TrainVariableLengthMarkovChain.cpp

/*
 *       TrainVariableLengthMarkovChain.cpp
 *
 *       Copyright 2011 Andre Yoshiaki Kashiwabara <akashiwabara@usp.br>
 *                      Ígor Bonádio <ibonadio@ime.usp.br>
 *                      Vitor Onuchic <vitoronuchic@gmail.com>
 *                      Alan Mitchell Durham <aland@usp.br>
 *
 *       This program is free software; you can redistribute it and/or modify
 *       it under the terms of the GNU  General Public License as published by
 *       the Free Software Foundation; either version 3 of the License, or
 *       (at your option) any later version.
 *
 *       This program 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 General Public License for more details.
 *
 *       You should have received a copy of the GNU General Public License
 *       along with this program; if not, write to the Free Software
 *       Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
 *       MA 02110-1301, USA.
 */

#include "util.hpp"

#include "ContextTree.hpp"
#include "ProbabilisticModelParameter.hpp"
#include "VariableLengthMarkovChain.hpp"
#include "TrainVariableLengthMarkovChain.hpp"

#include <iostream>
#include <fstream>
#include <set>

namespace tops {

ProbabilisticModelPtr TrainVariableLengthMarkovChain::create(
                ProbabilisticModelParameters & parameters) const {
        double loglike;
        int samplesize;
        return create(parameters, loglike, samplesize);
}

  ProbabilisticModelPtr TrainVariableLengthMarkovChain::create( ProbabilisticModelParameters & parameters, const std::vector<std::string> & training_set, double & loglikelihood, int & sample_size) const {
        ProbabilisticModelParameterValuePtr alphabet_parameter =
                        parameters.getMandatoryParameterValue("alphabet");
        ProbabilisticModelParameterValuePtr delta_parameter =
                        parameters.getMandatoryParameterValue("cut");
        if ((alphabet_parameter == NULL)|| (delta_parameter == NULL)) {
                std::cerr << help() << std::endl;
                exit(-1);
        }
        AlphabetPtr alphabet = AlphabetPtr(new Alphabet());
        alphabet->initializeFromVector(alphabet_parameter->getStringVector());
        SequenceFactory factory(alphabet);
        SequenceEntryList sample_set;
        for(int i = 0; i < (int)sample_set.size(); i++) {
          Sequence s = factory.createSequence(training_set[i]);
          SequenceEntryPtr e =  SequenceEntryPtr  (new SequenceEntry(alphabet));
          e->setSequence(s);
          sample_set.push_back(e);
        }

        ContextTreePtr tree = ContextTreePtr(new ContextTree(alphabet));
        tree->initializeContextTreeRissanen(sample_set);
        tree->pruneTree(delta_parameter->getDouble());
        tree->removeContextNotUsed();
        tree->normalize();
        VariableLengthMarkovChainPtr m = VariableLengthMarkovChainPtr(
                        new VariableLengthMarkovChain(tree));
        m->setAlphabet(alphabet);
        loglikelihood = 0.0;
        sample_size = 0;
        for (int i = 0; i < (int) sample_set.size(); i++) {
                loglikelihood
                  += m->evaluate((sample_set[i]->getSequence()), 0, (sample_set[i]->getSequence()).size() - 1);
                sample_size += (sample_set[i]->getSequence()).size();
        }
        return m;

  }

ProbabilisticModelPtr TrainVariableLengthMarkovChain::create(
                ProbabilisticModelParameters & parameters, double & loglikelihood,
                int & sample_size) const {
        ProbabilisticModelParameterValuePtr training_set_parameter =
                        parameters.getMandatoryParameterValue("training_set");
        ProbabilisticModelParameterValuePtr alphabet_parameter =
                        parameters.getMandatoryParameterValue("alphabet");
        ProbabilisticModelParameterValuePtr delta_parameter =
                        parameters.getMandatoryParameterValue("cut");

        if ((training_set_parameter == NULL) || (alphabet_parameter == NULL)
                        || (delta_parameter == NULL)) {
                std::cerr << help() << std::endl;
                exit(-1);
        }
        AlphabetPtr alphabet = AlphabetPtr(new Alphabet());
        alphabet->initializeFromVector(alphabet_parameter->getStringVector());

        SequenceEntryList sample_set;
        readSequencesFromFile(sample_set, alphabet, training_set_parameter->getString());

        ContextTreePtr tree = ContextTreePtr(new ContextTree(alphabet));
        tree->initializeContextTreeRissanen(sample_set);
        tree->pruneTree(delta_parameter->getDouble());
        tree->removeContextNotUsed();
        tree->normalize();
        VariableLengthMarkovChainPtr m = VariableLengthMarkovChainPtr(
                        new VariableLengthMarkovChain(tree));
        m->setAlphabet(alphabet);
        loglikelihood = 0.0;
        sample_size = 0;
        for (int i = 0; i < (int) sample_set.size(); i++) {
                loglikelihood
                  += m->evaluate((sample_set[i]->getSequence()), 0, (sample_set[i]->getSequence()).size() - 1);
                sample_size += (sample_set[i]->getSequence()).size();
        }
        return m;
}

std::string TrainVariableLengthMarkovChain::help() const {
      std::stringstream out;
      out << "\nUSAGE: " << std::endl;
      out << "Mandatory parameters: " << std::endl;
      out << "\ntraining_set" << std::endl;
      out << "\talphabet" << std::endl;
      out << "\tcut" << std::endl;
      out << "Example: " << std::endl;
      out << "\ttraining_algorithm=\"ContextAlgorithm\"" << std::endl;
      out << "\talphabet=(\"0\", \"1\")" << std::endl;
      out << "\ttraining_set= \"input.seq" << std::endl;
      out << "\tcut=1" << std::endl;
      return out.str();
}

}
;