properties = new Properties();
-		try {
-			properties.load(new FileInputStream(fileName));
-		} catch (IOException e) {
-			e.printStackTrace();
-			throw new AssertionError("Wrong properties file " + fileName);
-		}
-		System.out.println(properties.toString());
-		
-		return properties;
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/LogSummer.java b/gi/posterior-regularisation/prjava/src/util/LogSummer.java
deleted file mode 100644
index 117393b9..00000000
--- a/gi/posterior-regularisation/prjava/src/util/LogSummer.java
+++ /dev/null
@@ -1,86 +0,0 @@
-package util;
-
-import java.lang.Math;
-
-/*
- * Math tool for computing logs of sums, when the terms of the sum are already in log form.
- * (Useful if the terms of the sum are very small numbers.)
- */
-public class LogSummer {
-	
-	private LogSummer() {
-	}
-		
-	/**
-	 * Given log(a) and log(b), computes log(a + b).
-	 * 
-	 * @param  loga log of first sum term
-	 * @param  logb log of second sum term
-	 * @return     log(sum), where sum = a + b
-	 */
-	public static double sum(double loga, double logb) {
-		assert(!Double.isNaN(loga));
-		assert(!Double.isNaN(logb));
-		
-		if(Double.isInfinite(loga))
-			return logb;
-		if(Double.isInfinite(logb))
-			return loga;
-
-		double maxLog;
-		double difference;
-		if(loga > logb) {
-			difference = logb - loga;
-			maxLog = loga;
-		}
-		else {
-			difference = loga - logb;
-			maxLog = logb;
-		}
-
-		return Math.log1p(Math.exp(difference)) + maxLog;
-	}
-
-	/**
-	 * Computes log(exp(array[index]) + b), and
-	 * modifies array[index] to contain this new value.
-	 * 
-	 * @param array array to modify
-	 * @param index index at which to modify
-	 * @param logb  log of the second sum term
-	 */
-	public static void sum(double[] array, int index, double logb) {
-		array[index] = sum(array[index], logb);
-	}
-	
-	/**
-	 * Computes log(a + b + c + ...) from log(a), log(b), log(c), ...
-	 * by recursively splitting the input and delegating to the sum method.
-	 * 
-	 * @param  terms an array containing the log of all the terms for the sum
-	 * @return log(sum), where sum = exp(terms[0]) + exp(terms[1]) + ...
-	 */
-	public static double sumAll(double... terms) {
-		return sumAllHelper(terms, 0, terms.length);
-	}
-	
-	/**
-	 * Computes log(a_0 + a_1 + ...) from a_0 = exp(terms[begin]),
-	 * a_1 = exp(terms[begin + 1]), ..., a_{end - 1 - begin} = exp(terms[end - 1]).
-	 * 
-	 * @param  terms an array containing the log of all the terms for the sum,
-	 *               and possibly some other terms that will not go into the sum
-	 * @return log of the sum of the elements in the [begin, end) region of the terms array
-	 */
-	private static double sumAllHelper(final double[] terms, final int begin, final int end) {
-		int length = end - begin;
-		switch(length) {
-			case 0: return Double.NEGATIVE_INFINITY;
-			case 1: return terms[begin];
-			default:
-				int midIndex = begin + length/2;
-				return sum(sumAllHelper(terms, begin, midIndex), sumAllHelper(terms, midIndex, end));
-		}
-	}
-
-}
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/src/util/MathUtil.java b/gi/posterior-regularisation/prjava/src/util/MathUtil.java
deleted file mode 100644
index 799b1faf..00000000
--- a/gi/posterior-regularisation/prjava/src/util/MathUtil.java
+++ /dev/null
@@ -1,148 +0,0 @@
-package util;
-
-import java.util.Random;
-
-public class MathUtil {
-	public static final boolean closeToOne(double number){
-		return Math.abs(number-1) < 1.E-10;
-	}
-	
-	public static final boolean closeToZero(double number){
-		return Math.abs(number) < 1.E-5;
-	}
-	
-	/**
-	 * Return a ramdom multinominal distribution.
-	 * 
-	 * @param size
-	 * @return
-	 */
-	public static final double[] randomVector(int size, Random r){
-		double[] random = new double[size];
-		double sum=0;
-		for(int i = 0; i < size; i++){
-			double number = r.nextDouble();
-			random[i] = number;
-			sum+=number;
-		}
-		for(int i = 0; i < size; i++){
-			random[i] = random[i]/sum;
-		}
-		return random;
-	}
-	
-	
-
-	public static double sum(double[] ds) {
-		double res = 0;
-		for (int i = 0; i < ds.length; i++) {
-			res+=ds[i];
-		}
-		return res;
-	}
-
-	public static double max(double[] ds) {
-		double res = Double.NEGATIVE_INFINITY;
-		for (int i = 0; i < ds.length; i++) {
-			res = Math.max(res, ds[i]);
-		}
-		return res;
-	}
-
-	public static double min(double[] ds) {
-		double res = Double.POSITIVE_INFINITY;
-		for (int i = 0; i < ds.length; i++) {
-			res = Math.min(res, ds[i]);
-		}
-		return res;
-	}
-
-	
-	public static double KLDistance(double[] p, double[] q) {
-		int len = p.length;
-		double kl = 0;
-		for (int j = 0; j < len; j++) {
-				if (p[j] == 0 || q[j] == 0) {
-					continue;
-				} else {
-					kl += q[j] * Math.log(q[j] / p[j]);
-				}
-
-		}
-		return kl;
-	}
-	
-	public static double L2Distance(double[] p, double[] q) {
-		int len = p.length;
-		double l2 = 0;
-		for (int j = 0; j < len; j++) {
-				if (p[j] == 0 || q[j] == 0) {
-					continue;
-				} else {
-					l2 += (q[j] - p[j])*(q[j] - p[j]);
-				}
-
-		}
-		return Math.sqrt(l2);
-	}
-	
-	public static double L1Distance(double[] p, double[] q) {
-		int len = p.length;
-		double l1 = 0;
-		for (int j = 0; j < len; j++) {
-				if (p[j] == 0 || q[j] == 0) {
-					continue;
-				} else {
-					l1 += Math.abs(q[j] - p[j]);
-				}
-
-		}
-		return l1;
-	}
-
-	public static double dot(double[] ds, double[] ds2) {
-		double res = 0;
-		for (int i = 0; i < ds2.length; i++) {
-			res+= ds[i]*ds2[i];
-		}
-		return res;
-	}
-	
-	public static double expDigamma(double number){
-		return Math.exp(digamma(number));
-	}
-	
-	public static double digamma(double number){
-		if(number > 7){
-			return digammApprox(number-0.5);
-		}else{
-			return digamma(number+1) - 1.0/number;
-		}
-	}
-	
-	private static double digammApprox(double value){
-		return Math.log(value) + 0.04167*Math.pow(value, -2) - 0.00729*Math.pow(value, -4) 
-		+  0.00384*Math.pow(value, -6) - 0.00413*Math.pow(value, -8);
-	}
-
-	public static double eulerGamma = 0.57721566490152386060651209008240243;
-	// FIXME -- so far just the initialization from Minka's paper "Estimating a Dirichlet distribution". 
-	public static double invDigamma(double y) {
-		if (y>= -2.22) return Math.exp(y)+0.5;
-		return -1.0/(y+eulerGamma);
-	}
-
-	
-	
-	public static void main(String[] args) {
-		for(double i = 0; i < 10 ; i+=0.1){
-			System.out.println(i+"\t"+expDigamma(i)+"\t"+(i-0.5));
-		}
-//		double gammaValue = (expDigamma(3)/expDigamma(10) + expDigamma(3)/expDigamma(10) + expDigamma(4)/expDigamma(10));
-//		double normalValue = 3/10+3/4+10/10;
-//		System.out.println("Gamma " + gammaValue + " normal " + normalValue);
-	}
-
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/Matrix.java b/gi/posterior-regularisation/prjava/src/util/Matrix.java
deleted file mode 100644
index 8fb6d911..00000000
--- a/gi/posterior-regularisation/prjava/src/util/Matrix.java
+++ /dev/null
@@ -1,16 +0,0 @@
-package util;
-
-public class Matrix {
-	int x;
-	int y;
-	double[][] values;
-	
-	public Matrix(int x, int y){
-		this.x = x;
-		this.y=y;
-		values = new double[x][y];
-	}
-	
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/MemoryTracker.java b/gi/posterior-regularisation/prjava/src/util/MemoryTracker.java
deleted file mode 100644
index 83a65611..00000000
--- a/gi/posterior-regularisation/prjava/src/util/MemoryTracker.java
+++ /dev/null
@@ -1,47 +0,0 @@
-package util;
-
-
-public class MemoryTracker {
-	
-	double initM,finalM;
-	boolean start = false,finish = false;
-	
-	public MemoryTracker(){
-		
-	}
-	
-	public void start(){
-		System.gc();
-	    System.gc();
-	    System.gc();
-	    initM = (Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory())/(1024*1024);  
-	    start = true;
-	}
-	
-	public void finish(){
-		if(!start){
-			throw new RuntimeException("Canot stop before starting");
-		}
-		System.gc();
-	    System.gc();
-	    System.gc();
-	    finalM = (Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory())/(1024*1024);  
-	    finish = true;
-	}
-	
-	public String print(){
-		if(!finish){
-			throw new RuntimeException("Canot print before stopping");
-		}
-		return "Used: " + (finalM - initM) + "MB";
-	}
-	
-	public void clear(){
-		initM = 0;
-		finalM = 0;
-		finish = false;
-		start = false;
-	}
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/Pair.java b/gi/posterior-regularisation/prjava/src/util/Pair.java
deleted file mode 100644
index 7b1f108d..00000000
--- a/gi/posterior-regularisation/prjava/src/util/Pair.java
+++ /dev/null
@@ -1,31 +0,0 @@
-package util;
-
-public class Pair<O1, O2> {
-	public O1 _first;
-	public O2 _second;
-
-	public final O1 first() {
-		return _first;
-	}
-
-	public final O2 second() {
-		return _second;
-	}
-
-	public final void setFirst(O1 value){
-		_first = value;
-	}
-	
-	public final void setSecond(O2 value){
-		_second = value;
-	}
-	
-	public Pair(O1 first, O2 second) {
-		_first = first;
-		_second = second;
-	}
-
-	public String toString(){
-		return _first + " " + _second; 
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/Printing.java b/gi/posterior-regularisation/prjava/src/util/Printing.java
deleted file mode 100644
index 14fcbe91..00000000
--- a/gi/posterior-regularisation/prjava/src/util/Printing.java
+++ /dev/null
@@ -1,158 +0,0 @@
-package util;
-
-public class Printing {
-	static java.text.DecimalFormat fmt = new java.text.DecimalFormat();
-
-	public static String padWithSpace(String s, int len){
-		StringBuffer sb = new StringBuffer();
-		while(sb.length() +s.length() < len){
-			sb.append(" ");
-		}
-		sb.append(s);
-		return sb.toString();
-	}
-	
-	public static String prettyPrint(double d, String patt, int len) {
-		fmt.applyPattern(patt);
-		String s = fmt.format(d);
-		while (s.length() < len) {
-			s = " " + s;
-		}
-		return s;
-	}
-	
-	public static  String formatTime(long duration) {
-		StringBuilder sb = new StringBuilder();
-		double d = duration / 1000;
-		fmt.applyPattern("00");
-		sb.append(fmt.format((int) (d / (60 * 60))) + ":");
-		d -= ((int) d / (60 * 60)) * 60 * 60;
-		sb.append(fmt.format((int) (d / 60)) + ":");
-		d -= ((int) d / 60) * 60;
-		fmt.applyPattern("00.0");
-		sb.append(fmt.format(d));
-		return sb.toString();
-	}
-	
-	
-	public static String doubleArrayToString(double[] array, String[] labels, String arrayName) {
-		StringBuffer res = new StringBuffer();
-		res.append(arrayName);
-		res.append("\n");
-		for (int i = 0; i < array.length; i++) {
-			if (labels == null){
-				res.append(i+"       \t");
-			}else{
-				res.append(labels[i]+     "\t");
-			}
-		}
-		res.append("sum\n");
-		double sum = 0;
-		for (int i = 0; i < array.length; i++) {
-			res.append(prettyPrint(array[i],
-					"0.00000E00", 8) + "\t");
-			sum+=array[i];
-		}
-		res.append(prettyPrint(sum,
-				"0.00000E00", 8)+"\n");
-		return res.toString();
-	}
-	
-	
-	
-	public static void printDoubleArray(double[] array, String labels[], String arrayName) {
-		System.out.println(doubleArrayToString(array, labels,arrayName));
-	}
-	
-	
-	public static String doubleArrayToString(double[][] array, String[] labels1, String[] labels2,
-			String arrayName){
-		StringBuffer res = new StringBuffer();
-		res.append(arrayName);
-		res.append("\n\t");
-		//Calculates the column sum to keeps the sums
-		double[] sums = new double[array[0].length+1];
-		//Prints rows headings
-		for (int i = 0; i < array[0].length; i++) {
-			if (labels1 == null){
-				res.append(i+"        \t");
-			}else{
-				res.append(labels1[i]+"        \t");
-			}
-		}
-		res.append("sum\n");
-		double sum = 0;
-		//For each row print heading
-		for (int i = 0; i < array.length; i++) {
-			if (labels2 == null){
-				res.append(i+"\t");
-			}else{
-				res.append(labels2[i]+"\t");
-			}
-			//Print values for that row
-			for (int j = 0; j < array[0].length; j++) {
-				res.append(" " + prettyPrint(array[i][j],
-						"0.00000E00", 8) + "\t");
-				sums[j] += array[i][j]; 
-				sum+=array[i][j]; //Sum all values of that row
-			}
-			//Print row sum
-			res.append(prettyPrint(sum,"0.00000E00", 8)+"\n");
-			sums[array[0].length]+=sum;
-			sum=0;
-		}
-		res.append("sum\t");
-		//Print values for colums sum
-		for (int i = 0; i < array[0].length+1; i++) {
-			res.append(prettyPrint(sums[i],"0.00000E00", 8)+"\t");
-		}
-		res.append("\n");
-		return res.toString();
-	}
-	
-	public static void printDoubleArray(double[][] array, String[] labels1, String[] labels2
-			, String arrayName) {
-		System.out.println(doubleArrayToString(array, labels1,labels2,arrayName));
-	}
-	
-	
-	public static void printIntArray(int[][] array, String[] labels1, String[] labels2, String arrayName,
-			int size1, int size2) {
-		System.out.println(arrayName);
-		for (int i = 0; i < size1; i++) {
-			for (int j = 0; j < size2; j++) {
-				System.out.print(" " + array[i][j] +  " ");
-
-			}
-			System.out.println();
-		}
-		System.out.println();
-	}
-	
-	public static String intArrayToString(int[] array, String[] labels, String arrayName) {
-		StringBuffer res = new StringBuffer();
-		res.append(arrayName);
-		for (int i = 0; i < array.length; i++) {
-			res.append(" " + array[i] + " ");
-			
-		}
-		res.append("\n");
-		return res.toString();
-	}
-	
-	public static void printIntArray(int[] array, String[] labels, String arrayName) {
-		System.out.println(intArrayToString(array, labels,arrayName));
-	}
-	
-	public static String toString(double[][] d){
-		StringBuffer sb = new StringBuffer();
-		for (int i = 0; i < d.length; i++) {
-			for (int j = 0; j < d[0].length; j++) {
-				sb.append(prettyPrint(d[i][j], "0.00E0", 10));
-			}
-			sb.append("\n");
-		}
-		return sb.toString();
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/util/Sorters.java b/gi/posterior-regularisation/prjava/src/util/Sorters.java
deleted file mode 100644
index 836444e5..00000000
--- a/gi/posterior-regularisation/prjava/src/util/Sorters.java
+++ /dev/null
@@ -1,39 +0,0 @@
-package util;
-
-import java.util.Comparator;
-
-public class Sorters {
-	public static class sortWordsCounts implements Comparator{
-		
-		/**
-		 * Sorter for a pair of word id, counts. Sort ascending by counts
-		 */
-		public int compare(Object arg0, Object arg1) {
-			Pair<Integer,Integer> p1 = (Pair<Integer,Integer>)arg0;
-			Pair<Integer,Integer> p2 = (Pair<Integer,Integer>)arg1;
-			if(p1.second() > p2.second()){
-				return 1;
-			}else{
-				return -1;
-			}
-		}
-		
-	}
-	
-public static class sortWordsDouble implements Comparator{
-		
-		/**
-		 * Sorter for a pair of word id, counts. Sort by counts
-		 */
-		public int compare(Object arg0, Object arg1) {
-			Pair<Integer,Double> p1 = (Pair<Integer,Double>)arg0;
-			Pair<Integer,Double> p2 = (Pair<Integer,Double>)arg1;
-			if(p1.second() < p2.second()){
-				return 1;
-			}else{
-				return -1;
-			}
-		}
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/train-PR-cluster.sh b/gi/posterior-regularisation/prjava/train-PR-cluster.sh
deleted file mode 100755
index 67552c00..00000000
--- a/gi/posterior-regularisation/prjava/train-PR-cluster.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/bin/sh
-
-d=`dirname $0`
-java -ea -Xmx30g -cp $d/prjava.jar:$d/lib/trove-2.0.2.jar:$d/lib/optimization.jar:$d/lib/jopt-simple-3.2.jar:$d/lib/commons-math-2.1.jar phrase.Trainer $*
diff --git a/gi/posterior-regularisation/projected_gradient.cc b/gi/posterior-regularisation/projected_gradient.cc
deleted file mode 100644
index f7c39817..00000000
--- a/gi/posterior-regularisation/projected_gradient.cc
+++ /dev/null
@@ -1,87 +0,0 @@
-//
-// Minimises given functional using the projected gradient method. Based on
-// algorithm and demonstration example in Linear and Nonlinear Programming,
-// Luenberger and Ye, 3rd ed., p 370.
-//
-
-#include "invert.hh"
-#include <iostream>
-
-using namespace std;
-
-double 
-f(double x1, double x2, double x3, double x4)
-{
-    return x1 * x1 + x2 * x2 + x3 * x3 + x4 * x4 - 2 * x1 - 3 * x4;
-}
-
-ublas::vector<double> 
-g(double x1, double x2, double x3, double x4)
-{
-    ublas::vector<double> v(4);
-    v(0) = 2 * x1 - 2;
-    v(1) = 2 * x2;
-    v(2) = 2 * x3;
-    v(3) = 2 * x4 - 3;
-    return v;
-}
-
-ublas::matrix<double> 
-activeConstraints(double x1, double x2, double x3, double x4)
-{
-    int n = 2;
-    if (x1 == 0) ++n;
-    if (x2 == 0) ++n;
-    if (x3 == 0) ++n;
-    if (x4 == 0) ++n;
-
-    ublas::matrix<double> a(n,4);
-    a(0, 0) = 2; a(0, 1) = 1; a(0, 2) = 1; a(0, 3) = 4;
-    a(1, 0) = 1; a(1, 1) = 1; a(1, 2) = 2; a(1, 3) = 1;
-
-    int c = 2;
-    if (x1 == 0) a(c++, 0) = 1;
-    if (x2 == 0) a(c++, 1) = 1;
-    if (x3 == 0) a(c++, 2) = 1;
-    if (x4 == 0) a(c++, 3) = 1;
-
-    return a;
-}
-
-ublas::matrix<double>
-projection(const ublas::matrix<double> &a)
-{
-    ublas::matrix<double> aT = ublas::trans(a);
-    ublas::matrix<double> inv(a.size1(), a.size1());
-    bool ok = invert_matrix(ublas::matrix<double>(ublas::prod(a, aT)), inv);
-    assert(ok && "Failed to invert matrix");
-    return ublas::identity_matrix<double>(4) - 
-        ublas::prod(aT, ublas::matrix<double>(ublas::prod(inv, a)));
-}
-
-int main(int argc, char *argv[])
-{
-    double x1 = 2, x2 = 2, x3 = 1, x4 = 0;
-
-    double fval = f(x1, x2, x3, x4);
-    cout << "f = " << fval << endl;
-    ublas::vector<double> grad = g(x1, x2, x3, x4);
-    cout << "g = " << grad << endl;
-    ublas::matrix<double> A = activeConstraints(x1, x2, x3, x4);
-    cout << "A = " << A << endl;
-    ublas::matrix<double> P = projection(A);
-    cout << "P = " << P << endl;
-    // the direction of movement
-    ublas::vector<double> d = prod(P, grad);
-    cout << "d = " << (d / d(0)) << endl;
-
-    // special case for d = 0
-
-    // next solve for limits on the line search
-
-    // then use golden rule technique between these values (if bounded)
-
-    // or simple Armijo's rule technique
-
-    return 0;
-}
diff --git a/gi/posterior-regularisation/simplex_pg.py b/gi/posterior-regularisation/simplex_pg.py
deleted file mode 100644
index 5da796d3..00000000
--- a/gi/posterior-regularisation/simplex_pg.py
+++ /dev/null
@@ -1,55 +0,0 @@
-#
-# Following Leunberger and Ye, Linear and Nonlinear Progamming, 3rd ed. p367
-# "The gradient projection method"
-# applied to an equality constraint for a simplex.
-#
-#   min f(x)
-#   s.t. x >= 0, sum_i x = d
-#
-# FIXME: enforce the positivity constraint - a limit on the line search?
-#
-
-from numpy import *
-from scipy import *
-from linesearch import line_search
-# local copy of scipy's Amijo line_search - wasn't enforcing alpha max correctly
-import sys
-
-dims = 4
-
-def f(x):
-    fv = x[0]*x[0] + x[1]*x[1] + x[2]*x[2] + x[3]*x[3] - 2*x[0] - 3*x[3]
-    # print 'evaluating f at', x, 'value', fv
-    return fv
-
-def g(x):
-    return array([2*x[0] - 2, 2*x[1], 2*x[2], 2*x[3]-3])
-
-def pg(x):
-    gv = g(x)
-    return gv - sum(gv) / dims
-
-x = ones(dims) / dims
-old_fval = None
-
-while True:
-    fv = f(x)
-    gv = g(x)
-    dv = pg(x)
-
-    print 'x', x, 'f', fv, 'g', gv, 'd', dv
-
-    if old_fval == None:
-        old_fval = fv + 0.1
-
-    # solve for maximum step size i.e. when positivity constraints kick in
-    # x - alpha d = 0   => alpha = x/d
-    amax = max(x/dv)
-    if amax < 1e-8: break
-
-    stuff = line_search(f, pg, x, -dv, dv, fv, old_fval, amax=amax)
-    alpha = stuff[0] # Nb. can avoid next evaluation of f,g,d using 'stuff'
-    if alpha < 1e-8: break
-    x -= alpha * dv
-
-    old_fval = fv
diff --git a/gi/posterior-regularisation/split-languages.py b/gi/posterior-regularisation/split-languages.py
deleted file mode 100755
index 206da661..00000000
--- a/gi/posterior-regularisation/split-languages.py
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/python
-
-import sys
-
-sout = open(sys.argv[1], 'w')
-tout = open(sys.argv[2], 'w')
-for line in sys.stdin:
-	phrase, contexts = line.rstrip().split('\t')
-	sp, tp = phrase.split(' <SPLIT> ')
-	sout.write('%s\t' % sp)
-	tout.write('%s\t' % tp)
-	parts = contexts.split(' ||| ')
-	for i in range(0, len(parts), 2):
-		sc, tc = parts[i].split(' <SPLIT> ')
-		if i != 0:
-			sout.write(' ||| ')
-			tout.write(' ||| ')
-		sout.write('%s ||| %s' % (sc, parts[i+1]))
-		tout.write('%s ||| %s' % (tc, parts[i+1]))
-	sout.write('\n')
-	tout.write('\n')
-sout.close()
-tout.close()
diff --git a/gi/posterior-regularisation/train_pr_agree.py b/gi/posterior-regularisation/train_pr_agree.py
deleted file mode 100644
index 9d41362d..00000000
--- a/gi/posterior-regularisation/train_pr_agree.py
+++ /dev/null
@@ -1,400 +0,0 @@
-import sys
-import scipy.optimize
-from scipy.stats import geom
-from numpy import *
-from numpy.random import random, seed
-
-style = sys.argv[1]
-if len(sys.argv) >= 3:
-     seed(int(sys.argv[2]))
-
-#
-# Step 1: load the concordance counts
-# 
-
-edges = []
-word_types = {}
-phrase_types = {}
-context_types = {}
-
-for line in sys.stdin:
-    phrase, rest = line.strip().split('\t')
-    ptoks = tuple(map(lambda t: word_types.setdefault(t, len(word_types)), phrase.split()))
-    pid = phrase_types.setdefault(ptoks, len(phrase_types))
-
-    parts = rest.split('|||')
-    for i in range(0, len(parts), 2):
-        context, count = parts[i:i+2]
-
-        ctx = filter(lambda x: x != '<PHRASE>', context.split())
-        ctoks = tuple(map(lambda t: word_types.setdefault(t, len(word_types)), ctx))
-        cid = context_types.setdefault(ctoks, len(context_types))
-
-        cnt = int(count.strip()[2:])
-        edges.append((pid, cid, cnt))
-
-word_type_list = [None] * len(word_types)
-for typ, index in word_types.items():
-    word_type_list[index] = typ
-
-phrase_type_list = [None] * len(phrase_types)
-for typ, index in phrase_types.items():
-    phrase_type_list[index] = typ
-
-context_type_list = [None] * len(context_types)
-for typ, index in context_types.items():
-    context_type_list[index] = typ
-
-num_tags = 5
-num_types = len(word_types)
-num_phrases = len(phrase_types)
-num_contexts = len(context_types)
-num_edges = len(edges)
-
-print 'Read in', num_edges, 'edges', num_phrases, 'phrases', num_contexts, 'contexts and', num_types, 'word types'
-
-#
-# Step 2:  expectation maximisation 
-#
-
-def normalise(a):
-    return a / float(sum(a))
-
-class PhraseToContextModel:
-    def __init__(self):
-        # Pr(tag | phrase)
-        self.tagDist = [normalise(random(num_tags)+1) for p in range(num_phrases)]
-        # Pr(context at pos i = w | tag) indexed by i, tag, word
-        self.contextWordDist = [[normalise(random(num_types)+1) for t in range(num_tags)] for i in range(4)]
-
-    def prob(self, pid, cid):
-        # return distribution p(tag, context | phrase) as vector of length |tags|
-        context = context_type_list[cid]
-        dist = zeros(num_tags)
-        for t in range(num_tags):
-            prob = self.tagDist[pid][t]
-            for k, tokid in enumerate(context):
-                prob *= self.contextWordDist[k][t][tokid]
-            dist[t] = prob
-        return dist
-
-    def expectation_maximisation_step(self, lamba=None):
-        tagCounts = zeros((num_phrases, num_tags))
-        contextWordCounts = zeros((4, num_tags, num_types))
-
-        # E-step
-        llh = 0
-        for pid, cid, cnt in edges:
-            q = self.prob(pid, cid)
-            z = sum(q)
-            q /= z
-            llh += log(z)
-            context = context_type_list[cid]
-            if lamba != None:
-                q *= exp(lamba)
-                q /= sum(q)
-            for t in range(num_tags):
-                tagCounts[pid][t] += cnt * q[t]
-            for i in range(4):
-                for t in range(num_tags):
-                    contextWordCounts[i][t][context[i]] += cnt * q[t]
-
-        # M-step
-        for p in range(num_phrases):
-            self.tagDist[p] = normalise(tagCounts[p])
-        for i in range(4):
-            for t in range(num_tags):
-                self.contextWordDist[i][t] = normalise(contextWordCounts[i,t])
-
-        return llh
-
-class ContextToPhraseModel:
-    def __init__(self):
-        # Pr(tag | context) = Multinomial
-        self.tagDist = [normalise(random(num_tags)+1) for p in range(num_contexts)]
-        # Pr(phrase = w | tag) = Multinomial
-        self.phraseSingleDist = [normalise(random(num_types)+1) for t in range(num_tags)]
-        # Pr(phrase_1 = w | tag) = Multinomial
-        self.phraseLeftDist = [normalise(random(num_types)+1) for t in range(num_tags)]
-        # Pr(phrase_-1 = w | tag) = Multinomial
-        self.phraseRightDist = [normalise(random(num_types)+1) for t in range(num_tags)]
-        # Pr(|phrase| = l | tag) = Geometric
-        self.phraseLengthDist = [0.5] * num_tags
-        # n.b. internal words for phrases of length >= 3 are drawn from uniform distribution
-
-    def prob(self, pid, cid):
-        # return distribution p(tag, phrase | context) as vector of length |tags|
-        phrase = phrase_type_list[pid]
-        dist = zeros(num_tags)
-        for t in range(num_tags):
-            prob = self.tagDist[cid][t]
-            f = self.phraseLengthDist[t]
-            prob *= geom.pmf(len(phrase), f)
-            if len(phrase) == 1:
-                prob *= self.phraseSingleDist[t][phrase[0]]
-            else:
-                prob *= self.phraseLeftDist[t][phrase[0]]
-                prob *= self.phraseRightDist[t][phrase[-1]]
-            dist[t] = prob
-        return dist
-
-    def expectation_maximisation_step(self, lamba=None):
-        tagCounts = zeros((num_contexts, num_tags))
-        phraseSingleCounts = zeros((num_tags, num_types))
-        phraseLeftCounts = zeros((num_tags, num_types))
-        phraseRightCounts = zeros((num_tags, num_types))
-        phraseLength = zeros(num_types)
-
-        # E-step
-        llh = 0
-        for pid, cid, cnt in edges:
-            q = self.prob(pid, cid)
-            z = sum(q)
-            q /= z
-            llh += log(z)
-            if lamba != None:
-                q *= exp(lamba)
-                q /= sum(q)
-            #print 'p', phrase_type_list[pid], 'c', context_type_list[cid], 'q', q
-            phrase = phrase_type_list[pid]
-            for t in range(num_tags):
-                tagCounts[cid][t] += cnt * q[t]
-                phraseLength[t] += cnt * len(phrase) * q[t]
-                if len(phrase) == 1:
-                    phraseSingleCounts[t][phrase[0]] += cnt * q[t]
-                else:
-                    phraseLeftCounts[t][phrase[0]] += cnt * q[t]
-                    phraseRightCounts[t][phrase[-1]] += cnt * q[t]
-
-        # M-step
-        for t in range(num_tags):
-            self.phraseLengthDist[t] = min(max(sum(tagCounts[:,t]) / phraseLength[t], 1e-6), 1-1e-6)
-            self.phraseSingleDist[t] = normalise(phraseSingleCounts[t])
-            self.phraseLeftDist[t] = normalise(phraseLeftCounts[t])
-            self.phraseRightDist[t] = normalise(phraseRightCounts[t])
-        for c in range(num_contexts):
-            self.tagDist[c] = normalise(tagCounts[c])
-
-        #print 't', self.tagDist
-        #print 'l', self.phraseLengthDist
-        #print 's', self.phraseSingleDist
-        #print 'L', self.phraseLeftDist
-        #print 'R', self.phraseRightDist
-
-        return llh
-
-class ProductModel:
-    """
-    WARNING: I haven't verified the maths behind this model. It's quite likely to be incorrect.
-    """
-
-    def __init__(self):
-        self.pcm = PhraseToContextModel()
-        self.cpm = ContextToPhraseModel()
-
-    def prob(self, pid, cid):
-        p1 = self.pcm.prob(pid, cid)
-        p2 = self.cpm.prob(pid, cid)
-        return (p1 / sum(p1)) * (p2 / sum(p2))
-
-    def expectation_maximisation_step(self):
-        tagCountsGivenPhrase = zeros((num_phrases, num_tags))
-        contextWordCounts = zeros((4, num_tags, num_types))
-
-        tagCountsGivenContext = zeros((num_contexts, num_tags))
-        phraseSingleCounts = zeros((num_tags, num_types))
-        phraseLeftCounts = zeros((num_tags, num_types))
-        phraseRightCounts = zeros((num_tags, num_types))
-        phraseLength = zeros(num_types)
-
-        kl = llh1 = llh2 = 0
-        for pid, cid, cnt in edges:
-            p1 = self.pcm.prob(pid, cid)
-            llh1 += log(sum(p1)) * cnt
-            p2 = self.cpm.prob(pid, cid)
-            llh2 += log(sum(p2)) * cnt
-
-            q = (p1 / sum(p1)) * (p2 / sum(p2))
-            kl += log(sum(q)) * cnt
-            qi = sqrt(q)
-            qi /= sum(qi)
-
-            phrase = phrase_type_list[pid]
-            context = context_type_list[cid]
-            for t in range(num_tags):
-                tagCountsGivenPhrase[pid][t] += cnt * qi[t]
-                tagCountsGivenContext[cid][t] += cnt * qi[t]
-                phraseLength[t] += cnt * len(phrase) * qi[t]
-                if len(phrase) == 1:
-                    phraseSingleCounts[t][phrase[0]] += cnt * qi[t]
-                else:
-                    phraseLeftCounts[t][phrase[0]] += cnt * qi[t]
-                    phraseRightCounts[t][phrase[-1]] += cnt * qi[t]
-                for i in range(4):
-                    contextWordCounts[i][t][context[i]] += cnt * qi[t]
-
-        kl *= -2
-
-        for t in range(num_tags):
-            for i in range(4):
-                self.pcm.contextWordDist[i][t] = normalise(contextWordCounts[i,t])
-            self.cpm.phraseLengthDist[t] = min(max(sum(tagCountsGivenContext[:,t]) / phraseLength[t], 1e-6), 1-1e-6)
-            self.cpm.phraseSingleDist[t] = normalise(phraseSingleCounts[t])
-            self.cpm.phraseLeftDist[t] = normalise(phraseLeftCounts[t])
-            self.cpm.phraseRightDist[t] = normalise(phraseRightCounts[t])
-        for p in range(num_phrases):
-            self.pcm.tagDist[p] = normalise(tagCountsGivenPhrase[p])
-        for c in range(num_contexts):
-            self.cpm.tagDist[c] = normalise(tagCountsGivenContext[c])
-
-        # return the overall objective
-        return llh1 + llh2 + kl
-
-class RegularisedProductModel:
-    # as above, but with a slack regularisation term which kills the
-    # closed-form solution for the E-step
-
-    def __init__(self, epsilon):
-        self.pcm = PhraseToContextModel()
-        self.cpm = ContextToPhraseModel()
-        self.epsilon = epsilon
-        self.lamba = zeros(num_tags)
-
-    def prob(self, pid, cid):
-        p1 = self.pcm.prob(pid, cid)
-        p2 = self.cpm.prob(pid, cid)
-        return (p1 / sum(p1)) * (p2 / sum(p2))
-
-    def dual(self, lamba):
-        return self.logz(lamba) + self.epsilon * dot(lamba, lamba) ** 0.5
-
-    def dual_gradient(self, lamba):
-        return self.expected_features(lamba) + self.epsilon * 2 * lamba
-
-    def expectation_maximisation_step(self):
-        # PR-step: optimise lambda to minimise log(z_lambda) + eps ||lambda||_2
-        self.lamba = scipy.optimize.fmin_slsqp(self.dual, self.lamba,
-                                bounds=[(0, 1e100)] * num_tags,
-                                fprime=self.dual_gradient, iprint=1)
-
-        # E,M-steps: collect expected counts under q_lambda and normalise
-        llh1 = self.pcm.expectation_maximisation_step(self.lamba)
-        llh2 = self.cpm.expectation_maximisation_step(-self.lamba)
-
-        # return the overall objective: llh - KL(q||p1.p2)
-        # llh = llh1 + llh2
-        # kl = sum q log q / p1 p2 = sum q { lambda . phi } - log Z
-        return llh1 + llh2 + self.logz(self.lamba) \
-            - dot(self.lamba, self.expected_features(self.lamba))
-
-    def logz(self, lamba):
-        lz = 0
-        for pid, cid, cnt in edges:
-            p1 = self.pcm.prob(pid, cid)
-            z1 = dot(p1 / sum(p1), exp(lamba))
-            lz += log(z1) * cnt
-
-            p2 = self.cpm.prob(pid, cid)
-            z2 = dot(p2 / sum(p2), exp(-lamba))
-            lz += log(z2) * cnt
-        return lz
-
-    def expected_features(self, lamba):
-        fs = zeros(num_tags)
-        for pid, cid, cnt in edges:
-            p1 = self.pcm.prob(pid, cid)
-            q1 = (p1 / sum(p1)) * exp(lamba)
-            fs += cnt * q1 / sum(q1)
-
-            p2 = self.cpm.prob(pid, cid)
-            q2 = (p2 / sum(p2)) * exp(-lamba)
-            fs -= cnt * q2 / sum(q2)
-        return fs
-
-
-class InterpolatedModel:
-    def __init__(self, epsilon):
-        self.pcm = PhraseToContextModel()
-        self.cpm = ContextToPhraseModel()
-        self.epsilon = epsilon
-        self.lamba = zeros(num_tags)
-
-    def prob(self, pid, cid):
-        p1 = self.pcm.prob(pid, cid)
-        p2 = self.cpm.prob(pid, cid)
-        return (p1 + p2) / 2
-
-    def dual(self, lamba):
-        return self.logz(lamba) + self.epsilon * dot(lamba, lamba) ** 0.5
-
-    def dual_gradient(self, lamba):
-        return self.expected_features(lamba) + self.epsilon * 2 * lamba
-
-    def expectation_maximisation_step(self):
-        # PR-step: optimise lambda to minimise log(z_lambda) + eps ||lambda||_2
-        self.lamba = scipy.optimize.fmin_slsqp(self.dual, self.lamba,
-                                bounds=[(0, 1e100)] * num_tags,
-                                fprime=self.dual_gradient, iprint=2)
-
-        # E,M-steps: collect expected counts under q_lambda and normalise
-        llh1 = self.pcm.expectation_maximisation_step(self.lamba)
-        llh2 = self.cpm.expectation_maximisation_step(self.lamba)
-
-        # return the overall objective: llh1 + llh2 - KL(q||p1.p2)
-        # kl = sum_y q log q / 0.5 * (p1 + p2) = sum_y q(y) { -lambda . phi(y) } - log Z
-        #    = -log Z + lambda . (E_q1[-phi] + E_q2[-phi]) / 2
-        kl = -self.logz(self.lamba) + dot(self.lamba, self.expected_features(self.lamba))
-        return llh1 + llh2 - kl, llh1, llh2, kl
-        # FIXME: KL comes out negative...
