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();
}
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;
}
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();
}
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_dna("../data/fm_train_dna.dat");
string[] fm_test_dna = Load.load_dna("../data/fm_test_dna.dat");
DoubleMatrix label_train_dna = Load.load_labels("../data/label_train_dna.dat");
ArrayList fm_hmm_pos_builder = new ArrayList();
ArrayList fm_hmm_neg_builder = new ArrayList();
for(int i = 0; i < label_train_dna.Columns; i++)
{
if (label_train_dna.get(i) == 1)
{
fm_hmm_pos_builder.Add(fm_train_dna[i]);
}
else
{
fm_hmm_neg_builder.Add(fm_train_dna[i]);
}
}
int pos_size = fm_hmm_pos_builder.Count;
int neg_size = fm_hmm_neg_builder.Count;
string[] fm_hmm_pos = new string[pos_size];
string[] fm_hmm_neg = new string[neg_size];
for (int i = 0; i < pos_size; i++)
{
fm_hmm_pos[i] = (string)fm_hmm_pos_builder[i];
}
for (int i = 0; i < neg_size; i++)
{
fm_hmm_pos[i] = (string)fm_hmm_neg_builder[i];
}
StringCharFeatures charfeat = new StringCharFeatures(fm_hmm_pos, DNA);
StringWordFeatures hmm_pos_train = new StringWordFeatures(charfeat.get_alphabet());
hmm_pos_train.obtain_from_char(charfeat, order-1, order, gap, reverse);
HMM pos = new HMM(hmm_pos_train, N, M, pseudo);
pos.baum_welch_viterbi_train(BW_NORMAL);
charfeat = new StringCharFeatures(fm_hmm_neg, DNA);
StringWordFeatures hmm_neg_train = new StringWordFeatures(charfeat.get_alphabet());
hmm_neg_train.obtain_from_char(charfeat, order-1, order, gap, reverse);
HMM neg = new HMM(hmm_neg_train, N, M, pseudo);
neg.baum_welch_viterbi_train(BW_NORMAL);
charfeat = new StringCharFeatures(fm_train_dna, DNA);
StringWordFeatures wordfeats_train = new StringWordFeatures(charfeat.get_alphabet());
wordfeats_train.obtain_from_char(charfeat, order-1, order, gap, reverse);
charfeat = new StringCharFeatures(fm_test_dna, DNA);
StringWordFeatures wordfeats_test = new StringWordFeatures(charfeat.get_alphabet());
wordfeats_test.obtain_from_char(charfeat, order-1, order, gap, reverse);
pos.set_observations(wordfeats_train);
neg.set_observations(wordfeats_train);
FKFeatures feats_train = new FKFeatures(10, pos, neg);
feats_train.set_opt_a(-1);
PolyKernel kernel = new PolyKernel(feats_train, feats_train, 1, true);
DoubleMatrix km_train = kernel.get_kernel_matrix();
HMM pos_clone = new HMM(pos);
HMM neg_clone = new HMM(neg);
pos_clone.set_observations(wordfeats_test);
neg_clone.set_observations(wordfeats_test);
FKFeatures feats_test = new FKFeatures(10, pos_clone, neg_clone);
feats_test.set_a(feats_train.get_a());
kernel.init(feats_train, feats_test);
DoubleMatrix km_test =kernel.get_kernel_matrix();
ArrayList result = new ArrayList();
result.Add(km_train);
result.Add(km_test);
result.Add(kernel);
modshogun.exit_shogun();
return result;
}