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[]) {
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();
}