-
-    def logz(self, lamba):
-        lz = 0
-        for pid, cid, cnt in edges:
-            p1 = self.pcm.prob(pid, cid)
-            q1 = p1 / sum(p1) * exp(-lamba)
-            q1z = sum(q1)
-
-            p2 = self.cpm.prob(pid, cid)
-            q2 = p2 / sum(p2) * exp(-lamba)
-            q2z = sum(q2)
-
-            lz += log(0.5 * (q1z + q2z)) * cnt
-        return lz
-
-    # z = 1/2 * (sum_y p1(y|x) exp (-lambda . phi(y)) + sum_y p2(y|x) exp (-lambda . phi(y)))
-    #   = 1/2 (z1 + z2)
-    # d (log z) / dlambda = 1/2 (E_q1 [ -phi ] + E_q2 [ -phi ] )
-    def expected_features(self, lamba):
-        fs = zeros(num_tags)
-        for pid, cid, cnt in edges:
-            p1 = self.pcm.prob(pid, cid)
-            q1 = (p1 / sum(p1)) * exp(-lamba)
-            fs -= 0.5 * cnt * q1 / sum(q1)
-
-            p2 = self.cpm.prob(pid, cid)
-            q2 = (p2 / sum(p2)) * exp(-lamba)
-            fs -= 0.5 * cnt * q2 / sum(q2)
-        return fs
-
-if style == 'p2c':
-    m = PhraseToContextModel()
-elif style == 'c2p':
-    m = ContextToPhraseModel()
-elif style == 'prod':
-    m = ProductModel()
-elif style == 'prodslack':
-    m = RegularisedProductModel(0.5)
-elif style == 'sum':
-    m = InterpolatedModel(0.5)
-
-for iteration in range(30):
-    obj = m.expectation_maximisation_step()
-    print 'iteration', iteration, 'objective', obj
-
-for pid, cid, cnt in edges:
-    p = m.prob(pid, cid)
-    phrase = phrase_type_list[pid]
-    phrase_str = ' '.join(map(word_type_list.__getitem__, phrase))
-    context = context_type_list[cid]
-    context_str = ' '.join(map(word_type_list.__getitem__, context))
-    print '%s\t%s ||| C=%d' % (phrase_str, context_str, argmax(p))
diff --git a/gi/posterior-regularisation/train_pr_global.py b/gi/posterior-regularisation/train_pr_global.py
deleted file mode 100644
index 8521bccb..00000000
--- a/gi/posterior-regularisation/train_pr_global.py
+++ /dev/null
@@ -1,296 +0,0 @@
-import sys
-import scipy.optimize
-from numpy import *
-from numpy.random import random
-
-#
-# Step 1: load the concordance counts
-# 
-
-edges_phrase_to_context = []
-edges_context_to_phrase = []
-types = {}
-context_types = {}
-num_edges = 0
-
-for line in sys.stdin:
-    phrase, rest = line.strip().split('\t')
-    parts = rest.split('|||')
-    edges_phrase_to_context.append((phrase, []))
-    for i in range(0, len(parts), 2):
-        context, count = parts[i:i+2]
-
-        ctx = tuple(filter(lambda x: x != '<PHRASE>', context.split()))
-        cnt = int(count.strip()[2:])
-        edges_phrase_to_context[-1][1].append((ctx, cnt))
-
-        cid = context_types.get(ctx, len(context_types))
-        if cid == len(context_types):
-            context_types[ctx] = cid
-            edges_context_to_phrase.append((ctx, []))
-        edges_context_to_phrase[cid][1].append((phrase, cnt))
-
-        for token in ctx:
-            types.setdefault(token, len(types))
-        for token in phrase.split():
-            types.setdefault(token, len(types))
-
-        num_edges += 1
-
-print 'Read in', num_edges, 'edges and', len(types), 'word types'
-
-print 'edges_phrase_to_context', edges_phrase_to_context
-
-#
-# Step 2: initialise the model parameters
-#
-
-num_tags = 10
-num_types = len(types)
-num_phrases = len(edges_phrase_to_context)
-num_contexts = len(edges_context_to_phrase)
-delta = int(sys.argv[1])
-gamma = int(sys.argv[2])
-
-def normalise(a):
-    return a / float(sum(a))
-
-# Pr(tag | phrase)
-tagDist = [normalise(random(num_tags)+1) for p in range(num_phrases)]
-#tagDist = [normalise(array(range(1,num_tags+1))) for p in range(num_phrases)]
-# Pr(context at pos i = w | tag) indexed by i, tag, word
-#contextWordDist = [[normalise(array(range(1,num_types+1))) for t in range(num_tags)] for i in range(4)]
-contextWordDist = [[normalise(random(num_types)+1) for t in range(num_tags)] for i in range(4)]
-# PR langrange multipliers
-lamba = zeros(2 * num_edges * num_tags)
-omega_offset = num_edges * num_tags
-lamba_index = {}
-next = 0
-for phrase, ccs in edges_phrase_to_context:
-    for context, count in ccs:
-        lamba_index[phrase,context] = next
-        next += num_tags
-#print lamba_index
-
-#
-# Step 3: expectation maximisation
-#
-
-for iteration in range(20):
-    tagCounts = [zeros(num_tags) for p in range(num_phrases)]
-    contextWordCounts = [[zeros(num_types) for t in range(num_tags)] for i in range(4)]
-
-    #print 'tagDist', tagDist
-    #print 'contextWordCounts[0][0]', contextWordCounts[0][0]
-
-    # Tune lambda
-    # dual: min log Z(lamba) s.t. lamba >= 0;
-    # sum_c lamba_pct <= delta; sum_p lamba_pct <= gamma
-    def dual(ls):
-        logz = 0
-        for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-            for context, count in ccs:
-                conditionals = zeros(num_tags)
-                for t in range(num_tags):
-                    prob = tagDist[p][t]
-                    for i in range(4):
-                        prob *= contextWordDist[i][t][types[context[i]]]
-                    conditionals[t] = prob
-                cz = sum(conditionals)
-                conditionals /= cz
-
-                #print 'dual', phrase, context, count, 'p =', conditionals
-
-                local_z = 0
-                for t in range(num_tags):
-                    li = lamba_index[phrase,context] + t
-                    local_z += conditionals[t] * exp(-ls[li] - ls[omega_offset+li])
-                logz += log(local_z) * count
-
-        #print 'ls', ls
-        #print 'lambda', list(ls)
-        #print 'dual', logz
-        return logz
-
-    def loglikelihood():
-        llh = 0
-        for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-            for context, count in ccs:
-                conditionals = zeros(num_tags)
-                for t in range(num_tags):
-                    prob = tagDist[p][t]
-                    for i in range(4):
-                        prob *= contextWordDist[i][t][types[context[i]]]
-                    conditionals[t] = prob
-                cz = sum(conditionals)
-                llh += log(cz) * count
-        return llh
-
-    def primal(ls):
-        # FIXME: returns negative values for KL (impossible)
-        logz = dual(ls)
-        expectations = -dual_deriv(ls)
-        kl = -logz - dot(ls, expectations)
-        llh = loglikelihood()
-
-        pt_l1linf = 0
-        for phrase, ccs in edges_phrase_to_context:
-            for t in range(num_tags):
-                best = -1e500
-                for context, count in ccs:
-                    li = lamba_index[phrase,context] + t
-                    s = expectations[li]
-                    if s > best: best = s
-                pt_l1linf += best
-
-        ct_l1linf = 0
-        for context, pcs in edges_context_to_phrase:
-            for t in range(num_tags):
-                best = -1e500
-                for phrase, count in pcs:
-                    li = omega_offset + lamba_index[phrase,context] + t
-                    s = expectations[li]
-                    if s > best: best = s
-                ct_l1linf += best
-
-        return llh, kl, pt_l1linf, ct_l1linf, llh - kl - delta * pt_l1linf - gamma * ct_l1linf
-
-    def dual_deriv(ls):
-        # d/dl log(z) = E_q[phi]
-        deriv = zeros(2 * num_edges * num_tags)
-        for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-            for context, count in ccs:
-                conditionals = zeros(num_tags)
-                for t in range(num_tags):
-                    prob = tagDist[p][t]
-                    for i in range(4):
-                        prob *= contextWordDist[i][t][types[context[i]]]
-                    conditionals[t] = prob
-                cz = sum(conditionals)
-                conditionals /= cz
-
-                scores = zeros(num_tags)
-                for t in range(num_tags):
-                    li = lamba_index[phrase,context] + t
-                    scores[t] = conditionals[t] * exp(-ls[li] - ls[omega_offset + li])
-                local_z = sum(scores)
-
-                #print 'ddual', phrase, context, count, 'q =', scores / local_z
-
-                for t in range(num_tags):
-                    deriv[lamba_index[phrase,context] + t] -= count * scores[t] / local_z
-                    deriv[omega_offset + lamba_index[phrase,context] + t] -= count * scores[t] / local_z
-
-        #print 'ddual', list(deriv)
-        return deriv
-
-    def constraints(ls):
-        cons = zeros(num_phrases * num_tags + num_edges * num_tags)
-
-        index = 0
-        for phrase, ccs in edges_phrase_to_context:
-            for t in range(num_tags):
-                if delta > 0:
-                    total = delta
-                    for cprime, count in ccs:
-                        total -= ls[lamba_index[phrase, cprime] + t]
-                    cons[index] = total
-                index += 1
-
-        for context, pcs in edges_context_to_phrase:
-            for t in range(num_tags):
-                if gamma > 0:
-                    total = gamma
-                    for pprime, count in pcs:
-                        total -= ls[omega_offset + lamba_index[pprime, context] + t]
-                    cons[index] = total
-                index += 1
-
-        #print 'cons', cons
-        return cons
-
-    def constraints_deriv(ls):
-        cons = zeros((num_phrases * num_tags + num_edges * num_tags, 2 * num_edges * num_tags))
-
-        index = 0
-        for phrase, ccs in edges_phrase_to_context:
-            for t in range(num_tags):
-                if delta > 0:
-                    d = cons[index,:]#zeros(num_edges * num_tags)
-                    for cprime, count in ccs:
-                        d[lamba_index[phrase, cprime] + t] = -1
-                    #cons[index] = d
-                index += 1
-
-        for context, pcs in edges_context_to_phrase:
-            for t in range(num_tags):
-                if gamma > 0:
-                    d = cons[index,:]#d = zeros(num_edges * num_tags)
-                    for pprime, count in pcs:
-                        d[omega_offset + lamba_index[pprime, context] + t] = -1
-                    #cons[index] = d
-                index += 1
-        #print 'dcons', cons
-        return cons
-
-    print 'Pre lambda optimisation dual', dual(lamba), 'primal', primal(lamba)
-    #print 'lambda', lamba, lamba.shape
-    #print 'bounds', [(0, max(delta, gamma))] * (2 * num_edges * num_tags)
-
-    lamba = scipy.optimize.fmin_slsqp(dual, lamba,
-                            bounds=[(0, max(delta, gamma))] * (2 * num_edges * num_tags),
-                            f_ieqcons=constraints,
-                            fprime=dual_deriv,
-                            fprime_ieqcons=constraints_deriv,
-                            iprint=0)
-    print 'Post lambda optimisation dual', dual(lamba), 'primal', primal(lamba)
-
-    # E-step
-    llh = log_z = 0
-    for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-        for context, count in ccs:
-            conditionals = zeros(num_tags)
-            for t in range(num_tags):
-                prob = tagDist[p][t]
-                for i in range(4):
-                    prob *= contextWordDist[i][t][types[context[i]]]
-                conditionals[t] = prob
-            cz = sum(conditionals)
-            conditionals /= cz
-            llh += log(cz) * count
-
-            q = zeros(num_tags)
-            li = lamba_index[phrase, context]
-            for t in range(num_tags):
-                q[t] = conditionals[t] * exp(-lamba[li + t] - lamba[omega_offset + li + t])
-            qz = sum(q)
-            log_z += count * log(qz)
-
-            for t in range(num_tags):
-                tagCounts[p][t] += count * q[t] / qz
-
-            for i in range(4):
-                for t in range(num_tags):
-                    contextWordCounts[i][t][types[context[i]]] += count * q[t] / qz
-
-    print 'iteration', iteration, 'llh', llh, 'logz', log_z
-
-    # M-step
-    for p in range(num_phrases):
-        tagDist[p] = normalise(tagCounts[p])
-    for i in range(4):
-        for t in range(num_tags):
-            contextWordDist[i][t] = normalise(contextWordCounts[i][t])
-
-for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-    for context, count in ccs:
-        conditionals = zeros(num_tags)
-        for t in range(num_tags):
-            prob = tagDist[p][t]
-            for i in range(4):
-                prob *= contextWordDist[i][t][types[context[i]]]
-            conditionals[t] = prob
-        cz = sum(conditionals)
-        conditionals /= cz
-
-        print '%s\t%s ||| C=%d |||' % (phrase, context, argmax(conditionals)), conditionals
diff --git a/gi/posterior-regularisation/train_pr_parallel.py b/gi/posterior-regularisation/train_pr_parallel.py
deleted file mode 100644
index 3b9cefed..00000000
--- a/gi/posterior-regularisation/train_pr_parallel.py
+++ /dev/null
@@ -1,333 +0,0 @@
-import sys
-import scipy.optimize
-from numpy import *
-from numpy.random import random, seed
-
-#
-# Step 1: load the concordance counts
-# 
-
-edges_phrase_to_context = []
-edges_context_to_phrase = []
-types = {}
-context_types = {}
-num_edges = 0
-
-for line in sys.stdin:
-    phrase, rest = line.strip().split('\t')
-    parts = rest.split('|||')
-    edges_phrase_to_context.append((phrase, []))
-    for i in range(0, len(parts), 2):
-        context, count = parts[i:i+2]
-
-        ctx = tuple(filter(lambda x: x != '<PHRASE>', context.split()))
-        cnt = int(count.strip()[2:])
-        edges_phrase_to_context[-1][1].append((ctx, cnt))
-
-        cid = context_types.get(ctx, len(context_types))
-        if cid == len(context_types):
-            context_types[ctx] = cid
-            edges_context_to_phrase.append((ctx, []))
-        edges_context_to_phrase[cid][1].append((phrase, cnt))
-
-        for token in ctx:
-            types.setdefault(token, len(types))
-        for token in phrase.split():
-            types.setdefault(token, len(types))
-
-        num_edges += 1
-
-#
-# Step 2: initialise the model parameters
-#
-
-num_tags = 25
-num_types = len(types)
-num_phrases = len(edges_phrase_to_context)
-num_contexts = len(edges_context_to_phrase)
-delta = float(sys.argv[1])
-assert sys.argv[2] in ('local', 'global')
-local = sys.argv[2] == 'local'
-if len(sys.argv) >= 2:
-     seed(int(sys.argv[3]))
-
-print 'Read in', num_edges, 'edges', num_phrases, 'phrases', num_contexts, 'contexts and', len(types), 'word types'
-
-def normalise(a):
-    return a / float(sum(a))
-
-# Pr(tag | phrase)
-tagDist = [normalise(random(num_tags)+1) for p in range(num_phrases)]
-# Pr(context at pos i = w | tag) indexed by i, tag, word
-contextWordDist = [[normalise(random(num_types)+1) for t in range(num_tags)] for i in range(4)]
-
-#
-# Step 3: expectation maximisation
-#
-
-class GlobalDualObjective:
-    """
-    Objective, log(z), for all phrases s.t. lambda >= 0, sum_c lambda_pct <= scale 
-    """
-
-    def __init__(self, scale):
-        self.scale = scale
-        self.posterior = zeros((num_edges, num_tags))
-        self.q = zeros((num_edges, num_tags))
-        self.llh = 0
-
-        index = 0
-        for j, (phrase, edges) in enumerate(edges_phrase_to_context):
-            for context, count in edges:
-                for t in range(num_tags):
-                    prob = tagDist[j][t]
-                    for k, token in enumerate(context):
-                        prob *= contextWordDist[k][t][types[token]]
-                    self.posterior[index,t] = prob
-                z = sum(self.posterior[index,:])
-                self.posterior[index,:] /= z
-                self.llh += log(z) * count
-                index += 1
-
-    def objective(self, ls):
-        ls = ls.reshape((num_edges, num_tags))
-        logz = 0
-
-        index = 0
-        for j, (phrase, edges) in enumerate(edges_phrase_to_context):
-            for context, count in edges:
-                for t in range(num_tags):
-                    self.q[index,t] = self.posterior[index,t] * exp(-ls[index,t])
-                local_z = sum(self.q[index,:])
-                self.q[index,:] /= local_z
-                logz += log(local_z) * count
-                index += 1
-
-        return logz
-
-    # FIXME: recomputes q many more times than necessary
-
-    def gradient(self, ls):
-        ls = ls.reshape((num_edges, num_tags))
-        gradient = zeros((num_edges, num_tags))
-
-        index = 0
-        for j, (phrase, edges) in enumerate(edges_phrase_to_context):
-            for context, count in edges:
-                for t in range(num_tags):
-                    self.q[index,t] = self.posterior[index,t] * exp(-ls[index,t])
-                local_z = sum(self.q[index,:])
-                self.q[index,:] /= local_z
-                for t in range(num_tags):
-                    gradient[index,t] -= self.q[index,t] * count
-                index += 1
-
-        return gradient.ravel()
-
-    def constraints(self, ls):
-        ls = ls.reshape((num_edges, num_tags))
-        cons = ones((num_phrases, num_tags)) * self.scale
-        index = 0
-        for j, (phrase, edges) in enumerate(edges_phrase_to_context):
-            for i, (context, count) in enumerate(edges):
-                for t in range(num_tags):
-                    cons[j,t] -= ls[index,t] * count
-                index += 1
-        return cons.ravel()
-
-    def constraints_gradient(self, ls):
-        ls = ls.reshape((num_edges, num_tags))
-        gradient = zeros((num_phrases, num_tags, num_edges, num_tags))
-        index = 0
-        for j, (phrase, edges) in enumerate(edges_phrase_to_context):
-            for i, (context, count) in enumerate(edges):
-                for t in range(num_tags):
-                    gradient[j,t,index,t] -= count
-                index += 1
-        return gradient.reshape((num_phrases*num_tags, num_edges*num_tags))
-
-    def optimize(self):
-        ls = zeros(num_edges * num_tags)
-        #print '\tpre lambda optimisation dual', self.objective(ls) #, 'primal', primal(lamba)
-        ls = scipy.optimize.fmin_slsqp(self.objective, ls,
-                                bounds=[(0, self.scale)] * num_edges * num_tags,
-                                f_ieqcons=self.constraints,
-                                fprime=self.gradient,
-                                fprime_ieqcons=self.constraints_gradient,
-                                iprint=0) # =2 for verbose
-        #print '\tpost lambda optimisation dual', self.objective(ls) #, 'primal', primal(lamba)
-
-        # returns llh, kl and l1lmax contribution
-        l1lmax = 0
-        index = 0
-        for j, (phrase, edges) in enumerate(edges_phrase_to_context):
-            for t in range(num_tags):
-                lmax = None
-                for i, (context, count) in enumerate(edges):
-                    lmax = max(lmax, self.q[index+i,t])
-                l1lmax += lmax
-            index += len(edges)
-
-        return self.llh, -self.objective(ls) + dot(ls, self.gradient(ls)), l1lmax
-
-class LocalDualObjective:
-    """
-    Local part of objective, log(z) relevant to lambda_p**.
-    Optimised subject to lambda >= 0, sum_c lambda_pct <= scale forall t 
-    """
-
-    def __init__(self, phraseId, scale):
-        self.phraseId = phraseId
-        self.scale = scale
-        edges = edges_phrase_to_context[self.phraseId][1]
-        self.posterior = zeros((len(edges), num_tags))
-        self.q = zeros((len(edges), num_tags))
-        self.llh = 0
-
-        for i, (context, count) in enumerate(edges):
-            for t in range(num_tags):
-                prob = tagDist[phraseId][t]
-                for j, token in enumerate(context):
-                    prob *= contextWordDist[j][t][types[token]]
-                self.posterior[i,t] = prob
-            z = sum(self.posterior[i,:])
-            self.posterior[i,:] /= z
-            self.llh += log(z) * count
-
-    def objective(self, ls):
-        edges = edges_phrase_to_context[self.phraseId][1]
-        ls = ls.reshape((len(edges), num_tags))
-        logz = 0
-
-        for i, (context, count) in enumerate(edges):
-            for t in range(num_tags):
-                self.q[i,t] = self.posterior[i,t] * exp(-ls[i,t])
-            local_z = sum(self.q[i,:])
-            self.q[i,:] /= local_z
-            logz += log(local_z) * count
-
-        return logz
-
-    # FIXME: recomputes q many more times than necessary
-
-    def gradient(self, ls):
-        edges = edges_phrase_to_context[self.phraseId][1]
-        ls = ls.reshape((len(edges), num_tags))
-        gradient = zeros((len(edges), num_tags))
-
-        for i, (context, count) in enumerate(edges):
-            for t in range(num_tags):
-                self.q[i,t] = self.posterior[i,t] * exp(-ls[i,t])
-            local_z = sum(self.q[i,:])
-            self.q[i,:] /= local_z
-            for t in range(num_tags):
-                gradient[i,t] -= self.q[i,t] * count
-
-        return gradient.ravel()
-
-    def constraints(self, ls):
-        edges = edges_phrase_to_context[self.phraseId][1]
-        ls = ls.reshape((len(edges), num_tags))
-        cons = ones(num_tags) * self.scale
-        for t in range(num_tags):
-            for i, (context, count) in enumerate(edges):
-                cons[t] -= ls[i,t] * count
-        return cons
-
-    def constraints_gradient(self, ls):
-        edges = edges_phrase_to_context[self.phraseId][1]
-        ls = ls.reshape((len(edges), num_tags))
-        gradient = zeros((num_tags, len(edges), num_tags))
-        for t in range(num_tags):
-            for i, (context, count) in enumerate(edges):
-                gradient[t,i,t] -= count
-        return gradient.reshape((num_tags, len(edges)*num_tags))
-
-    def optimize(self, ls=None):
-        edges = edges_phrase_to_context[self.phraseId][1]
-        if ls == None:
-            ls = zeros(len(edges) * num_tags)
-        #print '\tpre lambda optimisation dual', self.objective(ls) #, 'primal', primal(lamba)
-        ls = scipy.optimize.fmin_slsqp(self.objective, ls,
-                                bounds=[(0, self.scale)] * len(edges) * num_tags,
-                                f_ieqcons=self.constraints,
-                                fprime=self.gradient,
-                                fprime_ieqcons=self.constraints_gradient,
-                                iprint=0) # =2 for verbose
-        #print '\tlambda', list(ls)
-        #print '\tpost lambda optimisation dual', self.objective(ls) #, 'primal', primal(lamba)
-
-        # returns llh, kl and l1lmax contribution
-        l1lmax = 0
-        for t in range(num_tags):
-            lmax = None
-            for i, (context, count) in enumerate(edges):
-                lmax = max(lmax, self.q[i,t])
-            l1lmax += lmax
-
-        return self.llh, -self.objective(ls) + dot(ls, self.gradient(ls)), l1lmax, ls
-
-ls = [None] * num_phrases
-for iteration in range(20):
-    tagCounts = [zeros(num_tags) for p in range(num_phrases)]
-    contextWordCounts = [[zeros(num_types) for t in range(num_tags)] for i in range(4)]
-
-    # E-step
-    llh = kl = l1lmax = 0
-    if local:
-        for p in range(num_phrases):
-            o = LocalDualObjective(p, delta)
-            #print '\toptimising lambda for phrase', p, '=', edges_phrase_to_context[p][0]
-            #print '\toptimising lambda for phrase', p, 'ls', ls[p]
-            obj = o.optimize(ls[p])
-            #print '\tphrase', p, 'deltas', obj
-            llh += obj[0]
-            kl += obj[1]
-            l1lmax += obj[2]
-            ls[p] = obj[3]
-
-            edges = edges_phrase_to_context[p][1]
-            for j, (context, count) in enumerate(edges):
-                for t in range(num_tags):
-                    tagCounts[p][t] += count * o.q[j,t]
-                for i in range(4):
-                    for t in range(num_tags):
-                        contextWordCounts[i][t][types[context[i]]] += count * o.q[j,t]
-
-        #print 'iteration', iteration, 'LOCAL objective', (llh + kl + delta * l1lmax), 'llh', llh, 'kl', kl, 'l1lmax', l1lmax
-    else:
-        o = GlobalDualObjective(delta)
-        obj = o.optimize()
-        llh, kl, l1lmax = o.optimize()
-
-        index = 0
-        for p, (phrase, edges) in enumerate(edges_phrase_to_context):
-            for context, count in edges:
-                for t in range(num_tags):
-                    tagCounts[p][t] += count * o.q[index,t]
-                for i in range(4):
-                    for t in range(num_tags):
-                        contextWordCounts[i][t][types[context[i]]] += count * o.q[index,t]
-                index += 1
-
-    print 'iteration', iteration, 'objective', (llh - kl - delta * l1lmax), 'llh', llh, 'kl', kl, 'l1lmax', l1lmax
-
-    # M-step
-    for p in range(num_phrases):
-        tagDist[p] = normalise(tagCounts[p])
-    for i in range(4):
-        for t in range(num_tags):
-            contextWordDist[i][t] = normalise(contextWordCounts[i][t])
-
-for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-    for context, count in ccs:
-        conditionals = zeros(num_tags)
-        for t in range(num_tags):
-            prob = tagDist[p][t]
-            for i in range(4):
-                prob *= contextWordDist[i][t][types[context[i]]]
-            conditionals[t] = prob
-        cz = sum(conditionals)
-        conditionals /= cz
-
-        print '%s\t%s ||| C=%d |||' % (phrase, context, argmax(conditionals)), conditionals
diff --git a/gi/pyp-topics/scripts/contexts2documents.py b/gi/pyp-topics/scripts/contexts2documents.py
deleted file mode 100755
index 9be4ebbb..00000000
--- a/gi/pyp-topics/scripts/contexts2documents.py
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/usr/bin/python
-
-import sys
-from operator import itemgetter
-
-if len(sys.argv) > 3:
-  print "Usage: contexts2documents.py [contexts_index_out] [phrases_index_out]"
-  exit(1)
-
-context_index = {} 
-phrase_index = {}
-for line in sys.stdin:
-  phrase, line_tail = line.split('\t')
-
-  raw_contexts = line_tail.split('|||')
-  contexts = [c.strip() for x,c in enumerate(raw_contexts) if x%2 == 0]
-  counts   = [int(c.split('=')[1].strip()) for x,c in enumerate(raw_contexts) if x%2 != 0]
-  phrase_index.setdefault(phrase, len(phrase_index))
-  print len(contexts),
-  for context,count in zip(contexts,counts): 
-    c = context_index.setdefault(context, len(context_index))
-    print "%d:%d" % (c,count),
-  print
-if 1 < len(sys.argv) < 4:
-  contexts_out = open(sys.argv[1],'w')
-  contexts = context_index.items()
-  contexts.sort(key = itemgetter(1))
-  for context in contexts: 
-    print >>contexts_out, context[0]
-  contexts_out.close()
-if len(sys.argv) == 3:
-  phrases_out = open(sys.argv[2],'w')
-  phrases = phrase_index.items()
-  phrases.sort(key = itemgetter(1))
-  for phrase in phrases: 
-    print >>phrases_out, phrase[0]
-  phrases_out.close()
diff --git a/gi/pyp-topics/scripts/extract_contexts.py b/gi/pyp-topics/scripts/extract_contexts.py
deleted file mode 100755
index b2723f2a..00000000
--- a/gi/pyp-topics/scripts/extract_contexts.py
+++ /dev/null
@@ -1,144 +0,0 @@
-#!/usr/bin/python
-
-import sys,collections
-
-def extract_backoff(context_list, order):
-  assert len(context_list) == (2*order)
-  backoffs = []
-  for i in range(1,order+1):
-    if i == order:
-      backoffs.append(([context_list[i-1]+"|"], ["|"+context_list[i]]))
-    else:
-      right_limit = 2*order-i
-      core = context_list[i:right_limit]
-      left = [context_list[i-1]+"|"*(order-i+1)]
-      right = ["|"*(order-i+1)+context_list[right_limit]]
-      backoffs.append((core, left, right))
-# print context_list, backoffs
-  return backoffs
-
-def tuple_to_str(t):
-  s=""
-  for i,x in enumerate(t):
-    if i > 0: s += "|"
-    s += str(x)
-  return s
-
-if len(sys.argv) < 3:
-  print "Usage: extract-contexts.py output_filename order cutoff lowercase"
-  exit(1)
-
-output_filename = sys.argv[1]
-order = int(sys.argv[2])
-cutoff = 0
-if len(sys.argv) > 3:
-  cutoff = int(sys.argv[3])
-lowercase = False
-if len(sys.argv) > 4:
-  lowercase = bool(sys.argv[4])
-
-contexts_dict={}
-contexts_list=[]
-contexts_freq=collections.defaultdict(int)
-contexts_backoff={}
-
-token_dict={}
-token_list=[]
-documents_dict=collections.defaultdict(dict)
-
-contexts_at_order = [i for i in range(order+1)]
-
-prefix = ["<s%d>|<s>"%i for i in range(order)]
-suffix = ["</s%d>|</s>"%i for i in range(order)]
-
-for line in sys.stdin:
-  tokens = list(prefix)
-  tokens.extend(line.split())
-  tokens.extend(suffix)
-  if lowercase:
-    tokens = map(lambda x: x.lower(), tokens)
-
-  for i in range(order, len(tokens)-order):
-    context_list = []
-    term=""
-    for j in range(i-order, i+order+1):
-      token,tag = tokens[j].rsplit('|',2)
-      if j != i:
-        context_list.append(token)
-      else:
-        if token not in token_dict: 
-          token_dict[token] = len(token_dict)
-          token_list.append(token)
-        term = token_dict[token] 
-
-    context = tuple_to_str(tuple(context_list))
-
-    if context not in contexts_dict: 
-      context_index = len(contexts_dict)
-      contexts_dict[context] = context_index
-      contexts_list.append(context)
-      contexts_at_order[0] += 1
-
-      # handle backoff
-      backoff_contexts = extract_backoff(context_list, order)
-      bo_indexes=[(context_index,)]
-#     bo_indexes=[(context,)]
-      for i,bo in enumerate(backoff_contexts):
-        factor_indexes=[]
-        for factor in bo:
-          bo_tuple = tuple_to_str(tuple(factor))
-          if bo_tuple not in contexts_dict:
-            contexts_dict[bo_tuple] = len(contexts_dict)
-            contexts_list.append(bo_tuple)
-            contexts_at_order[i+1] += 1
-#         factor_indexes.append(bo_tuple)
-          factor_indexes.append(contexts_dict[bo_tuple])
-        bo_indexes.append(tuple(factor_indexes))
-      
-      for i in range(len(bo_indexes)-1):
-        contexts_backoff[bo_indexes[i][0]] = bo_indexes[i+1]
-
-    context_index = contexts_dict[context]
-    contexts_freq[context_index] += 1
-
-    if context_index not in documents_dict[term]:
-      documents_dict[term][context_index] = 1
-    else:
-      documents_dict[term][context_index] += 1
-
-term_file = open(output_filename+".terms",'w')
-for t in token_list: print >>term_file, t
-term_file.close()
-
-contexts_file = open(output_filename+".contexts",'w')
-for c in contexts_list: 
-  print >>contexts_file, c
-contexts_file.close()
-
-data_file = open(output_filename+".data",'w')
-for t in range(len(token_list)): 
-  line=""
-  num_active=0
-  for c in documents_dict[t]:
-    count = documents_dict[t][c]
-    if contexts_freq[c] >= cutoff:
-      line += (' ' + str(c) + ':' + str(count))
-      num_active += 1
-  if num_active > 0:
-    print >>data_file, "%d%s" % (num_active,line)
-data_file.close()
-
-contexts_backoff_file = open(output_filename+".contexts_backoff",'w')
-print >>contexts_backoff_file, len(contexts_list), order,
-#for x in contexts_at_order: 
-#  print >>contexts_backoff_file, x,
-#print >>contexts_backoff_file
-for x in range(order-1):
-  print >>contexts_backoff_file, 3,
-print >>contexts_backoff_file, 2
-
-for x in contexts_backoff: 
-  print >>contexts_backoff_file, x, 
-  for y in contexts_backoff[x]: print >>contexts_backoff_file, y,
-  print >>contexts_backoff_file 
-contexts_backoff_file.close()
diff --git a/gi/pyp-topics/scripts/extract_contexts_test.py b/gi/pyp-topics/scripts/extract_contexts_test.py
deleted file mode 100755
index 693b6e0b..00000000
--- a/gi/pyp-topics/scripts/extract_contexts_test.py
+++ /dev/null
@@ -1,72 +0,0 @@
-#!/usr/bin/python
-
-import sys,collections
-
-def tuple_to_str(t):
-  s=""
-  for i,x in enumerate(t):
-    if i > 0: s += "|"
-    s += str(x)
-  return s
-
-if len(sys.argv) < 5:
-  print "Usage: extract-contexts_test.py output_filename vocab contexts order lowercase"
-  exit(1)
-
-output_filename = sys.argv[1]
-output = open(output_filename+".test_data",'w')
-
-unk_term="-UNK-"
-vocab_dict={}
-for i,x in enumerate(file(sys.argv[2], 'r').readlines()): 
-  vocab_dict[x.strip()]=i
-
-contexts_dict={}
-contexts_list=[]
-for i,x in enumerate(file(sys.argv[3], 'r').readlines()): 
-  contexts_dict[x.strip()]=i
-  contexts_list.append(x.strip())
-
-order = int(sys.argv[4])
-
-lowercase = False
-if len(sys.argv) > 5:
-  lowercase = bool(sys.argv[5])
-if lowercase: unk_term = unk_term.lower()
-
-prefix = ["<s%d>|<s>"%i for i in range(order)]
-suffix = ["</s%d>|</s>"%i for i in range(order)]
-
-assert unk_term in vocab_dict
-for line in sys.stdin:
-  tokens = list(prefix)
-  tokens.extend(line.split())
-  tokens.extend(suffix)
-  if lowercase:
-    tokens = map(lambda x: x.lower(), tokens)
-
-  for i in range(order, len(tokens)-order):
-    context_list=[]
-    term=""
-    for j in range(i-order, i+order+1):
-      token,tag = tokens[j].rsplit('|',2)
-      if j != i:
-        context_list.append(token)
-      else:
-        if token not in vocab_dict: 
-          term = vocab_dict[unk_term] 
-        else:
-          term = vocab_dict[token] 
-    context = tuple_to_str(context_list)
-    if context not in contexts_dict: 
-      contexts_dict[context] = len(contexts_dict)
-      contexts_list.append(context)
-    context_index = contexts_dict[context]
-    print >>output, "%d:%d" % (term,context_index),
-  print >>output
-output.close()
-
-contexts_file = open(output_filename+".test_contexts",'w')
-for c in contexts_list: 
-  print >>contexts_file, c
-contexts_file.close()
diff --git a/gi/pyp-topics/scripts/extract_leaves.py b/gi/pyp-topics/scripts/extract_leaves.py
deleted file mode 100755
index 14783b36..00000000
--- a/gi/pyp-topics/scripts/extract_leaves.py
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/usr/bin/python
-
-import nltk
-import nltk.probability
-import sys
-import getopt 
-
-lexicalise=False
-rm_traces=False
-cutoff=100
-length_cutoff=10000
-try:                                
-  opts, args = getopt.getopt(sys.argv[1:], "hs:c:l", ["help", "lexicalise", "cutoff","sentence-length","remove-traces"])
-except getopt.GetoptError:          
-  print "Usage: extract_leaves.py [-lsc]"                        
-  sys.exit(2)                     
-for opt, arg in opts:                
-  if opt in ("-h", "--help"):      
-    print "Usage: extract_leaves.py [-lsc]"                        
-    sys.exit()                  
-  elif opt in ("-l", "--lexicalise"):                
-    lexicalise = True                 
-  elif opt in ("-c", "--cutoff"):                
-    cutoff = int(arg) 
-  elif opt in ("-s", "--sentence-length"):                
-    length_cutoff = int(arg) 
-  elif opt in ("--remove-traces"):                
-    rm_traces = True                 
-
-token_freq = nltk.probability.FreqDist()
-lines = []
-for line in sys.stdin:
-  t = nltk.Tree.parse(line)
-  pos = t.pos()
-  if len(pos) <= length_cutoff:
-    lines.append(pos)
-    for token, tag in pos:
-      token_freq.inc(token)  
-
-for line in lines:
-  for token,tag in line:
-    if not (rm_traces and tag == "-NONE-"):
-      if lexicalise:
-        if token_freq[token] < cutoff:
-          token = '-UNK-'
-        print '%s|%s' % (token,tag),
-      else:
-        print '%s' % tag,
-  print
diff --git a/gi/pyp-topics/scripts/map-documents.py b/gi/pyp-topics/scripts/map-documents.py
deleted file mode 100755
index 703de312..00000000
--- a/gi/pyp-topics/scripts/map-documents.py
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/usr/bin/python
-
-import sys
-
-if len(sys.argv) != 2:
-  print "Usage: map-documents.py vocab-file"
-  exit(1)
-
-vocab = file(sys.argv[1], 'r').readlines()
-term_dict = map(lambda x: x.strip(), vocab)
-
-for line in sys.stdin:
-  tokens = line.split()
-  for token in tokens:
-    elements = token.split(':')
-    if len(elements) == 1:
-      print "%s" % (term_dict[int(elements[0])]),
-    else:
-      print "%s:%s" % (term_dict[int(elements[0])], elements[1]),
-  print
diff --git a/gi/pyp-topics/scripts/map-terms.py b/gi/pyp-topics/scripts/map-terms.py
deleted file mode 100755
index eb0298d7..00000000
--- a/gi/pyp-topics/scripts/map-terms.py
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/usr/bin/python
-
-import sys
-
-if len(sys.argv) != 2:
-  print "Usage: map-terms.py vocab-file"
-  exit(1)
-
-vocab = file(sys.argv[1], 'r').readlines()
-term_dict = map(lambda x: x.strip().replace(' ','_'), vocab)
-
-for line in sys.stdin:
-  tokens = line.split()
-  for token in tokens:
-    elements = token.split(':')
-    if len(elements) == 1:
-      print "%s" % (term_dict[int(elements[0])]),
-    else:
-      print "%s:%s" % (term_dict[int(elements[0])], elements[1]),
-  print
diff --git a/gi/pyp-topics/scripts/run.sh b/gi/pyp-topics/scripts/run.sh
deleted file mode 100644
index 19e625b1..00000000
--- a/gi/pyp-topics/scripts/run.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/sh
-
-
-./simple-extract-context.sh ~/workspace/clsp2010/jhuws2010/data/btec/split.zh-en.al 1 | ~/workspace/pyp-topics/scripts/contexts2documents.py > split.zh-en.data
-
-~/workspace/pyp-topics/bin/pyp-topics-train -d split.zh-en.data -t 50 -s 100 -o split.zh-en.documents.gz -w split.zh-en.topics.gz
-gunzip split.zh-en.documents.gz
-
-~/workspace/cdec/extools/extractor -i ../jhuws2010/data/btec/split.zh-en.al -S 1 -c 500000 -L 12 --base_phrase_spans | ~/workspace/pyp-topics/scripts/spans2labels.py split.zh-en.phrases split.zh-en.contexts split.zh-en.documents > corpus.zh-en.labelled_spans
-
-paste -d " " ~/workspace/clsp2010/jhuws2010/data/btec/split.zh-en.al corpus.labelled_spans > split.zh-en.labelled_spans
-
-./simple-extract.sh ~/workspace/clsp2010/scratch/split.zh-en.labelled_spans
