public static void main(String argv[]) {
modshogun.init_shogun_with_defaults();
double width = 1.4;
int size_cache = 10;
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
NormOne preproc = new NormOne();
preproc.init(feats_train);
feats_train.add_preprocessor(preproc);
feats_train.apply_preprocessor();
feats_test.add_preprocessor(preproc);
feats_test.apply_preprocessor();
Chi2Kernel kernel = new Chi2Kernel(feats_train, feats_train, width, size_cache);
DoubleMatrix km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
DoubleMatrix km_test = kernel.get_kernel_matrix();
System.out.println(km_train.toString());
System.out.println(km_test.toString());
}
public static void main(String argv[]) {
modshogun.init_shogun_with_defaults();
double width = 2.1;
double epsilon = 1e-5;
double C = 1.0;
int mkl_norm = 2;
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
DoubleMatrix trainlab = Load.load_labels("../data/label_train_multiclass.dat");
CombinedKernel kernel = new CombinedKernel();
CombinedFeatures feats_train = new CombinedFeatures();
CombinedFeatures feats_test = new CombinedFeatures();
RealFeatures subkfeats1_train = new RealFeatures(traindata_real);
RealFeatures subkfeats1_test = new RealFeatures(testdata_real);
GaussianKernel subkernel = new GaussianKernel(10, width);
feats_train.append_feature_obj(subkfeats1_train);
feats_test.append_feature_obj(subkfeats1_test);
kernel.append_kernel(subkernel);
RealFeatures subkfeats2_train = new RealFeatures(traindata_real);
RealFeatures subkfeats2_test = new RealFeatures(testdata_real);
LinearKernel subkernel2 = new LinearKernel();
feats_train.append_feature_obj(subkfeats2_train);
feats_test.append_feature_obj(subkfeats2_test);
kernel.append_kernel(subkernel2);
RealFeatures subkfeats3_train = new RealFeatures(traindata_real);
RealFeatures subkfeats3_test = new RealFeatures(testdata_real);
PolyKernel subkernel3 = new PolyKernel(10, 2);
feats_train.append_feature_obj(subkfeats3_train);
feats_test.append_feature_obj(subkfeats3_test);
kernel.append_kernel(subkernel3);
kernel.init(feats_train, feats_train);
Labels labels = new Labels(trainlab);
MKLMultiClass mkl = new MKLMultiClass(C, kernel, labels);
mkl.set_epsilon(epsilon);
mkl.set_mkl_epsilon(epsilon);
mkl.set_mkl_norm(mkl_norm);
mkl.train();
kernel.init(feats_train, feats_test);
DoubleMatrix out = mkl.apply().get_labels();
modshogun.exit_shogun();
}
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[]) {
modshogun.init_shogun_with_defaults();
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
DoubleMatrix dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
DoubleMatrix dm_test = distance.get_distance_matrix();
System.out.println(dm_train.toString());
System.out.println(dm_test.toString());
}
public static void main(String argv[]) {
modshogun.init_shogun_with_defaults();
DoubleMatrix ground_truth = Load.load_labels("../data/label_train_twoclass.dat");
DoubleMatrix predicted = randn(1, ground_truth.getLength());
Labels ground_truth_labels = new Labels(ground_truth);
Labels predicted_labels = new Labels(predicted);
ROCEvaluation evaluator = new ROCEvaluation();
evaluator.evaluate(predicted_labels, ground_truth_labels);
System.out.println(evaluator.get_ROC());
System.out.println(evaluator.get_auROC());
modshogun.exit_shogun();
}
public static void main(String argv[]) {
modshogun.init_shogun_with_defaults();
DoubleMatrix ground_truth = Load.load_labels("../data/label_train_twoclass.dat");
DoubleMatrix predicted = randn(1, ground_truth.getLength());
Labels ground_truth_labels = new Labels(ground_truth);
Labels predicted_labels = new Labels(predicted);
ContingencyTableEvaluation base_evaluator = new ContingencyTableEvaluation();
base_evaluator.evaluate(predicted_labels, ground_truth_labels);
AccuracyMeasure evaluator1 = new AccuracyMeasure();
double accuracy = evaluator1.evaluate(predicted_labels, ground_truth_labels);
ErrorRateMeasure evaluator2 = new ErrorRateMeasure();
double errorrate = evaluator2.evaluate(predicted_labels, ground_truth_labels);
BALMeasure evaluator3 = new BALMeasure();
double bal = evaluator3.evaluate(predicted_labels, ground_truth_labels);
WRACCMeasure evaluator4 = new WRACCMeasure();
double wracc = evaluator4.evaluate(predicted_labels, ground_truth_labels);
F1Measure evaluator5 = new F1Measure();
double f1 = evaluator5.evaluate(predicted_labels, ground_truth_labels);
CrossCorrelationMeasure evaluator6 = new CrossCorrelationMeasure();
double crosscorrelation = evaluator6.evaluate(predicted_labels, ground_truth_labels);
RecallMeasure evaluator7 = new RecallMeasure();
double recall = evaluator7.evaluate(predicted_labels, ground_truth_labels);
PrecisionMeasure evaluator8 = new PrecisionMeasure();
double precision = evaluator8.evaluate(predicted_labels, ground_truth_labels);
SpecificityMeasure evaluator9 = new SpecificityMeasure();
double specificity = evaluator9.evaluate(predicted_labels, ground_truth_labels);
System.out.printf(
"%f, %f, %f, %f, %f, %f, %f, %f, %f\n",
accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity);
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();
}
static ArrayList run(int para) {
modshogun.init_shogun_with_defaults();
int k = para;
init_random(17);
DoubleMatrix fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
KMeans kmeans = new KMeans(k, distance);
kmeans.train();
DoubleMatrix out_centers = kmeans.get_cluster_centers();
kmeans.get_radiuses();
ArrayList result = new ArrayList();
result.add(kmeans);
result.add(out_centers);
modshogun.exit_shogun();
return result;
}
