void trainhmm(string race, int numStates, int maxIterations)
{
Hmm hmm;
hmm.makeEmitAndTransFiles(race, numStates);
hmm.loadFromRace(race);
ifstream istrm((race + "/data.csv").c_str());
vector<vector<unsigned long>*> trainingSequences;
hmm.readSeqs(istrm, trainingSequences);
hmm.baumWelch(trainingSequences, maxIterations);
hmm.saveProbs(race + "/hmm");
}
// construit les matrices emit et trans à l'aide de l'algorithme Baum-Welch
void learn(const std::string& name, unsigned int maxIterations)
{
Hmm hmm;
std::string output = (boost::format("seq-%1%-result") %
name).str();
ifstream istrm((boost::format("seq-%1%.input") % name).str().c_str());
std:: vector < std::vector < unsigned long >* > trainingSequences;
hmm.loadProbs((boost::format("seq-%1%-init") % name).str().c_str());
hmm.readSeqs(istrm, trainingSequences);
hmm.baumWelch(trainingSequences, maxIterations);
hmm.saveProbs(output.c_str());
}
int main(int argc, char* argv[])
{
Hmm hmm;
if (argc<3) {
cerr << "USAGE: trainhmm INIT-HMM RESULT-HMM DATA [MAX-ITERATIONS]" << endl;
exit(1);
}
hmm.loadProbs(argv[1]);
const char* output = argv[2];
ifstream istrm(argv[3]);
int maxIterations = 10;
if (argc>4)
maxIterations = atoi(argv[4]);
vector<vector<unsigned long>*> trainingSequences;
hmm.readSeqs(istrm, trainingSequences);
hmm.baumWelch(trainingSequences, maxIterations);
hmm.saveProbs(output);
}