diff --git a/gi/pyp-topics/scripts/score-mkcls.py b/gi/pyp-topics/scripts/score-mkcls.py
deleted file mode 100755
index 6bd33fc5..00000000
--- a/gi/pyp-topics/scripts/score-mkcls.py
+++ /dev/null
@@ -1,61 +0,0 @@
-#!/usr/bin/python
-
-import sys
-from collections import defaultdict
-
-def dict_max(d):
-  max_val=-1
-  max_key=None
-  for k in d:
-    if d[k] > max_val: 
-      max_val = d[k]
-      max_key = k
-  assert max_key
-  return max_key
-
-if len(sys.argv) != 3:
-  print "Usage: score-mkcls.py gold classes"
-  exit(1)
-
-gold_file=open(sys.argv[1],'r')
-
-term_to_topics = {}
-for line in open(sys.argv[2],'r'):
-  term,cls = line.split()
-  term_to_topics[term] = cls
-
-gold_to_topics = defaultdict(dict)
-topics_to_gold = defaultdict(dict)
-
-for gold_line in gold_file:
-  gold_tokens = gold_line.split()
-  for gold_token in gold_tokens:
-    gold_term,gold_tag = gold_token.rsplit('|',1)
-    pred_token = term_to_topics[gold_term]
-    gold_to_topics[gold_tag][pred_token] \
-      = gold_to_topics[gold_tag].get(pred_token, 0) + 1
-    topics_to_gold[pred_token][gold_tag] \
-      = topics_to_gold[pred_token].get(gold_tag, 0) + 1
-
-pred=0
-correct=0
-gold_file=open(sys.argv[1],'r')
-for gold_line in gold_file:
-  gold_tokens = gold_line.split()
-
-  for gold_token in gold_tokens:
-    gold_term,gold_tag = gold_token.rsplit('|',1)
-    pred_token = term_to_topics[gold_term]
-    print "%s|%s|%s" % (gold_token, pred_token, dict_max(topics_to_gold[pred_token])),
-    pred += 1
-    if gold_tag == dict_max(topics_to_gold[pred_token]):
-      correct += 1
-  print
-print >>sys.stderr, "Many-to-One Accuracy = %f" % (float(correct) / pred)
-#for x in gold_to_topics: 
-#  print x,dict_max(gold_to_topics[x])
-#print "###################################################"
-#for x in range(len(topics_to_gold)): 
-#  print x,dict_max(topics_to_gold[str(x)])
-#  print x,topics_to_gold[str(x)]
-#print term_to_topics
diff --git a/gi/pyp-topics/scripts/score-topics.py b/gi/pyp-topics/scripts/score-topics.py
deleted file mode 100755
index 1d8a1fcd..00000000
--- a/gi/pyp-topics/scripts/score-topics.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/usr/bin/python
-
-import sys
-from collections import defaultdict
-
-def dict_max(d):
-  max_val=-1
-  max_key=None
-  for k in d:
-    if d[k] > max_val: 
-      max_val = d[k]
-      max_key = k
-  assert max_key
-  return max_key
-
-if len(sys.argv) != 3:
-  print "Usage: score-topics.py gold pred"
-  exit(1)
-
-gold_file=open(sys.argv[1],'r')
-pred_file=open(sys.argv[2],'r')
-
-gold_to_topics = defaultdict(dict)
-topics_to_gold = defaultdict(dict)
-term_to_topics = defaultdict(dict)
-
-for gold_line,pred_line in zip(gold_file,pred_file):
-  gold_tokens = gold_line.split()
-  pred_tokens = pred_line.split()
-  assert len(gold_tokens) == len(pred_tokens)
-
-  for gold_token,pred_token in zip(gold_tokens,pred_tokens):
-    gold_term,gold_tag = gold_token.rsplit('|',1)
-    gold_to_topics[gold_tag][pred_token] \
-      = gold_to_topics[gold_tag].get(pred_token, 0) + 1
-    term_to_topics[gold_term][pred_token] \
-      = term_to_topics[gold_term].get(pred_token, 0) + 1
-    topics_to_gold[pred_token][gold_tag] \
-      = topics_to_gold[pred_token].get(gold_tag, 0) + 1
-
-pred=0
-correct=0
-gold_file=open(sys.argv[1],'r')
-pred_file=open(sys.argv[2],'r')
-for gold_line,pred_line in zip(gold_file,pred_file):
-  gold_tokens = gold_line.split()
-  pred_tokens = pred_line.split()
-
-  for gold_token,pred_token in zip(gold_tokens,pred_tokens):
-    gold_term,gold_tag = gold_token.rsplit('|',1)
-#   print "%s|%s" % (gold_token, dict_max(gold_to_topics[gold_tag])),
-    print "%s|%s|%s" % (gold_token, pred_token, dict_max(topics_to_gold[pred_token])),
-    pred += 1
-    if gold_tag == dict_max(topics_to_gold[pred_token]):
-      correct += 1
-  print
-print >>sys.stderr, "Many-to-One Accuracy = %f" % (float(correct) / pred)
-#for x in gold_to_topics: 
-#  print x,dict_max(gold_to_topics[x])
-#print "###################################################"
-#for x in range(len(topics_to_gold)): 
-#  print x,dict_max(topics_to_gold[str(x)])
-#  print x,topics_to_gold[str(x)]
-#print term_to_topics
diff --git a/gi/pyp-topics/scripts/spans2labels.py b/gi/pyp-topics/scripts/spans2labels.py
deleted file mode 100755
index 50fa8106..00000000
--- a/gi/pyp-topics/scripts/spans2labels.py
+++ /dev/null
@@ -1,137 +0,0 @@
-#!/usr/bin/python
-
-import sys
-from operator import itemgetter
-
-if len(sys.argv) <= 2:
-  print "Usage: spans2labels.py phrase_context_index [order] [threshold] [languages={s,t,b}{s,t,b}] [type={tag,tok,both},{tag,tok,both}]"
-  exit(1)
-
-order=1
-threshold = 0
-cutoff_cat = "<UNK>"
-if len(sys.argv) > 2:
-  order = int(sys.argv[2])
-if len(sys.argv) > 3:
-  threshold = float(sys.argv[3])
-phr=ctx='t'
-if len(sys.argv) > 4:
-  phr, ctx = sys.argv[4]
-  assert phr in 'stb'
-  assert ctx in 'stb'
-phr_typ = ctx_typ = 'both'
-if len(sys.argv) > 5:
-  phr_typ, ctx_typ = sys.argv[5].split(',')
-  assert phr_typ in ('tag', 'tok', 'both')
-  assert ctx_typ in ('tag', 'tok', 'both')
-
-#print >>sys.stderr, "Loading phrase index"
-phrase_context_index = {}
-for line in file(sys.argv[1], 'r'):
-  phrase,tail= line.split('\t')
-  contexts = tail.split(" ||| ")
-  try: # remove Phil's bizarre integer pair
-       x,y = contexts[0].split()
-       x=int(x); y=int(y)
-       contexts = contexts[1:]
-  except:
-       pass
-  if len(contexts) == 1: continue
-  assert len(contexts) % 2 == 0
-  for i in range(0, len(contexts), 2):
-    #parse contexts[i+1] = " C=1 P=0.8 ... "
-    features=dict([ keyval.split('=') for keyval in contexts[i+1].split()])
-    category = features['C']    
-    if features.has_key('P') and float(features['P']) < threshold:
-	category = cutoff_cat
-    
-    phrase_context_index[(phrase,contexts[i])] = category 
-    #print (phrase,contexts[i]), category
-
-#print >>sys.stderr, "Labelling spans"
-for line in sys.stdin:
-  #print >>sys.stderr, "line", line.strip()
-  line_segments = line.split(' ||| ')
-  assert len(line_segments) >= 3
-  source = ['<s>' for x in range(order)] + line_segments[0].split() + ['</s>' for x in range(order)]
-  target = ['<s>' for x in range(order)] + line_segments[1].split() + ['</s>' for x in range(order)]
-  phrases = [ [int(i) for i in x.split('-')] for x in line_segments[2].split()]
-
-  if phr_typ != 'both' or ctx_typ != 'both':
-    if phr in 'tb' or ctx in 'tb':
-        target_toks = ['<s>' for x in range(order)] + map(lambda x: x.rsplit('_', 1)[0], line_segments[1].split()) + ['</s>' for x in range(order)]
-        target_tags = ['<s>' for x in range(order)] + map(lambda x: x.rsplit('_', 1)[-1], line_segments[1].split()) + ['</s>' for x in range(order)]
-
-        if phr in 'tb':
-            if phr_typ == 'tok':
-                targetP = target_toks
-            elif phr_typ == 'tag':
-                targetP = target_tags
-        if ctx in 'tb':
-            if ctx_typ == 'tok':
-                targetC = target_toks
-            elif ctx_typ == 'tag':
-                targetC = target_tags
-
-    if phr in 'sb' or ctx in 'sb':
-        source_toks = ['<s>' for x in range(order)] + map(lambda x: x.rsplit('_', 1)[0], line_segments[0].split()) + ['</s>' for x in range(order)]
-        source_tags = ['<s>' for x in range(order)] + map(lambda x: x.rsplit('_', 1)[-1], line_segments[0].split()) + ['</s>' for x in range(order)]
-
-        if phr in 'sb':
-            if phr_typ == 'tok':
-                sourceP = source_toks
-            elif phr_typ == 'tag':
-                sourceP = source_tags
-        if ctx in 'sb':
-            if ctx_typ == 'tok':
-                sourceC = source_toks
-            elif ctx_typ == 'tag':
-                sourceC = source_tags
-  else:
-    sourceP = sourceC = source
-    targetP = targetC = target
-
-  #print >>sys.stderr, "line", source, '---', target, 'phrases', phrases
-
-  print "|||",
-
-  for s1,s2,t1,t2 in phrases:
-    s1 += order
-    s2 += order
-    t1 += order
-    t2 += order
-
-    phraset = phrases = contextt = contexts = ''
-    if phr in 'tb':
-        phraset = reduce(lambda x, y: x+y+" ", targetP[t1:t2], "").strip()
-    if phr in 'sb':
-        phrases = reduce(lambda x, y: x+y+" ", sourceP[s1:s2], "").strip()
-
-    if ctx in 'tb':
-        left_context = reduce(lambda x, y: x+y+" ", targetC[t1-order:t1], "")
-        right_context = reduce(lambda x, y: x+y+" ", targetC[t2:t2+order], "").strip()
-        contextt = "%s<PHRASE> %s" % (left_context, right_context)
-    if ctx in 'sb':
-        left_context = reduce(lambda x, y: x+y+" ", sourceC[s1-order:s1], "")
-        right_context = reduce(lambda x, y: x+y+" ", sourceC[s2:s2+order], "").strip()
-        contexts = "%s<PHRASE> %s" % (left_context, right_context)
-
-    if phr == 'b':
-        phrase = phraset + ' <SPLIT> ' + phrases
-    elif phr == 's':
-        phrase = phrases
-    else:
-        phrase = phraset
-
-    if ctx == 'b':
-        context = contextt + ' <SPLIT> ' + contexts
-    elif ctx == 's':
-        context = contexts
-    else:
-        context = contextt
-
-    #print "%d-%d-%d-%d looking up" % (s1-order,s2-order,t1-order,t2-order), (phrase, context)
-    label = phrase_context_index.get((phrase,context), cutoff_cat)
-    if label != cutoff_cat: #cutoff'd spans are left unlabelled
-      print "%d-%d-%d-%d:X%s" % (s1-order,s2-order,t1-order,t2-order,label),
-  print
diff --git a/gi/pyp-topics/scripts/tokens2classes.py b/gi/pyp-topics/scripts/tokens2classes.py
deleted file mode 100755
index 33df255f..00000000
--- a/gi/pyp-topics/scripts/tokens2classes.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/usr/bin/python
-
-import sys
-
-if len(sys.argv) != 3:
-  print "Usage: tokens2classes.py source_classes target_classes"
-  exit(1)
-
-source_to_topics = {}
-for line in open(sys.argv[1],'r'):
-  term,cls = line.split()
-  source_to_topics[term] = cls
-
-target_to_topics = {}
-for line in open(sys.argv[2],'r'):
-  term,cls = line.split()
-  target_to_topics[term] = cls
-
-for line in sys.stdin:
-  source, target, tail = line.split(" ||| ")
-
-  for token in source.split():
-    print source_to_topics[token],
-  print "|||",
-  for token in target.split():
-    print target_to_topics[token],
-  print "|||", tail,
diff --git a/gi/pyp-topics/scripts/topics.py b/gi/pyp-topics/scripts/topics.py
deleted file mode 100755
index 0db1af71..00000000
--- a/gi/pyp-topics/scripts/topics.py
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/usr/bin/python
-
-import sys
-
-if len(sys.argv) != 2:
-  print "Usage: topics.py words-per-topic"
-  exit(1)
-
-for t,line in enumerate(sys.stdin):
-  tokens = line.split()
-  terms = []
-  for token in tokens:
-    elements = token.rsplit(':',1)
-    terms.append((int(elements[1]),elements[0]))
-  terms.sort()
-  terms.reverse()
-
-  print "Topic %d:" % t
-  map(lambda (x,y) : sys.stdout.write("   %s:%s\n" % (y,x)), terms[:int(sys.argv[1])])
-  print
diff --git a/gi/pyp-topics/src/Makefile.am b/gi/pyp-topics/src/Makefile.am
deleted file mode 100644
index d3f95d0b..00000000
--- a/gi/pyp-topics/src/Makefile.am
+++ /dev/null
@@ -1,16 +0,0 @@
-bin_PROGRAMS = pyp-topics-train pyp-contexts-train #mpi-pyp-contexts-train
-
-contexts_lexer.cc: contexts_lexer.l
-	$(LEX) -s -CF -8 -o$@ $<
-
-pyp_topics_train_SOURCES = mt19937ar.c corpus.cc gzstream.cc pyp-topics.cc train.cc contexts_lexer.cc contexts_corpus.cc
-pyp_topics_train_LDADD = $(top_srcdir)/utils/libutils.a -lz
-
-pyp_contexts_train_SOURCES = mt19937ar.c corpus.cc gzstream.cc pyp-topics.cc contexts_lexer.cc contexts_corpus.cc train-contexts.cc
-pyp_contexts_train_LDADD = $(top_srcdir)/utils/libutils.a -lz
-
-#mpi_pyp_contexts_train_SOURCES = mt19937ar.c corpus.cc gzstream.cc mpi-pyp-topics.cc contexts_lexer.cc contexts_corpus.cc mpi-train-contexts.cc
-#mpi_pyp_contexts_train_LDADD = $(top_srcdir)/utils/libutils.a -lz
-
-AM_CPPFLAGS = -W -Wall -Wno-sign-compare -funroll-loops -I../../../utils
-
diff --git a/gi/pyp-topics/src/Makefile.mpi b/gi/pyp-topics/src/Makefile.mpi
deleted file mode 100644
index b7b8a290..00000000
--- a/gi/pyp-topics/src/Makefile.mpi
+++ /dev/null
@@ -1,26 +0,0 @@
-BLD_ARCH=$(shell uname -s)
--include macros.${BLD_ARCH}
-
-local_objs = mt19937ar.o corpus.o gzstream.o mpi-pyp-topics.o contexts_lexer.o contexts_corpus.o mpi-train-contexts.o
-
-all: mpi-pyp-contexts-train
-
--include makefile.depend
-
-#-----------------------#
-# Local stuff
-#-----------------------#
-
-mpi-pyp-contexts-train: mpi-train-contexts.o $(local_objs)
-	$(CXX) -o $@ $^ $(LDFLAGS)
-
-.PHONY: depend echo
-depend:
-#$(CXX) -MM $(CXXFLAGS) *.cc *.c | sed 's/^\(.*\.o:\)/obj\/\1/' > makefile.depend
-	$(CXX) -MM $(CXXFLAGS) *.cc *.c > makefile.depend
-
-clean:
-	rm -f *.o
-
-#clobber: clean
-#	rm makefile.depend ../bin/${ARCH}/*
diff --git a/gi/pyp-topics/src/clock_gettime_stub.c b/gi/pyp-topics/src/clock_gettime_stub.c
deleted file mode 100644
index 4883b7c1..00000000
--- a/gi/pyp-topics/src/clock_gettime_stub.c
+++ /dev/null
@@ -1,141 +0,0 @@
-/*
- * Copyright (c), MM Weiss
- * All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without modification, 
- * are permitted provided that the following conditions are met:
- * 
- *     1. Redistributions of source code must retain the above copyright notice, 
- *     this list of conditions and the following disclaimer.
- *     
- *     2. Redistributions in binary form must reproduce the above copyright notice, 
- *     this list of conditions and the following disclaimer in the documentation 
- *     and/or other materials provided with the distribution.
- *     
- *     3. Neither the name of the MM Weiss nor the names of its contributors 
- *     may be used to endorse or promote products derived from this software without 
- *     specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY 
- * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT 
- * SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 
- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT 
- * OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) 
- * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR 
- * TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, 
- * EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- */
-
-/*
- *  clock_gettime_stub.c
- *  gcc -Wall -c clock_gettime_stub.c
- *  posix realtime functions; MacOS user space glue
- */
- 
-/*  @comment
- *  other possible implementation using intel builtin rdtsc
- *  rdtsc-workaround: http://www.mcs.anl.gov/~kazutomo/rdtsc.html
- *  
- *  we could get the ticks by doing this
- * 
- *  __asm __volatile("mov %%ebx, %%esi\n\t"
- *  		"cpuid\n\t"
- *  		"xchg %%esi, %%ebx\n\t"
- *  		"rdtsc"
- *  		: "=a" (a),
- *  		  "=d" (d)
- * 	);
- 
- *  we could even replace our tricky sched_yield call by assembly code to get a better accurency,
- *  anyway the following C stub will satisfy 99% of apps using posix clock_gettime call, 
- *  moreover, the setter version (clock_settime) could be easly written using mach primitives:
- *  http://www.opensource.apple.com/source/xnu/xnu-${VERSION}/osfmk/man/ (clock_[set|get]_time)
- *  
- *  hackers don't be crackers, don't you use a flush toilet?
- * 
- *
- *  @see draft: ./posix-realtime-stub/posix-realtime-stub.c
- *
- */
- 
-
-#ifdef __APPLE__
-
-#pragma weak clock_gettime
-
-#include <sys/time.h>
-#include <sys/resource.h>
-#include <mach/mach.h>
-#include <mach/clock.h>
-#include <mach/mach_time.h>
-#include <errno.h>
-#include <unistd.h>
-#include <sched.h>
-
-typedef enum {
-	CLOCK_REALTIME,
-	CLOCK_MONOTONIC,
-	CLOCK_PROCESS_CPUTIME_ID,
-	CLOCK_THREAD_CPUTIME_ID
-} clockid_t;
-
-static mach_timebase_info_data_t __clock_gettime_inf;
-
-static int clock_gettime(clockid_t clk_id, struct timespec *tp) {
-	kern_return_t   ret;
-	clock_serv_t    clk;
-	clock_id_t clk_serv_id;
-	mach_timespec_t tm;
-	
-	uint64_t start, end, delta, nano;
-	
-	//task_basic_info_data_t tinfo;
-	//task_thread_times_info_data_t ttinfo;
-	//mach_msg_type_number_t tflag;
-	
-	int retval = -1;
-	switch (clk_id) {
-		case CLOCK_REALTIME:
-		case CLOCK_MONOTONIC:
-			clk_serv_id = clk_id == CLOCK_REALTIME ? CALENDAR_CLOCK : SYSTEM_CLOCK;
-			if (KERN_SUCCESS == (ret = host_get_clock_service(mach_host_self(), clk_serv_id, &clk))) {
-				if (KERN_SUCCESS == (ret = clock_get_time(clk, &tm))) {
-					tp->tv_sec  = tm.tv_sec;
-					tp->tv_nsec = tm.tv_nsec;
-					retval = 0;
-				}
-			}
-			if (KERN_SUCCESS != ret) {
-				errno = EINVAL;
-				retval = -1;
-			}
-		break;
-		case CLOCK_PROCESS_CPUTIME_ID:
-		case CLOCK_THREAD_CPUTIME_ID:
-			start = mach_absolute_time();
-			if (clk_id == CLOCK_PROCESS_CPUTIME_ID) {
-				getpid();
-			} else {
-				sched_yield();
-			}
-			end = mach_absolute_time();
-			delta = end - start;	
-			if (0 == __clock_gettime_inf.denom) {
-				mach_timebase_info(&__clock_gettime_inf);
-			}
-			nano = delta * __clock_gettime_inf.numer / __clock_gettime_inf.denom;
-			tp->tv_sec = nano * 1e-9;  
-			tp->tv_nsec = nano - (tp->tv_sec * 1e9);
-			retval = 0;
-		break;
-		default:
-			errno = EINVAL;
-			retval = -1;
-	}
-	return retval;
-}
-
-#endif // __APPLE__
-
-/* EOF */
diff --git a/gi/pyp-topics/src/contexts_corpus.cc b/gi/pyp-topics/src/contexts_corpus.cc
deleted file mode 100644
index 92b1b34c..00000000
--- a/gi/pyp-topics/src/contexts_corpus.cc
+++ /dev/null
@@ -1,164 +0,0 @@
-#include <sstream>
-#include <iostream>
-#include <set>
-
-#include "contexts_corpus.hh"
-#include "gzstream.hh"
-#include "contexts_lexer.h"
-
-#include <boost/tuple/tuple.hpp>
-
-
-using namespace std;
-
-//////////////////////////////////////////////////
-// ContextsCorpus
-//////////////////////////////////////////////////
-
-bool read_callback_binary_contexts = false;
-
-void read_callback(const ContextsLexer::PhraseContextsType& new_contexts, void* extra) {
-  assert(new_contexts.contexts.size() == new_contexts.counts.size());
-
-  boost::tuple<ContextsCorpus*, BackoffGenerator*, map<string,int>* >* extra_pair
-    = static_cast< boost::tuple<ContextsCorpus*, BackoffGenerator*, map<string,int>* >* >(extra);
-
-  ContextsCorpus* corpus_ptr = extra_pair->get<0>();
-  BackoffGenerator* backoff_gen = extra_pair->get<1>();
-  //map<string,int>* counts = extra_pair->get<2>();
-
-  Document* doc(new Document());
-
-  //cout << "READ: " << new_contexts.phrase << "\t";
-  for (int i=0; i < (int)new_contexts.counts.size(); ++i) {
-    int cache_word_count = corpus_ptr->m_dict.max();
-
-    //string context_str = corpus_ptr->m_dict.toString(new_contexts.contexts[i]);
-    int context_index = new_contexts.counts.at(i).first;
-    string context_str = corpus_ptr->m_dict.toString(new_contexts.contexts[context_index]);
-
-    // filter out singleton contexts
-    //if (!counts->empty()) {
-    //  map<string,int>::const_iterator find_it = counts->find(context_str);
-    //  if (find_it == counts->end() || find_it->second < 2)
-    //    continue;
-    //}
-
-    WordID id = corpus_ptr->m_dict.Convert(context_str);
-    if (cache_word_count != corpus_ptr->m_dict.max()) {
-      corpus_ptr->m_backoff->terms_at_level(0)++;
-      corpus_ptr->m_num_types++;
-    }
-
-    //int count = new_contexts.counts[i];
-    int count = new_contexts.counts.at(i).second;
-    if (read_callback_binary_contexts) {
-      doc->push_back(id);
-      corpus_ptr->m_num_terms++;
-    }
-    else {
-      for (int j=0; j<count; ++j)
-        doc->push_back(id);
-      corpus_ptr->m_num_terms += count;
-    }
-
-    // generate the backoff map
-    if (backoff_gen) {
-      int order = 1;
-      WordID backoff_id = id;
-      //ContextsLexer::Context backedoff_context = new_contexts.contexts[i];
-      ContextsLexer::Context backedoff_context = new_contexts.contexts[context_index];
-      while (true) {
-        if (!corpus_ptr->m_backoff->has_backoff(backoff_id)) {
-          //cerr << "Backing off from " << corpus_ptr->m_dict.Convert(backoff_id) << " to ";
-          backedoff_context = (*backoff_gen)(backedoff_context);
-
-          if (backedoff_context.empty()) {
-            //cerr << "Nothing." << endl;
-            (*corpus_ptr->m_backoff)[backoff_id] = -1;
-            break;
-          }
-
-          if (++order > corpus_ptr->m_backoff->order())
-            corpus_ptr->m_backoff->order(order);
-
-          int cache_word_count = corpus_ptr->m_dict.max();
-          int new_backoff_id = corpus_ptr->m_dict.Convert(backedoff_context);
-          if (cache_word_count != corpus_ptr->m_dict.max())
-            corpus_ptr->m_backoff->terms_at_level(order-1)++;
-
-          //cerr << corpus_ptr->m_dict.Convert(new_backoff_id) << " ." << endl;
-
-          backoff_id = ((*corpus_ptr->m_backoff)[backoff_id] = new_backoff_id);
-        }
-        else break;
-      }
-    }
-    //cout << context_str << " (" << id << ") ||| C=" << count << " ||| ";
-  }
-  //cout << endl;
-
-  //if (!doc->empty()) {
-    corpus_ptr->m_documents.push_back(doc);
-    corpus_ptr->m_keys.push_back(new_contexts.phrase);
-  //}
-}
-
-void filter_callback(const ContextsLexer::PhraseContextsType& new_contexts, void* extra) {
-  assert(new_contexts.contexts.size() == new_contexts.counts.size());
-
-  map<string,int>* context_counts = (static_cast<map<string,int>*>(extra));
-
-  for (int i=0; i < (int)new_contexts.counts.size(); ++i) {
-    int context_index = new_contexts.counts.at(i).first;
-    int count = new_contexts.counts.at(i).second;
-    //if (read_callback_binary_contexts) count = 1;
-    //int count = new_contexts.counts[i];
-    pair<map<string,int>::iterator,bool> result 
-      = context_counts->insert(make_pair(Dict::toString(new_contexts.contexts[context_index]),count));
-      //= context_counts->insert(make_pair(Dict::toString(new_contexts.contexts[i]),count));
-    if (!result.second)
-      result.first->second += count;
-  }
-}
-
-
-unsigned ContextsCorpus::read_contexts(const string &filename, 
-                                       BackoffGenerator* backoff_gen_ptr,
-                                       bool /*filter_singeltons*/,
-                                       bool binary_contexts) {
-  read_callback_binary_contexts = binary_contexts;
-
-  map<string,int> counts;
-  //if (filter_singeltons) 
-  {
-  //  cerr << "--- Filtering singleton contexts ---" << endl;
-
-    igzstream in(filename.c_str());
-    ContextsLexer::ReadContexts(&in, filter_callback, &counts);
-  }
-
-  m_num_terms = 0;
-  m_num_types = 0;
-
-  igzstream in(filename.c_str());
-  boost::tuple<ContextsCorpus*, BackoffGenerator*, map<string,int>* > extra_pair(this,backoff_gen_ptr,&counts);
-  ContextsLexer::ReadContexts(&in, read_callback, &extra_pair);
-
-  //m_num_types = m_dict.max();
-
-  cerr << "Read backoff with order " << m_backoff->order() << "\n";
-  for (int o=0; o<m_backoff->order(); o++)
-    cerr << "  Terms at " << o << " = " << m_backoff->terms_at_level(o) << endl;
-  //cerr << endl;
-
-  int i=0; double av_freq=0;
-  for (map<string,int>::const_iterator it=counts.begin(); it != counts.end(); ++it, ++i) {
-    WordID id = m_dict.Convert(it->first);
-    m_context_counts[id] = it->second;
-    av_freq += it->second;
-  }
-  cerr << "  Average term frequency = " << av_freq / (double) i << endl;
-
-  return m_documents.size();
-}
diff --git a/gi/pyp-topics/src/contexts_corpus.hh b/gi/pyp-topics/src/contexts_corpus.hh
deleted file mode 100644
index 2527f655..00000000
--- a/gi/pyp-topics/src/contexts_corpus.hh
+++ /dev/null
@@ -1,90 +0,0 @@
-#ifndef _CONTEXTS_CORPUS_HH
-#define _CONTEXTS_CORPUS_HH
-
-#include <vector>
-#include <string>
-#include <map>
-#include <tr1/unordered_map>
-
-#include <boost/ptr_container/ptr_vector.hpp>
-
-#include "corpus.hh"
-#include "contexts_lexer.h"
-#include "dict.h"
-
-
-class BackoffGenerator {
-public:
-  virtual ContextsLexer::Context
-    operator()(const ContextsLexer::Context& c) = 0;
-
-protected:
-  ContextsLexer::Context strip_edges(const ContextsLexer::Context& c) {
-    if (c.size() <= 1) return ContextsLexer::Context();
-    assert(c.size() % 2 == 1);
-    return ContextsLexer::Context(c.begin() + 1, c.end() - 1);
-  }
-};
-
-class NullBackoffGenerator : public BackoffGenerator {
-  virtual ContextsLexer::Context
-    operator()(const ContextsLexer::Context&) 
-    { return ContextsLexer::Context(); }
-};
-
-class SimpleBackoffGenerator : public BackoffGenerator {
-  virtual ContextsLexer::Context
-    operator()(const ContextsLexer::Context& c) { 
-      if (c.size() <= 3)
-        return ContextsLexer::Context();
-      return strip_edges(c); 
-    }
-};
-
-
-////////////////////////////////////////////////////////////////
-// ContextsCorpus
-////////////////////////////////////////////////////////////////
-
-class ContextsCorpus : public Corpus {
-  friend void read_callback(const ContextsLexer::PhraseContextsType&, void*);
-
-public:
-    ContextsCorpus() : m_backoff(new TermBackoff) {}
-    virtual ~ContextsCorpus() {}
-
-    virtual unsigned read_contexts(const std::string &filename, 
-                                   BackoffGenerator* backoff_gen=0,
-                                   bool filter_singeltons=false,
-                                   bool binary_contexts=false);
-
-    TermBackoffPtr backoff_index() {
-      return m_backoff;
-    }
-
-    std::vector<std::string> context2string(const WordID& id) const {
-      std::vector<std::string> res;
-      assert (id >= 0);
-      m_dict.AsVector(id, &res);
-      return res;
-    }
-
-    virtual int context_count(const WordID& id) const {
-      return m_context_counts.find(id)->second;
-    }
-
-
-    const std::string& key(const int& i) const {
-      return m_keys.at(i);
-    }
-
-    const Dict& dict() const { return m_dict; }
-
-protected:
-    TermBackoffPtr m_backoff;
-    Dict m_dict;
-    std::vector<std::string> m_keys;
-    std::tr1::unordered_map<int,int> m_context_counts;
-};
-
-#endif // _CONTEXTS_CORPUS_HH
diff --git a/gi/pyp-topics/src/contexts_lexer.h b/gi/pyp-topics/src/contexts_lexer.h
deleted file mode 100644
index 66004990..00000000
--- a/gi/pyp-topics/src/contexts_lexer.h
+++ /dev/null
@@ -1,22 +0,0 @@
-#ifndef _CONTEXTS_LEXER_H_
-#define _CONTEXTS_LEXER_H_ 
-
-#include <iostream>
-#include <vector>
-#include <string>
-
-#include "dict.h" 
-
-struct ContextsLexer {
-  typedef std::vector<std::string> Context;
-  struct PhraseContextsType {
-    std::string          phrase;
-    std::vector<Context> contexts;
-    std::vector< std::pair<int,int> >     counts;
-  };
-
-  typedef void (*ContextsCallback)(const PhraseContextsType& new_contexts, void* extra);
-  static void ReadContexts(std::istream* in, ContextsCallback func, void* extra);
-};
-
-#endif
diff --git a/gi/pyp-topics/src/contexts_lexer.l b/gi/pyp-topics/src/contexts_lexer.l
deleted file mode 100644
index 64cd7ca3..00000000
--- a/gi/pyp-topics/src/contexts_lexer.l
+++ /dev/null
@@ -1,113 +0,0 @@
-%{
-#include "contexts_lexer.h"
-
-#include <string>
-#include <iostream>
-#include <sstream>
-#include <cstring>
-#include <cassert>
-#include <algorithm>
-
-int lex_line = 0;
-std::istream* contextslex_stream = NULL;
-ContextsLexer::ContextsCallback contexts_callback = NULL;
-void* contexts_callback_extra = NULL;
-
-#undef YY_INPUT
-#define YY_INPUT(buf, result, max_size) (result = contextslex_stream->read(buf, max_size).gcount())
-
-#define YY_SKIP_YYWRAP 1
-int num_phrases = 0;
-int yywrap() { return 1; }
-
-#define MAX_TOKEN_SIZE 255
-std::string contextslex_tmp_token(MAX_TOKEN_SIZE, '\0');
-ContextsLexer::PhraseContextsType current_contexts;
-
-#define MAX_CONTEXT_SIZE 255
-//std::string tmp_context[MAX_CONTEXT_SIZE];
-ContextsLexer::Context tmp_context;
-
-
-void contextslex_reset() {
-  current_contexts.phrase.clear();
-  current_contexts.contexts.clear();
-  current_contexts.counts.clear();
-  tmp_context.clear();
-}
-
-%}
-
-INT [\-+]?[0-9]+|inf|[\-+]inf
-
-%x CONTEXT COUNT COUNT_END
-%%
-
-<INITIAL>[^\t]+	{ 
-    contextslex_reset();
-    current_contexts.phrase.assign(yytext, yyleng);
-    BEGIN(CONTEXT);
-  }
-<INITIAL>\t	{ 
-    ; 
-  }
-
-<INITIAL,CONTEXT,COUNT>\n	{
-    std::cerr << "ERROR: contexts_lexer.l: unexpected newline while trying to read phrase|context|count." << std::endl;
-    abort();
-  }
-
-<CONTEXT>\|\|\|	{
-    current_contexts.contexts.push_back(tmp_context);
-    tmp_context.clear();
-		BEGIN(COUNT);
-	}
-<CONTEXT>[^ \t]+	{ 
-		contextslex_tmp_token.assign(yytext, yyleng);
-    tmp_context.push_back(contextslex_tmp_token);
-  }
-<CONTEXT>[ \t]+	{ ; }
-
-<COUNT>[ \t]+	{ ; }
-<COUNT>C={INT} { 
-		current_contexts.counts.push_back(std::make_pair(current_contexts.counts.size(), atoi(yytext+2)));
-    BEGIN(COUNT_END);
-  }
-<COUNT>.	{ 
-    std::cerr << "ERROR: contexts_lexer.l: unexpected content while reading count." << std::endl;
-    abort();
-  }
-
-<COUNT_END>[ \t]+  { ; }
-<COUNT_END>\|\|\|	{
-		BEGIN(CONTEXT);
-  }
-<COUNT_END>\n {
-    //std::cerr << "READ:" << current_contexts.phrase << " with " << current_contexts.contexts.size() 
-    //  << " contexts, and " << current_contexts.counts.size() << " counts." << std::endl;
-    std::sort(current_contexts.counts.rbegin(), current_contexts.counts.rend()); 
-
-		contexts_callback(current_contexts, contexts_callback_extra);
-    current_contexts.phrase.clear();
-    current_contexts.contexts.clear();
-    current_contexts.counts.clear();
-		BEGIN(INITIAL);
-  }
-<COUNT_END>.  { 
-		contextslex_tmp_token.assign(yytext, yyleng);
-    std::cerr << "ERROR: contexts_lexer.l: unexpected content while looking for ||| closing count." << std::endl;
-    abort();
-  }
-
-%%
-
-#include "filelib.h" 
-
-void ContextsLexer::ReadContexts(std::istream* in, ContextsLexer::ContextsCallback func, void* extra) {
-  lex_line = 1;
-  contextslex_stream = in;
-  contexts_callback_extra = extra,
-  contexts_callback = func;
-  yylex();
-}
-
diff --git a/gi/pyp-topics/src/corpus.cc b/gi/pyp-topics/src/corpus.cc
deleted file mode 100644
index f182381f..00000000
--- a/gi/pyp-topics/src/corpus.cc
+++ /dev/null
@@ -1,104 +0,0 @@
-#include <sstream>
-#include <iostream>
-#include <set>
-
-#include "corpus.hh"
-#include "gzstream.hh"
-
-using namespace std;
-
-//////////////////////////////////////////////////
-// Corpus
-//////////////////////////////////////////////////
-
-Corpus::Corpus() : m_num_terms(0), m_num_types(0) {}
-
-unsigned Corpus::read(const std::string &filename) {
-  m_num_terms = 0;
-  m_num_types = 0;
-  std::set<int> seen_types;
-
-  igzstream in(filename.c_str());
-
-  string buf;
-  int token;
-  unsigned doc_count=0;
-  while (getline(in, buf)) {
-    Document* doc(new Document());
-    istringstream ss(buf);
-
-    ss >> token; // the number of unique terms
-
-    char delimeter;
-    int count;
-    while(ss >> token >> delimeter >> count) {
-      for (int i=0; i<count; ++i)
-        doc->push_back(token);
-      m_num_terms += count;
-      seen_types.insert(token);
-    }
-
-    m_documents.push_back(doc);
-    doc_count++;
-  }
-
-  m_num_types = seen_types.size();
-
-  return doc_count;
-}
-
-
-//////////////////////////////////////////////////
-// TestCorpus
-//////////////////////////////////////////////////
-
-TestCorpus::TestCorpus() {}
-
-void TestCorpus::read(const std::string &filename) {
-  igzstream in(filename.c_str());
-
-  string buf;
-  Term term;
-  DocumentId doc;
-  char delimeter;
-  while (getline(in, buf)) {
-    DocumentTerms* line(new DocumentTerms());
-    istringstream ss(buf);
-
-    while(ss >> doc >> delimeter >> term)
-      line->push_back(DocumentTerm(doc, term));
-
-    m_lines.push_back(line);
-  }
-}
-
-//////////////////////////////////////////////////
-// TermBackoff
-//////////////////////////////////////////////////
-
-void TermBackoff::read(const std::string &filename) {
-  igzstream in(filename.c_str());
-
-  string buf;
-  int num_terms;
-  getline(in, buf);
-  istringstream ss(buf); 
-  ss >> num_terms >> m_backoff_order;
-
-  m_dict.resize(num_terms, -1);
-  for (int i=0; i<m_backoff_order; ++i) {
-    int count; ss >> count;
-    m_terms_at_order.push_back(count);
-  }
-
-  Term term, backoff;
-  while (getline(in, buf)) {
-    istringstream ss(buf);
-    ss >> term >> backoff;
-
-    assert(term < num_terms);
-    assert(term >= 0);
-
-    m_dict[term] = backoff;
-  }
-}
diff --git a/gi/pyp-topics/src/corpus.hh b/gi/pyp-topics/src/corpus.hh
deleted file mode 100644
index 2aa03527..00000000
--- a/gi/pyp-topics/src/corpus.hh
+++ /dev/null
@@ -1,133 +0,0 @@
-#ifndef _CORPUS_HH
-#define _CORPUS_HH
-
-#include <vector>
-#include <string>
-#include <map>
-#include <limits>
-
-#include <boost/shared_ptr.hpp>
-#include <boost/ptr_container/ptr_vector.hpp>
-
-////////////////////////////////////////////////////////////////
-// Corpus
-////////////////////////////////////////////////////////////////
-typedef int Term;
-
-typedef std::vector<Term> Document;
-typedef std::vector<Term> Terms;
-
-class Corpus {
-public:
-    typedef boost::ptr_vector<Document>::const_iterator const_iterator;
-
-public:
-    Corpus();
-    virtual ~Corpus() {}
-
-    virtual unsigned read(const std::string &filename);
-
-    const_iterator begin() const { return m_documents.begin(); }
-    const_iterator end() const { return m_documents.end(); }
-
-    const Document& at(size_t i) const { return m_documents.at(i); }
-
-    int num_documents() const { return m_documents.size(); }
-    int num_terms() const { return m_num_terms; }
-    int num_types() const { return m_num_types; }
-
-    virtual int context_count(const int&) const {
-      return std::numeric_limits<int>::max();
-    }
-
-protected:
-    int m_num_terms, m_num_types;
-    boost::ptr_vector<Document> m_documents; 
-};
-
-typedef int DocumentId;
-struct DocumentTerm {
-  DocumentTerm(DocumentId d, Term t) : term(t), doc(d) {}
-  Term term;
-  DocumentId doc;
-};
-typedef std::vector<DocumentTerm> DocumentTerms;
-
-class TestCorpus {
-public:
-    typedef boost::ptr_vector<DocumentTerms>::const_iterator const_iterator;
-
-public:
-    TestCorpus();
-    ~TestCorpus() {}
-
-    void read(const std::string &filename);
-
-    const_iterator begin() const { return m_lines.begin(); }
-    const_iterator end() const { return m_lines.end(); }
-
-    int num_instances() const { return m_lines.size(); }
-
-protected:
-    boost::ptr_vector<DocumentTerms> m_lines; 
-};
-
-class TermBackoff {
-public:
-    typedef std::vector<Term> dictionary_type;
-    typedef dictionary_type::const_iterator const_iterator;
-    const static int NullBackoff=-1;
-
-public:
-    TermBackoff() { order(1); }
-    ~TermBackoff() {}
-
-    void read(const std::string &filename);
-
-    const_iterator begin() const { return m_dict.begin(); }
-    const_iterator end() const { return m_dict.end(); }
-
-    const Term& operator[](const Term& t) const {
-      assert(t < static_cast<int>(m_dict.size()));
-      return m_dict[t];
-    }
-
-    Term& operator[](const Term& t) {
-      if (t >= static_cast<int>(m_dict.size()))
-        m_dict.resize(t+1, -1);
-      return m_dict[t];
-    }
-
-    bool has_backoff(const Term& t) {
-      return t >= 0 && t < static_cast<int>(m_dict.size()) && m_dict[t] >= 0;
-    }
-
-    int order() const { return m_backoff_order; }
-    void order(int o) { 
-      if (o >= (int)m_terms_at_order.size())
-        m_terms_at_order.resize(o, 0);
-      m_backoff_order = o; 
-    }
-
-//    int levels() const { return m_terms_at_order.size(); }
-    bool is_null(const Term& term) const { return term < 0; }
-    int terms_at_level(int level) const { 
-      assert (level < (int)m_terms_at_order.size());
-      return m_terms_at_order.at(level);
-    }
-
-    int& terms_at_level(int level) { 
-      assert (level < (int)m_terms_at_order.size());
-      return m_terms_at_order.at(level);
-    }
-
-    int size() const { return m_dict.size(); }
-
-protected:
-    dictionary_type m_dict; 
-    int m_backoff_order;
-    std::vector<int> m_terms_at_order;
-};
-typedef boost::shared_ptr<TermBackoff> TermBackoffPtr;
-
-#endif // _CORPUS_HH
diff --git a/gi/pyp-topics/src/gammadist.c b/gi/pyp-topics/src/gammadist.c
deleted file mode 100644
index 4e260db8..00000000
--- a/gi/pyp-topics/src/gammadist.c
+++ /dev/null
@@ -1,247 +0,0 @@
-/* gammadist.c -- computes probability of samples under / produces samples from a Gamma distribution
- *
- * Mark Johnson, 22nd March 2008
- *
- * WARNING: you need to set the flag -std=c99 to compile
- *
- * gammavariate() was translated from random.py in Python library
- *
- * The Gamma distribution is:
- *
- *   Gamma(x | alpha, beta) = pow(x/beta, alpha-1) * exp(-x/beta) / (gamma(alpha)*beta)
- *
- * shape parameter alpha > 0 (also called c), scale parameter beta > 0 (also called s); 
- * mean is alpha*beta, variance is alpha*beta**2
- *
- * Note that many parameterizations of the Gamma function are in terms of an _inverse_
- * scale parameter beta, which is the inverse of the beta given here.
- *
- * To define a main() that tests the routines, uncomment the following #define:
- */
-/* #define GAMMATEST */
-
-#include <assert.h>
-#include <math.h> 
-
-#include "gammadist.h"
-#include "mt19937ar.h"
-
-/* gammadist() returns the probability density of x under a Gamma(alpha,beta) 
- * distribution
- */
-
-long double gammadist(long double x, long double alpha, long double beta) {
-  assert(alpha > 0);
-  assert(beta > 0);
-  return  pow(x/beta, alpha-1) * exp(-x/beta) / (tgamma(alpha)*beta);
-}
-
-/* lgammadist() returns the log probability density of x under a Gamma(alpha,beta)
- * distribution
- */
-
-long double lgammadist(long double x, long double alpha, long double beta) {
-  assert(alpha > 0);
-  assert(beta > 0);
-  return (alpha-1)*log(x) - alpha*log(beta) - x/beta - lgamma(alpha);
-}
-
-/* This definition of gammavariate is from Python code in
- * the Python random module.
