Ejemplo n.º 1
0
    public static void Main()
    {
        bool reverse = false;

        modshogun.init_shogun_with_defaults();
        int    N      = 1;
        int    M      = 512;
        double pseudo = 1e-5;
        int    order  = 3;
        int    gap    = 0;

        string[] fm_train_dna = Load.load_cubes("../data/fm_train_cube.dat");

        StringCharFeatures charfeat = new StringCharFeatures(fm_train_dna, EAlphabet.CUBE);
        StringWordFeatures feats    = new StringWordFeatures(charfeat.get_alphabet());

        feats.obtain_from_char(charfeat, order - 1, order, gap, reverse);

        HMM hmm = new HMM(feats, N, M, pseudo);

        hmm.train();
        hmm.baum_welch_viterbi_train(BaumWelchViterbiType.BW_NORMAL);

        int num_examples = feats.get_num_vectors();
        int num_param    = hmm.get_num_model_parameters();

        for (int i = 0; i < num_examples; i++)
        {
            for (int j = 0; j < num_param; j++)
            {
                hmm.get_log_derivative(j, i);
            }
        }

        int best_path       = 0;
        int best_path_state = 0;

        for (int i = 0; i < num_examples; i++)
        {
            best_path += (int)hmm.best_path(i);
            for (int j = 0; j < N; j++)
            {
                best_path_state += hmm.get_best_path_state(i, j);
            }
        }

        double[] lik_example = hmm.get_log_likelihood();
        double   lik_sample  = hmm.get_log_likelihood_sample();

        modshogun.exit_shogun();
    }
Ejemplo n.º 2
0
    internal static ArrayList run(IList para)
    {
        bool reverse = false;
        modshogun.init_shogun_with_defaults();
        int N = (int)((int?)para[0]);
        int M = (int)((int?)para[1]);
        double pseudo = (double)((double?)para[2]);
        int order = (int)((int?)para[3]);
        int gap = (int)((int?)para[4]);

        string[] fm_train_dna = Load.load_cubes("../data/fm_train_cube.dat");

        StringCharFeatures charfeat = new StringCharFeatures(fm_train_dna, CUBE);
        StringWordFeatures feats = new StringWordFeatures(charfeat.get_alphabet());
        feats.obtain_from_char(charfeat, order-1, order, gap, reverse);

        HMM hmm = new HMM(feats, N, M, pseudo);
        hmm.train();
        hmm.baum_welch_viterbi_train(BW_NORMAL);

        int num_examples = feats.get_num_vectors();
        int num_param = hmm.get_num_model_parameters();
        for (int i = 0; i < num_examples; i++)
        {
            for(int j = 0; j < num_param; j++)
            {
            hmm.get_log_derivative(j, i);
        }
        }

        int best_path = 0;
        int best_path_state = 0;
        for(int i = 0; i < num_examples; i++)
        {
            best_path += hmm.best_path(i);
            for(int j = 0; j < N; j++)
            {
                best_path_state += hmm.get_best_path_state(i, j);
            }
        }

        DoubleMatrix lik_example = hmm.get_log_likelihood();
        double lik_sample = hmm.get_log_likelihood_sample();

        ArrayList result = new ArrayList();
        result.Add(lik_example);
        result.Add(lik_sample);
        result.Add(hmm);
        modshogun.exit_shogun();
        return result;
    }
Ejemplo n.º 3
0
    public static void Main()
    {
        bool reverse = false;
        modshogun.init_shogun_with_defaults();
        int N = 1;
        int M = 512;
        double pseudo = 1e-5;
        int order = 3;
        int gap = 0;

        string[] fm_train_dna = Load.load_cubes("../data/fm_train_cube.dat");

        StringCharFeatures charfeat = new StringCharFeatures(fm_train_dna, EAlphabet.CUBE);
        StringWordFeatures feats = new StringWordFeatures(charfeat.get_alphabet());
        feats.obtain_from_char(charfeat, order-1, order, gap, reverse);

        HMM hmm = new HMM(feats, N, M, pseudo);
        hmm.train();
        hmm.baum_welch_viterbi_train(BaumWelchViterbiType.BW_NORMAL);

        int  num_examples = feats.get_num_vectors();
        int num_param = hmm.get_num_model_parameters();
        for (int i = 0; i < num_examples; i++)
            for(int j = 0; j < num_param; j++) {
            hmm.get_log_derivative(j, i);
        }

        int best_path = 0;
        int best_path_state = 0;
        for(int i = 0; i < num_examples; i++){
            best_path += (int)hmm.best_path(i);
            for(int j = 0; j < N; j++)
                best_path_state += hmm.get_best_path_state(i, j);
        }

        double[] lik_example = hmm.get_log_likelihood();
        double lik_sample = hmm.get_log_likelihood_sample();

        modshogun.exit_shogun();
    }