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mpdsvm.sg
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mpdsvm.sg
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File f_feats_train = csv_file("../../data/classifier_binary_2d_linear_features_train.dat")
File f_feats_test = csv_file("../../data/classifier_binary_2d_linear_features_test.dat")
File f_labels_train = csv_file("../../data/classifier_binary_2d_linear_labels_train.dat")
File f_labels_test = csv_file("../../data/classifier_binary_2d_linear_labels_test.dat")
Features feats_train = features(f_feats_train)
Features feats_test = features(f_feats_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
Kernel gaussian = kernel("GaussianKernel", log_width=0.01)
gaussian.init(feats_train, feats_train)
real C=1.0
Machine svm = machine("MPDSVM", C1=C, C2=C, kernel=gaussian, epsilon=0.00001, labels=labels_train)
svm.train()
Labels labels_predict = svm.apply(feats_test)
Evaluation eval = evaluation("AccuracyMeasure")
real accuracy = eval.evaluate(labels_predict, labels_test)