- */
-
-long double gammavariate(long double alpha, long double beta) {
-
-  assert(alpha > 0);
-  assert(beta > 0);
-
-  if (alpha > 1.0) {
-    
-    /* Uses R.C.H. Cheng, "The generation of Gamma variables with
-       non-integral shape parameters", Applied Statistics, (1977), 26,
-       No. 1, p71-74 */
-
-    long double ainv = sqrt(2.0 * alpha - 1.0);
-    long double bbb = alpha - log(4.0);
-    long double ccc = alpha + ainv;
-    
-    while (1) {
-      long double u1 = mt_genrand_real3();
-      if (u1 > 1e-7  || u1 < 0.9999999) {
-	long double u2 = 1.0 - mt_genrand_real3();
-	long double v = log(u1/(1.0-u1))/ainv;
-	long double x = alpha*exp(v);
-	long double z = u1*u1*u2;
-	long double r = bbb+ccc*v-x;
-	if (r + (1.0+log(4.5)) - 4.5*z >= 0.0 || r >= log(z))
-	  return x * beta;
-      }
-    }
-  }
-  else if (alpha == 1.0) {
-    long double u = mt_genrand_real3();
-    while (u <= 1e-7)
-      u = mt_genrand_real3();
-    return -log(u) * beta;
-  }
-  else { 
-    /* alpha is between 0 and 1 (exclusive) 
-       Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle */
-    
-    while (1) {
-      long double u = mt_genrand_real3();
-      long double b = (exp(1) + alpha)/exp(1);
-      long double p = b*u;
-      long double x = (p <= 1.0) ? pow(p, 1.0/alpha) : -log((b-p)/alpha);
-      long double u1 = mt_genrand_real3();
-      if (! (((p <= 1.0) && (u1 > exp(-x))) ||
-	     ((p > 1.0)  &&  (u1 > pow(x, alpha - 1.0)))))
-	return x * beta;
-    }
-  }
-}
-
-/* betadist() returns the probability density of x under a Beta(alpha,beta)
- * distribution.
- */
-
-long double betadist(long double x, long double alpha, long double beta) {
-  assert(x >= 0);
-  assert(x <= 1);
-  assert(alpha > 0);
-  assert(beta > 0);
-  return pow(x,alpha-1)*pow(1-x,beta-1)*tgamma(alpha+beta)/(tgamma(alpha)*tgamma(beta));
-}
-
-/* lbetadist() returns the log probability density of x under a Beta(alpha,beta)
- * distribution.
- */
-
-long double lbetadist(long double x, long double alpha, long double beta) {
-  assert(x > 0);
-  assert(x < 1);
-  assert(alpha > 0);
-  assert(beta > 0);
-  return (alpha-1)*log(x)+(beta-1)*log(1-x)+lgamma(alpha+beta)-lgamma(alpha)-lgamma(beta);
-}
-
-/* betavariate() generates a sample from a Beta distribution with
- * parameters alpha and beta.
- *
- * 0 < alpha < 1, 0 < beta < 1, mean is alpha/(alpha+beta)
- */
-
-long double betavariate(long double alpha, long double beta) {
-  long double x = gammavariate(alpha, 1);
-  long double y = gammavariate(beta, 1);
-  return x/(x+y);
-}
-
-#ifdef GAMMATEST
-#include <stdio.h>
-
-int main(int argc, char **argv) {
-  int iteration, niterations = 1000;
-
-  for (iteration = 0; iteration < niterations; ++iteration) {
-    long double alpha = 100*mt_genrand_real3();
-    long double gv = gammavariate(alpha, 1);
-    long double pgv = gammadist(gv, alpha, 1);
-    long double pgvl = exp(lgammadist(gv, alpha, 1));
-    fprintf(stderr, "iteration = %d, gammavariate(%lg,1) = %lg, gammadist(%lg,%lg,1) = %lg, exp(lgammadist(%lg,%lg,1) = %lg\n",
-	    iteration, alpha, gv, gv, alpha, pgv, gv, alpha, pgvl);
-  }
-  return 0;
-}
-
-#endif /* GAMMATEST */
-
-
-/* Other routines I tried, but which weren't as good as the ones above */
-
-#if 0
-
-/*! gammavariate() returns samples from a Gamma distribution
- *! where alpha is the shape parameter and beta is the scale 
- *! parameter, using the algorithm described on p. 94 of 
- *! Gentle (1998) Random Number Generation and Monte Carlo Methods, 
- *! Springer.
- */
-
-long double gammavariate(long double alpha) {
-
-  assert(alpha > 0); 
-  
-  if (alpha > 1.0) {
-    while (1) {
-      long double u1 = mt_genrand_real3();
-      long double u2 = mt_genrand_real3();
-      long double v = (alpha - 1/(6*alpha))*u1/(alpha-1)*u2;
-      if (2*(u2-1)/(alpha-1) + v + 1/v <= 2 
-         || 2*log(u2)/(alpha-1) - log(v) + v <= 1)
-	return (alpha-1)*v;
-    }
-  } else if (alpha < 1.0) {  
-    while (1) {
-      long double t = 0.07 + 0.75*sqrt(1-alpha);
-      long double b = alpha + exp(-t)*alpha/t;
-      long double u1 = mt_genrand_real3();
-      long double u2 = mt_genrand_real3();
-      long double v = b*u1;
-      if (v <= 1) {
-	long double x = t*pow(v, 1/alpha);
-	if (u2 <= (2 - x)/(2 + x))
-	  return x;
-	if (u2 <= exp(-x))
-	  return x;
-      }
-      else {
-	long double x = log(t*(b-v)/alpha);
-	long double y = x/t;
-	if (u2*(alpha + y*(1-alpha)) <= 1)
-	  return x;
-	if (u2 <= pow(y,alpha-1))
-	  return x;
-      }
-    }
-  }
-  else  
-    return -log(mt_genrand_real3());
-} 
-
-
-/*! gammavariate() returns a deviate distributed as a gamma
- *! distribution of order alpha, beta, i.e., a waiting time to the alpha'th
- *! event in a Poisson process of unit mean.
- *!
- *! Code from Numerical Recipes
- */
-
-long double nr_gammavariate(long double ia) {
-  int j;
-  long double am,e,s,v1,v2,x,y;
-  assert(ia > 0);
-  if (ia < 10) { 
-    x=1.0; 
-    for (j=1;j<=ia;j++) 
-      x *= mt_genrand_real3();
-    x = -log(x);
-  } else { 
-    do {
-      do {
-	do { 
-	  v1=mt_genrand_real3();
-	  v2=2.0*mt_genrand_real3()-1.0;
-	} while (v1*v1+v2*v2 > 1.0); 
-	y=v2/v1;
-	am=ia-1;
-	s=sqrt(2.0*am+1.0);
-	x=s*y+am;
-      } while (x <= 0.0);
-      e=(1.0+y*y)*exp(am*log(x/am)-s*y);
-    } while (mt_genrand_real3() > e);
-  }
-  return x;
-} 
-
-#endif
diff --git a/gi/pyp-topics/src/gammadist.h b/gi/pyp-topics/src/gammadist.h
deleted file mode 100644
index b6ad6c40..00000000
--- a/gi/pyp-topics/src/gammadist.h
+++ /dev/null
@@ -1,72 +0,0 @@
-/* gammadist.h -- computes probability of samples under / produces samples from a Gamma distribution
- *
- * Mark Johnson, 22nd March 2008
- *
- * gammavariate() was translated from random.py in Python library
- *
- * The Gamma distribution is:
- *
- *   Gamma(x | alpha, beta) = pow(x/beta, alpha-1) * exp(-x/beta) / (gamma(alpha)*beta)
- *
- * shape parameter alpha > 0 (also called c), scale parameter beta > 0 (also called s); 
- * mean is alpha*beta, variance is alpha*beta**2
- *
- * Note that many parameterizations of the Gamma function are in terms of an _inverse_
- * scale parameter beta, which is the inverse of the beta given here.
- */
-
-#ifndef GAMMADIST_H
-#define GAMMADIST_H
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-  
-  /* gammadist() returns the probability density of x under a Gamma(alpha,beta) 
-   * distribution
-   */
-
-  long double gammadist(long double x, long double alpha, long double beta);
-
-  /* lgammadist() returns the log probability density of x under a Gamma(alpha,beta)
-   * distribution
-   */
-
-  long double lgammadist(long double x, long double alpha, long double beta);
-
-  /* gammavariate() generates samples from a Gamma distribution
-   * conditioned on the parameters alpha and beta.
-   * 
-   * alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2
-   *
-   * Warning: a few older sources define the gamma distribution in terms
-   * of alpha > -1.0
-   */
-
-  long double gammavariate(long double alpha, long double beta);
-
-  /* betadist() returns the probability density of x under a Beta(alpha,beta)
-   * distribution.
-   */
-
-  long double betadist(long double x, long double alpha, long double beta);
-
-  /* lbetadist() returns the log probability density of x under a Beta(alpha,beta)
-   * distribution.
-   */
-  
-  long double lbetadist(long double x, long double alpha, long double beta);
-
-  /* betavariate() generates a sample from a Beta distribution with
-   * parameters alpha and beta.
-   *
-   * 0 < alpha < 1, 0 < beta < 1, mean is alpha/(alpha+beta)
-   */
-
-  long double betavariate(long double alpha, long double beta);
-
-#ifdef __cplusplus
-};
-#endif
-
-#endif /* GAMMADIST_H */
diff --git a/gi/pyp-topics/src/gzstream.cc b/gi/pyp-topics/src/gzstream.cc
deleted file mode 100644
index 7c4d3a12..00000000
--- a/gi/pyp-topics/src/gzstream.cc
+++ /dev/null
@@ -1,165 +0,0 @@
-// ============================================================================
-// gzstream, C++ iostream classes wrapping the zlib compression library.
-// Copyright (C) 2001  Deepak Bandyopadhyay, Lutz Kettner
-//
-// This library is free software; you can redistribute it and/or
-// modify it under the terms of the GNU Lesser General Public
-// License as published by the Free Software Foundation; either
-// version 2.1 of the License, or (at your option) any later version.
-//
-// This library is distributed in the hope that it will be useful,
-// but WITHOUT ANY WARRANTY; without even the implied warranty of
-// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-// Lesser General Public License for more details.
-//
-// You should have received a copy of the GNU Lesser General Public
-// License along with this library; if not, write to the Free Software
-// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
-// ============================================================================
-//
-// File          : gzstream.C
-// Revision      : $Revision: 1.1 $
-// Revision_date : $Date: 2006/03/30 04:05:52 $
-// Author(s)     : Deepak Bandyopadhyay, Lutz Kettner
-// 
-// Standard streambuf implementation following Nicolai Josuttis, "The 
-// Standard C++ Library".
-// ============================================================================
-
-#include "gzstream.hh"
-#include <iostream>
-#include <string.h>  // for memcpy
-
-#ifdef GZSTREAM_NAMESPACE
-namespace GZSTREAM_NAMESPACE {
-#endif
-
-// ----------------------------------------------------------------------------
-// Internal classes to implement gzstream. See header file for user classes.
-// ----------------------------------------------------------------------------
-
-// --------------------------------------
-// class gzstreambuf:
-// --------------------------------------
-
-gzstreambuf* gzstreambuf::open( const char* name, int open_mode) {
-    if ( is_open())
-        return (gzstreambuf*)0;
-    mode = open_mode;
-    // no append nor read/write mode
-    if ((mode & std::ios::ate) || (mode & std::ios::app)
-        || ((mode & std::ios::in) && (mode & std::ios::out)))
-        return (gzstreambuf*)0;
-    char  fmode[10];
-    char* fmodeptr = fmode;
-    if ( mode & std::ios::in)
-        *fmodeptr++ = 'r';
-    else if ( mode & std::ios::out)
-        *fmodeptr++ = 'w';
-    *fmodeptr++ = 'b';
-    *fmodeptr = '\0';
-    file = gzopen( name, fmode);
-    if (file == 0)
-        return (gzstreambuf*)0;
-    opened = 1;
-    return this;
-}
-
-gzstreambuf * gzstreambuf::close() {
-    if ( is_open()) {
-        sync();
-        opened = 0;
-        if ( gzclose( file) == Z_OK)
-            return this;
-    }
-    return (gzstreambuf*)0;
-}
-
-int gzstreambuf::underflow() { // used for input buffer only
-    if ( gptr() && ( gptr() < egptr()))
-        return * reinterpret_cast<unsigned char *>( gptr());
-
-    if ( ! (mode & std::ios::in) || ! opened)
-        return EOF;
-    // Josuttis' implementation of inbuf
-    int n_putback = gptr() - eback();
-    if ( n_putback > 4)
-        n_putback = 4;
-    memcpy( buffer + (4 - n_putback), gptr() - n_putback, n_putback);
-
-    int num = gzread( file, buffer+4, bufferSize-4);
-    if (num <= 0) // ERROR or EOF
-        return EOF;
-
-    // reset buffer pointers
-    setg( buffer + (4 - n_putback),   // beginning of putback area
-          buffer + 4,                 // read position
-          buffer + 4 + num);          // end of buffer
-
-    // return next character
-    return * reinterpret_cast<unsigned char *>( gptr());    
-}
-
-int gzstreambuf::flush_buffer() {
-    // Separate the writing of the buffer from overflow() and
-    // sync() operation.
-    int w = pptr() - pbase();
-    if ( gzwrite( file, pbase(), w) != w)
-        return EOF;
-    pbump( -w);
-    return w;
-}
-
-int gzstreambuf::overflow( int c) { // used for output buffer only
-    if ( ! ( mode & std::ios::out) || ! opened)
-        return EOF;
-    if (c != EOF) {
-        *pptr() = c;
-        pbump(1);
-    }
-    if ( flush_buffer() == EOF)
-        return EOF;
-    return c;
-}
-
-int gzstreambuf::sync() {
-    // Changed to use flush_buffer() instead of overflow( EOF)
-    // which caused improper behavior with std::endl and flush(),
-    // bug reported by Vincent Ricard.
-    if ( pptr() && pptr() > pbase()) {
-        if ( flush_buffer() == EOF)
-            return -1;
-    }
-    return 0;
-}
-
-// --------------------------------------
-// class gzstreambase:
-// --------------------------------------
-
-gzstreambase::gzstreambase( const char* name, int mode) {
-    init( &buf);
-    open( name, mode);
-}
-
-gzstreambase::~gzstreambase() {
-    buf.close();
-}
-
-void gzstreambase::open( const char* name, int open_mode) {
-    if ( ! buf.open( name, open_mode))
-        clear( rdstate() | std::ios::badbit);
-}
-
-void gzstreambase::close() {
-    if ( buf.is_open())
-        if ( ! buf.close())
-            clear( rdstate() | std::ios::badbit);
-}
-
-#ifdef GZSTREAM_NAMESPACE
-} // namespace GZSTREAM_NAMESPACE
-#endif
-
-// ============================================================================
-// EOF //
diff --git a/gi/pyp-topics/src/gzstream.hh b/gi/pyp-topics/src/gzstream.hh
deleted file mode 100644
index ad9785fd..00000000
--- a/gi/pyp-topics/src/gzstream.hh
+++ /dev/null
@@ -1,121 +0,0 @@
-// ============================================================================
-// gzstream, C++ iostream classes wrapping the zlib compression library.
-// Copyright (C) 2001  Deepak Bandyopadhyay, Lutz Kettner
-//
-// This library is free software; you can redistribute it and/or
-// modify it under the terms of the GNU Lesser General Public
-// License as published by the Free Software Foundation; either
-// version 2.1 of the License, or (at your option) any later version.
-//
-// This library is distributed in the hope that it will be useful,
-// but WITHOUT ANY WARRANTY; without even the implied warranty of
-// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-// Lesser General Public License for more details.
-//
-// You should have received a copy of the GNU Lesser General Public
-// License along with this library; if not, write to the Free Software
-// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
-// ============================================================================
-//
-// File          : gzstream.h
-// Revision      : $Revision: 1.1 $
-// Revision_date : $Date: 2006/03/30 04:05:52 $
-// Author(s)     : Deepak Bandyopadhyay, Lutz Kettner
-// 
-// Standard streambuf implementation following Nicolai Josuttis, "The 
-// Standard C++ Library".
-// ============================================================================
-
-#ifndef GZSTREAM_H
-#define GZSTREAM_H 1
-
-// standard C++ with new header file names and std:: namespace
-#include <iostream>
-#include <fstream>
-#include <zlib.h>
-
-#ifdef GZSTREAM_NAMESPACE
-namespace GZSTREAM_NAMESPACE {
-#endif
-
-// ----------------------------------------------------------------------------
-// Internal classes to implement gzstream. See below for user classes.
-// ----------------------------------------------------------------------------
-
-class gzstreambuf : public std::streambuf {
-private:
-    static const int bufferSize = 47+256;    // size of data buff
-    // totals 512 bytes under g++ for igzstream at the end.
-
-    gzFile           file;               // file handle for compressed file
-    char             buffer[bufferSize]; // data buffer
-    char             opened;             // open/close state of stream
-    int              mode;               // I/O mode
-
-    int flush_buffer();
-public:
-    gzstreambuf() : opened(0) {
-        setp( buffer, buffer + (bufferSize-1));
-        setg( buffer + 4,     // beginning of putback area
-              buffer + 4,     // read position
-              buffer + 4);    // end position      
-        // ASSERT: both input & output capabilities will not be used together
-    }
-    int is_open() { return opened; }
-    gzstreambuf* open( const char* name, int open_mode);
-    gzstreambuf* close();
-    ~gzstreambuf() { close(); }
-    
-    virtual int     overflow( int c = EOF);
-    virtual int     underflow();
-    virtual int     sync();
-};
-
-class gzstreambase : virtual public std::ios {
-protected:
-    gzstreambuf buf;
-public:
-    gzstreambase() { init(&buf); }
-    gzstreambase( const char* name, int open_mode);
-    ~gzstreambase();
-    void open( const char* name, int open_mode);
-    void close();
-    gzstreambuf* rdbuf() { return &buf; }
-};
-
-// ----------------------------------------------------------------------------
-// User classes. Use igzstream and ogzstream analogously to ifstream and
-// ofstream respectively. They read and write files based on the gz* 
-// function interface of the zlib. Files are compatible with gzip compression.
-// ----------------------------------------------------------------------------
-
-class igzstream : public gzstreambase, public std::istream {
-public:
-    igzstream() : std::istream( &buf) {} 
-    igzstream( const char* name, int open_mode = std::ios::in)
-        : gzstreambase( name, open_mode), std::istream( &buf) {}  
-    gzstreambuf* rdbuf() { return gzstreambase::rdbuf(); }
-    void open( const char* name, int open_mode = std::ios::in) {
-        gzstreambase::open( name, open_mode);
-    }
-};
-
-class ogzstream : public gzstreambase, public std::ostream {
-public:
-    ogzstream() : std::ostream( &buf) {}
-    ogzstream( const char* name, int mode = std::ios::out)
-        : gzstreambase( name, mode), std::ostream( &buf) {}  
-    gzstreambuf* rdbuf() { return gzstreambase::rdbuf(); }
-    void open( const char* name, int open_mode = std::ios::out) {
-        gzstreambase::open( name, open_mode);
-    }
-};
-
-#ifdef GZSTREAM_NAMESPACE
-} // namespace GZSTREAM_NAMESPACE
-#endif
-
-#endif // GZSTREAM_H
-// ============================================================================
-// EOF //
-
diff --git a/gi/pyp-topics/src/log_add.h b/gi/pyp-topics/src/log_add.h
deleted file mode 100644
index e0620c5a..00000000
--- a/gi/pyp-topics/src/log_add.h
+++ /dev/null
@@ -1,30 +0,0 @@
-#ifndef log_add_hh
-#define log_add_hh
-
-#include <limits>
-#include <iostream>
-#include <cassert>
-#include <cmath>
-
-template <typename T>
-struct Log
-{
-    static T zero() { return -std::numeric_limits<T>::infinity(); } 
-
-    static T add(T l1, T l2)
-    {
-        if (l1 == zero()) return l2;
-        if (l1 > l2) 
-            return l1 + std::log(1 + exp(l2 - l1));
-        else
-            return l2 + std::log(1 + exp(l1 - l2));
-    }
-
-    static T subtract(T l1, T l2)
-    {
-        //std::assert(l1 >= l2);
-        return l1 + log(1 - exp(l2 - l1));
-    }
-};
-
-#endif
diff --git a/gi/pyp-topics/src/macros.Linux b/gi/pyp-topics/src/macros.Linux
deleted file mode 100644
index 7c6e7fa7..00000000
--- a/gi/pyp-topics/src/macros.Linux
+++ /dev/null
@@ -1,18 +0,0 @@
-CC = /home/pblunsom/software/bin/mpicc
-CXX = /home/pblunsom/software/bin/mpicxx
-LD = /home/pblunsom/software/bin/mpicxx
-FC = /home/pblunsom/software/bin/mpif77
-
-SOFTWARE_DIR=/export/ws10smt/software
-
-CXXFLAGS = -Wall -I${SOFTWARE_DIR}/include
-CFLAGS = -Wall -I${SOFTWARE_DIR}/include
-FFLAGS = -Wall
-LDFLAGS = -lm -lz -L${SOFTWARE_DIR}/lib \
-	-lboost_program_options -lboost_mpi -lboost_serialization \
-  -lboost_regex -L../../../decoder -lcdec
-
-FFLAGS += -g -O6  -march=native
-CFLAGS += -g -O6  -march=native
-CXXFLAGS += -g -O6  -march=native
-LDFLAGS += -g -O6  -march=native
diff --git a/gi/pyp-topics/src/makefile.darwin b/gi/pyp-topics/src/makefile.darwin
deleted file mode 100644
index af608fd8..00000000
--- a/gi/pyp-topics/src/makefile.darwin
+++ /dev/null
@@ -1,15 +0,0 @@
-CC = /usr/bin/gcc
-CXX = /usr/bin/g++
-LD = /usr/bin/g++
-FC=/usr/bin/g77
-
-ARCH=i686-m64
-CXXFLAGS = -m64 -Wall -I/Users/pblunsom/packages/include 
-CFLAGS = -m64 -Wall -I/Users/pblunsom/packages/include 
-FFLAGS = -m64 -Wall
-LDFLAGS = -L/Users/pblunsom/packages/lib -lboost_program_options -lm -lz
-
-FFLAGS += -g -O3 -funroll-loops #-pg
-CFLAGS += -g -O3 -funroll-loops #-pg
-CXXFLAGS += -g -O3 -funroll-loops #-pg
-LDFLAGS += -g -O3 -funroll-loops #-pg
diff --git a/gi/pyp-topics/src/makefile.depend b/gi/pyp-topics/src/makefile.depend
deleted file mode 100644
index 9b8e306c..00000000
--- a/gi/pyp-topics/src/makefile.depend
+++ /dev/null
@@ -1,4042 +0,0 @@
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- /home/pblunsom/packages/include/boost/preprocessor/seq/push_front.hpp \
- /home/pblunsom/packages/include/boost/preprocessor/seq/remove.hpp \
- /home/pblunsom/packages/include/boost/preprocessor/seq/replace.hpp \
- /home/pblunsom/packages/include/boost/preprocessor/seq/subseq.hpp \
- /home/pblunsom/packages/include/boost/preprocessor/seq/to_array.hpp \
- /home/pblunsom/packages/include/boost/preprocessor/seq/to_tuple.hpp \
- /home/pblunsom/packages/include/boost/preprocessor/slot.hpp \
- /home/pblunsom/packages/include/boost/preprocessor/tuple.hpp \
- /home/pblunsom/packages/include/boost/preprocessor/tuple/to_seq.hpp \
- /home/pblunsom/packages/include/boost/utility/detail/result_of_iterate.hpp \
- /home/pblunsom/packages/include/boost/pointee.hpp \
- /home/pblunsom/packages/include/boost/detail/is_incrementable.hpp \
- /home/pblunsom/packages/include/boost/ptr_container/detail/void_ptr_iterator.hpp \
- /home/pblunsom/packages/include/boost/random/uniform_real.hpp \
- /home/pblunsom/packages/include/boost/random/detail/config.hpp \
- /home/pblunsom/packages/include/boost/random/variate_generator.hpp \
- /home/pblunsom/packages/include/boost/random/uniform_01.hpp \
- /home/pblunsom/packages/include/boost/random/detail/pass_through_engine.hpp \
- /home/pblunsom/packages/include/boost/random/detail/ptr_helper.hpp \
- /home/pblunsom/packages/include/boost/random/detail/disable_warnings.hpp \
- /home/pblunsom/packages/include/boost/random/detail/enable_warnings.hpp \
- /home/pblunsom/packages/include/boost/random/detail/uniform_int_float.hpp \
- /home/pblunsom/packages/include/boost/random/mersenne_twister.hpp \
- /home/pblunsom/packages/include/boost/random/linear_congruential.hpp \
- /home/pblunsom/packages/include/boost/random/detail/const_mod.hpp \
- /home/pblunsom/packages/include/boost/random/detail/seed.hpp pyp.hh \
- slice-sampler.h log_add.h mt19937ar.h corpus.hh workers.hh \
- /home/pblunsom/packages/include/boost/bind.hpp \
- /home/pblunsom/packages/include/boost/bind/bind.hpp \
- /home/pblunsom/packages/include/boost/is_placeholder.hpp \
- /home/pblunsom/packages/include/boost/bind/arg.hpp \
- /home/pblunsom/packages/include/boost/visit_each.hpp \
- /home/pblunsom/packages/include/boost/bind/storage.hpp \
- /home/pblunsom/packages/include/boost/bind/bind_template.hpp \
- /home/pblunsom/packages/include/boost/bind/bind_cc.hpp \
- /home/pblunsom/packages/include/boost/bind/bind_mf_cc.hpp \
- /home/pblunsom/packages/include/boost/bind/bind_mf2_cc.hpp \
- /home/pblunsom/packages/include/boost/bind/placeholders.hpp \
- /home/pblunsom/packages/include/boost/thread/thread.hpp \
- /home/pblunsom/packages/include/boost/thread/detail/platform.hpp \
- /home/pblunsom/packages/include/boost/config/requires_threads.hpp \
- /home/pblunsom/packages/include/boost/thread/pthread/thread_data.hpp \
- /home/pblunsom/packages/include/boost/thread/detail/config.hpp \
- /home/pblunsom/packages/include/boost/thread/detail/platform.hpp \
- /home/pblunsom/packages/include/boost/thread/exceptions.hpp \
- /home/pblunsom/packages/include/boost/config/abi_prefix.hpp \
- /home/pblunsom/packages/include/boost/config/abi_suffix.hpp \
- /home/pblunsom/packages/include/boost/enable_shared_from_this.hpp \
- /home/pblunsom/packages/include/boost/smart_ptr/enable_shared_from_this.hpp \
- /home/pblunsom/packages/include/boost/smart_ptr/weak_ptr.hpp \
- /home/pblunsom/packages/include/boost/thread/mutex.hpp \
- /home/pblunsom/packages/include/boost/thread/pthread/mutex.hpp \
- /home/pblunsom/packages/include/boost/thread/locks.hpp \
- /home/pblunsom/packages/include/boost/thread/detail/move.hpp \
- /home/pblunsom/packages/include/boost/thread/thread_time.hpp \
- /home/pblunsom/packages/include/boost/date_time/microsec_time_clock.hpp \
- /home/pblunsom/packages/include/boost/date_time/compiler_config.hpp \
- /home/pblunsom/packages/include/boost/date_time/locale_config.hpp \
- /home/pblunsom/packages/include/boost/date_time/c_time.hpp \
- /home/pblunsom/packages/include/boost/date_time/time_clock.hpp \
- /home/pblunsom/packages/include/boost/date_time/filetime_functions.hpp \
- /home/pblunsom/packages/include/boost/date_time/posix_time/posix_time_types.hpp \
- /home/pblunsom/packages/include/boost/date_time/posix_time/ptime.hpp \
- /home/pblunsom/packages/include/boost/date_time/posix_time/posix_time_system.hpp \
- /home/pblunsom/packages/include/boost/date_time/posix_time/posix_time_config.hpp \
- /home/pblunsom/packages/include/boost/config/no_tr1/cmath.hpp \
- /home/pblunsom/packages/include/boost/date_time/time_duration.hpp \
- /home/pblunsom/packages/include/boost/operators.hpp \
- /home/pblunsom/packages/include/boost/date_time/time_defs.hpp \
- /home/pblunsom/packages/include/boost/date_time/special_defs.hpp \
- /home/pblunsom/packages/include/boost/date_time/time_resolution_traits.hpp \
- /home/pblunsom/packages/include/boost/date_time/int_adapter.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/gregorian_types.hpp \
- /home/pblunsom/packages/include/boost/date_time/date.hpp \
- /home/pblunsom/packages/include/boost/date_time/year_month_day.hpp \
- /home/pblunsom/packages/include/boost/date_time/period.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/greg_calendar.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/greg_weekday.hpp \
- /home/pblunsom/packages/include/boost/date_time/constrained_value.hpp \
- /home/pblunsom/packages/include/boost/type_traits/is_base_of.hpp \
- /home/pblunsom/packages/include/boost/type_traits/is_base_and_derived.hpp \
- /home/pblunsom/packages/include/boost/date_time/date_defs.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/greg_day_of_year.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian_calendar.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian_calendar.ipp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/greg_ymd.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/greg_day.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/greg_year.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/greg_month.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/greg_duration.hpp \
- /home/pblunsom/packages/include/boost/date_time/date_duration.hpp \
- /home/pblunsom/packages/include/boost/date_time/date_duration_types.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/greg_duration_types.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/greg_date.hpp \
- /home/pblunsom/packages/include/boost/date_time/adjust_functors.hpp \
- /home/pblunsom/packages/include/boost/date_time/wrapping_int.hpp \
- /home/pblunsom/packages/include/boost/date_time/date_generators.hpp \
- /home/pblunsom/packages/include/boost/date_time/date_clock_device.hpp \
- /home/pblunsom/packages/include/boost/date_time/date_iterator.hpp \
- /home/pblunsom/packages/include/boost/date_time/time_system_split.hpp \
- /home/pblunsom/packages/include/boost/date_time/time_system_counted.hpp \
- /home/pblunsom/packages/include/boost/date_time/time.hpp \
- /home/pblunsom/packages/include/boost/date_time/posix_time/date_duration_operators.hpp \
- /home/pblunsom/packages/include/boost/date_time/posix_time/posix_time_duration.hpp \
- /home/pblunsom/packages/include/boost/date_time/posix_time/time_period.hpp \
- /home/pblunsom/packages/include/boost/date_time/time_iterator.hpp \
- /home/pblunsom/packages/include/boost/date_time/dst_rules.hpp \
- /home/pblunsom/packages/include/boost/thread/xtime.hpp \
- /home/pblunsom/packages/include/boost/date_time/posix_time/conversion.hpp \
- /home/pblunsom/packages/include/boost/date_time/gregorian/conversion.hpp \
- /home/pblunsom/packages/include/boost/thread/pthread/timespec.hpp \
- /home/pblunsom/packages/include/boost/thread/pthread/pthread_mutex_scoped_lock.hpp \
- /home/pblunsom/packages/include/boost/optional.hpp \
- /home/pblunsom/packages/include/boost/optional/optional.hpp \
- /home/pblunsom/packages/include/boost/type_traits/type_with_alignment.hpp \
- /home/pblunsom/packages/include/boost/detail/reference_content.hpp \
- /home/pblunsom/packages/include/boost/type_traits/has_nothrow_copy.hpp \
- /home/pblunsom/packages/include/boost/none.hpp \
- /home/pblunsom/packages/include/boost/none_t.hpp \
- /home/pblunsom/packages/include/boost/utility/compare_pointees.hpp \
- /home/pblunsom/packages/include/boost/optional/optional_fwd.hpp \
- /home/pblunsom/packages/include/boost/thread/pthread/condition_variable_fwd.hpp \
- /home/pblunsom/packages/include/boost/thread/detail/thread.hpp \
- /home/pblunsom/packages/include/boost/thread/detail/thread_heap_alloc.hpp \
- /home/pblunsom/packages/include/boost/thread/pthread/thread_heap_alloc.hpp \
- /home/pblunsom/packages/include/boost/thread/detail/thread_interruption.hpp \
- /home/pblunsom/packages/include/boost/thread/detail/thread_group.hpp \
- /home/pblunsom/packages/include/boost/thread/shared_mutex.hpp \
- /home/pblunsom/packages/include/boost/thread/pthread/shared_mutex.hpp \
- /home/pblunsom/packages/include/boost/thread/condition_variable.hpp \
- /home/pblunsom/packages/include/boost/thread/pthread/condition_variable.hpp \
- /home/pblunsom/packages/include/boost/thread/pthread/thread_data.hpp \
- /home/pblunsom/packages/include/boost/thread/future.hpp \
- /home/pblunsom/packages/include/boost/exception_ptr.hpp \
- /home/pblunsom/packages/include/boost/exception/detail/exception_ptr.hpp \
- /home/pblunsom/packages/include/boost/type_traits/is_fundamental.hpp \
- /home/pblunsom/packages/include/boost/thread/condition.hpp timing.h \
- clock_gettime_stub.c contexts_corpus.hh contexts_lexer.h \
- ../../../decoder/dict.h \
- /home/pblunsom/packages/include/boost/functional/hash.hpp \
- /home/pblunsom/packages/include/boost/functional/hash/hash.hpp \
- /home/pblunsom/packages/include/boost/functional/hash/hash_fwd.hpp \
- /home/pblunsom/packages/include/boost/functional/hash/detail/hash_float.hpp \
- /home/pblunsom/packages/include/boost/functional/hash/detail/float_functions.hpp \
- /home/pblunsom/packages/include/boost/functional/hash/detail/limits.hpp \
- /home/pblunsom/packages/include/boost/integer/static_log2.hpp \
- /home/pblunsom/packages/include/boost/functional/hash/detail/hash_float_generic.hpp \
- /home/pblunsom/packages/include/boost/functional/hash/extensions.hpp \
- /home/pblunsom/packages/include/boost/detail/container_fwd.hpp \
- ../../../decoder/wordid.h gzstream.hh
-clock_gettime_stub.o: clock_gettime_stub.c
-gammadist.o: gammadist.c gammadist.h mt19937ar.h
-mt19937ar.o: mt19937ar.c mt19937ar.h
diff --git a/gi/pyp-topics/src/mpi-corpus.hh b/gi/pyp-topics/src/mpi-corpus.hh
deleted file mode 100644
index f5c478a9..00000000
--- a/gi/pyp-topics/src/mpi-corpus.hh
+++ /dev/null
@@ -1,69 +0,0 @@
-#ifndef _MPI_CORPUS_HH
-#define _MPI_CORPUS_HH
-
-#include <vector>
-#include <string>
-#include <map>
-#include <tr1/unordered_map>
-
-#include <boost/ptr_container/ptr_vector.hpp>
-#include <boost/mpi/environment.hpp>
-#include <boost/mpi/communicator.hpp>
-
-#include "contexts_corpus.hh"
-
-
-////////////////////////////////////////////////////////////////
-// MPICorpus
-////////////////////////////////////////////////////////////////
-
-class MPICorpus : public ContextsCorpus {
-public:
-  MPICorpus() : ContextsCorpus() {
-    boost::mpi::communicator world;
-    m_rank = world.rank();
-    m_size = world.size();
-    m_start = -1;
-    m_end = -1;
-  }
-  virtual ~MPICorpus() {}
-
-  virtual unsigned read_contexts(const std::string &filename, 
-                                 BackoffGenerator* backoff_gen=0,
-                                 bool filter_singeltons=false,
-                                 bool binary_contexts=false) {
-    unsigned result = ContextsCorpus::read_contexts(filename, backoff_gen, filter_singeltons, binary_contexts);
-
-    if (m_rank == 0) std::cerr << "\tLoad balancing terms per mpi segment:" << std::endl;
-    float segment_size = num_terms() / m_size;
-    float term_threshold = segment_size;
-    int seen_terms = 0;
-    std::vector<int> end_points;
-    for (int i=0; i < num_documents(); ++i) {
-      seen_terms += m_documents.at(i).size();
-      if (seen_terms >= term_threshold) {
-        end_points.push_back(i+1);
-        term_threshold += segment_size;
-        if (m_rank == 0) std::cerr << "\t\t" << i+1 << ": " <<  seen_terms << " terms, " << 100*seen_terms / (float)num_terms() << "%" << std::endl;
-      }
-    }
-    m_start = (m_rank == 0 ? 0 : end_points.at(m_rank-1));
-    m_end = (m_rank == m_size-1 ? num_documents() : end_points.at(m_rank));
-
-    return result;
-  }
-
-  void
-  bounds(int* start, int* end) const {
-    *start = m_start;
-    *end = m_end;
-  }
-
-
-
-protected:
-  int m_rank, m_size;
-  int m_start, m_end;
-};
-
-#endif // _MPI_CORPUS_HH
diff --git a/gi/pyp-topics/src/mpi-pyp-topics.cc b/gi/pyp-topics/src/mpi-pyp-topics.cc
deleted file mode 100644
index d6e22af6..00000000
--- a/gi/pyp-topics/src/mpi-pyp-topics.cc
+++ /dev/null
@@ -1,466 +0,0 @@
-#include <boost/mpi/communicator.hpp>
-
-#include "timing.h"
-#include "mpi-pyp-topics.hh"
-
-//#include <boost/date_time/posix_time/posix_time_types.hpp>
-void MPIPYPTopics::sample_corpus(const MPICorpus& corpus, int samples,
-                              int freq_cutoff_start, int freq_cutoff_end,
-                              int freq_cutoff_interval,
-                              int max_contexts_per_document) {
-  Timer timer;
-
-  //int documents = corpus.num_documents();
-  /*
-  m_mpi_start = 0;
-  m_mpi_end = documents;
-  if (m_size != 1) {
-      assert(documents < std::numeric_limits<int>::max());
-      m_mpi_start = (documents / m_size) * m_rank;
-      if (m_rank == m_size-1) m_mpi_end = documents;
-      else m_mpi_end = (documents / m_size)*(m_rank+1);
-  }
-  */
-  corpus.bounds(&m_mpi_start, &m_mpi_end);
-  int local_documents = m_mpi_end - m_mpi_start;
-
-  if (!m_backoff.get()) {
-    m_word_pyps.clear();
-    m_word_pyps.push_back(MPIPYPs());
-  }
-
-  if (m_am_root) std::cerr << "\n Training with " << m_word_pyps.size()-1 << " backoff level"
-    << (m_word_pyps.size()>1 ? ":" : "s:") << std::endl;
-
-  for (int i=0; i<(int)m_word_pyps.size(); ++i) {
-    m_word_pyps.at(i).reserve(m_num_topics);
-    for (int j=0; j<m_num_topics; ++j)
-      m_word_pyps.at(i).push_back(new MPIPYP<int>(0.5, 1.0));
-  }
-  if (m_am_root) std::cerr << std::endl;
-
-  m_document_pyps.reserve(local_documents);
-  //m_document_pyps.reserve(corpus.num_documents());
-  //for (int j=0; j<corpus.num_documents(); ++j)
-  for (int j=0; j<local_documents; ++j)
-    m_document_pyps.push_back(new PYP<int>(0.5, 1.0));
-
-  m_topic_p0 = 1.0/m_num_topics;
-  m_term_p0 = 1.0/corpus.num_types();
-  m_backoff_p0 = 1.0/corpus.num_documents();
-
-  if (m_am_root) std::cerr << " Documents: " << corpus.num_documents() << "(" 
-    << local_documents << ")" << " Terms: " << corpus.num_types() << std::endl;
-
-  int frequency_cutoff = freq_cutoff_start;
-  if (m_am_root) std::cerr << " Context frequency cutoff set to " << frequency_cutoff << std::endl;
-
-  timer.Reset();
-  // Initialisation pass
-  int document_id=0, topic_counter=0;
-  for (int i=0; i<local_documents; ++i) {
-    document_id = i+m_mpi_start;
-
-  //for (Corpus::const_iterator corpusIt=corpus.begin();
-  //     corpusIt != corpus.end(); ++corpusIt, ++document_id) {
-    m_corpus_topics.push_back(DocumentTopics(corpus.at(document_id).size(), 0));
-
-    int term_index=0;
-    for (Document::const_iterator docIt=corpus.at(document_id).begin();
-         docIt != corpus.at(document_id).end(); ++docIt, ++term_index) {
-      topic_counter++;
-      Term term = *docIt;
-
-      // sample a new_topic
-      //int new_topic = (topic_counter % m_num_topics);
-      int freq = corpus.context_count(term);
-      int new_topic = -1;
-      if (freq > frequency_cutoff
-          && (!max_contexts_per_document || term_index < max_contexts_per_document)) {
-        new_topic = sample(i, term);
-        //new_topic = document_id % m_num_topics;
-
-        // add the new topic to the PYPs
-        increment(term, new_topic);
-
-        if (m_use_topic_pyp) {
-          F p0 = m_topic_pyp.prob(new_topic, m_topic_p0);
-          int table_delta = m_document_pyps.at(i).increment(new_topic, p0);
-          if (table_delta)
-            m_topic_pyp.increment(new_topic, m_topic_p0, rnd);
-        }
-        else m_document_pyps.at(i).increment(new_topic, m_topic_p0);
-      }
-
-      m_corpus_topics.at(i).at(term_index) = new_topic;
-    }
-  }
-
-  // Synchronise the topic->word counds across the processes.
