cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 201 deletions

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;
-}