public static void main(String argv[]) {
boolean reverse = false;
modshogun.init_shogun_with_defaults();
int order = 3;
int gap = 4;
String[] fm_train_dna = Load.load_dna("../data/fm_train_dna.dat");
StringCharFeatures charfeat = new StringCharFeatures(fm_train_dna, DNA);
StringWordFeatures feats = new StringWordFeatures(charfeat.get_alphabet());
feats.obtain_from_char(charfeat, order - 1, order, gap, reverse);
PositionalPWM ppwm = new PositionalPWM();
ppwm.set_sigma(5.0);
ppwm.set_mean(10.0);
DoubleMatrix pwm =
new DoubleMatrix(
new double[][] {
{0.0, 0.5, 0.1, 1.0}, {0.0, 0.5, 0.5, 0.0}, {1.0, 0.0, 0.4, 0.0}, {0.0, 0.0, 0.0, 0.0}
});
ppwm.set_pwm(logi(pwm));
ppwm.compute_w(20);
DoubleMatrix w = ppwm.get_w();
modshogun.exit_shogun();
}
public static void main(String argv[]) {
boolean 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, 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();
modshogun.exit_shogun();
}