-  synchronise();
-
-  if (m_am_root) std::cerr << "  Initialized in " << timer.Elapsed() << " seconds\n";
-
-  int* randomDocIndices = new int[local_documents];
-  for (int i = 0; i < local_documents; ++i)
-	  randomDocIndices[i] = i;
-
-  // Sampling phase
-  for (int curr_sample=0; curr_sample < samples; ++curr_sample) {
-    if (freq_cutoff_interval > 0 && curr_sample != 1
-        && curr_sample % freq_cutoff_interval == 1
-        && frequency_cutoff > freq_cutoff_end) {
-      frequency_cutoff--;
-      if (m_am_root) std::cerr << "\n Context frequency cutoff set to " << frequency_cutoff << std::endl;
-    }
-
-    if (m_am_root) std::cerr << "\n  -- Sample " << curr_sample << " "; std::cerr.flush();
-
-    // Randomize the corpus indexing array
-    int tmp;
-    int processed_terms=0;
-    for (int i = (local_documents-1); i > 0; --i) {
-      //i+1 since j \in [0,i] but rnd() \in [0,1)
-    	int j = (int)(rnd() * (i+1));
-      assert(j >= 0 && j <= i);
-     	tmp = randomDocIndices[i];
-    	randomDocIndices[i] = randomDocIndices[j];
-    	randomDocIndices[j] = tmp;
-    }
-
-    // for each document in the corpus
-    for (int rand_doc=0; rand_doc<local_documents; ++rand_doc) {
-    	int doc_index = randomDocIndices[rand_doc];
-    	int document_id = doc_index + m_mpi_start;
-      const Document& doc = corpus.at(document_id);
-
-      // for each term in the document
-      int term_index=0;
-      Document::const_iterator docEnd = doc.end();
-      for (Document::const_iterator docIt=doc.begin();
-           docIt != docEnd; ++docIt, ++term_index) {
-
-        if (max_contexts_per_document && term_index > max_contexts_per_document)
-          break;
-        
-        Term term = *docIt;
-        int freq = corpus.context_count(term);
-        if (freq < frequency_cutoff)
-          continue;
-
-        processed_terms++;
-
-        // remove the prevous topic from the PYPs
-        int current_topic = m_corpus_topics.at(doc_index).at(term_index);
-        // a negative label mean that term hasn't been sampled yet
-        if (current_topic >= 0) {
-          decrement(term, current_topic);
-
-          int table_delta = m_document_pyps.at(doc_index).decrement(current_topic);
-          if (m_use_topic_pyp && table_delta < 0)
-            m_topic_pyp.decrement(current_topic, rnd);
-        }
-
-        // sample a new_topic
-        int new_topic = sample(doc_index, term);
-
-        // add the new topic to the PYPs
-        m_corpus_topics.at(doc_index).at(term_index) = new_topic;
-        increment(term, new_topic);
-
-        if (m_use_topic_pyp) {
-          F p0 = m_topic_pyp.prob(new_topic, m_topic_p0);
-          int table_delta = m_document_pyps.at(doc_index).increment(new_topic, p0);
-          if (table_delta)
-            m_topic_pyp.increment(new_topic, m_topic_p0, rnd);
-        }
-        else m_document_pyps.at(doc_index).increment(new_topic, m_topic_p0);
-      }
-      if (document_id && document_id % 10000 == 0) {
-        if (m_am_root) std::cerr << "."; std::cerr.flush();
-      }
-    }
-    std::cerr << "|"; std::cerr.flush();  
-
-    // Synchronise the topic->word counds across the processes.
-    synchronise();
-
-    if (m_am_root) std::cerr << " ||| sampled " << processed_terms << " terms.";
-
-    if (curr_sample != 0 && curr_sample % 10 == 0) {
-      if (m_am_root) std::cerr << " ||| time=" << (timer.Elapsed() / 10.0) << " sec/sample" << std::endl;
-      timer.Reset();
-      if (m_am_root) std::cerr << "     ... Resampling hyperparameters"; std::cerr.flush();
-
-      // resample the hyperparamters
-      F log_p=0.0;
-      for (std::vector<MPIPYPs>::iterator levelIt=m_word_pyps.begin();
-           levelIt != m_word_pyps.end(); ++levelIt) {
-        for (MPIPYPs::iterator pypIt=levelIt->begin();
-             pypIt != levelIt->end(); ++pypIt) {
-          pypIt->resample_prior(rnd);
-          log_p += pypIt->log_restaurant_prob();
-        }
-      }
-
-      for (PYPs::iterator pypIt=m_document_pyps.begin();
-           pypIt != m_document_pyps.end(); ++pypIt) {
-        pypIt->resample_prior(rnd);
-        log_p += pypIt->log_restaurant_prob();
-      }
-
-      if (m_use_topic_pyp) {
-        m_topic_pyp.resample_prior(rnd);
-        log_p += m_topic_pyp.log_restaurant_prob();
-      }
-
-      std::cerr.precision(10);
-      if (m_am_root) std::cerr << " ||| LLH=" << log_p << " ||| resampling time=" << timer.Elapsed() << " sec" << std::endl;
-      timer.Reset();
-
-      int k=0;
-      if (m_am_root) std::cerr << "Topics distribution: ";
-      std::cerr.precision(2);
-      for (MPIPYPs::iterator pypIt=m_word_pyps.front().begin();
-           pypIt != m_word_pyps.front().end(); ++pypIt, ++k) {
-        if (m_am_root && k % 5 == 0) std::cerr << std::endl << '\t';
-        if (m_am_root) std::cerr << "<" << k << ":" << pypIt->num_customers() << ","
-          << pypIt->num_types() << "," << m_topic_pyp.prob(k, m_topic_p0) << "> ";
-      }
-      std::cerr.precision(4);
-      if (m_am_root) std::cerr << std::endl;
-    }
-  }
-  delete [] randomDocIndices;
-}
-
-void MPIPYPTopics::synchronise() {
-  // Synchronise the topic->word counds across the processes.
-  //for (std::vector<MPIPYPs>::iterator levelIt=m_word_pyps.begin();
-  //     levelIt != m_word_pyps.end(); ++levelIt) {
-//  std::vector<MPIPYPs>::iterator levelIt=m_word_pyps.begin(); 
-//  {
-//    for (MPIPYPs::iterator pypIt=levelIt->begin(); pypIt != levelIt->end(); ++pypIt) {
-    for (size_t label=0; label < m_word_pyps.at(0).size(); ++label) {
-      MPIPYP<int>& pyp = m_word_pyps.at(0).at(label);
-
-      //if (!m_am_root) boost::mpi::communicator().barrier();
-      //std::cerr << "Before Sync Process " << m_rank << ":";
-      //pyp.debug_info(std::cerr); std::cerr << std::endl;
-      //if (m_am_root) boost::mpi::communicator().barrier();
-
-      MPIPYP<int>::dish_delta_type delta;
-      pyp.synchronise(&delta);
-
-      for (MPIPYP<int>::dish_delta_type::const_iterator it=delta.begin(); it != delta.end(); ++it) {
-        int count = it->second;
-        if (count > 0)
-          for (int i=0; i < count; ++i) increment(it->first, label);
-        if (count < 0)
-          for (int i=0; i > count; --i) decrement(it->first, label);
-      }
-      pyp.reset_deltas();
-
-      //if (!m_am_root) boost::mpi::communicator().barrier();
-      //std::cerr << "After Sync Process " << m_rank << ":";
-      //pyp.debug_info(std::cerr); std::cerr << std::endl;
-      //if (m_am_root) boost::mpi::communicator().barrier();
-    }
-//  }
-    // Synchronise the hierarchical topic pyp
-    MPIPYP<int>::dish_delta_type topic_delta;
-    m_topic_pyp.synchronise(&topic_delta);
-    for (MPIPYP<int>::dish_delta_type::const_iterator it=topic_delta.begin(); it != topic_delta.end(); ++it) {
-      int count = it->second;
-      if (count > 0)
-        for (int i=0; i < count; ++i) 
-          m_topic_pyp.increment(it->first, m_topic_p0, rnd);
-      if (count < 0)
-        for (int i=0; i > count; --i) 
-          m_topic_pyp.decrement(it->first, rnd);
-    }
-    m_topic_pyp.reset_deltas();
-}
-
-void MPIPYPTopics::decrement(const Term& term, int topic, int level) {
-  //std::cerr << "MPIPYPTopics::decrement(" << term << "," << topic << "," << level << ")" << std::endl;
-  m_word_pyps.at(level).at(topic).decrement(term, rnd);
-  if (m_backoff.get()) {
-    Term backoff_term = (*m_backoff)[term];
-    if (!m_backoff->is_null(backoff_term))
-      decrement(backoff_term, topic, level+1);
-  }
-}
-
-void MPIPYPTopics::increment(const Term& term, int topic, int level) {
-  //std::cerr << "MPIPYPTopics::increment(" << term << "," << topic << "," << level << ")" << std::endl;
-  m_word_pyps.at(level).at(topic).increment(term, word_pyps_p0(term, topic, level), rnd);
-
-  if (m_backoff.get()) {
-    Term backoff_term = (*m_backoff)[term];
-    if (!m_backoff->is_null(backoff_term))
-      increment(backoff_term, topic, level+1);
-  }
-}
-
-int MPIPYPTopics::sample(const DocumentId& doc, const Term& term) {
-  // First pass: collect probs
-  F sum=0.0;
-  std::vector<F> sums;
-  for (int k=0; k<m_num_topics; ++k) {
-    F p_w_k = prob(term, k);
-
-    F topic_prob = m_topic_p0;
-    if (m_use_topic_pyp) topic_prob = m_topic_pyp.prob(k, m_topic_p0);
-
-    //F p_k_d = m_document_pyps[doc].prob(k, topic_prob);
-    F p_k_d = m_document_pyps.at(doc).unnormalised_prob(k, topic_prob);
-
-    sum += (p_w_k*p_k_d);
-    sums.push_back(sum);
-  }
-  // Second pass: sample a topic
-  F cutoff = rnd() * sum;
-  for (int k=0; k<m_num_topics; ++k) {
-    if (cutoff <= sums[k])
-      return k;
-  }
-  std::cerr << cutoff << " " << sum << std::endl;
-  assert(false);
-}
-
-MPIPYPTopics::F MPIPYPTopics::word_pyps_p0(const Term& term, int topic, int level) const {
-  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
-  //std::cerr << "MPIPYPTopics::word_pyps_p0(" << term << "," << topic << "," << level << ")" << std::endl;
-
-  F p0 = m_term_p0;
-  if (m_backoff.get()) {
-    //static F fudge=m_backoff_p0; // TODO
-
-    Term backoff_term = (*m_backoff)[term];
-    if (!m_backoff->is_null(backoff_term)) {
-      assert (level < m_backoff->order());
-      //p0 = (1.0/(double)m_backoff->terms_at_level(level))*prob(backoff_term, topic, level+1);
-      p0 = prob(backoff_term, topic, level+1);
-    }
-    else
-      p0 = m_term_p0;
-  }
-  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
-  //std::cerr << "MPIPYPTopics::word_pyps_p0(" << term << "," << topic << "," << level << ") = " << p0 << std::endl;
-  return p0;
-}
-
-MPIPYPTopics::F MPIPYPTopics::prob(const Term& term, int topic, int level) const {
-  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
-  //std::cerr << "MPIPYPTopics::prob(" << term << "," << topic << "," << level << " " << factor << ")" << std::endl;
-
-  F p0 = word_pyps_p0(term, topic, level);
-  F p_w_k = m_word_pyps.at(level).at(topic).prob(term, p0);
-
-  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
-  //std::cerr << "MPIPYPTopics::prob(" << term << "," << topic << "," << level << ") = " << p_w_k << std::endl;
-
-  return p_w_k;
-}
-
-int MPIPYPTopics::max_topic() const {
-  if (!m_use_topic_pyp)
-    return -1;
-
-  F current_max=0.0;
-  int current_topic=-1;
-  for (int k=0; k<m_num_topics; ++k) {
-    F prob = m_topic_pyp.prob(k, m_topic_p0);
-    if (prob > current_max) {
-      current_max = prob;
-      current_topic = k;
-    }
-  }
-  assert(current_topic >= 0);
-  assert(current_max >= 0);
-  return current_max;
-}
-
-std::pair<int,MPIPYPTopics::F> MPIPYPTopics::max(const DocumentId& true_doc) const {
-  //std::cerr << "MPIPYPTopics::max(" << doc << "," << term << ")" << std::endl;
-  // collect probs
-  F current_max=0.0;
-  DocumentId local_doc = true_doc - m_mpi_start;
-  int current_topic=-1;
-  for (int k=0; k<m_num_topics; ++k) {
-    //F p_w_k = prob(term, k);
-
-    F topic_prob = m_topic_p0;
-    if (m_use_topic_pyp)
-      topic_prob = m_topic_pyp.prob(k, m_topic_p0);
-
-    F prob = 0;
-    if (local_doc < 0) prob = topic_prob;
-    else               prob = m_document_pyps.at(local_doc).prob(k, topic_prob);
-
-    if (prob > current_max) {
-      current_max = prob;
-      current_topic = k;
-    }
-  }
-  assert(current_topic >= 0);
-  assert(current_max >= 0);
-  return std::make_pair(current_topic, current_max);
-}
-
-std::pair<int,MPIPYPTopics::F> MPIPYPTopics::max(const DocumentId& true_doc, const Term& term) const {
-  //std::cerr << "MPIPYPTopics::max(" << doc << "," << term << ")" << std::endl;
-  // collect probs
-  F current_max=0.0;
-  DocumentId local_doc = true_doc - m_mpi_start;
-  int current_topic=-1;
-  for (int k=0; k<m_num_topics; ++k) {
-    F p_w_k = prob(term, k);
-
-    F topic_prob = m_topic_p0;
-    if (m_use_topic_pyp)
-      topic_prob = m_topic_pyp.prob(k, m_topic_p0);
-
-    F p_k_d = 0;
-    if (local_doc < 0) p_k_d = topic_prob;
-    else               p_k_d = m_document_pyps.at(local_doc).prob(k, topic_prob);
-
-    F prob = (p_w_k*p_k_d);
-    if (prob > current_max) {
-      current_max = prob;
-      current_topic = k;
-    }
-  }
-  assert(current_topic >= 0);
-  assert(current_max >= 0);
-  return std::make_pair(current_topic, current_max);
-}
-
-std::ostream& MPIPYPTopics::print_document_topics(std::ostream& out) const {
-  for (CorpusTopics::const_iterator corpusIt=m_corpus_topics.begin();
-       corpusIt != m_corpus_topics.end(); ++corpusIt) {
-    int term_index=0;
-    for (DocumentTopics::const_iterator docIt=corpusIt->begin();
-         docIt != corpusIt->end(); ++docIt, ++term_index) {
-      if (term_index) out << " ";
-      out << *docIt;
-    }
-    out << std::endl;
-  }
-  return out;
-}
-
-std::ostream& MPIPYPTopics::print_topic_terms(std::ostream& out) const {
-  for (PYPs::const_iterator pypsIt=m_word_pyps.front().begin();
-       pypsIt != m_word_pyps.front().end(); ++pypsIt) {
-    int term_index=0;
-    for (PYP<int>::const_iterator termIt=pypsIt->begin();
-         termIt != pypsIt->end(); ++termIt, ++term_index) {
-      if (term_index) out << " ";
-      out << termIt->first << ":" << termIt->second;
-    }
-    out << std::endl;
-  }
-  return out;
-}
diff --git a/gi/pyp-topics/src/mpi-pyp-topics.hh b/gi/pyp-topics/src/mpi-pyp-topics.hh
deleted file mode 100644
index d96bc4e5..00000000
--- a/gi/pyp-topics/src/mpi-pyp-topics.hh
+++ /dev/null
@@ -1,106 +0,0 @@
-#ifndef MPI_PYP_TOPICS_HH
-#define MPI_PYP_TOPICS_HH
-
-#include <vector>
-#include <iostream>
-
-#include <boost/ptr_container/ptr_vector.hpp>
-#include <boost/random/uniform_real.hpp>
-#include <boost/random/variate_generator.hpp>
-#include <boost/random/mersenne_twister.hpp>
-#include <boost/random/inversive_congruential.hpp>
-#include <boost/random/linear_congruential.hpp>
-#include <boost/random/lagged_fibonacci.hpp>
-#include <boost/mpi/environment.hpp>
-#include <boost/mpi/communicator.hpp>
-
-
-#include "mpi-pyp.hh"
-#include "mpi-corpus.hh"
-
-class MPIPYPTopics {
-public:
-  typedef std::vector<int> DocumentTopics;
-  typedef std::vector<DocumentTopics> CorpusTopics;
-  typedef double F;
-
-public:
-  MPIPYPTopics(int num_topics, bool use_topic_pyp=false, unsigned long seed = 0) 
-    : m_num_topics(num_topics), m_word_pyps(1), 
-    m_topic_pyp(0.5,1.0), m_use_topic_pyp(use_topic_pyp),
-    m_seed(seed),
-    uni_dist(0,1), rng(seed == 0 ? (unsigned long)this : seed), 
-    rnd(rng, uni_dist), m_mpi_start(-1), m_mpi_end(-1) {
-      boost::mpi::communicator m_world;
-      m_rank = m_world.rank(); 
-      m_size = m_world.size();
-      m_am_root = (m_rank == 0);
-    }
-
-  void sample_corpus(const MPICorpus& corpus, int samples,
-                     int freq_cutoff_start=0, int freq_cutoff_end=0, 
-                     int freq_cutoff_interval=0,
-                     int max_contexts_per_document=0);
-
-  int sample(const DocumentId& doc, const Term& term);
-  std::pair<int,F> max(const DocumentId& doc, const Term& term) const;
-  std::pair<int,F> max(const DocumentId& doc) const;
-  int max_topic() const;
-
-  void set_backoff(const std::string& filename) {
-    m_backoff.reset(new TermBackoff);
-    m_backoff->read(filename);
-    m_word_pyps.clear();
-    m_word_pyps.resize(m_backoff->order(), MPIPYPs());
-  }
-  void set_backoff(TermBackoffPtr backoff) {
-    m_backoff = backoff;
-    m_word_pyps.clear();
-    m_word_pyps.resize(m_backoff->order(), MPIPYPs());
-  }
-
-  F prob(const Term& term, int topic, int level=0) const;
-  void decrement(const Term& term, int topic, int level=0);
-  void increment(const Term& term, int topic, int level=0);
-
-  std::ostream& print_document_topics(std::ostream& out) const;
-  std::ostream& print_topic_terms(std::ostream& out) const;
-
-  void synchronise();
-
-private:
-  F word_pyps_p0(const Term& term, int topic, int level) const;
-
-  int m_num_topics;
-  F m_term_p0, m_topic_p0, m_backoff_p0;
-
-  CorpusTopics m_corpus_topics;
-  typedef boost::ptr_vector< PYP<int> > PYPs;
-  typedef boost::ptr_vector< MPIPYP<int> > MPIPYPs;
-  PYPs m_document_pyps;
-  std::vector<MPIPYPs> m_word_pyps;
-  MPIPYP<int> m_topic_pyp;
-  bool m_use_topic_pyp;
-
-  unsigned long m_seed;
-
-  //typedef boost::mt19937 base_generator_type;
-  //typedef boost::hellekalek1995 base_generator_type;
-  typedef boost::lagged_fibonacci607 base_generator_type;
-  typedef boost::uniform_real<> uni_dist_type;
-  typedef boost::variate_generator<base_generator_type&, uni_dist_type> gen_type;
-
-  uni_dist_type uni_dist;
-  base_generator_type rng; //this gets the seed
-  gen_type rnd; //instantiate: rnd(rng, uni_dist)
-                //call: rnd() generates uniform on [0,1)
-
-  TermBackoffPtr m_backoff;
-
-  boost::mpi::communicator m_world;
-  bool m_am_root;
-  int m_rank, m_size;
-  int m_mpi_start, m_mpi_end;
-};
-
-#endif // PYP_TOPICS_HH
diff --git a/gi/pyp-topics/src/mpi-pyp.hh b/gi/pyp-topics/src/mpi-pyp.hh
deleted file mode 100644
index c2341b9e..00000000
--- a/gi/pyp-topics/src/mpi-pyp.hh
+++ /dev/null
@@ -1,447 +0,0 @@
-#ifndef _mpipyp_hh
-#define _mpipyp_hh
-
-#include <math.h>
-#include <map>
-#include <tr1/unordered_map>
-//#include <google/sparse_hash_map>
-
-#include <boost/random/uniform_real.hpp>
-#include <boost/random/variate_generator.hpp>
-#include <boost/random/mersenne_twister.hpp>
-#include <boost/tuple/tuple.hpp>
-#include <boost/serialization/map.hpp>
-#include <boost/mpi.hpp>
-#include <boost/mpi/environment.hpp>
-#include <boost/mpi/communicator.hpp>
-#include <boost/mpi/operations.hpp>
-
-
-#include "pyp.hh"
-
-//
-// Pitman-Yor process with customer and table tracking
-//
-
-template <typename Dish, typename Hash=std::tr1::hash<Dish> >
-class MPIPYP : public PYP<Dish, Hash> {
-public:
-  typedef std::map<Dish, int> dish_delta_type;
-
-  MPIPYP(double a, double b, Hash hash=Hash());
-
-  template < typename Uniform01 >
-    int increment(Dish d, double p0, Uniform01& rnd);
-  template < typename Uniform01 >
-    int decrement(Dish d, Uniform01& rnd);
-
-  void clear();
-  void reset_deltas();
-
-  void synchronise(dish_delta_type* result);
-
-private:
-  typedef std::map<Dish, typename PYP<Dish,Hash>::TableCounter> table_delta_type;
-
-  dish_delta_type m_count_delta;
-  table_delta_type m_table_delta;
-};
-
-template <typename Dish, typename Hash>
-MPIPYP<Dish,Hash>::MPIPYP(double a, double b, Hash h)
-: PYP<Dish,Hash>(a, b, 0, h) {}
-
-template <typename Dish, typename Hash>
-  template <typename Uniform01>
-int 
-MPIPYP<Dish,Hash>::increment(Dish dish, double p0, Uniform01& rnd) {
-  //std::cerr << "-----INCREMENT DISH " << dish << std::endl;
-  int delta = 0;
-  int table_joined=-1;
-  typename PYP<Dish,Hash>::TableCounter &tc = PYP<Dish,Hash>::_dish_tables[dish];
-
-  // seated on a new or existing table?
-  int c = PYP<Dish,Hash>::count(dish); 
-  int t = PYP<Dish,Hash>::num_tables(dish); 
-  int T = PYP<Dish,Hash>::num_tables();
-  double& a = PYP<Dish,Hash>::_a;
-  double& b = PYP<Dish,Hash>::_b;
-  double pshare = (c > 0) ? (c - a*t) : 0.0;
-  double pnew = (b + a*T) * p0;
-  if (pshare < 0.0) {
-    std::cerr << pshare << " " << c << " " << a << " " << t << std::endl;
-    assert(false);
-  }
-
-  if (rnd() < pnew / (pshare + pnew)) {
-    // assign to a new table
-    tc.tables += 1;
-    tc.table_histogram[1] += 1;
-    PYP<Dish,Hash>::_total_tables += 1;
-    delta = 1;
-    table_joined = 1;
-  }
-  else {
-    // randomly assign to an existing table
-    // remove constant denominator from inner loop
-    double r = rnd() * (c - a*t);
-    for (std::map<int,int>::iterator
-         hit = tc.table_histogram.begin();
-         hit != tc.table_histogram.end(); ++hit) {
-      r -= ((hit->first - a) * hit->second);
-      if (r <= 0) {
-        tc.table_histogram[hit->first+1] += 1;
-        hit->second -= 1;
-        table_joined = hit->first+1;
-        if (hit->second == 0)
-          tc.table_histogram.erase(hit);
-        break;
-      }
-    }
-    if (r > 0) {
-      std::cerr << r << " " << c << " " << a << " " << t << std::endl;
-      assert(false);
-    }
-    delta = 0;
-  }
-
-  std::tr1::unordered_map<Dish,int,Hash>::operator[](dish) += 1;
-  //google::sparse_hash_map<Dish,int,Hash>::operator[](dish) += 1;
-  PYP<Dish,Hash>::_total_customers += 1;
-
-  // MPI Delta handling
-  // track the customer entering
-  typename dish_delta_type::iterator customer_it; 
-  bool customer_insert_result; 
-  boost::tie(customer_it, customer_insert_result) 
-    = m_count_delta.insert(std::make_pair(dish,0)); 
-
-  customer_it->second += 1;
-  if (customer_it->second == 0)
-    m_count_delta.erase(customer_it);
-
-  // increment the histogram bar for the table joined
-  /*
-  typename PYP<Dish,Hash>::TableCounter &delta_tc = m_table_delta[dish];
-
-  std::map<int,int> &histogram = delta_tc.table_histogram;
-  assert (table_joined > 0);
-
-  typename std::map<int,int>::iterator table_it; bool table_insert_result; 
-  boost::tie(table_it, table_insert_result) = histogram.insert(std::make_pair(table_joined,0)); 
-  table_it->second += 1;
-  if (delta == 0) {
-    // decrement the histogram bar for the table left 
-    typename std::map<int,int>::iterator left_table_it; 
-    boost::tie(left_table_it, table_insert_result) 
-      = histogram.insert(std::make_pair(table_joined-1,0)); 
-    left_table_it->second -= 1;
-    if (left_table_it->second == 0) histogram.erase(left_table_it);
-  }
-  else delta_tc.tables += 1;
-
-  if (table_it->second == 0) histogram.erase(table_it);
-
-    //std::cerr << "Added (" << delta << ") " << dish << " to table " << table_joined << "\n"; 
-    //std::cerr << "Dish " << dish << " has " << count(dish) << " customers, and is sitting at " << PYP<Dish,Hash>::num_tables(dish) << " tables.\n"; 
-    //for (std::map<int,int>::const_iterator 
-    //     hit = delta_tc.table_histogram.begin();
-    //     hit != delta_tc.table_histogram.end(); ++hit) {
-    //  std::cerr << "    " << hit->second << " tables with " << hit->first << " customers." << std::endl; 
-    //}
-    //std::cerr << "Added (" << delta << ") " << dish << " to table " << table_joined << "\n"; 
-    //std::cerr << "Dish " << dish << " has " << count(dish) << " customers, and is sitting at " << PYP<Dish,Hash>::num_tables(dish) << " tables.\n"; 
-    int x_num_customers=0, x_num_table=0;
-    for (std::map<int,int>::const_iterator 
-         hit = delta_tc.table_histogram.begin();
-         hit != delta_tc.table_histogram.end(); ++hit) {
-      x_num_table += hit->second;
-      x_num_customers += (hit->second*hit->first);
-    }
-    int tmp_c = PYP<Dish,Hash>::count(dish);
-    int tmp_t = PYP<Dish,Hash>::num_tables(dish);
-    assert (x_num_customers <= tmp_c); 
-    assert (x_num_table <= tmp_t); 
-
-  if (delta_tc.table_histogram.empty()) {
-    assert (delta_tc.tables == 0);
-    m_table_delta.erase(dish);
-  }
-  */
-
-  //PYP<Dish,Hash>::debug_info(std::cerr);
-  //std::cerr << "   Dish " << dish << " has count " << PYP<Dish,Hash>::count(dish) << " tables " << PYP<Dish,Hash>::num_tables(dish) << std::endl;
-
-  return delta;
-}
-
-template <typename Dish, typename Hash>
-  template <typename Uniform01>
-int 
-MPIPYP<Dish,Hash>::decrement(Dish dish, Uniform01& rnd)
-{
-  //std::cerr << "-----DECREMENT DISH " << dish << std::endl;
-  typename std::tr1::unordered_map<Dish, int>::iterator dcit = find(dish);
-  //typename google::sparse_hash_map<Dish, int>::iterator dcit = find(dish);
-  if (dcit == PYP<Dish,Hash>::end()) {
-    std::cerr << dish << std::endl;
-    assert(false);
-  } 
-
-  int delta = 0, table_left=-1;
-
-  typename std::tr1::unordered_map<Dish, typename PYP<Dish,Hash>::TableCounter>::iterator dtit 
-    = PYP<Dish,Hash>::_dish_tables.find(dish);
-  //typename google::sparse_hash_map<Dish, TableCounter>::iterator dtit = _dish_tables.find(dish);
-  if (dtit == PYP<Dish,Hash>::_dish_tables.end()) {
-    std::cerr << dish << std::endl;
-    assert(false);
-  } 
-  typename PYP<Dish,Hash>::TableCounter &tc = dtit->second;
-
-  double r = rnd() * PYP<Dish,Hash>::count(dish);
-  for (std::map<int,int>::iterator hit = tc.table_histogram.begin();
-       hit != tc.table_histogram.end(); ++hit) {
-    r -= (hit->first * hit->second);
-    if (r <= 0) {
-      table_left = hit->first;
-      if (hit->first > 1) {
-        tc.table_histogram[hit->first-1] += 1;
-      }
-      else {
-        delta = -1;
-        tc.tables -= 1;
-        PYP<Dish,Hash>::_total_tables -= 1;
-      }
-
-      hit->second -= 1;
-      if (hit->second == 0) tc.table_histogram.erase(hit);
-      break;
-    }
-  }
-  if (r > 0) {
-    std::cerr << r << " " << PYP<Dish,Hash>::count(dish) << " " << PYP<Dish,Hash>::_a << " " 
-      << PYP<Dish,Hash>::num_tables(dish) << std::endl;
-    assert(false);
-  }
-
-  // remove the customer
-  dcit->second -= 1;
-  PYP<Dish,Hash>::_total_customers -= 1;
-  assert(dcit->second >= 0);
-  if (dcit->second == 0) {
-    PYP<Dish,Hash>::erase(dcit);
-    PYP<Dish,Hash>::_dish_tables.erase(dtit);
-  }
-
-  // MPI Delta processing
-  typename dish_delta_type::iterator it; 
-  bool insert_result; 
-  boost::tie(it, insert_result) = m_count_delta.insert(std::make_pair(dish,0)); 
-  it->second -= 1;
-  if (it->second == 0) m_count_delta.erase(it);
-
-  assert (table_left > 0);
-  typename PYP<Dish,Hash>::TableCounter& delta_tc = m_table_delta[dish];
-  if (table_left > 1) {
-    std::map<int,int>::iterator tit;
-    boost::tie(tit, insert_result) = delta_tc.table_histogram.insert(std::make_pair(table_left-1,0));
-    tit->second += 1;
-    if (tit->second == 0) delta_tc.table_histogram.erase(tit);
-  }
-  else delta_tc.tables -= 1;
-
-  std::map<int,int>::iterator tit;
-  boost::tie(tit, insert_result) = delta_tc.table_histogram.insert(std::make_pair(table_left,0));
-  tit->second -= 1;
-  if (tit->second == 0) delta_tc.table_histogram.erase(tit);
-
-  //  std::cerr << "Dish " << dish << " has " << count(dish) << " customers, and is sitting at " << PYP<Dish,Hash>::num_tables(dish) << " tables.\n"; 
-  //  for (std::map<int,int>::const_iterator 
-  //       hit = delta_tc.table_histogram.begin();
-  //       hit != delta_tc.table_histogram.end(); ++hit) {
-  //    std::cerr << "    " << hit->second << " tables with " << hit->first << " customers." << std::endl; 
-  //  }
-    int x_num_customers=0, x_num_table=0;
-    for (std::map<int,int>::const_iterator 
-         hit = delta_tc.table_histogram.begin();
-         hit != delta_tc.table_histogram.end(); ++hit) {
-      x_num_table += hit->second;
-      x_num_customers += (hit->second*hit->first);
-    }
-    int tmp_c = PYP<Dish,Hash>::count(dish);
-    int tmp_t = PYP<Dish,Hash>::num_tables(dish);
-    assert (x_num_customers <= tmp_c); 
-    assert (x_num_table <= tmp_t); 
-
-  if (delta_tc.table_histogram.empty()) {
-  //  std::cerr << "   DELETING " << dish << std::endl;
-    assert (delta_tc.tables == 0);
-    m_table_delta.erase(dish);
-  }
-
-  //PYP<Dish,Hash>::debug_info(std::cerr);
-  //std::cerr << "   Dish " << dish << " has count " << PYP<Dish,Hash>::count(dish) << " tables " << PYP<Dish,Hash>::num_tables(dish) << std::endl;
-  return delta;
-}
-
-template <typename Dish, typename Hash>
-void 
-MPIPYP<Dish,Hash>::clear() {
-  PYP<Dish,Hash>::clear();
-  reset_deltas();
-}
-
-template <typename Dish, typename Hash>
-void 
-MPIPYP<Dish,Hash>::reset_deltas() { 
-  m_count_delta.clear(); 
-  m_table_delta.clear();
-}
-
-template <typename Dish>
-struct sum_maps {
-  typedef std::map<Dish,int> map_type;
-  map_type& operator() (map_type& l, map_type const & r) const {
-    for (typename map_type::const_iterator it=r.begin(); it != r.end(); it++)
-      l[it->first] += it->second;
-    return l;
-  }
-};
-
-template <typename Dish>
-struct subtract_maps {
-  typedef std::map<Dish,int> map_type;
-  map_type& operator() (map_type& l, map_type const & r) const {
-    for (typename map_type::const_iterator it=r.begin(); it != r.end(); it++)
-      l[it->first] -= it->second;
-    return l;
-  }
-};
-
-// Needed Boost definitions
-namespace boost { 
-  namespace mpi {
-    template <>
-    struct is_commutative< sum_maps<int>, std::map<int,int> > : mpl::true_ {};
-  }
-
-  namespace serialization {
-    template<class Archive>
-    void serialize(Archive & ar, PYP<int>::TableCounter& t, const unsigned int version) {
-      ar & t.table_histogram;
-      ar & t.tables;
-    }
-
-  } // namespace serialization
-} // namespace boost
-
-template <typename A, typename B, typename C>
-struct triple {
-  triple() {}
-  triple(const A& a, const B& b, const C& c) : first(a), second(b), third(c) {}
-  A first;
-  B second;
-  C third;
-
-  template<class Archive>
-  void serialize(Archive &ar, const unsigned int version){
-      ar & first;
-      ar & second;
-      ar & third;
-  }
-};
-
-BOOST_IS_BITWISE_SERIALIZABLE(MPIPYP<int>::dish_delta_type)
-BOOST_CLASS_TRACKING(MPIPYP<int>::dish_delta_type,track_never)
-
-template <typename Dish, typename Hash>
-void 
-MPIPYP<Dish,Hash>::synchronise(dish_delta_type* result) {
-  boost::mpi::communicator world; 
-  //int rank = world.rank(), size = world.size();
-
-  boost::mpi::all_reduce(world, m_count_delta, *result, sum_maps<Dish>());
-  subtract_maps<Dish>()(*result, m_count_delta);
- 
-/*
-  // communicate the customer count deltas
-  dish_delta_type global_dish_delta;
-  boost::mpi::all_reduce(world, m_count_delta, global_dish_delta, sum_maps<Dish>());
-
-  // update this restaurant
-  for (typename dish_delta_type::const_iterator it=global_dish_delta.begin(); 
-       it != global_dish_delta.end(); ++it) {
-    int global_delta = it->second - m_count_delta[it->first];
-    if (global_delta == 0) continue;
-    typename std::tr1::unordered_map<Dish,int,Hash>::iterator dit; bool inserted;
-    boost::tie(dit, inserted) 
-      = std::tr1::unordered_map<Dish,int,Hash>::insert(std::make_pair(it->first, 0));
-    dit->second += global_delta;
-    assert(dit->second >= 0);
-    if (dit->second == 0) {
-      std::tr1::unordered_map<Dish,int,Hash>::erase(dit);
-    }
-
-    PYP<Dish,Hash>::_total_customers += (it->second - m_count_delta[it->first]);
-    int tmp = PYP<Dish,Hash>::_total_customers;
-    assert(tmp >= 0);
-    //std::cerr << "Process " << rank << " adding " <<  (it->second - m_count_delta[it->first]) << " of customer " << it->first << std::endl;
-  }
-*/
-/*
-  // communicate the table count deltas
-  for (int process = 0; process < size; ++process) {
-    typename std::vector< triple<Dish, int, int> > message;
-    if (rank == process) {
-      // broadcast deltas
-      for (typename table_delta_type::const_iterator dish_it=m_table_delta.begin(); 
-           dish_it != m_table_delta.end(); ++dish_it) {
-        //assert (dish_it->second.tables > 0);
-        for (std::map<int,int>::const_iterator it=dish_it->second.table_histogram.begin(); 
-             it != dish_it->second.table_histogram.end(); ++it) {
-          triple<Dish, int, int> m(dish_it->first, it->first, it->second);
-          message.push_back(m);
-        }
-        // append a special message with the total table delta for this dish
-        triple<Dish, int, int> m(dish_it->first, -1, dish_it->second.tables);
-        message.push_back(m);
-      }
-      boost::mpi::broadcast(world, message, process);
-    }
-    else {
-      // receive deltas
-      boost::mpi::broadcast(world, message, process);
-      for (typename std::vector< triple<Dish, int, int> >::const_iterator it=message.begin(); it != message.end(); ++it) {
-        typename PYP<Dish,Hash>::TableCounter& tc = PYP<Dish,Hash>::_dish_tables[it->first];
-        if (it->second >= 0) {
-          std::map<int,int>::iterator tit; bool inserted;
-          boost::tie(tit, inserted) = tc.table_histogram.insert(std::make_pair(it->second, 0));
-          tit->second += it->third;
-          if (tit->second < 0) {
-            std::cerr << tit->first << " " << tit->second << " " << it->first << " " << it->second << " " << it->third << std::endl;
-            assert(tit->second >= 0);
-          }
-          if (tit->second == 0) {
-            tc.table_histogram.erase(tit);
-          }
-        }
-        else {
-          tc.tables += it->third;
-          PYP<Dish,Hash>::_total_tables += it->third;
-          assert(tc.tables >= 0);
-          if (tc.tables == 0) assert(tc.table_histogram.empty());
-          if (tc.table_histogram.empty()) {
-            assert (tc.tables == 0);
-            PYP<Dish,Hash>::_dish_tables.erase(it->first);
-          }
-        }
-      }
-    }
-  }
-*/
-
-//  reset_deltas();
-}
-
-#endif
diff --git a/gi/pyp-topics/src/mpi-train-contexts.cc b/gi/pyp-topics/src/mpi-train-contexts.cc
deleted file mode 100644
index e05e0eac..00000000
--- a/gi/pyp-topics/src/mpi-train-contexts.cc
+++ /dev/null
@@ -1,201 +0,0 @@
-// STL
-#include <iostream>
-#include <fstream>
-#include <algorithm>
-#include <iterator>
-
-// Boost
-#include <boost/program_options/parsers.hpp>
-#include <boost/program_options/variables_map.hpp>
-#include <boost/scoped_ptr.hpp>
-#include <boost/mpi/environment.hpp>
-#include <boost/mpi/communicator.hpp>
-#include <boost/lexical_cast.hpp>
-
-// Local
-#include "mpi-pyp-topics.hh"
-#include "corpus.hh"
-#include "mpi-corpus.hh"
-#include "gzstream.hh"
-
-static const char *REVISION = "$Rev: 170 $";
-
-// Namespaces
-using namespace boost;
-using namespace boost::program_options;
-using namespace std;
-
-int main(int argc, char **argv)
-{
-  mpi::environment env(argc, argv);
-  mpi::communicator world;
-  int rank = world.rank();
-  bool am_root = (rank==0);
-  if (am_root) cout << "Pitman Yor topic models: Copyright 2010 Phil Blunsom\n";
-  if (am_root) std::cout << "I am process " << world.rank() << " of " << world.size() << "." << std::endl;
-  if (am_root) cout << REVISION << '\n' <<endl;
-
-  ////////////////////////////////////////////////////////////////////////////////////////////
-  // Command line processing
-  variables_map vm; 
-
-  // Command line processing
-  {
-    options_description cmdline_specific("Command line specific options");
-    cmdline_specific.add_options()
-      ("help,h", "print help message")
-      ("config,c", value<string>(), "config file specifying additional command line options")
-      ;
-    options_description config_options("Allowed options");
-    config_options.add_options()
-      ("help,h", "print help message")
-      ("data,d", value<string>(), "file containing the documents and context terms")
-      ("topics,t", value<int>()->default_value(50), "number of topics")
-      ("document-topics-out,o", value<string>(), "file to write the document topics to")
-      ("default-topics-out", value<string>(), "file to write default term topic assignments.")
-      ("topic-words-out,w", value<string>(), "file to write the topic word distribution to")
-      ("samples,s", value<int>()->default_value(10), "number of sampling passes through the data")
-      ("backoff-type", value<string>(), "backoff type: none|simple")
-//      ("filter-singleton-contexts", "filter singleton contexts")
-      ("hierarchical-topics", "Use a backoff hierarchical PYP as the P0 for the document topics distribution.")
-      ("binary-counts,b", "Use binary rather than integer counts for contexts.")
-      ("freq-cutoff-start", value<int>()->default_value(0), "initial frequency cutoff.")
-      ("freq-cutoff-end", value<int>()->default_value(0), "final frequency cutoff.")
-      ("freq-cutoff-interval", value<int>()->default_value(0), "number of iterations between frequency decrement.")
-      ("max-contexts-per-document", value<int>()->default_value(0), "Only sample the n most frequent contexts for a document.")
-      ;
-
-    cmdline_specific.add(config_options);
-
-    store(parse_command_line(argc, argv, cmdline_specific), vm); 
-    notify(vm);
-
-    if (vm.count("config") > 0) {
-      ifstream config(vm["config"].as<string>().c_str());
-      store(parse_config_file(config, config_options), vm); 
-    }
-
-    if (vm.count("help")) { 
-      cout << cmdline_specific << "\n"; 
-      return 1; 
-    }
-  }
-  ////////////////////////////////////////////////////////////////////////////////////////////
-
-  if (!vm.count("data")) {
-    cerr << "Please specify a file containing the data." << endl;
-    return 1;
-  }
-
-  // seed the random number generator: 0 = automatic, specify value otherwise
-  unsigned long seed = 0; 
-  MPIPYPTopics model(vm["topics"].as<int>(), vm.count("hierarchical-topics"), seed);
-
-  // read the data
-  BackoffGenerator* backoff_gen=0;
-  if (vm.count("backoff-type")) {
-    if (vm["backoff-type"].as<std::string>() == "none") {
-      backoff_gen = 0;
-    }
-    else if (vm["backoff-type"].as<std::string>() == "simple") {
-      backoff_gen = new SimpleBackoffGenerator();
-    }
-    else {
-     cerr << "Backoff type (--backoff-type) must be one of none|simple." <<endl;
-      return(1);
-    }
-  }
-
-  //ContextsCorpus contexts_corpus;
-  MPICorpus contexts_corpus;
-  contexts_corpus.read_contexts(vm["data"].as<string>(), backoff_gen, /*vm.count("filter-singleton-contexts")*/ false, vm.count("binary-counts"));
-  int mpi_start = 0, mpi_end = 0;
-  contexts_corpus.bounds(&mpi_start, &mpi_end);
-  std::cerr << "\tProcess " << rank << " has documents " << mpi_start << " -> " << mpi_end << "." << std::endl;
-
-  model.set_backoff(contexts_corpus.backoff_index());
-
-  if (backoff_gen) 
-    delete backoff_gen;
-
-  // train the sampler
-  model.sample_corpus(contexts_corpus, vm["samples"].as<int>(),
-                      vm["freq-cutoff-start"].as<int>(),
-                      vm["freq-cutoff-end"].as<int>(),
-                      vm["freq-cutoff-interval"].as<int>(),
-                      vm["max-contexts-per-document"].as<int>());
-
-  if (vm.count("document-topics-out")) {
-    std::ofstream documents_out((vm["document-topics-out"].as<string>() + ".pyp-process-" + boost::lexical_cast<std::string>(rank)).c_str());
-    //int documents = contexts_corpus.num_documents();
-    /*
-    int mpi_start = 0, mpi_end = documents;
-    if (world.size() != 1) {
-      mpi_start = (documents / world.size()) * rank;
-      if (rank == world.size()-1) mpi_end = documents;
-      else mpi_end = (documents / world.size())*(rank+1);
-    }
-    */
-
-    map<int,int> all_terms;
-    for (int document_id=mpi_start; document_id<mpi_end; ++document_id) {
-      assert (document_id < contexts_corpus.num_documents());
-      const Document& doc = contexts_corpus.at(document_id);
-      vector<int> unique_terms;
-      for (Document::const_iterator docIt=doc.begin(); docIt != doc.end(); ++docIt) {
-        if (unique_terms.empty() || *docIt != unique_terms.back())
-          unique_terms.push_back(*docIt);
-        // increment this terms frequency
-        pair<map<int,int>::iterator,bool> insert_result = all_terms.insert(make_pair(*docIt,1));
-        if (!insert_result.second) 
-          all_terms[*docIt] = all_terms[*docIt] + 1;
-      }
-      documents_out << contexts_corpus.key(document_id) << '\t';
-      documents_out << model.max(document_id).first << " " << doc.size() << " ||| ";
-      for (std::vector<int>::const_iterator termIt=unique_terms.begin(); termIt != unique_terms.end(); ++termIt) {
-        if (termIt != unique_terms.begin())
-          documents_out << " ||| ";
-        vector<std::string> strings = contexts_corpus.context2string(*termIt);
-        copy(strings.begin(), strings.end(),ostream_iterator<std::string>(documents_out, " "));
-        std::pair<int,MPIPYPTopics::F> maxinfo = model.max(document_id, *termIt);
-        documents_out << "||| C=" << maxinfo.first << " P=" << maxinfo.second;
-      }
-      documents_out <<endl;
-    }
-    documents_out.close();
-    world.barrier();
-
-    if (am_root) {
-      ogzstream root_documents_out(vm["document-topics-out"].as<string>().c_str());
-      for (int p=0; p < world.size(); ++p) {
-        std::string rank_p_prefix((vm["document-topics-out"].as<string>() + ".pyp-process-" + boost::lexical_cast<std::string>(p)).c_str());
-        std::ifstream rank_p_trees_istream(rank_p_prefix.c_str(), std::ios_base::binary);
-        root_documents_out << rank_p_trees_istream.rdbuf();
-        rank_p_trees_istream.close();
-        remove((rank_p_prefix).c_str());
-      }
-      root_documents_out.close();
-    }
-
-    if (am_root && vm.count("default-topics-out")) {
-      ofstream default_topics(vm["default-topics-out"].as<string>().c_str());
-      default_topics << model.max_topic() <<endl;
-      for (std::map<int,int>::const_iterator termIt=all_terms.begin(); termIt != all_terms.end(); ++termIt) {
-        vector<std::string> strings = contexts_corpus.context2string(termIt->first);
-        default_topics << model.max(-1, termIt->first).first << " ||| " << termIt->second << " ||| ";
-        copy(strings.begin(), strings.end(),ostream_iterator<std::string>(default_topics, " "));
-        default_topics <<endl;
-      }
-    }
-  }
-
-  if (am_root && vm.count("topic-words-out")) {
-    ogzstream topics_out(vm["topic-words-out"].as<string>().c_str());
-    model.print_topic_terms(topics_out);
-    topics_out.close();
-  }
-
-  cout <<endl;
-
-  return 0;
-}
diff --git a/gi/pyp-topics/src/mt19937ar.c b/gi/pyp-topics/src/mt19937ar.c
deleted file mode 100644
index 6551ea39..00000000
--- a/gi/pyp-topics/src/mt19937ar.c
+++ /dev/null
@@ -1,194 +0,0 @@
-/* 
-   A C-program for MT19937, with initialization improved 2002/1/26.
-   Coded by Takuji Nishimura and Makoto Matsumoto.
-
-   Before using, initialize the state by using mt_init_genrand(seed)  
-   or mt_init_by_array(init_key, key_length).
-
-   Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
-   All rights reserved.                          
-
-   Redistribution and use in source and binary forms, with or without
-   modification, are permitted provided that the following conditions
-   are met:
-
-     1. Redistributions of source code must retain the above copyright
-        notice, this list of conditions and the following disclaimer.
-
-     2. Redistributions in binary form must reproduce the above copyright
-        notice, this list of conditions and the following disclaimer in the
-        documentation and/or other materials provided with the distribution.
-
-     3. The names of its contributors may not be used to endorse or promote 
-        products derived from this software without specific prior written 
-        permission.
-
-   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-   "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
-   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
-   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-
-   Any feedback is very welcome.
-   http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html
-   email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space)
-*/
-
-#include "mt19937ar.h"   /* XXX MJ 17th March 2006 */
-
-/* Period parameters */  
-#define N 624
-#define M 397
-#define MATRIX_A 0x9908b0dfUL   /* constant vector a */
-#define UPPER_MASK 0x80000000UL /* most significant w-r bits */
-#define LOWER_MASK 0x7fffffffUL /* least significant r bits */
-
-static unsigned long mt[N]; /* the array for the state vector  */
-static int mti=N+1; /* mti==N+1 means mt[N] is not initialized */
-
-/* initializes mt[N] with a seed */
-void mt_init_genrand(unsigned long s)
-{
-    mt[0]= s & 0xffffffffUL;
-    for (mti=1; mti<N; mti++) {
-        mt[mti] = 
-	    (1812433253UL * (mt[mti-1] ^ (mt[mti-1] >> 30)) + mti); 
-        /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */
-        /* In the previous versions, MSBs of the seed affect   */
-        /* only MSBs of the array mt[].                        */
-        /* 2002/01/09 modified by Makoto Matsumoto             */
-        mt[mti] &= 0xffffffffUL;
-        /* for >32 bit machines */
-    }
-}
-
-/* initialize by an array with array-length */
-/* init_key is the array for initializing keys */
-/* key_length is its length */
-/* slight change for C++, 2004/2/26 */
-void mt_init_by_array(unsigned long init_key[], int key_length)
-{
-    int i, j, k;
-    mt_init_genrand(19650218UL);
-    i=1; j=0;
-    k = (N>key_length ? N : key_length);
-    for (; k; k--) {
-        mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1664525UL))
-          + init_key[j] + j; /* non linear */
-        mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */
-        i++; j++;
-        if (i>=N) { mt[0] = mt[N-1]; i=1; }
-        if (j>=key_length) j=0;
-    }
-    for (k=N-1; k; k--) {
-        mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1566083941UL))
-          - i; /* non linear */
-        mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */
-        i++;
-        if (i>=N) { mt[0] = mt[N-1]; i=1; }
-    }
-
-    mt[0] = 0x80000000UL; /* MSB is 1; assuring non-zero initial array */ 
-}
-
-/* generates a random number on [0,0xffffffff]-interval */
-unsigned long mt_genrand_int32(void)
-{
-    unsigned long y;
-    static unsigned long mag01[2]={0x0UL, MATRIX_A};
-    /* mag01[x] = x * MATRIX_A  for x=0,1 */
-
-    if (mti >= N) { /* generate N words at one time */
-        int kk;
-
-        if (mti == N+1)   /* if mt_init_genrand() has not been called, */
-            mt_init_genrand(5489UL); /* a default initial seed is used */
-
-        for (kk=0;kk<N-M;kk++) {
-            y = (mt[kk]&UPPER_MASK)|(mt[kk+1]&LOWER_MASK);
-            mt[kk] = mt[kk+M] ^ (y >> 1) ^ mag01[y & 0x1UL];
-        }
-        for (;kk<N-1;kk++) {
-            y = (mt[kk]&UPPER_MASK)|(mt[kk+1]&LOWER_MASK);
-            mt[kk] = mt[kk+(M-N)] ^ (y >> 1) ^ mag01[y & 0x1UL];
-        }
-        y = (mt[N-1]&UPPER_MASK)|(mt[0]&LOWER_MASK);
-        mt[N-1] = mt[M-1] ^ (y >> 1) ^ mag01[y & 0x1UL];
-
-        mti = 0;
-    }
-  
-    y = mt[mti++];
-
-    /* Tempering */
-    y ^= (y >> 11);
-    y ^= (y << 7) & 0x9d2c5680UL;
-    y ^= (y << 15) & 0xefc60000UL;
-    y ^= (y >> 18);
-
-    return y;
-}
-
-/* generates a random number on [0,0x7fffffff]-interval */
-long mt_genrand_int31(void)
-{
-    return (long)( mt_genrand_int32()>>1);
-}
-
-/* generates a random number on [0,1]-real-interval */
-double mt_genrand_real1(void)
-{
-    return mt_genrand_int32()*(1.0/4294967295.0); 
-    /* divided by 2^32-1 */ 
-}
-
-/* generates a random number on [0,1)-real-interval */
-double mt_genrand_real2(void)
-{
-    return mt_genrand_int32()*(1.0/4294967296.0); 
-    /* divided by 2^32 */
-}
-
-/* generates a random number on (0,1)-real-interval */
-double mt_genrand_real3(void)
-{
-    return (((double) mt_genrand_int32()) + 0.5)*(1.0/4294967296.0); 
-    /* divided by 2^32 */
-}
-
-/* generates a random number on [0,1) with 53-bit resolution*/
-double mt_genrand_res53(void) 
-{ 
-    unsigned long a=mt_genrand_int32()>>5, b=mt_genrand_int32()>>6; 
-    return(a*67108864.0+b)*(1.0/9007199254740992.0); 
-} 
-/* These real versions are due to Isaku Wada, 2002/01/09 added */
-
-/*
-#include <stdio.h>
-
-int main(void)
-{
-    int i;
-    unsigned long init[4]={0x123, 0x234, 0x345, 0x456}, length=4;
-    mt_init_by_array(init, length);
-    printf("1000 outputs of genrand_int32()\n");
-    for (i=0; i<1000; i++) {
-      printf("%10lu ", mt_genrand_int32());
-      if (i%5==4) printf("\n");
-    }
-    printf("\n1000 outputs of genrand_real2()\n");
-    for (i=0; i<1000; i++) {
-      printf("%10.8f ", mt_genrand_real2());
-      if (i%5==4) printf("\n");
-    }
-    return 0;
-}
-*/
diff --git a/gi/pyp-topics/src/mt19937ar.h b/gi/pyp-topics/src/mt19937ar.h
deleted file mode 100644
index caab4045..00000000
--- a/gi/pyp-topics/src/mt19937ar.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/* mt19937ar.h
- *
- * Mark Johnson, 17th March 2006
- */
-
-#ifndef MT19937AR_H
-#define MT19937AR_H
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-  /* initializes mt[N] with a seed */
-  void mt_init_genrand(unsigned long s);
-
-  /* initialize by an array with array-length */
-  /* init_key is the array for initializing keys */
-  /* key_length is its length */
-  /* slight change for C++, 2004/2/26 */
-  void mt_init_by_array(unsigned long init_key[], int key_length);
-
-  /* generates a random number on [0,0xffffffff]-interval */
-  unsigned long mt_genrand_int32(void);
-
-  /* generates a random number on [0,0x7fffffff]-interval */
-  long mt_genrand_int31(void);
-
-  /* generates a random number on [0,1]-real-interval */
-  double mt_genrand_real1(void);
-
-  /* generates a random number on [0,1)-real-interval */
-  double mt_genrand_real2(void);
-
-  /* generates a random number on (0,1)-real-interval */
-  double mt_genrand_real3(void);
-
-  /* generates a random number on [0,1) with 53-bit resolution*/
-  double mt_genrand_res53(void);
-
-#ifdef __cplusplus
-};
-#endif
-
-#endif /* MT19937AR_H */
diff --git a/gi/pyp-topics/src/pyp-topics.cc b/gi/pyp-topics/src/pyp-topics.cc
deleted file mode 100644
index 4de52fd7..00000000
--- a/gi/pyp-topics/src/pyp-topics.cc
+++ /dev/null
@@ -1,499 +0,0 @@
-#include "timing.h"
-#include "pyp-topics.hh"
-#include "contexts_corpus.hh"
-
-//Dict const *dict;
-
-//#include <boost/date_time/posix_time/posix_time_types.hpp>
-void PYPTopics::sample_corpus(const Corpus& corpus, int samples,
-                              int freq_cutoff_start, int freq_cutoff_end,
-                              int freq_cutoff_interval,
-                              int max_contexts_per_document,
-                              F temp_start, F temp_end) {
-  Timer timer;
-  //dict = &((ContextsCorpus*) &corpus)->dict();
-
-  if (!m_backoff.get()) {
-    m_word_pyps.clear();
-    m_word_pyps.push_back(PYPs());
-  }
-
-  std::cerr << "\n Training with " << m_word_pyps.size()-1 << " backoff level"
-    << (m_word_pyps.size()==2 ? ":" : "s:") << std::endl;
-
-
-  for (int i=0; i<(int)m_word_pyps.size(); ++i)
-  {
-    m_word_pyps.at(i).reserve(m_num_topics);
-    for (int j=0; j<m_num_topics; ++j)
-      m_word_pyps.at(i).push_back(new PYP<int>(0.01, 1.0, m_seed));
-  }
-  std::cerr << std::endl;
-
-  m_document_pyps.reserve(corpus.num_documents());
-  for (int j=0; j<corpus.num_documents(); ++j)
-    m_document_pyps.push_back(new PYP<int>(0.01, 1.0, m_seed));
-
-  m_topic_p0 = 1.0/m_num_topics;
-  m_term_p0 = 1.0/(F)m_backoff->terms_at_level(m_word_pyps.size()-1);
-  //m_term_p0 = 1.0/corpus.num_types();
-  m_backoff_p0 = 1.0/corpus.num_documents();
-
-  std::cerr << " Documents: " << corpus.num_documents() << " Terms: "
-    << corpus.num_types() << std::endl;
-
-  int frequency_cutoff = freq_cutoff_start;
-  std::cerr << " Context frequency cutoff set to " << frequency_cutoff << std::endl;
-
-  timer.Reset();
-  // Initialisation pass
-  int document_id=0, topic_counter=0;
-  for (Corpus::const_iterator corpusIt=corpus.begin();
-       corpusIt != corpus.end(); ++corpusIt, ++document_id) {
-    m_corpus_topics.push_back(DocumentTopics(corpusIt->size(), 0));
-
-    int term_index=0;
-    for (Document::const_iterator docIt=corpusIt->begin();
-         docIt != corpusIt->end(); ++docIt, ++term_index) {
-      topic_counter++;
-      Term term = *docIt;
-
-      // sample a new_topic
-      //int new_topic = (topic_counter % m_num_topics);
-      int freq = corpus.context_count(term);
-      int new_topic = -1;
-      if (freq > frequency_cutoff
-          && (!max_contexts_per_document || term_index < max_contexts_per_document)) {
-        //new_topic = sample(document_id, term);
-        //new_topic = document_id % m_num_topics;
-        new_topic = (int) (rnd() * m_num_topics);
-
-        // add the new topic to the PYPs
-        increment(term, new_topic);
-
-        if (m_use_topic_pyp) {
-          F p0 = m_topic_pyp.prob(new_topic, m_topic_p0);
-          int table_delta = m_document_pyps[document_id].increment(new_topic, p0);
-          if (table_delta)
-            m_topic_pyp.increment(new_topic, m_topic_p0);
-        }
-        else m_document_pyps[document_id].increment(new_topic, m_topic_p0);
-      }
-
-      m_corpus_topics[document_id][term_index] = new_topic;
-    }
-  }
-  std::cerr << "  Initialized in " << timer.Elapsed() << " seconds\n";
-
-  int* randomDocIndices = new int[corpus.num_documents()];
-  for (int i = 0; i < corpus.num_documents(); ++i)
-	  randomDocIndices[i] = i;
-
-  if (num_jobs < max_threads)
-    num_jobs = max_threads;
-  int job_incr = (int) ( (float)m_document_pyps.size() / float(num_jobs) );
-
-  // Sampling phase
-  for (int curr_sample=0; curr_sample < samples; ++curr_sample) {
-    if (freq_cutoff_interval > 0 && curr_sample != 1
-        && curr_sample % freq_cutoff_interval == 1
-        && frequency_cutoff > freq_cutoff_end) {
-      frequency_cutoff--;
-      std::cerr << "\n Context frequency cutoff set to " << frequency_cutoff << std::endl;
-    }
-
-    F temp = 1.0 / (temp_start - curr_sample*(temp_start-temp_end)/samples);
-    std::cerr << "\n  -- Sample " << curr_sample << " (T=" << temp << ") "; std::cerr.flush();
-
-    // Randomize the corpus indexing array
-    int tmp;
-    int processed_terms=0;
-    /*
-    for (int i = corpus.num_documents()-1; i > 0; --i)
-    {
-        //i+1 since j \in [0,i] but rnd() \in [0,1)
-    	int j = (int)(rnd() * (i+1));
-      assert(j >= 0 && j <= i);
-     	tmp = randomDocIndices[i];
-    	randomDocIndices[i] = randomDocIndices[j];
-    	randomDocIndices[j] = tmp;
-    }
-    */
-
-    // for each document in the corpus
-    int document_id;
-    for (int i=0; i<corpus.num_documents(); ++i) {
-    	document_id = randomDocIndices[i];
-
-      // for each term in the document
-      int term_index=0;
-      Document::const_iterator docEnd = corpus.at(document_id).end();
-      for (Document::const_iterator docIt=corpus.at(document_id).begin();
-           docIt != docEnd; ++docIt, ++term_index) {
-        if (max_contexts_per_document && term_index > max_contexts_per_document)
-          break;
-        
-        Term term = *docIt;
-
-        int freq = corpus.context_count(term);
-        if (freq < frequency_cutoff)
-          continue;
-
-        processed_terms++;
-
-        // remove the prevous topic from the PYPs
-        int current_topic = m_corpus_topics[document_id][term_index];
-        // a negative label mean that term hasn't been sampled yet
-        if (current_topic >= 0) {
-          decrement(term, current_topic);
-
-          int table_delta = m_document_pyps[document_id].decrement(current_topic);
-          if (m_use_topic_pyp && table_delta < 0)
-            m_topic_pyp.decrement(current_topic);
-        }
-
-        // sample a new_topic
-        int new_topic = sample(document_id, term, temp);
-        //std::cerr << "TERM: " << dict->Convert(term) << " (" << term << ") " << " Old Topic: " 
-        //  << current_topic << " New Topic: " << new_topic << "\n" << std::endl;
-
-        // add the new topic to the PYPs
-        m_corpus_topics[document_id][term_index] = new_topic;
-        increment(term, new_topic);
-
-        if (m_use_topic_pyp) {
-          F p0 = m_topic_pyp.prob(new_topic, m_topic_p0);
-          int table_delta = m_document_pyps[document_id].increment(new_topic, p0);
-          if (table_delta)
-            m_topic_pyp.increment(new_topic, m_topic_p0);
-        }
-        else m_document_pyps[document_id].increment(new_topic, m_topic_p0);
-      }
-      if (document_id && document_id % 10000 == 0) {
-        std::cerr << "."; std::cerr.flush();
-      }
-    }
-    std::cerr << " ||| LLH= " << log_likelihood();
-
-    if (curr_sample != 0 && curr_sample % 10 == 0) {
-    //if (true) {
-      std::cerr << " ||| time=" << (timer.Elapsed() / 10.0) << " sec/sample" << std::endl;
-      timer.Reset();
-      std::cerr << "     ... Resampling hyperparameters (";
-      
-      // resample the hyperparamters
-      F log_p=0.0;
-      if (max_threads == 1)
-      { 
-        std::cerr << "1 thread)" << std::endl; std::cerr.flush();
-        log_p += hresample_topics();
-        log_p += hresample_docs(0, m_document_pyps.size());
-      }
-      else
-      { //parallelize
-        std::cerr << max_threads << " threads, " << num_jobs << " jobs)" << std::endl; std::cerr.flush();
-        
-        WorkerPool<JobReturnsF, F> pool(max_threads); 
-        int i=0, sz = m_document_pyps.size();
-        //documents...
-        while (i <= sz - 2*job_incr)
-        {    
-          JobReturnsF job = boost::bind(&PYPTopics::hresample_docs, this, i, i+job_incr);
-          pool.addJob(job);
-          i += job_incr;
-        }
-        //  do all remaining documents
-        JobReturnsF job = boost::bind(&PYPTopics::hresample_docs, this, i,sz);
-        pool.addJob(job);
-        
-        //topics...
-        JobReturnsF topics_job = boost::bind(&PYPTopics::hresample_topics, this);
-        pool.addJob(topics_job);
-
-        log_p += pool.get_result(); //blocks
-
-      }
-
-      if (m_use_topic_pyp) {
-        m_topic_pyp.resample_prior(rnd);
-        log_p += m_topic_pyp.log_restaurant_prob();
-      }
-
-      std::cerr.precision(10);
-      std::cerr << " ||| LLH=" << log_likelihood() << " ||| resampling time=" << timer.Elapsed() << " sec" << std::endl;
-      timer.Reset();
-
-      int k=0;
-      std::cerr << "Topics distribution: ";
-      std::cerr.precision(2);
-      for (PYPs::iterator pypIt=m_word_pyps.front().begin();
-           pypIt != m_word_pyps.front().end(); ++pypIt, ++k) {
-        if (k % 5 == 0) std::cerr << std::endl << '\t';
-        std::cerr << "<" << k << ":" << pypIt->num_customers() << ","
-          << pypIt->num_types() << "," << m_topic_pyp.prob(k, m_topic_p0) << "> ";
-      }
-      std::cerr.precision(10);
-      std::cerr << std::endl;
-    }
-  }
-  delete [] randomDocIndices;
-}
-
-PYPTopics::F PYPTopics::hresample_docs(int start, int end)
-{
-  int resample_counter=0;
-  F log_p = 0.0;
-  assert(start >= 0);
-  assert(end >= 0);
-  assert(start <= end);
-  for (int i=start; i < end; ++i)
-  {
-    m_document_pyps[i].resample_prior(rnd);
-    log_p += m_document_pyps[i].log_restaurant_prob();
-    if (resample_counter++ % 5000 == 0) {
-      std::cerr << "."; std::cerr.flush();
-    }
-  }
-  return log_p;
-}
-
-PYPTopics::F PYPTopics::hresample_topics()
-{
-  F log_p = 0.0;
-  for (std::vector<PYPs>::iterator levelIt=m_word_pyps.begin();
-      levelIt != m_word_pyps.end(); ++levelIt) {
-    for (PYPs::iterator pypIt=levelIt->begin();
-        pypIt != levelIt->end(); ++pypIt) {
-
-      pypIt->resample_prior(rnd);
-      log_p += pypIt->log_restaurant_prob();
-    }
-    std::cerr << log_p << std::endl;
-  }
-  return log_p;
-}
-
-PYPTopics::F PYPTopics::log_likelihood() const 
-{
-  F log_p = 0.0;
-
-  // LLH of topic term distribution
-  size_t i=0;
-  for (std::vector<PYPs>::const_iterator levelIt=m_word_pyps.begin();
-      levelIt != m_word_pyps.end(); ++levelIt, ++i) {
-    for (PYPs::const_iterator pypIt=levelIt->begin();
-        pypIt != levelIt->end(); ++pypIt, ++i) {
-      log_p += pypIt->log_restaurant_prob();
-
-      if (i == m_word_pyps.size()-1)
-        log_p += (pypIt->num_tables() * -log(m_backoff->terms_at_level(i)));
-      else
-        log_p += (pypIt->num_tables() * log(m_term_p0));
-    }
-  }
-  std::cerr << " TERM LLH: " << log_p << " "; //std::endl;
-
-  // LLH of document topic distribution
-  for (size_t i=0; i < m_document_pyps.size(); ++i) {
-    log_p += m_document_pyps[i].log_restaurant_prob();
-    if (!m_use_topic_pyp) log_p += (m_document_pyps[i].num_tables() * m_topic_p0);
-  }
-  if (m_use_topic_pyp) {
-    log_p += m_topic_pyp.log_restaurant_prob();
-    log_p += (m_topic_pyp.num_tables() * log(m_topic_p0));
-  }
-
-  return log_p;
-}
-
-void PYPTopics::decrement(const Term& term, int topic, int level) {
-  //std::cerr << "PYPTopics::decrement(" << term << "," << topic << "," << level << ")" << std::endl;
-  int table_delta = m_word_pyps.at(level).at(topic).decrement(term);
-  if (table_delta && m_backoff.get()) {
-    Term backoff_term = (*m_backoff)[term];
-    if (!m_backoff->is_null(backoff_term))
-      decrement(backoff_term, topic, level+1);
-  }
-}
-
-void PYPTopics::increment(const Term& term, int topic, int level) {
-  //std::cerr << "PYPTopics::increment(" << term << "," << topic << "," << level << ")" << std::endl;
-  int table_delta = m_word_pyps.at(level).at(topic).increment(term, word_pyps_p0(term, topic, level));
-
-  if (table_delta && m_backoff.get()) {
-    Term backoff_term = (*m_backoff)[term];
-    if (!m_backoff->is_null(backoff_term))
-      increment(backoff_term, topic, level+1);
-  }
-}
-
-int PYPTopics::sample(const DocumentId& doc, const Term& term, F inv_temp) {
-  // First pass: collect probs
-  F sum=0.0;
-  std::vector<F> sums;
-  for (int k=0; k<m_num_topics; ++k) {
-    F p_w_k = prob(term, k);
-
-    F topic_prob = m_topic_p0;
-    if (m_use_topic_pyp) topic_prob = m_topic_pyp.prob(k, m_topic_p0);
-
-    //F p_k_d = m_document_pyps[doc].prob(k, topic_prob);
-    F p_k_d = m_document_pyps[doc].unnormalised_prob(k, topic_prob);
-
-    F prob = p_w_k*p_k_d;
-    /*
-    if (prob < 0.0) { std::cerr << "\n\n" << prob << " " << p_w_k << " " << p_k_d << std::endl; assert(false); }
-    if (prob > 1.0) { std::cerr << "\n\n" << prob << " " << p_w_k << " " << p_k_d << std::endl; assert(false); }
-    assert (pow(prob, inv_temp) >= 0.0);
-    assert (pow(prob, inv_temp) <= 1.0);
-    */
-    sum += pow(prob, inv_temp);
-    sums.push_back(sum);
-  }
-  // Second pass: sample a topic
-  F cutoff = rnd() * sum;
-  for (int k=0; k<m_num_topics; ++k) {
-    if (cutoff <= sums[k])
-      return k;
-  }
-  assert(false);
-}
-
-PYPTopics::F PYPTopics::word_pyps_p0(const Term& term, int topic, int level) const {
-  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
-  //std::cerr << "PYPTopics::word_pyps_p0(" << term << "," << topic << "," << level << ")" << std::endl;
-
-  F p0 = m_term_p0;
-  if (m_backoff.get()) {
-    //static F fudge=m_backoff_p0; // TODO
-
-    Term backoff_term = (*m_backoff)[term];
-    //std::cerr << "T: " << term << " BO: " << backoff_term << std::endl;
-    if (!m_backoff->is_null(backoff_term)) {
-      assert (level < m_backoff->order());
-      //p0 = (1.0/(F)m_backoff->terms_at_level(level))*prob(backoff_term, topic, level+1);
-      p0 = m_term_p0*prob(backoff_term, topic, level+1);
-      p0 = prob(backoff_term, topic, level+1);
-    }
-    else
-      p0 = (1.0/(F) m_backoff->terms_at_level(level));
-      //p0 = m_term_p0;
-  }
-  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
-  //std::cerr << "PYPTopics::word_pyps_p0(" << term << "," << topic << "," << level << ") = " << p0 << std::endl;
-  return p0;
-}
-
-PYPTopics::F PYPTopics::prob(const Term& term, int topic, int level) const {
-  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
-  //std::cerr << "PYPTopics::prob(" << dict->Convert(term) << "," << topic << "," << level << ")" << std::endl;
-
-  F p0 = word_pyps_p0(term, topic, level);
-  F p_w_k = m_word_pyps.at(level).at(topic).prob(term, p0);
-
-  /*
-  for (int i=0; i<level+1; ++i) std::cerr << "  ";
-  std::cerr << "PYPTopics::prob(" << dict->Convert(term) << "," << topic << "," << level << ") = " << p_w_k << std::endl;
-  for (int i=0; i<level+1; ++i) std::cerr << "  ";
-  m_word_pyps.at(level).at(topic).debug_info(std::cerr);
-  */
-  return p_w_k;
-}
-
-int PYPTopics::max_topic() const {
-  if (!m_use_topic_pyp)
-    return -1;
-
-  F current_max=0.0;
-  int current_topic=-1;
-  for (int k=0; k<m_num_topics; ++k) {
-    F prob = m_topic_pyp.prob(k, m_topic_p0);
-    if (prob > current_max) {
-      current_max = prob;
-      current_topic = k;
-    }
-  }
-  assert(current_topic >= 0);
-  return current_topic;
-}
-
-std::pair<int,PYPTopics::F> PYPTopics::max(const DocumentId& doc) const {
-  //std::cerr << "PYPTopics::max(" << doc << "," << term << ")" << std::endl;
-  // collect probs
-  F current_max=0.0;
-  int current_topic=-1;
-  for (int k=0; k<m_num_topics; ++k) {
-    //F p_w_k = prob(term, k);
-
-    F topic_prob = m_topic_p0;
-    if (m_use_topic_pyp)
-      topic_prob = m_topic_pyp.prob(k, m_topic_p0);
-
-    F prob = 0;
-    if (doc < 0) prob = topic_prob;
-    else         prob = m_document_pyps[doc].prob(k, topic_prob);
-
-    if (prob > current_max) {
-      current_max = prob;
-      current_topic = k;
-    }
-  }
-  assert(current_topic >= 0);
-  assert(current_max >= 0);
-  return std::make_pair(current_topic, current_max);
-}
-
-std::pair<int,PYPTopics::F> PYPTopics::max(const DocumentId& doc, const Term& term) const {
-  //std::cerr << "PYPTopics::max(" << doc << "," << term << ")" << std::endl;
-  // collect probs
-  F current_max=0.0;
-  int current_topic=-1;
-  for (int k=0; k<m_num_topics; ++k) {
-    F p_w_k = prob(term, k);
-
-    F topic_prob = m_topic_p0;
-    if (m_use_topic_pyp)
-      topic_prob = m_topic_pyp.prob(k, m_topic_p0);
-
-    F p_k_d = 0;
-    if (doc < 0) p_k_d = topic_prob;
-    else         p_k_d = m_document_pyps[doc].prob(k, topic_prob);
-
-    F prob = (p_w_k*p_k_d);
-    if (prob > current_max) {
-      current_max = prob;
-      current_topic = k;
-    }
-  }
-  assert(current_topic >= 0);
-  assert(current_max >= 0);
-  return std::make_pair(current_topic,current_max);
-}
-
-std::ostream& PYPTopics::print_document_topics(std::ostream& out) const {
-  for (CorpusTopics::const_iterator corpusIt=m_corpus_topics.begin();
-       corpusIt != m_corpus_topics.end(); ++corpusIt) {
-    int term_index=0;
-    for (DocumentTopics::const_iterator docIt=corpusIt->begin();
-         docIt != corpusIt->end(); ++docIt, ++term_index) {
-      if (term_index) out << " ";
-      out << *docIt;
-    }
-    out << std::endl;
-  }
-  return out;
-}
-
-std::ostream& PYPTopics::print_topic_terms(std::ostream& out) const {
-  for (PYPs::const_iterator pypsIt=m_word_pyps.front().begin();
-       pypsIt != m_word_pyps.front().end(); ++pypsIt) {
-    int term_index=0;
-    for (PYP<int>::const_iterator termIt=pypsIt->begin();
-         termIt != pypsIt->end(); ++termIt, ++term_index) {
-      if (term_index) out << " ";
-      out << termIt->first << ":" << termIt->second;
-    }
-    out << std::endl;
-  }
-  return out;
-}
diff --git a/gi/pyp-topics/src/pyp-topics.hh b/gi/pyp-topics/src/pyp-topics.hh
deleted file mode 100644
index 3a910540..00000000
--- a/gi/pyp-topics/src/pyp-topics.hh
+++ /dev/null
@@ -1,98 +0,0 @@
-#ifndef PYP_TOPICS_HH
-#define PYP_TOPICS_HH
-
-#include <vector>
-#include <iostream>
-#include <boost/ptr_container/ptr_vector.hpp>
-
-#include <boost/random/uniform_real.hpp>
-#include <boost/random/variate_generator.hpp>
-#include <boost/random/mersenne_twister.hpp>
-
-#include "pyp.hh"
-#include "corpus.hh"
-#include "workers.hh"
-
-class PYPTopics {
-public:
-  typedef std::vector<int> DocumentTopics;
-  typedef std::vector<DocumentTopics> CorpusTopics;
-  typedef long double F;
-
-public:
-  PYPTopics(int num_topics, bool use_topic_pyp=false, unsigned long seed = 0,
-        int max_threads = 1, int num_jobs = 1) 
-    : m_num_topics(num_topics), m_word_pyps(1), 
-    m_topic_pyp(0.5,1.0,seed), m_use_topic_pyp(use_topic_pyp),
-    m_seed(seed),
-    uni_dist(0,1), rng(seed == 0 ? (unsigned long)this : seed), 
-    rnd(rng, uni_dist), max_threads(max_threads), num_jobs(num_jobs) {}
-
-  void sample_corpus(const Corpus& corpus, int samples,
-                     int freq_cutoff_start=0, int freq_cutoff_end=0, 
-                     int freq_cutoff_interval=0,
-                     int max_contexts_per_document=0,
-                     F temp_start=1.0, F temp_end=1.0);
-
-  int sample(const DocumentId& doc, const Term& term, F inv_temp=1.0);
-  std::pair<int,F> max(const DocumentId& doc, const Term& term) const;
-  std::pair<int,F> max(const DocumentId& doc) const;
-  int max_topic() const;
-
-  void set_backoff(const std::string& filename) {
-    m_backoff.reset(new TermBackoff);
-    m_backoff->read(filename);
-    m_word_pyps.clear();
-    m_word_pyps.resize(m_backoff->order(), PYPs());
-  }
-  void set_backoff(TermBackoffPtr backoff) {
-    m_backoff = backoff;
-    m_word_pyps.clear();
-    m_word_pyps.resize(m_backoff->order(), PYPs());
-  }
-
-  F prob(const Term& term, int topic, int level=0) const;
-  void decrement(const Term& term, int topic, int level=0);
-  void increment(const Term& term, int topic, int level=0);
-
-  F log_likelihood() const;
-
-  std::ostream& print_document_topics(std::ostream& out) const;
-  std::ostream& print_topic_terms(std::ostream& out) const;
-
-private:
-  F word_pyps_p0(const Term& term, int topic, int level) const;
-
-  int m_num_topics;
-  F m_term_p0, m_topic_p0, m_backoff_p0;
-
-  CorpusTopics m_corpus_topics;
-  typedef boost::ptr_vector< PYP<int> > PYPs;
-  PYPs m_document_pyps;
-  std::vector<PYPs> m_word_pyps;
-  PYP<int> m_topic_pyp;
-  bool m_use_topic_pyp;
-
-  unsigned long m_seed;
-
-  typedef boost::mt19937 base_generator_type;
-  typedef boost::uniform_real<> uni_dist_type;
-  typedef boost::variate_generator<base_generator_type&, uni_dist_type> gen_type;
-
-  uni_dist_type uni_dist;
-  base_generator_type rng; //this gets the seed
-  gen_type rnd; //instantiate: rnd(rng, uni_dist)
-                //call: rnd() generates uniform on [0,1)
-
-  typedef boost::function<F()> JobReturnsF;
-
-  F hresample_docs(int start, int end); //does i in [start, end)
-
-  F hresample_topics();
-  
-  int max_threads;
-  int num_jobs;
-  TermBackoffPtr m_backoff;
-};
-
-#endif // PYP_TOPICS_HH
diff --git a/gi/pyp-topics/src/pyp.hh b/gi/pyp-topics/src/pyp.hh
deleted file mode 100644
index b1cb62be..00000000
--- a/gi/pyp-topics/src/pyp.hh
+++ /dev/null
@@ -1,566 +0,0 @@
-#ifndef _pyp_hh
-#define _pyp_hh
-
-#include "slice-sampler.h"
-#include <math.h>
-#include <map>
-#include <tr1/unordered_map>
-//#include <google/sparse_hash_map>
-
-#include <boost/random/uniform_real.hpp>
-#include <boost/random/variate_generator.hpp>
-#include <boost/random/mersenne_twister.hpp>
-
-#include "log_add.h"
-#include "mt19937ar.h"
-
-//
-// Pitman-Yor process with customer and table tracking
-//
-
-template <typename Dish, typename Hash=std::tr1::hash<Dish> >
-class PYP : protected std::tr1::unordered_map<Dish, int, Hash>
-//class PYP : protected google::sparse_hash_map<Dish, int, Hash>
-{
-public:
-  using std::tr1::unordered_map<Dish,int>::const_iterator;
-  using std::tr1::unordered_map<Dish,int>::iterator;
-  using std::tr1::unordered_map<Dish,int>::begin;
-  using std::tr1::unordered_map<Dish,int>::end;
-//  using google::sparse_hash_map<Dish,int>::const_iterator;
-//  using google::sparse_hash_map<Dish,int>::iterator;
-//  using google::sparse_hash_map<Dish,int>::begin;
-//  using google::sparse_hash_map<Dish,int>::end;
-
-  PYP(double a, double b, unsigned long seed = 0, Hash hash=Hash());
-
-  virtual int increment(Dish d, double p0);
-  virtual int decrement(Dish d);
-
-  // lookup functions
-  int count(Dish d) const;
-  double prob(Dish dish, double p0) const;
-  double prob(Dish dish, double dcd, double dca, 
-              double dtd, double dta, double p0) const;
-  double unnormalised_prob(Dish dish, double p0) const;
-
-  int num_customers() const { return _total_customers; }
-  int num_types() const { return std::tr1::unordered_map<Dish,int>::size(); }
-  //int num_types() const { return google::sparse_hash_map<Dish,int>::size(); }
-  bool empty() const { return _total_customers == 0; }
-
-  double log_prob(Dish dish, double log_p0) const;
-  // nb. d* are NOT logs
-  double log_prob(Dish dish, double dcd, double dca, 
-                       double dtd, double dta, double log_p0) const;
-
-  int num_tables(Dish dish) const;
-  int num_tables() const;
-
-  double a() const { return _a; }
-  void set_a(double a) { _a = a; }
-
-  double b() const { return _b; }
-  void set_b(double b) { _b = b; }
-
-  virtual void clear();
-  std::ostream& debug_info(std::ostream& os) const;
-
-  double log_restaurant_prob() const;
-  double log_prior() const;
-  static double log_prior_a(double a, double beta_a, double beta_b);
-  static double log_prior_b(double b, double gamma_c, double gamma_s);
-
-  template <typename Uniform01>
-    void resample_prior(Uniform01& rnd);
-  template <typename Uniform01>
-    void resample_prior_a(Uniform01& rnd);
-  template <typename Uniform01>
-    void resample_prior_b(Uniform01& rnd);
-
-protected:
-  double _a, _b; // parameters of the Pitman-Yor distribution
-  double _a_beta_a, _a_beta_b; // parameters of Beta prior on a
-  double _b_gamma_s, _b_gamma_c; // parameters of Gamma prior on b
-
-  struct TableCounter {
-    TableCounter() : tables(0) {};
-    int tables;
-    std::map<int, int> table_histogram; // num customers at table -> number tables
-  };
-  typedef std::tr1::unordered_map<Dish, TableCounter, Hash> DishTableType;
-  //typedef google::sparse_hash_map<Dish, TableCounter, Hash> DishTableType;
-  DishTableType _dish_tables;
-  int _total_customers, _total_tables;
-
-  typedef boost::mt19937 base_generator_type;
-  typedef boost::uniform_real<> uni_dist_type;
-  typedef boost::variate_generator<base_generator_type&, uni_dist_type> gen_type;
-
-//  uni_dist_type uni_dist;
-//  base_generator_type rng; //this gets the seed
-//  gen_type rnd; //instantiate: rnd(rng, uni_dist)
-                //call: rnd() generates uniform on [0,1)
-
-  // Function objects for calculating the parts of the log_prob for 
-  // the parameters a and b
-  struct resample_a_type {
-    int n, m; double b, a_beta_a, a_beta_b;
-    const DishTableType& dish_tables;
-    resample_a_type(int n, int m, double b, double a_beta_a, 
-                    double a_beta_b, const DishTableType& dish_tables)
-      : n(n), m(m), b(b), a_beta_a(a_beta_a), a_beta_b(a_beta_b), dish_tables(dish_tables) {}
-
-    double operator() (double proposed_a) const {
-      double log_prior = log_prior_a(proposed_a, a_beta_a, a_beta_b);
-      double log_prob = 0.0;
-      double lgamma1a = lgamma(1.0 - proposed_a);
-      for (typename DishTableType::const_iterator dish_it=dish_tables.begin(); dish_it != dish_tables.end(); ++dish_it) 
-        for (std::map<int, int>::const_iterator table_it=dish_it->second.table_histogram.begin(); 
-             table_it !=dish_it->second.table_histogram.end(); ++table_it) 
-          log_prob += (table_it->second * (lgamma(table_it->first - proposed_a) - lgamma1a));
-
-      log_prob += (proposed_a == 0.0 ? (m-1.0)*log(b) 
-                   : ((m-1.0)*log(proposed_a) + lgamma((m-1.0) + b/proposed_a) - lgamma(b/proposed_a)));
-      assert(std::isfinite(log_prob));
-      return log_prob + log_prior;
-    }
-  };
-
-  struct resample_b_type {
-    int n, m; double a, b_gamma_c, b_gamma_s;
-    resample_b_type(int n, int m, double a, double b_gamma_c, double b_gamma_s)
-      : n(n), m(m), a(a), b_gamma_c(b_gamma_c), b_gamma_s(b_gamma_s) {}
-
-    double operator() (double proposed_b) const {
-      double log_prior = log_prior_b(proposed_b, b_gamma_c, b_gamma_s);
-      double log_prob = 0.0;
-      log_prob += (a == 0.0  ? (m-1.0)*log(proposed_b) 
-                  : ((m-1.0)*log(a) + lgamma((m-1.0) + proposed_b/a) - lgamma(proposed_b/a)));
-      log_prob += (lgamma(1.0+proposed_b) - lgamma(n+proposed_b));
-      return log_prob + log_prior;
-    }
-  };
-   
-  /* lbetadist() returns the log probability density of x under a Beta(alpha,beta)
-   * distribution. - copied from Mark Johnson's gammadist.c
-   */
-  static long double lbetadist(long double x, long double alpha, long double beta);
-
-  /* lgammadist() returns the log probability density of x under a Gamma(alpha,beta)
-   * distribution - copied from Mark Johnson's gammadist.c
-   */
-  static long double lgammadist(long double x, long double alpha, long double beta);
-
-};
-
-template <typename Dish, typename Hash>
-PYP<Dish,Hash>::PYP(double a, double b, unsigned long seed, Hash)
-: std::tr1::unordered_map<Dish, int, Hash>(10), _a(a), _b(b), 
-//: google::sparse_hash_map<Dish, int, Hash>(10), _a(a), _b(b), 
-  _a_beta_a(1), _a_beta_b(1), _b_gamma_s(1), _b_gamma_c(1),
-  //_a_beta_a(1), _a_beta_b(1), _b_gamma_s(10), _b_gamma_c(0.1),
-  _total_customers(0), _total_tables(0)//,
-  //uni_dist(0,1), rng(seed == 0 ? (unsigned long)this : seed), rnd(rng, uni_dist)
-{
-//  std::cerr << "\t##PYP<Dish,Hash>::PYP(a=" << _a << ",b=" << _b << ")" << std::endl;
-  //set_deleted_key(-std::numeric_limits<Dish>::max());
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::prob(Dish dish, double p0) const
-{
-  int c = count(dish), t = num_tables(dish);
-  double r = num_tables() * _a + _b;
-  //std::cerr << "\t\t\t\tPYP<Dish,Hash>::prob(" << dish << "," << p0 << ") c=" << c << " r=" << r << std::endl;
-  if (c > 0)
-    return (c - _a * t + r * p0) / (num_customers() + _b);
-  else
-    return r * p0 / (num_customers() + _b);
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::unnormalised_prob(Dish dish, double p0) const
-{
-  int c = count(dish), t = num_tables(dish);
-  double r = num_tables() * _a + _b;
-  if (c > 0) return (c - _a * t + r * p0);
-  else       return r * p0;
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::prob(Dish dish, double dcd, double dca, 
-                     double dtd, double dta, double p0)
-const
-{
-  int c = count(dish) + dcd, t = num_tables(dish) + dtd;
-  double r = (num_tables() + dta) * _a + _b;
-  if (c > 0)
-    return (c - _a * t + r * p0) / (num_customers() + dca + _b);
-  else
-    return r * p0 / (num_customers() + dca + _b);
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_prob(Dish dish, double log_p0) const
-{
-  using std::log;
-  int c = count(dish), t = num_tables(dish);
-  double r = log(num_tables() * _a + b);
-  if (c > 0)
-    return Log<double>::add(log(c - _a * t), r + log_p0)
-      - log(num_customers() + _b);
-  else
-    return r + log_p0 - log(num_customers() + b);
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_prob(Dish dish, double dcd, double dca, 
-                         double dtd, double dta, double log_p0)
-const
-{
-  using std::log;
-  int c = count(dish) + dcd, t = num_tables(dish) + dtd;
-  double r = log((num_tables() + dta) * _a + b);
-  if (c > 0)
-    return Log<double>::add(log(c - _a * t), r + log_p0)
-      - log(num_customers() + dca + _b);
-  else
-    return r + log_p0 - log(num_customers() + dca + b);
-}
-
-template <typename Dish, typename Hash>
-int 
-PYP<Dish,Hash>::increment(Dish dish, double p0) {
-  int delta = 0;
-  TableCounter &tc = _dish_tables[dish];
-
-  // seated on a new or existing table?
-  int c = count(dish), t = num_tables(dish), T = num_tables();
-  double pshare = (c > 0) ? (c - _a*t) : 0.0;
-  double pnew = (_b + _a*T) * p0;
-  assert (pshare >= 0.0);
-  //assert (pnew > 0.0);
-
-  //if (rnd() < pnew / (pshare + pnew)) {
-  if (mt_genrand_res53() < pnew / (pshare + pnew)) {
-    // assign to a new table
-    tc.tables += 1;
-    tc.table_histogram[1] += 1;
-    _total_tables += 1;
-    delta = 1;
-  }
-  else {
-    // randomly assign to an existing table
-    // remove constant denominator from inner loop
-    //double r = rnd() * (c - _a*t);
-    double r = mt_genrand_res53() * (c - _a*t);
-    for (std::map<int,int>::iterator
-         hit = tc.table_histogram.begin();
-         hit != tc.table_histogram.end(); ++hit) {
-      r -= ((hit->first - _a) * hit->second);
-      if (r <= 0) {
-        tc.table_histogram[hit->first+1] += 1;
-        hit->second -= 1;
-        if (hit->second == 0)
-          tc.table_histogram.erase(hit);
-        break;
-      }
-    }
-    if (r > 0) {
-      std::cerr << r << " " << c << " " << _a << " " << t << std::endl;
-      assert(false);
-    }
-    delta = 0;
-  }
-
-  std::tr1::unordered_map<Dish,int,Hash>::operator[](dish) += 1;
-  //google::sparse_hash_map<Dish,int,Hash>::operator[](dish) += 1;
-  _total_customers += 1;
-
-  return delta;
-}
-
-template <typename Dish, typename Hash>
-int 
-PYP<Dish,Hash>::count(Dish dish) const
-{
-  typename std::tr1::unordered_map<Dish, int>::const_iterator 
-  //typename google::sparse_hash_map<Dish, int>::const_iterator 
-    dcit = find(dish);
-  if (dcit != end())
-    return dcit->second;
-  else
-    return 0;
-}
-
-template <typename Dish, typename Hash>
-int 
-PYP<Dish,Hash>::decrement(Dish dish)
-{
-  typename std::tr1::unordered_map<Dish, int>::iterator dcit = find(dish);
-  //typename google::sparse_hash_map<Dish, int>::iterator dcit = find(dish);
-  if (dcit == end()) {
-    std::cerr << dish << std::endl;
-    assert(false);
-  } 
-
-  int delta = 0;
-
-  typename std::tr1::unordered_map<Dish, TableCounter>::iterator dtit = _dish_tables.find(dish);
-  //typename google::sparse_hash_map<Dish, TableCounter>::iterator dtit = _dish_tables.find(dish);
-  if (dtit == _dish_tables.end()) {
-    std::cerr << dish << std::endl;
-    assert(false);
-  } 
-  TableCounter &tc = dtit->second;
-
-  //std::cerr << "\tdecrement for " << dish << "\n";
-  //std::cerr << "\tBEFORE histogram: " << tc.table_histogram << " ";
-  //std::cerr << "count: " << count(dish) << " ";
-  //std::cerr << "tables: " << tc.tables << "\n";
-
-  //double r = rnd() * count(dish);
-  double r = mt_genrand_res53() * count(dish);
-  for (std::map<int,int>::iterator hit = tc.table_histogram.begin();
-       hit != tc.table_histogram.end(); ++hit)
-  {
-    //r -= (hit->first - _a) * hit->second;
-    r -= (hit->first) * hit->second;
-    if (r <= 0)
-    {
-      if (hit->first > 1)
-        tc.table_histogram[hit->first-1] += 1;
-      else
-      {
-        delta = -1;
-        tc.tables -= 1;
-        _total_tables -= 1;
-      }
-
-      hit->second -= 1;
-      if (hit->second == 0) tc.table_histogram.erase(hit);
-      break;
-    }
-  }
-  if (r > 0) {
-    std::cerr << r << " " << count(dish) << " " << _a << " " << num_tables(dish) << std::endl;
-    assert(false);
-  }
-
-  // remove the customer
-  dcit->second -= 1;
-  _total_customers -= 1;
-  assert(dcit->second >= 0);
-  if (dcit->second == 0) {
-    erase(dcit);
-    _dish_tables.erase(dtit);
-    //std::cerr << "\tAFTER histogram: Empty\n";
-  }
-  else {
-    //std::cerr << "\tAFTER histogram: " << _dish_tables[dish].table_histogram << " ";
-    //std::cerr << "count: " << count(dish) << " ";
-    //std::cerr << "tables: " << _dish_tables[dish].tables << "\n";
-  }
-
-  return delta;
-}
-
-template <typename Dish, typename Hash>
-int 
-PYP<Dish,Hash>::num_tables(Dish dish) const
-{
-  typename std::tr1::unordered_map<Dish, TableCounter, Hash>::const_iterator 
-  //typename google::sparse_hash_map<Dish, TableCounter, Hash>::const_iterator 
-    dtit = _dish_tables.find(dish);
-
-  //assert(dtit != _dish_tables.end());
-  if (dtit == _dish_tables.end())
-    return 0;
-
-  return dtit->second.tables;
-}
-
-template <typename Dish, typename Hash>
-int 
-PYP<Dish,Hash>::num_tables() const
-{
-  return _total_tables;
-}
-
-template <typename Dish, typename Hash>
-std::ostream&
-PYP<Dish,Hash>::debug_info(std::ostream& os) const
-{
-  int hists = 0, tables = 0;
-  for (typename std::tr1::unordered_map<Dish, TableCounter, Hash>::const_iterator 
-  //for (typename google::sparse_hash_map<Dish, TableCounter, Hash>::const_iterator 
-       dtit = _dish_tables.begin(); dtit != _dish_tables.end(); ++dtit)
-  {
-    hists += dtit->second.table_histogram.size();
-    tables += dtit->second.tables;
-
-//    if (dtit->second.tables <= 0)
-//      std::cerr << dtit->first << " " << count(dtit->first) << std::endl;
-    assert(dtit->second.tables > 0);
-    assert(!dtit->second.table_histogram.empty());
-
-//    os << "Dish " << dtit->first << " has " << count(dtit->first) << " customers, and is sitting at " << dtit->second.tables << " tables.\n"; 
-    for (std::map<int,int>::const_iterator 
-         hit = dtit->second.table_histogram.begin();
-         hit != dtit->second.table_histogram.end(); ++hit) {
-//      os << "    " << hit->second << " tables with " << hit->first << " customers." << std::endl; 
-      assert(hit->second > 0);
-    }
-  }
-
-  os << "restaurant has " 
-    << _total_customers << " customers; "
-    << _total_tables << " tables; " 
-    << tables << " tables'; " 
-    << num_types() << " dishes; "
-    << _dish_tables.size() << " dishes'; and "
-    << hists << " histogram entries\n";
-
-  return os;
-}
-
-template <typename Dish, typename Hash>
-void 
-PYP<Dish,Hash>::clear()
-{
-  this->std::tr1::unordered_map<Dish,int,Hash>::clear();
-  //this->google::sparse_hash_map<Dish,int,Hash>::clear();
-  _dish_tables.clear();
-  _total_tables = _total_customers = 0;
-}
-
-// log_restaurant_prob returns the log probability of the PYP table configuration.
-// Excludes Hierarchical P0 term which must be calculated separately.
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_restaurant_prob() const {
-  if (_total_customers < 1)
-    return (double)0.0;
-
-  double log_prob = 0.0;
-  double lgamma1a = lgamma(1.0-_a);
-
-  //std::cerr << "-------------------\n" << std::endl;
-  for (typename DishTableType::const_iterator dish_it=_dish_tables.begin(); 
-       dish_it != _dish_tables.end(); ++dish_it) {
-    for (std::map<int, int>::const_iterator table_it=dish_it->second.table_histogram.begin(); 
-         table_it !=dish_it->second.table_histogram.end(); ++table_it) {
-      log_prob += (table_it->second * (lgamma(table_it->first - _a) - lgamma1a));
-      //std::cerr << "|" << dish_it->first->parent << " --> " << dish_it->first->rhs << " " << table_it->first << " " << table_it->second << " " << log_prob;
-    }
-  }
-  //std::cerr << std::endl;
-
-  log_prob += (_a == (double)0.0 ? (_total_tables-1.0)*log(_b) : (_total_tables-1.0)*log(_a) + lgamma((_total_tables-1.0) + _b/_a) - lgamma(_b/_a));
-  //std::cerr << "\t\t" << log_prob << std::endl;
-  log_prob += (lgamma(1.0 + _b) - lgamma(_total_customers + _b));
-
-  //std::cerr << _total_customers << " " << _total_tables << " " << log_prob << " " << log_prior() << std::endl;
-  //std::cerr << _a << " " << _b << std::endl;
-  if (!std::isfinite(log_prob)) {
-    assert(false);
-  }
-  //return log_prob;
-  if (log_prob > 0.0)
-    std::cerr << log_prob << std::endl;
-  return log_prob;// + log_prior();
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_prior() const {
-  double prior = 0.0;
-  if (_a_beta_a > 0.0 && _a_beta_b > 0.0 && _a > 0.0)
-    prior += log_prior_a(_a, _a_beta_a, _a_beta_b);
-  if (_b_gamma_s > 0.0 && _b_gamma_c > 0.0)
-    prior += log_prior_b(_b, _b_gamma_c, _b_gamma_s);
-
-  return prior;
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_prior_a(double a, double beta_a, double beta_b) {
-  return lbetadist(a, beta_a, beta_b); 
-}
-
-template <typename Dish, typename Hash>
-double 
-PYP<Dish,Hash>::log_prior_b(double b, double gamma_c, double gamma_s) {
-  return lgammadist(b, gamma_c, gamma_s); 
-}
-
-template <typename Dish, typename Hash>
-long double PYP<Dish,Hash>::lbetadist(long double x, long double alpha, long double beta) {
-  assert(x > 0);
-  assert(x < 1);
-  assert(alpha > 0);
-  assert(beta > 0);
-  return (alpha-1)*log(x)+(beta-1)*log(1-x)+lgamma(alpha+beta)-lgamma(alpha)-lgamma(beta);
-//boost::math::lgamma
-}
-
-template <typename Dish, typename Hash>
-long double PYP<Dish,Hash>::lgammadist(long double x, long double alpha, long double beta) {
-  assert(alpha > 0);
-  assert(beta > 0);
-  return (alpha-1)*log(x) - alpha*log(beta) - x/beta - lgamma(alpha);
-}
-
-
-template <typename Dish, typename Hash>
-  template <typename Uniform01>
-void 
-PYP<Dish,Hash>::resample_prior(Uniform01& rnd) {
-  for (int num_its=5; num_its >= 0; --num_its) {
-    resample_prior_b(rnd);
-    resample_prior_a(rnd);
-  }
-  resample_prior_b(rnd);
-}
-
-template <typename Dish, typename Hash>
-  template <typename Uniform01>
-void 
-PYP<Dish,Hash>::resample_prior_b(Uniform01& rnd) {
-  if (_total_tables == 0) 
-    return;
-
-  //int niterations = 10;   // number of resampling iterations
-  int niterations = 5;   // number of resampling iterations
-  //std::cerr << "\n## resample_prior_b(), initial a = " << _a << ", b = " << _b << std::endl;
-  resample_b_type b_log_prob(_total_customers, _total_tables, _a, _b_gamma_c, _b_gamma_s);
-  _b = slice_sampler1d(b_log_prob, _b, rnd, (double) 0.0, std::numeric_limits<double>::infinity(), 
-  //_b = slice_sampler1d(b_log_prob, _b, mt_genrand_res53, (double) 0.0, std::numeric_limits<double>::infinity(), 
-                       (double) 0.0, niterations, 100*niterations);
-  //std::cerr << "\n## resample_prior_b(), final a = " << _a << ", b = " << _b << std::endl;
-}
-
-template <typename Dish, typename Hash>
-  template <typename Uniform01>
-void 
-PYP<Dish,Hash>::resample_prior_a(Uniform01& rnd) {
-  if (_total_tables == 0) 
-    return;
-
-  //int niterations = 10;
-  int niterations = 5;
-  //std::cerr << "\n## Initial a = " << _a << ", b = " << _b << std::endl;
-  resample_a_type a_log_prob(_total_customers, _total_tables, _b, _a_beta_a, _a_beta_b, _dish_tables);
-  _a = slice_sampler1d(a_log_prob, _a, rnd, std::numeric_limits<double>::min(), 
-  //_a = slice_sampler1d(a_log_prob, _a, mt_genrand_res53, std::numeric_limits<double>::min(), 
-                       (double) 1.0, (double) 0.0, niterations, 100*niterations);
-}
-
-#endif
diff --git a/gi/pyp-topics/src/slice-sampler.h b/gi/pyp-topics/src/slice-sampler.h
deleted file mode 100644
index 3108a0f7..00000000
--- a/gi/pyp-topics/src/slice-sampler.h
+++ /dev/null
@@ -1,192 +0,0 @@
-//! slice-sampler.h is an MCMC slice sampler
-//!
-//! Mark Johnson, 1st August 2008
-
-#ifndef SLICE_SAMPLER_H
-#define SLICE_SAMPLER_H
-
-#include <algorithm>
-#include <cassert>
-#include <cmath>
-#include <iostream>
-#include <limits>
-
-//! slice_sampler_rfc_type{} returns the value of a user-specified
-//! function if the argument is within range, or - infinity otherwise
-//
-template <typename F, typename Fn, typename U>
-struct slice_sampler_rfc_type {
-  F min_x, max_x;
-  const Fn& f;
-  U max_nfeval, nfeval;
-  slice_sampler_rfc_type(F min_x, F max_x, const Fn& f, U max_nfeval) 
-    : min_x(min_x), max_x(max_x), f(f), max_nfeval(max_nfeval), nfeval(0) { }
-    
-  F operator() (F x) {
-    if (min_x < x && x < max_x) {
-      assert(++nfeval <= max_nfeval);
-      F fx = f(x);
-      assert(std::isfinite(fx));
-      return fx;
-    }
-    else
-      return -std::numeric_limits<F>::infinity();
-  }
-};  // slice_sampler_rfc_type{}
-
-//! slice_sampler1d() implements the univariate "range doubling" slice sampler
-//! described in Neal (2003) "Slice Sampling", The Annals of Statistics 31(3), 705-767.
-//
-template <typename F, typename LogF, typename Uniform01>
-F slice_sampler1d(const LogF& logF0,               //!< log of function to sample
-		  F x,                             //!< starting point
-		  Uniform01& u01,                  //!< uniform [0,1) random number generator
-		  F min_x = -std::numeric_limits<F>::infinity(),  //!< minimum value of support
-		  F max_x = std::numeric_limits<F>::infinity(),   //!< maximum value of support
-		  F w = 0.0,                       //!< guess at initial width
-		  unsigned nsamples=1,             //!< number of samples to draw
-		  unsigned max_nfeval=200)         //!< max number of function evaluations
-{
-  typedef unsigned U;
-  slice_sampler_rfc_type<F,LogF,U> logF(min_x, max_x, logF0, max_nfeval);
-
-  assert(std::isfinite(x));
-
-  if (w <= 0.0) {                           // set w to a default width 
-    if (min_x > -std::numeric_limits<F>::infinity() && max_x < std::numeric_limits<F>::infinity())
-      w = (max_x - min_x)/4;
-    else
-      w = std::max(((x < 0.0) ? -x : x)/4, (F) 0.1);
-  }
-  assert(std::isfinite(w));
-
-  F logFx = logF(x);
-  for (U sample = 0; sample < nsamples; ++sample) {
-    F logY = logFx + log(u01()+1e-100);     //! slice logFx at this value
-    assert(std::isfinite(logY));
-
-    F xl = x - w*u01();                     //! lower bound on slice interval
-    F logFxl = logF(xl);
-    F xr = xl + w;                          //! upper bound on slice interval
-    F logFxr = logF(xr);
-
-    while (logY < logFxl || logY < logFxr)  // doubling procedure
-      if (u01() < 0.5) 
-	logFxl = logF(xl -= xr - xl);
-      else
-	logFxr = logF(xr += xr - xl);
-	
-    F xl1 = xl;
-    F xr1 = xr;
-    while (true) {                          // shrinking procedure
-      F x1 = xl1 + u01()*(xr1 - xl1);
-      if (logY < logF(x1)) {
-	F xl2 = xl;                         // acceptance procedure
-	F xr2 = xr; 
-	bool d = false;
-	while (xr2 - xl2 > 1.1*w) {
-	  F xm = (xl2 + xr2)/2;
-	  if ((x < xm && x1 >= xm) || (x >= xm && x1 < xm))
-	    d = true;
-	  if (x1 < xm)
-	    xr2 = xm;
-	  else
-	    xl2 = xm;
-	  if (d && logY >= logF(xl2) && logY >= logF(xr2))
-	    goto unacceptable;
-	}
-	x = x1;
-	goto acceptable;
-      }
-      goto acceptable;
-    unacceptable:
-      if (x1 < x)                           // rest of shrinking procedure
-	xl1 = x1;
-      else 
-	xr1 = x1;
-    }
-  acceptable:
-    w = (4*w + (xr1 - xl1))/5;              // update width estimate
-  }
-  return x;
-}
-
-/*
-//! slice_sampler1d() implements a 1-d MCMC slice sampler.
-//! It should be correct for unimodal distributions, but
-//! not for multimodal ones.
-//
-template <typename F, typename LogP, typename Uniform01>
-F slice_sampler1d(const LogP& logP,     //!< log of distribution to sample
-		  F x,                  //!< initial sample
-		  Uniform01& u01,       //!< uniform random number generator
-		  F min_x = -std::numeric_limits<F>::infinity(),  //!< minimum value of support
-		  F max_x = std::numeric_limits<F>::infinity(),   //!< maximum value of support
-		  F w = 0.0,            //!< guess at initial width
-		  unsigned nsamples=1,  //!< number of samples to draw
-		  unsigned max_nfeval=200)  //!< max number of function evaluations
-{
-  typedef unsigned U;
-  assert(std::isfinite(x));
-  if (w <= 0.0) {
-    if (min_x > -std::numeric_limits<F>::infinity() && max_x < std::numeric_limits<F>::infinity())
-      w = (max_x - min_x)/4;
-    else
-      w = std::max(((x < 0.0) ? -x : x)/4, 0.1);
-  }
-  // TRACE4(x, min_x, max_x, w);
-  F logPx = logP(x);
-  assert(std::isfinite(logPx));
-  U nfeval = 1;
-  for (U sample = 0; sample < nsamples; ++sample) {
-    F x0 = x;
-    F logU = logPx + log(u01()+1e-100);
-    assert(std::isfinite(logU));
-    F r = u01();
-    F xl = std::max(min_x, x - r*w);
-    F xr = std::min(max_x, x + (1-r)*w);
-    // TRACE3(x, logPx, logU);
-    while (xl > min_x && logP(xl) > logU) {
-      xl -= w;
-      w *= 2;
-      ++nfeval;
-      if (nfeval >= max_nfeval)
-	std::cerr << "## Error: nfeval = " << nfeval << ", max_nfeval = " << max_nfeval << ", sample = " << sample << ", nsamples = " << nsamples << ", r = " << r << ", w = " << w << ", xl = " << xl << std::endl;
-      assert(nfeval < max_nfeval);
-    }
-    xl = std::max(xl, min_x);
-    while (xr < max_x && logP(xr) > logU) {
-      xr += w;
-      w *= 2;
-      ++nfeval;
-      if (nfeval >= max_nfeval)
-	std::cerr << "## Error: nfeval = " << nfeval << ", max_nfeval = " << max_nfeval << ", sample = " << sample << ", nsamples = " << nsamples << ", r = " << r << ", w = " << w << ", xr = " << xr << std::endl;
-      assert(nfeval < max_nfeval);
-    }
-    xr = std::min(xr, max_x);
-    while (true) {
-      r = u01();
-      x = r*xl + (1-r)*xr;
-      assert(std::isfinite(x));
-      logPx = logP(x);
-      // TRACE4(logPx, x, xl, xr);
-      assert(std::isfinite(logPx));
-      ++nfeval;
-      if (nfeval >= max_nfeval)
-	std::cerr << "## Error: nfeval = " << nfeval << ", max_nfeval = " << max_nfeval << ", sample = " << sample << ", nsamples = " << nsamples << ", r = " << r << ", w = " << w << ", xl = " << xl << ", xr = " << xr << ", x = " << x << std::endl;
-      assert(nfeval < max_nfeval);
-      if (logPx > logU)
-        break;
-      else if (x > x0)
-          xr = x;
-        else
-          xl = x;
-    }
-    // w = (4*w + (xr-xl))/5;   // gradually adjust w
-  }
-  // TRACE2(logPx, x);
-  return x;
-}  // slice_sampler1d()
-*/
-
-#endif  // SLICE_SAMPLER_H
diff --git a/gi/pyp-topics/src/timing.h b/gi/pyp-topics/src/timing.h
deleted file mode 100644
index 08360b0f..00000000
--- a/gi/pyp-topics/src/timing.h
+++ /dev/null
@@ -1,37 +0,0 @@
-#ifndef TIMING_H
-#define TIMING_H
-
-#ifdef __CYGWIN__
-# ifndef _POSIX_MONOTONIC_CLOCK
-#  define _POSIX_MONOTONIC_CLOCK
-// this modifies <time.h>
-# endif
-// in case someone included <time.h> before we got here (this is lifted from time.h>)
-# ifndef CLOCK_MONOTONIC
-#  define CLOCK_MONOTONIC (clockid_t)4
-# endif
-#endif
-
-
-#include <time.h>
-#include <sys/time.h>
-#include "clock_gettime_stub.c"
-
-struct Timer {
-  Timer() { Reset(); }
-  void Reset()
-  {
-    clock_gettime(CLOCK_MONOTONIC, &start_t);
-  }
-  double Elapsed() const {
-    timespec end_t;
-    clock_gettime(CLOCK_MONOTONIC, &end_t);
-    const double elapsed = (end_t.tv_sec - start_t.tv_sec)
-                + (end_t.tv_nsec - start_t.tv_nsec) / 1000000000.0;
-    return elapsed;
-  }
- private:
-  timespec start_t;
-};
-
-#endif
diff --git a/gi/pyp-topics/src/train-contexts.cc b/gi/pyp-topics/src/train-contexts.cc
deleted file mode 100644
index 9463f9fc..00000000
--- a/gi/pyp-topics/src/train-contexts.cc
+++ /dev/null
@@ -1,174 +0,0 @@
-// STL
-#include <iostream>
-#include <fstream>
-#include <algorithm>
-#include <iterator>
-
-// Boost
-#include <boost/program_options/parsers.hpp>
-#include <boost/program_options/variables_map.hpp>
-#include <boost/scoped_ptr.hpp>
-
-// Local
-#include "pyp-topics.hh"
-#include "corpus.hh"
-#include "contexts_corpus.hh"
-#include "gzstream.hh"
-
-static const char *REVISION = "$Rev$";
-
-// Namespaces
-using namespace boost;
-using namespace boost::program_options;
-using namespace std;
-
-int main(int argc, char **argv)
-{
- cout << "Pitman Yor topic models: Copyright 2010 Phil Blunsom\n";
- cout << REVISION << '\n' <<endl;
-
-  ////////////////////////////////////////////////////////////////////////////////////////////
-  // Command line processing
-  variables_map vm; 
-
-  // Command line processing
-  {
-    options_description cmdline_specific("Command line specific options");
-    cmdline_specific.add_options()
-      ("help,h", "print help message")
-      ("config,c", value<string>(), "config file specifying additional command line options")
-      ;
-    options_description config_options("Allowed options");
-    config_options.add_options()
-      ("data,d", value<string>(), "file containing the documents and context terms")
-      ("topics,t", value<int>()->default_value(50), "number of topics")
-      ("document-topics-out,o", value<string>(), "file to write the document topics to")
-      ("default-topics-out", value<string>(), "file to write default term topic assignments.")
-      ("topic-words-out,w", value<string>(), "file to write the topic word distribution to")
-      ("samples,s", value<int>()->default_value(10), "number of sampling passes through the data")
-      ("backoff-type", value<string>(), "backoff type: none|simple")
-//      ("filter-singleton-contexts", "filter singleton contexts")
-      ("hierarchical-topics", "Use a backoff hierarchical PYP as the P0 for the document topics distribution.")
-      ("freq-cutoff-start", value<int>()->default_value(0), "initial frequency cutoff.")
-      ("freq-cutoff-end", value<int>()->default_value(0), "final frequency cutoff.")
-      ("freq-cutoff-interval", value<int>()->default_value(0), "number of iterations between frequency decrement.")
-      ("max-threads", value<int>()->default_value(1), "maximum number of simultaneous threads allowed")
-      ("max-contexts-per-document", value<int>()->default_value(0), "Only sample the n most frequent contexts for a document.")
-      ("num-jobs", value<int>()->default_value(1), "allows finer control over parallelization")
-      ("temp-start", value<double>()->default_value(1.0), "starting annealing temperature.")
-      ("temp-end", value<double>()->default_value(1.0), "end annealing temperature.")
-      ;
-
-    cmdline_specific.add(config_options);
-
-    store(parse_command_line(argc, argv, cmdline_specific), vm); 
-    notify(vm);
-
-    if (vm.count("config") > 0) {
-      ifstream config(vm["config"].as<string>().c_str());
-      store(parse_config_file(config, config_options), vm); 
-    }
-
-    if (vm.count("help")) { 
-      cout << cmdline_specific << "\n"; 
-      return 1; 
-    }
-  }
-  ////////////////////////////////////////////////////////////////////////////////////////////
-
-  if (!vm.count("data")) {
-    cerr << "Please specify a file containing the data." << endl;
-    return 1;
-  }
-  assert(vm["max-threads"].as<int>() > 0);
-  assert(vm["num-jobs"].as<int>() > -1);
-  // seed the random number generator: 0 = automatic, specify value otherwise
-  unsigned long seed = 0; 
-  PYPTopics model(vm["topics"].as<int>(), vm.count("hierarchical-topics"), seed, vm["max-threads"].as<int>(), vm["num-jobs"].as<int>());
-
-  // read the data
-  BackoffGenerator* backoff_gen=0;
-  if (vm.count("backoff-type")) {
-    if (vm["backoff-type"].as<std::string>() == "none") {
-      backoff_gen = 0;
-    }
-    else if (vm["backoff-type"].as<std::string>() == "simple") {
-      backoff_gen = new SimpleBackoffGenerator();
-    }
-    else {
-     cerr << "Backoff type (--backoff-type) must be one of none|simple." <<endl;
-      return(1);
-    }
-  }
-
-  ContextsCorpus contexts_corpus;
-  contexts_corpus.read_contexts(vm["data"].as<string>(), backoff_gen, /*vm.count("filter-singleton-contexts")*/ false);
-  model.set_backoff(contexts_corpus.backoff_index());
-
-  if (backoff_gen) 
-    delete backoff_gen;
-
-  // train the sampler
-  model.sample_corpus(contexts_corpus, vm["samples"].as<int>(),
-                      vm["freq-cutoff-start"].as<int>(),
-                      vm["freq-cutoff-end"].as<int>(),
-                      vm["freq-cutoff-interval"].as<int>(),
-                      vm["max-contexts-per-document"].as<int>(),
-                      vm["temp-start"].as<double>(), vm["temp-end"].as<double>());
-
-  if (vm.count("document-topics-out")) {
-    ogzstream documents_out(vm["document-topics-out"].as<string>().c_str());
-
-    int document_id=0;
-    map<int,int> all_terms;
-    for (Corpus::const_iterator corpusIt=contexts_corpus.begin(); 
-         corpusIt != contexts_corpus.end(); ++corpusIt, ++document_id) {
-      vector<int> unique_terms;
-      for (Document::const_iterator docIt=corpusIt->begin();
-           docIt != corpusIt->end(); ++docIt) {
-        if (unique_terms.empty() || *docIt != unique_terms.back())
-          unique_terms.push_back(*docIt);
-        // increment this terms frequency
-        pair<map<int,int>::iterator,bool> insert_result = all_terms.insert(make_pair(*docIt,1));
-        if (!insert_result.second) 
-          all_terms[*docIt] = all_terms[*docIt] + 1;
-          //insert_result.first++;
-      }
-      documents_out << contexts_corpus.key(document_id) << '\t';
-      documents_out << model.max(document_id).first << " " << corpusIt->size() << " ||| ";
-      for (std::vector<int>::const_iterator termIt=unique_terms.begin();
-           termIt != unique_terms.end(); ++termIt) {
-        if (termIt != unique_terms.begin())
-          documents_out << " ||| ";
-       vector<std::string> strings = contexts_corpus.context2string(*termIt);
-       copy(strings.begin(), strings.end(),ostream_iterator<std::string>(documents_out, " "));
-        std::pair<int,PYPTopics::F> maxinfo = model.max(document_id, *termIt);
-        documents_out << "||| C=" << maxinfo.first << " P=" << maxinfo.second;
-
-      }
-      documents_out <<endl;
-    }
-    documents_out.close();
-
-    if (vm.count("default-topics-out")) {
-      ofstream default_topics(vm["default-topics-out"].as<string>().c_str());
-      default_topics << model.max_topic() <<endl;
-      for (std::map<int,int>::const_iterator termIt=all_terms.begin(); termIt != all_terms.end(); ++termIt) {
-       vector<std::string> strings = contexts_corpus.context2string(termIt->first);
-        default_topics << model.max(-1, termIt->first).first << " ||| " << termIt->second << " ||| ";
-       copy(strings.begin(), strings.end(),ostream_iterator<std::string>(default_topics, " "));
-        default_topics <<endl;
-      }
-    }
-  }
-
-  if (vm.count("topic-words-out")) {
-    ogzstream topics_out(vm["topic-words-out"].as<string>().c_str());
-    model.print_topic_terms(topics_out);
-    topics_out.close();
-  }
-
- cout <<endl;
-
-  return 0;
-}
diff --git a/gi/pyp-topics/src/train.cc b/gi/pyp-topics/src/train.cc
deleted file mode 100644
index db7ca46e..00000000
--- a/gi/pyp-topics/src/train.cc
+++ /dev/null
@@ -1,135 +0,0 @@
-// STL
-#include <iostream>
-#include <fstream>
-
-// Boost
-#include <boost/program_options/parsers.hpp>
-#include <boost/program_options/variables_map.hpp>
-#include <boost/scoped_ptr.hpp>
-
-// Local
-#include "pyp-topics.hh"
-#include "corpus.hh"
-#include "contexts_corpus.hh"
-#include "gzstream.hh"
-
-static const char *REVISION = "$Rev$";
-
-// Namespaces
-using namespace boost;
-using namespace boost::program_options;
-using namespace std;
-
-int main(int argc, char **argv)
-{
-  std::cout << "Pitman Yor topic models: Copyright 2010 Phil Blunsom\n";
-  std::cout << REVISION << '\n' << std::endl;
-
-  ////////////////////////////////////////////////////////////////////////////////////////////
-  // Command line processing
-  variables_map vm; 
-
-  // Command line processing
-  options_description cmdline_specific("Command line specific options");
-  cmdline_specific.add_options()
-    ("help,h", "print help message")
-    ("config,c", value<string>(), "config file specifying additional command line options")
-    ;
-  options_description generic("Allowed options");
-  generic.add_options()
-    ("documents,d", value<string>(), "file containing the documents")
-    ("topics,t", value<int>()->default_value(50), "number of topics")
-    ("document-topics-out,o", value<string>(), "file to write the document topics to")
-    ("topic-words-out,w", value<string>(), "file to write the topic word distribution to")
-    ("samples,s", value<int>()->default_value(10), "number of sampling passes through the data")
-    ("test-corpus", value<string>(), "file containing the test data")
-    ("backoff-paths", value<string>(), "file containing the term backoff paths")
-    ;
-  options_description config_options, cmdline_options;
-  config_options.add(generic);
-  cmdline_options.add(generic).add(cmdline_specific);
-
-  store(parse_command_line(argc, argv, cmdline_options), vm); 
-  if (vm.count("config") > 0) {
-    ifstream config(vm["config"].as<string>().c_str());
-    store(parse_config_file(config, cmdline_options), vm); 
-  }
-  notify(vm);
-  ////////////////////////////////////////////////////////////////////////////////////////////
-
-  if (vm.count("documents") == 0) {
-    cerr << "Please specify a file containing the documents." << endl;
-    cout << cmdline_options << "\n"; 
-    return 1;
-  }
-
-  if (vm.count("help")) { 
-    cout << cmdline_options << "\n"; 
-    return 1; 
-  }
-
-  // seed the random number generator: 0 = automatic, specify value otherwise
-  unsigned long seed = 0; 
-  PYPTopics model(vm["topics"].as<int>(), false, seed);
-
-  // read the data
-  Corpus corpus;
-  corpus.read(vm["documents"].as<string>());
-
-  // read the backoff dictionary
-  if (vm.count("backoff-paths"))
-    model.set_backoff(vm["backoff-paths"].as<string>());
-
-  // train the sampler
-  model.sample_corpus(corpus, vm["samples"].as<int>());
-
-  if (vm.count("document-topics-out")) {
-    ogzstream documents_out(vm["document-topics-out"].as<string>().c_str());
-    //model.print_document_topics(documents_out);
-
-    int document_id=0;
-    for (Corpus::const_iterator corpusIt=corpus.begin(); 
-         corpusIt != corpus.end(); ++corpusIt, ++document_id) {
-      std::vector<int> unique_terms;
-      for (Document::const_iterator docIt=corpusIt->begin();
-           docIt != corpusIt->end(); ++docIt) {
-        if (unique_terms.empty() || *docIt != unique_terms.back())
-          unique_terms.push_back(*docIt);
-      }
-      documents_out << unique_terms.size();
-      for (std::vector<int>::const_iterator termIt=unique_terms.begin();
-           termIt != unique_terms.end(); ++termIt)
-        documents_out << " " << *termIt << ":" << model.max(document_id, *termIt).first;
-      documents_out << std::endl;
-    }
-    documents_out.close();
-  }
-
-  if (vm.count("topic-words-out")) {
-    ogzstream topics_out(vm["topic-words-out"].as<string>().c_str());
-    model.print_topic_terms(topics_out);
-    topics_out.close();
-  }
-
-  if (vm.count("test-corpus")) {
-    TestCorpus test_corpus;
-    test_corpus.read(vm["test-corpus"].as<string>());
-    ogzstream topics_out((vm["test-corpus"].as<string>() + ".topics.gz").c_str());
-
-    for (TestCorpus::const_iterator corpusIt=test_corpus.begin(); 
-         corpusIt != test_corpus.end(); ++corpusIt) {
-      int index=0;
-      for (DocumentTerms::const_iterator instanceIt=corpusIt->begin();
-           instanceIt != corpusIt->end(); ++instanceIt, ++index) {
-        int topic = model.max(instanceIt->doc, instanceIt->term).first;
-        if (index != 0) topics_out << " ";
-        topics_out << topic;
-      }
-      topics_out << std::endl;
-    }
-    topics_out.close();
-  }
-  std::cout << std::endl;
-
-  return 0;
-}
diff --git a/gi/pyp-topics/src/utility.h b/gi/pyp-topics/src/utility.h
deleted file mode 100644
index 405a5b0a..00000000
--- a/gi/pyp-topics/src/utility.h
+++ /dev/null
@@ -1,962 +0,0 @@
-// utility.h
-//
-// (c) Mark Johnson, 24th January 2005
-//
-// modified 6th May 2002 to ensure write/read consistency, fixed 18th July 2002
-// modified 14th July 2002 to include insert() (generic inserter)
-// modified 26th September 2003 to use mapped_type instead of data_type
-// 25th August 2004 added istream >> const char*
-// 24th January 2005 added insert_newkey()
-//
-// Defines:
-//  loop macros foreach, cforeach
-//  dfind (default find function)
-//  afind (find function that asserts key exists)
-//  insert_newkey (inserts a new key into a map)
-//  insert (generic inserter into standard data structures)
-//  disjoint (set operation)
-//  first_lessthan and second_lessthan (compares elements of pairs)
-//
-// Simplified interfaces to STL routines:
-//
-//  includes (simplified interface)
-//  set_intersection (simplified interface)
-//  inserter (simplified interface)
-//  max_element (simplified interface)
-//  min_element (simplified interface)
-//  hash functions for pairs, vectors, lists, slists and maps
-//  input and output for pairs and vectors
-//  resource_usage (interface improved)
-
-
-#ifndef UTILITY_H
-#define UTILITY_H
-
-#include <algorithm>
-// #include <boost/smart_ptr.hpp>    // Comment out this line if boost is not used
-#include <cassert>
-#include <cmath>
-#include <cctype>
-#include <cstdio>
-#include <unordered_map>
-#include <unordered_set>
-#include <ext/slist>
-#include <iostream>
-#include <iterator>
-#include <list>
-#include <map>
-#include <set>
-#include <string>
-#include <utility>
-#include <vector>
-#include <memory>
-
-#if (__GNUC__ > 3) || (__GNUC__ >= 3 && __GNUC_MINOR__ >= 1)
-#define EXT_NAMESPACE __gnu_cxx
-#else
-#define EXT_NAMESPACE std
-#endif
-
-namespace ext = EXT_NAMESPACE;
-
-inline float power(float x, float y) { return powf(x, y); }
-inline double power(double x, double y) { return pow(x, y); }
-inline long double power(long double x, long double y) { return powl(x, y); }
-
-typedef unsigned U;
-typedef long double F;  // slower than double, but underflows less
-
-///////////////////////////////////////////////////////////////////////////
-//                                                                       //
-//                              Looping constructs                       //
-//                                                                       //
-///////////////////////////////////////////////////////////////////////////
-
-// foreach is a simple loop construct
-//
-//   STORE should be an STL container
-//   TYPE is the typename of STORE
-//   VAR will be defined as a local variable of type TYPE::iterator
-//
-#define foreach(TYPE, VAR, STORE) \
-  for (TYPE::iterator VAR = (STORE).begin(); VAR != (STORE).end(); ++VAR)
-
-// cforeach is just like foreach, except that VAR is a const_iterator
-//
-//   STORE should be an STL container
-//   TYPE is the typename of STORE
-//   VAR will be defined as a local variable of type TYPE::const_iterator
-//
-#define cforeach(TYPE, VAR, STORE) \
-  for (TYPE::const_iterator VAR = (STORE).begin(); VAR != (STORE).end(); ++VAR)
-
-
-///////////////////////////////////////////////////////////////////////////
-//                                                                       //
-//                             Map searching                             //
-//                                                                       //
-// dfind(map, key) returns the key's value in map, or map's default      //
-//   value if no such key exists (the default value is not inserted)     //
-//                                                                       //
-// afind(map, key) returns a reference to the key's value in map, and    //
-//    asserts that this value exists                                     //
-//                                                                       //
-///////////////////////////////////////////////////////////////////////////
-
-// dfind(Map, Key) returns the value Map associates with Key, or the
-//  Map's default value if no such Key exists
-//
-template <class Map, class Key>
-inline typename Map::mapped_type dfind(Map& m, const Key& k)
-{
-  typename Map::iterator i = m.find(k);
-  if (i == m.end())
-    return typename Map::mapped_type();
-  else
-    return i->second;
-}
-
-template <class Map, class Key>
-inline const typename Map::mapped_type dfind(const Map& m, const Key& k)
-{
-  typename Map::const_iterator i = m.find(k);
-  if (i == m.end())
-    return typename Map::mapped_type();
-  else
-    return i->second;
-}
-
-
-// afind(map, key) returns a reference to the value associated
-//  with key in map.  It uses assert to check that the key's value
-//  is defined.
-//
-template <class Map, class Key>
-inline typename Map::mapped_type& afind(Map& m, const Key& k)
-{
-  typename Map::iterator i = m.find(k);
-  assert(i != m.end());
-  return i->second;
-}
-
-template <class Map, class Key>
-inline const typename Map::mapped_type& afind(const Map& m, const Key& k)
-{
-  typename Map::const_iterator i = m.find(k);
-  assert(i != m.end());
-  return i->second;
-}
-
-//! insert_newkey(map, key, value) checks that map does not contain
-//! key, and binds key to value.
-//
-template <class Map, class Key, class Value>
-inline typename Map::value_type& 
-insert_newkey(Map& m, const Key& k,const Value& v) 
-{
-  std::pair<typename Map::iterator, bool> itb 
-    = m.insert(Map::value_type(k, v));
-  assert(itb.second);
-  return *(itb.first);
-}  // insert_newkey()
-
-
-///////////////////////////////////////////////////////////////////////////
-//                                                                       //
-//                        Insert operations                              //
-//                                                                       //
-///////////////////////////////////////////////////////////////////////////
-
-
-template <typename T>
-void insert(std::list<T>& xs, const T& x) {
-  xs.push_back(x);
-}
-
-template <typename T>
-void insert(std::set<T>& xs, const T& x) {
-  xs.insert(x);
-}
-
-template <typename T>
-void insert(std::vector<T>& xs, const T& x) {
-  xs.push_back(x);
-}
-
-
-///////////////////////////////////////////////////////////////////////////
-//                                                                       //
-//                Additional versions of standard algorithms             //
-//                                                                       //
-///////////////////////////////////////////////////////////////////////////
-
-template <typename Set1, typename Set2>
-inline bool includes(const Set1& set1, const Set2& set2)
-{
-  return std::includes(set1.begin(), set1.end(), set2.begin(), set2.end());
-}
-
-template <typename Set1, typename Set2, typename Compare>
-inline bool includes(const Set1& set1, const Set2& set2, Compare comp)
-{
-  return std::includes(set1.begin(), set1.end(), set2.begin(), set2.end(), comp);
-}
-
-
-template <typename InputIter1, typename InputIter2>
-bool disjoint(InputIter1 first1, InputIter1 last1,
-              InputIter2 first2, InputIter2 last2)
-{
-  while (first1 != last1 && first2 != last2)
-    if (*first1 < *first2)
-      ++first1;
-    else if (*first2 < *first1)
-      ++first2;
-    else // *first1 == *first2
-      return false;
-  return true;
-}
-
-template <typename InputIter1, typename InputIter2, typename Compare>
-bool disjoint(InputIter1 first1, InputIter1 last1,
-              InputIter2 first2, InputIter2 last2, Compare comp)
-{
-  while (first1 != last1 && first2 != last2)
-    if (comp(*first1, *first2))
-      ++first1;
-    else if (comp(*first2, *first1))
-      ++first2;
-    else // *first1 == *first2
-      return false;
-  return true;
-}
-
-template <typename Set1, typename Set2>
-inline bool disjoint(const Set1& set1, const Set2& set2)
-{
-  return disjoint(set1.begin(), set1.end(), set2.begin(), set2.end());
-}
-
-template <typename Set1, typename Set2, typename Compare>
-inline bool disjoint(const Set1& set1, const Set2& set2, Compare comp)
-{
-  return disjoint(set1.begin(), set1.end(), set2.begin(), set2.end(), comp);
-}
-
-
-template <typename Set1, typename Set2, typename OutputIterator>
-inline OutputIterator set_intersection(const Set1& set1, const Set2& set2, 
-                                       OutputIterator result)
-{
-  return set_intersection(set1.begin(), set1.end(), set2.begin(), set2.end(), result);
-}
-
-template <typename Set1, typename Set2, typename OutputIterator, typename Compare>
-inline OutputIterator set_intersection(const Set1& set1, const Set2& set2, 
-                                       OutputIterator result, Compare comp)
-{
-  return set_intersection(set1.begin(), set1.end(), set2.begin(), set2.end(), result, comp);
-}
-
-
-template <typename Container>
-inline std::insert_iterator<Container> inserter(Container& container)
-{
-  return std::inserter(container, container.begin());
-}
-
-// max_element
-//
-template <class Es> inline typename Es::iterator max_element(Es& es)
-{
-  return std::max_element(es.begin(), es.end());
-}
-
-template <class Es> inline typename Es::const_iterator max_element(const Es& es)
-{
-  return std::max_element(es.begin(), es.end());
-}
-
-template <class Es, class BinaryPredicate> 
-inline typename Es::iterator max_element(Es& es, BinaryPredicate comp)
-{
-  return std::max_element(es.begin(), es.end(), comp);
-}
-
-template <class Es, class BinaryPredicate> 
-inline typename Es::const_iterator max_element(const Es& es, BinaryPredicate comp)
-{
-  return std::max_element(es.begin(), es.end(), comp);
-}
-
-// min_element
-//
-template <class Es> inline typename Es::iterator min_element(Es& es)
-{
-  return std::min_element(es.begin(), es.end());
-}
-
-template <class Es> inline typename Es::const_iterator min_element(const Es& es)
-{
-  return std::min_element(es.begin(), es.end());
-}
-
-template <class Es, class BinaryPredicate> 
-inline typename Es::iterator min_element(Es& es, BinaryPredicate comp)
-{
-  return std::min_element(es.begin(), es.end(), comp);
-}
-
-template <class Es, class BinaryPredicate> 
-inline typename Es::const_iterator min_element(const Es& es, BinaryPredicate comp)
-{
-  return std::min_element(es.begin(), es.end(), comp);
-}
-
-// first_lessthan and second_lessthan
-//
-struct first_lessthan {
-  template <typename T1, typename T2>
-    bool operator() (const T1& e1, const T2& e2) {
-      return e1.first < e2.first;
-    }
-};
-
-struct second_lessthan {
-  template <typename T1, typename T2>
-    bool operator() (const T1& e1, const T2& e2) {
-      return e1.second < e2.second;
-    }
-};
-
-// first_greaterthan and second_greaterthan
-//
-struct first_greaterthan {
-  template <typename T1, typename T2>
-    bool operator() (const T1& e1, const T2& e2) {
-      return e1.first > e2.first;
-    }
-};
-
-struct second_greaterthan {
-  template <typename T1, typename T2>
-    bool operator() (const T1& e1, const T2& e2) {
-      return e1.second > e2.second;
-    }
-};
-
-
-///////////////////////////////////////////////////////////////////////////
-//                                                                       //
-//                          hash<> specializations                       //
-//                                                                       //
-// These must be in namespace std.   They permit the corresponding STL   //
-// container to be used as a key in an STL hash table.                   //
-//                                                                       //
-///////////////////////////////////////////////////////////////////////////
-
-//namespace EXT_NAMESPACE {
-namespace std {
-  /*
-  // hash function for bool
-  //
-  template <> struct hash<bool>
-  {
-  size_t operator() (bool b) const 
-  {
-  return b;
-  } // operator()
-  }; // hash<bool>{}
-
-  // hash function for double
-  //
-  template <> struct hash<double>
-  {
-  size_t operator() (double d) const 
-  {
-  int exponent;
-  double fraction = frexp(d, &exponent);
-  return size_t(exponent) ^ size_t(1000000.0*(fabs(fraction-0.5)));
-  } // operator()
-  }; // hash<double>{}
-
-  // hash function for strings
-  //
-  template <> struct hash<std::string> 
-  {
-  size_t operator()(const std::string& s) const 
-  {
-  typedef std::string::const_iterator CI;
-
-  unsigned long h = 0; 
-  unsigned long g;
-  CI p = s.begin();
-  CI end = s.end();
-
-  while (p!=end) {
-  h = (h << 4) + (*p++);
-  if ((g = h&0xf0000000)) {
-  h = h ^ (g >> 24);
-  h = h ^ g;
-  }}
-  return size_t(h);
-  }  // operator()
-  };  // hash<string>{}
-
-*/
-  // hash function for arbitrary pairs
-  //
-  template<class T1, class T2> struct hash<std::pair<T1,T2> > {
-    size_t operator()(const std::pair<T1,T2>& p) const
-    {
-      size_t h1 = hash<T1>()(p.first);
-      size_t h2 = hash<T2>()(p.second);
-      return h1 ^ (h1 >> 1) ^ h2 ^ (h2 << 1);
-    }
-  };
-
-
-  // hash function for vectors
-  //
-  template<class T> struct hash<std::vector<T> > 
-  { //  This is the fn hashpjw of Aho, Sethi and Ullman, p 436.
-    size_t operator()(const std::vector<T>& s) const 
-    {
-      typedef typename std::vector<T>::const_iterator CI;
-
-      unsigned long h = 0; 
-      unsigned long g;
-      CI p = s.begin();
-      CI end = s.end();
-
-      while (p!=end) {
-        h = (h << 5) + hash<T>()(*p++);
-        if ((g = h&0xff000000)) {
-          h = h ^ (g >> 23);
-          h = h ^ g;
-        }}
-        return size_t(h);
-    }
-  };
-
-  // hash function for slists
-  //
-  template<class T> struct hash<ext::slist<T> > 
-  { //  This is the fn hashpjw of Aho, Sethi and Ullman, p 436.
-    size_t operator()(const ext::slist<T>& s) const 
-    {
-      typedef typename ext::slist<T>::const_iterator CI;
-
-      unsigned long h = 0; 
-      unsigned long g;
-      CI p = s.begin();
-      CI end = s.end();
-
-      while (p!=end) {
-        h = (h << 7) + hash<T>()(*p++);
-        if ((g = h&0xff000000)) {
-          h = h ^ (g >> 23);
-          h = h ^ g;
-        }}
-        return size_t(h);
-    }
-  };
-
-  // hash function for maps
-  //
-  template<typename T1, typename T2> struct hash<std::map<T1,T2> >
-  {
-    size_t operator()(const std::map<T1,T2>& m) const
-    {
-      typedef typename std::map<T1,T2> M;
-      typedef typename M::const_iterator CI;
-
-      unsigned long h = 0;
-      unsigned long g;
-      CI p = m.begin();
-      CI end = m.end();
-
-      while (p != end) {
-        h = (h << 11) + hash<typename M::value_type>()(*p++);
-        if ((g = h&0xff000000)) {
-          h = h ^ (g >> 23);
-          h = h ^ g;
-        }}
-        return size_t(h);
-    }
-  };
-
-} // namespace EXT_NAMESPACE
-
-
-
-///////////////////////////////////////////////////////////////////////////
-//                                                                       //
-//                           Write/Read code                             //
-//                                                                       //
-// These routines should possess write/read invariance IF their elements //
-// also have write-read invariance.  Whitespace, '(' and ')' are used as //
-// delimiters.                                                           //
-//                                                                       //
-///////////////////////////////////////////////////////////////////////////
-
-
-// Define istream >> const char* so that it consumes the characters from the
-// istream.  Just as in scanf, a space consumes an arbitrary amount of whitespace.
-//
-inline std::istream& operator>> (std::istream& is, const char* cp)
-{
-  if (*cp == '\0')
-    return is;
-  else if (*cp == ' ') {
-    char c;
-    if (is.get(c)) {
-      if (isspace(c))
-        return is >> cp;
-      else {
-        is.unget();
-        return is >> (cp+1);
-      }
-    }
-    else {
-      is.clear(is.rdstate() & ~std::ios::failbit);  // clear failbit
-      return is >> (cp+1);
-    }
-  }
-  else {
-    char c;
-    if (is.get(c)) {
-      if (c == *cp)
-        return is >> (cp+1);
-      else {
-        is.unget();
-        is.setstate(std::ios::failbit);
-      }
-    }
-    return is;
-  }
-}
-
-
-// Write out an auto_ptr object just as you would write out the pointer object
-//
-template <typename T> 
-inline std::ostream& operator<<(std::ostream& os, const std::auto_ptr<T>& sp)
-{
-  return os << sp.get();
-}
-
-
-// Pairs
-//
-template <class T1, class T2> 
-std::ostream& operator<< (std::ostream& os, const std::pair<T1,T2>& p)
-{
-  return os << '(' << p.first << ' ' << p.second << ')';
-}
-
-template <class T1, class T2>
-std::istream& operator>> (std::istream& is, std::pair<T1,T2>& p)
-{
-  char c;
-  if (is >> c) {
-    if (c == '(') {
-      if (is >> p.first >> p.second >> c && c == ')')
-        return is;
-      else 
-        is.setstate(std::ios::badbit);
-    }
-    else
-      is.putback(c);
-  }
-  is.setstate(std::ios::failbit);
-  return is;
-}
-
-// Lists
-//
-template <class T>
-std::ostream& operator<< (std::ostream& os, const std::list<T>& xs)
-{
-  os << '(';
-  for (typename std::list<T>::const_iterator xi = xs.begin(); xi != xs.end(); ++xi) {
-    if (xi != xs.begin())
-      os << ' ';
-    os << *xi;
-  }
-  return os << ')';
-}
-
-template <class T>
-std::istream& operator>> (std::istream& is, std::list<T>& xs)
-{
-  char c;                          // This code avoids unnecessary copy
-  if (is >> c) {                   // read the initial '('
-    if (c == '(') {
-      xs.clear();                  // clear the list
-      do {
-        xs.push_back(T());         // create a new elt in list
-        is >> xs.back();           // read element
-      }
-      while (is.good());           // read as long as possible
-      xs.pop_back();               // last read failed; pop last elt
-      is.clear(is.rdstate() & ~std::ios::failbit);  // clear failbit
-      if (is >> c && c == ')')     // read terminating ')'
-        return is;                 // successful return
-      else 
-        is.setstate(std::ios::badbit); // something went wrong, set badbit
-    }
-    else                           // c is not '('
-      is.putback(c);               //  put c back into input
-  }
-  is.setstate(std::ios::failbit);  // read failed, set failbit
-  return is;
-}
-
-// Vectors
-//
-template <class T>
-std::ostream& operator<< (std::ostream& os, const std::vector<T>& xs)
-{
-  os << '(';
-  for (typename std::vector<T>::const_iterator xi = xs.begin(); xi != xs.end(); ++xi) {
-    if (xi != xs.begin())
-      os << ' ';
-    os << *xi;
-  }
-  return os << ')';
-}
-
-template <class T>
-std::istream& operator>> (std::istream& is, std::vector<T>& xs)
-{
-  char c;                          // This code avoids unnecessary copy
-  if (is >> c) {                   // read the initial '('
-    if (c == '(') {
-      xs.clear();                  // clear the list
-      do {
-        xs.push_back(T());         // create a new elt in list
-        is >> xs.back();           // read element
-      }
-      while (is.good());           // read as long as possible
-      xs.pop_back();               // last read failed; pop last elt
-      is.clear(is.rdstate() & ~std::ios::failbit);  // clear failbit
-      if (is >> c && c == ')')     // read terminating ')'
-        return is;                 // successful return
-      else 
-        is.setstate(std::ios::badbit); // something went wrong, set badbit
-    }
-    else                           // c is not '('
-      is.putback(c);               //  put c back into input
-  }
-  is.setstate(std::ios::failbit);  // read failed, set failbit
-  return is;
-}
-
-// Slists
-//
-template <class T>
-std::ostream& operator<< (std::ostream& os, const ext::slist<T>& xs)
-{
-  os << '(';
-  for (typename ext::slist<T>::const_iterator xi = xs.begin(); xi != xs.end(); ++xi) {
-    if (xi != xs.begin())
-      os << ' ';
-    os << *xi;
-  }
-  return os << ')';
-}
-
-template <class T>
-std::istream& operator>> (std::istream& is, ext::slist<T>& xs)
-{
-  char c; 
-  if (is >> c) { 
-    if (c == '(') {
-      xs.clear();
-      T e;
-      if (is >> e) {   
-        xs.push_front(e);
-        typename ext::slist<T>::iterator xi = xs.begin();
-        while (is >> e)
-          xi = xs.insert_after(xi, e);
-        is.clear(is.rdstate() & ~std::ios::failbit);
-        if (is >> c && c == ')')
-          return is;
-        else 
-          is.setstate(std::ios::badbit); 
-      }
-      else { // empty list
-        is.clear(is.rdstate() & ~std::ios::failbit);
-        if (is >> c && c == ')')
-          return is;
-        else  // didn't see closing ')'
-          is.setstate(std::ios::badbit);
-      }
-    }
-    else  // didn't read '('
-      is.putback(c);
-  }
-  is.setstate(std::ios::failbit);
-  return is;
-}
-
-// Sets
-//
-template <class T>
-std::ostream& operator<< (std::ostream& os, const std::set<T>& s)
-{
-  os << '(';
-  for (typename std::set<T>::const_iterator i = s.begin(); i != s.end(); ++i) {
-    if (i != s.begin())
-      os << ' ';
-    os << *i;
-  }
-  return os << ')';
-}
-
-template <class T>
-std::istream& operator>> (std::istream& is, std::set<T>& s)
-{
-  char c;
-  if (is >> c) {
-    if (c == '(') {
-      s.clear();
-      T e;
-      while (is >> e)
-        s.insert(e);
-      is.clear(is.rdstate() & ~std::ios::failbit);
-      if (is >> c && c == ')')
-        return is;
-      else
-        is.setstate(std::ios::badbit);
-    }
-    else
-      is.putback(c);
-  }
-  is.setstate(std::ios::failbit);
-  return is;
-}
-
-// Hash_sets
-//
-template <class T>
-std::ostream& operator<< (std::ostream& os, const std::unordered_set<T>& s)
-{
-  os << '(';
-  for (typename std::unordered_set<T>::const_iterator i = s.begin(); i != s.end(); ++i) {
-    if (i != s.begin())
-      os << ' ';
-    os << *i;
-  }
-  return os << ')';
-}
-
-template <class T>
-std::istream& operator>> (std::istream& is, std::unordered_set<T>& s)
-{
-  char c;
-  if (is >> c) {
-    if (c == '(') {
-      s.clear();
-      T e;
-      while (is >> e)
-        s.insert(e);
-      is.clear(is.rdstate() & ~std::ios::failbit);
-      if (is >> c && c == ')')
-        return is;
-      else
-        is.setstate(std::ios::badbit);
-    }
-    else
-      is.putback(c);
-  }
-  is.setstate(std::ios::failbit);
-  return is;
-}
-
-
-// Maps
-//
-template <class Key, class Value>
-std::ostream& operator<< (std::ostream& os, const std::map<Key,Value>& m)
-{
-  typedef std::map<Key,Value> M;
-  os << '(';
-  for (typename M::const_iterator it = m.begin(); it != m.end(); ++it) {
-    if (it != m.begin())
-      os << ' ';
-    os << *it;
-  }
-  return os << ")";
-}
-
-template <class Key, class Value>
-std::istream& operator>> (std::istream& is, std::map<Key,Value>& m)
-{
-  char c;
-  if (is >> c) {
-    if (c == '(') {
-      m.clear();
-      std::pair<Key,Value> e;
-      while (is >> e)
-        m.insert(e);
-      is.clear(is.rdstate() & ~std::ios::failbit);
-      if (is >> c && c == ')')
-        return is;
-      else
-        is.setstate(std::ios::badbit);
-    }
-    else
-      is.putback(c);
-  }
-  is.setstate(std::ios::failbit);
-  return is;
-}
-
-// Hash_maps
-//
-template <class Key, class Value>
-std::ostream& operator<< (std::ostream& os, const std::unordered_map<Key,Value>& m)
-{
-  typedef std::unordered_map<Key,Value> M;
-  os << '(';
-  for (typename M::const_iterator it = m.begin(); it != m.end(); ++it) {
-    if (it != m.begin())
-      os << ' ';
-    os << *it;
-  }
-  return os << ")";
-}
-
-template <class Key, class Value>
-std::istream& operator>> (std::istream& is, std::unordered_map<Key,Value>& m)
-{
-  char c;
-  if (is >> c) {
-    if (c == '(') {
-      m.clear();
-      std::pair<Key,Value> e;
-      while (is >> e)
-        m.insert(e);
-      is.clear(is.rdstate() & ~std::ios::failbit);
-      if (is >> c && c == ')')
-        return is;
-      else
-        is.setstate(std::ios::badbit);
-    }
-    else
-      is.putback(c);
-  }
-  is.setstate(std::ios::failbit);
-  return is;
-}
-
-
-///////////////////////////////////////////////////////////////////////////
-//                                                                       //
-//                       Boost library additions                         //
-//                                                                       //
-///////////////////////////////////////////////////////////////////////////
-
-#ifdef BOOST_SHARED_PTR_HPP_INCLUDED
-
-// enhancements to boost::shared_ptr so it can be used with hash
-//
-namespace std {
-  template <typename T> struct equal_to<boost::shared_ptr<T> > 
-    : public binary_function<boost::shared_ptr<T>, boost::shared_ptr<T>, bool> {
-      bool operator() (const boost::shared_ptr<T>& p1, const boost::shared_ptr<T>& p2) const {
-        return equal_to<T*>()(p1.get(), p2.get());
-      }
-    };
-}  // namespace std
-
-//namespace EXT_NAMESPACE {
-namespace std {
-  template <typename T> struct hash<boost::shared_ptr<T> > {
-    size_t operator() (const boost::shared_ptr<T>& a) const {
-      return hash<T*>()(a.get());
-    }
-  };
-}  // namespace ext
-
-template <typename T> 
-inline std::ostream& operator<< (std::ostream& os, const boost::shared_ptr<T>& sp)
-{
-  return os << sp.get();
-}
-
-#endif  // BOOST_SHARED_PTR_HPP_INCLUDED
-
-struct resource_usage { };
-
-#ifndef __i386
-inline std::ostream& operator<< (std::ostream& os, resource_usage r)
-{
-  return os;
-}
-#else // Assume we are on a 586 linux
-inline std::ostream& operator<< (std::ostream& os, resource_usage r)
-{
-  FILE* fp = fopen("/proc/self/stat", "r");
-  assert(fp);
-  int utime;
-  int stime;
-  unsigned int vsize;
-  unsigned int rss;
-  int result = 
-    fscanf(fp, "%*d %*s %*c %*d %*d %*d %*d %*d %*u %*u %*u %*u %*u %d %d %*d %*d %*d %*d"
-           "%*u %*u %*d %u %u", &utime, &stime, &vsize, &rss);
-  assert(result == 4);
-  fclose(fp);
-  // s << "utime = " << utime << ", stime = " << stime << ", vsize = " << vsize << ", rss = " << rss
-  ;
-  // return s << "utime = " << utime << ", vsize = " << vsize;
-  return os << "utime " << float(utime)/1.0e2 << "s, vsize " 
-    << float(vsize)/1048576.0 << " Mb.";
-}
-#endif
-
-//! A default_value_type{} object is used to read an object from a stream,
-//! assigning a default value if the read fails.  Users should not need to
-//! construct such objects, but should use default_value() instead.
-//
-template <typename object_type, typename default_type>
-struct default_value_type {
-  object_type& object;
-  const default_type defaultvalue;
-  default_value_type(object_type& object, const default_type defaultvalue)
-    : object(object), defaultvalue(defaultvalue) { }
-};
-
-//! default_value() is used to read an object from a stream, assigning a
-//! default value if the read fails.  It returns a default_value_type{}
-//! object, which does the actual reading.
-//
-template <typename object_type, typename default_type>
-default_value_type<object_type,default_type>
-default_value(object_type& object, const default_type defaultvalue=default_type()) {
-  return default_value_type<object_type,default_type>(object, defaultvalue);
-}
-
-//! This version of operator>>() reads default_value_type{} from an input stream.
-//
-template <typename object_type, typename default_type>
-std::istream& operator>> (std::istream& is, 
-                          default_value_type<object_type, default_type> dv) {
-  if (is) {
-    if (is >> dv.object)
-      ;
-    else {
-      is.clear(is.rdstate() & ~std::ios::failbit);  // clear failbit
-      dv.object = dv.defaultvalue;
-    }
-  }
-  return is;
-}
-
-// inline F random1() { return rand()/(RAND_MAX+1.0); }
-inline F random1() { return mt_genrand_res53(); }
-
-#endif  // UTILITY_H
diff --git a/gi/pyp-topics/src/workers.hh b/gi/pyp-topics/src/workers.hh
deleted file mode 100644
index 95b18947..00000000
--- a/gi/pyp-topics/src/workers.hh
+++ /dev/null
@@ -1,275 +0,0 @@
-/**
-  Basic thread-pool tools using Boost.Thread.
-  (Jan Botha, 7/2010)
-
-  --Simple usage--
-  Use SimpleWorker.
-    Example, call a function that returns an int in a new thread:
-    typedef boost::function<int()> JobType;
-    JobType job = boost::bind(funcname);
-      //or boost::bind(&class::funcname, this) for a member function
-    SimpleWorker<JobType, int> worker(job);
-    int result = worker.getResult(); //blocks until result is ready
-
-  --Extended usage--
-  Use WorkerPool, which uses Queuemt (a synchronized queue) and Worker.
-  Example:
-    (same context and typedef
-    WorkerPool<JobType, int> pool(num_threads);
-    JobType job = ...
-    pool.addJob(job);
-    ...
-    pool.get_result(); //blocks until all workers are done, returns the some of their results.  
-    
-    Jobs added to a WorkerPool need to be the same type. A WorkerPool instance should not be reused (e.g. adding jobs) after calling get_result(). 
-*/
-
-#ifndef WORKERS_HH
-#define WORKERS_HH
-
-#include <iostream>
-#include <boost/bind.hpp>
-#include <boost/function.hpp>
-#include <queue>
-#include <boost/ptr_container/ptr_vector.hpp>
-#include <boost/thread/thread.hpp>
-#include <boost/thread/mutex.hpp>
-#include <boost/thread/shared_mutex.hpp>
-#include <boost/thread/future.hpp>
-#include <boost/thread/condition.hpp>
-
-#include <boost/date_time/posix_time/posix_time_types.hpp>
-#include "timing.h"
-
-/** Implements a synchronized queue*/
-template<typename J>
-class Queuemt
-{
-
-public:
-    boost::condition_variable_any cond;
-    const bool& running;
-
-    Queuemt() { }
-    Queuemt(const bool& running) : running(running), maxsize(0), qsize(0) 
-    { 
-    }
-
-    ~Queuemt() { 
-     }
-
-    J pop()
-    {
-        J job;
-        {
-            boost::unique_lock<boost::shared_mutex> qlock(q_mutex);
-            while (running && qsize == 0)
-                cond.wait(qlock);
-
-            if (qsize > 0)
-            {
-                job = q.front();
-                q.pop();
-                --qsize;      
-            }
-        }
-        if (job)
-            cond.notify_one();
-        return job;
-
-    }
-
-    void push(J job)
-    {
-        {
-            boost::unique_lock<boost::shared_mutex> lock(q_mutex);
-            q.push(job);
-            ++qsize;
-        }
-        if (qsize > maxsize)
-            maxsize = qsize;
-        
-        cond.notify_one();
-    }
-
-    int getMaxsize()
-    {
-        return maxsize;
-    }
-
-    int size()
-    {
-        return qsize;
-    }
-
-private:
-    boost::shared_mutex q_mutex;
-    std::queue<J> q;
-    int maxsize;
-    volatile int qsize;
-};
-
-
-template<typename J, typename R>
-class Worker
-{
-typedef boost::packaged_task<R> PackagedTask;
-public:
-    Worker(Queuemt<J>& queue, int id, int num_workers) :  
-      q(queue), tasktime(0.0), id(id), num_workers(num_workers)
-    {
-        PackagedTask task(boost::bind(&Worker<J, R>::run, this));
-        future = task.get_future();
-        boost::thread t(boost::move(task));
-    }
-
-    R run() //this is called upon thread creation
-    {
-        R wresult = 0;
-        while (isRunning())
-        {
-            J job = q.pop();
-
-            if (job)
-            {
-                timer.Reset();
-                wresult += job();
-                tasktime += timer.Elapsed();
-            }
-        }
-        return wresult;
-    }
-
-    R getResult()
-    {
-        if (!future.is_ready())
-            future.wait();
-        assert(future.is_ready());
-        return future.get();
-    }
-
-    double getTaskTime()
-    {
-        return tasktime;
-    }
-
-private:
-
-    Queuemt<J>& q;
-
-    boost::unique_future<R> future;
-
-    bool isRunning()
-    {
-        return q.running || q.size() > 0;
-    }
-    
-    Timer timer;
-    double tasktime;
-    int id;
-    int num_workers;
-};
-
-template<typename J, typename R>
-class WorkerPool
-{
-typedef boost::packaged_task<R> PackagedTask;
-typedef Worker<J,R> WJR;
-typedef boost::ptr_vector<WJR> WorkerVector;
-public:
-
-    WorkerPool(int num_workers)
-    {
-        q.reset(new Queuemt<J>(running));
-        running = true;
-        for (int i = 0; i < num_workers; ++i)
-            workers.push_back( new Worker<J, R>(*q, i, num_workers) );
-    }
-
-    ~WorkerPool()
-    {
-    }
-
-    R get_result()
-    {
-        running = false;
-        q->cond.notify_all();
-        R tmp = 0;
-        double tasktime = 0.0;
-        for (typename WorkerVector::iterator it = workers.begin(); it != workers.end(); it++)
-        {
-            R res = it->getResult();
-            tmp += res;
-            //std::cerr << "tasktime: " << it->getTaskTime() << std::endl; 
-            tasktime += it->getTaskTime();
-        }
-//        std::cerr << " maxQ = " << q->getMaxsize() << std::endl;
-        return tmp;
-    }
-
-    void addJob(J job)
-    {
-        q->push(job);
-    }
-
-private:
-
-    WorkerVector workers;
-
-    boost::shared_ptr<Queuemt<J> > q;
-
-    bool running;
-};
-
-///////////////////
-template <typename J, typename R>
-class SimpleWorker
-{
-typedef boost::packaged_task<R> PackagedTask;
-public:
-    SimpleWorker(J& job) : job(job), tasktime(0.0)
-    {
-        PackagedTask task(boost::bind(&SimpleWorker<J, R>::run, this));
-        future = task.get_future();
-        boost::thread t(boost::move(task));
-    }
-
-    R run() //this is called upon thread creation
-    {
-        R wresult = 0;
-
-        assert(job);
-        timer.Reset();
-        wresult = job();
-        tasktime = timer.Elapsed();
-        std::cerr << tasktime << " s" << std::endl; 
-        return wresult;
-    }
-
-    R getResult()
-    {
-        if (!future.is_ready())
-            future.wait();
-        assert(future.is_ready());
-        return future.get();
-    }
-
-    double getTaskTime()
-    {
-        return tasktime;
-    }
-
-private:
-
-    J job;
-
-    boost::unique_future<R> future;
-
-    Timer timer;
-    double tasktime;
-
-};
-
-
-
-#endif 
diff --git a/gi/scripts/buck2utf8.pl b/gi/scripts/buck2utf8.pl
deleted file mode 100755
index 1acfae8d..00000000
--- a/gi/scripts/buck2utf8.pl
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-use utf8;
-binmode(STDOUT, ":utf8");
-while(<>) {
-  chomp;
-  my @words = split /\s+/;
-  for my $w (@words) {
-    $_ = $w;
-    if ($w =~ /^__NTK__/o) {
-      s/__NTK__//go;
-      next if /^$/;
-      print STDOUT "$_ ";
-      next;
-    }
-s/tR/\x{0679}/g;  # retroflex t
-s/dR/\x{0688}/g;  # retroflex d
-s/rR/\x{0691}/g;  # retroflex r
-s/p/\x{067E}/g;   # peh
-s/c/\x{0686}/g;   # tcheh
-s/g/\x{06AF}/g;   # geh (G=ghain)
-s/@/\x{06BE}/g;   # heh doachashmee
-s/h'/\x{06c2}/g;  # heh goal + hamza
-s/h/\x{06c1}/g;   # heh goal
-s/J/\x{0698}/g;   # zheh (rare, usually persian loan words)
-s/k/\x{06A9}/g;   # k
-s/Y'/\x{06d3}/g;  # yeh barree + hamza above (ligature)
-s/y/\x{06cc}/g;   # same as ya' in arabic
-s/Y/\x{06d2}/g;   # yeh barree
-s/N/\x{06BA}/g;  # Ghunna
-
-    s/\'/\x{0621}/g;
-    s/\|/\x{0622}/g;
-    s/\>/\x{0623}/g;
-    s/\&/\x{0624}/g;
-    s/\</\x{0625}/g;
-    s/\}/\x{0626}/g;
-    s/A/\x{0627}/g;
-    s/b/\x{0628}/g;
-    s/t/\x{062A}/g;
-    s/v/\x{062B}/g;
-    s/j/\x{062C}/g;
-    s/H/\x{062D}/g;
-    s/x/\x{062E}/g;
-    s/d/\x{062F}/g;
-    s/\*/\x{0630}/g;
-    s/r/\x{0631}/g;
-    s/z/\x{0632}/g;
-    s/s/\x{0633}/g;
-    s/\$/\x{0634}/g;
-    s/S/\x{0635}/g;
-    s/D/\x{0636}/g;
-    s/T/\x{0637}/g;
-    s/Z/\x{0638}/g;
-    s/E/\x{0639}/g;
-    s/g/\x{063A}/g;
-    s/_/\x{0640}/g;
-    s/f/\x{0641}/g;
-    s/q/\x{0642}/g;
-    s/k/\x{0643}/g;
-    s/l/\x{0644}/g;
-    s/m/\x{0645}/g;
-    s/n/\x{0646}/g;
-    s/h/\x{0647}/g;
-    s/w/\x{0648}/g;
-    s/Y/\x{0649}/g;
-    s/y/\x{064A}/g;
-    s/F/\x{064B}/g;
-    s/N/\x{064C}/g;
-    s/K/\x{064D}/g;
-    s/a/\x{064E}/g;
-    s/u/\x{064F}/g;
-    s/i/\x{0650}/g;
-    s/\~/\x{0651}/g;
-    s/o/\x{0652}/g;
-    s/\`/\x{0670}/g;
-    s/\{/\x{0671}/g;
-    s/P/\x{067E}/g;
-    s/J/\x{0686}/g;
-    s/V/\x{06A4}/g;
-    s/G/\x{06AF}/g;
-
-
-print STDOUT "$_ ";
-  }
-  print STDOUT "\n";
-}