public static void Main() { modshogun.init_shogun_with_defaults(); double width = 2.1; double epsilon = 1e-5; double C = 1.0; double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat"); double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat"); double[] trainlab = Load.load_labels("../data/label_train_multiclass.dat"); RealFeatures feats_train = new RealFeatures(); feats_train.set_feature_matrix(traindata_real); RealFeatures feats_test = new RealFeatures(); feats_test.set_feature_matrix(testdata_real); GaussianKernel kernel = new GaussianKernel(feats_train, feats_train, width); MulticlassLabels labels = new MulticlassLabels(trainlab); GMNPSVM svm = new GMNPSVM(C, kernel, labels); svm.set_epsilon(epsilon); svm.train(); kernel.init(feats_train, feats_test); double[] out_labels = LabelsFactory.to_multiclass(svm.apply(feats_test)).get_labels(); foreach(double item in out_labels) { Console.Write(item); } }
static void Main(string[] argv) { modshogun.init_shogun_with_defaults(); double width = 2.1; double epsilon = 1e-5; double C = 1.0; 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"); RealFeatures feats_train = new RealFeatures(); feats_train.set_feature_matrix(traindata_real); RealFeatures feats_test = new RealFeatures(); feats_test.set_feature_matrix(testdata_real); GaussianKernel kernel = new GaussianKernel(feats_train, feats_train, width); Labels labels = new Labels(trainlab); GMNPSVM svm = new GMNPSVM(C, kernel, labels); svm.set_epsilon(epsilon); svm.train(); kernel.init(feats_train, feats_test); DoubleMatrix out_labels = svm.apply(feats_test).get_labels(); Console.WriteLine(out_labels.ToString()); modshogun.exit_shogun(); }
static void Main(string[] argv) { modshogun.init_shogun_with_defaults(); int num = 1000; double dist = 1.0; double width = 2.1; double C = 1.0; DoubleMatrix offs =ones(2, num).mmul(dist); DoubleMatrix x = randn(2, num).sub(offs); DoubleMatrix y = randn(2, num).add(offs); DoubleMatrix traindata_real = concatHorizontally(x, y); DoubleMatrix o = ones(1,num); DoubleMatrix trainlab = concatHorizontally(o.neg(), o); DoubleMatrix testlab = concatHorizontally(o.neg(), o); RealFeatures feats = new RealFeatures(traindata_real); GaussianKernel kernel = new GaussianKernel(feats, feats, width); Labels labels = new Labels(trainlab); GMNPSVM svm = new GMNPSVM(C, kernel, labels); feats.add_preprocessor(new NormOne()); feats.add_preprocessor(new LogPlusOne()); feats.set_preprocessed(1); svm.train(feats); SerializableAsciiFile fstream = new SerializableAsciiFile("blaah.asc", 'w'); //svm.save_serializable(fstream); modshogun.exit_shogun(); }
public static void Main() { modshogun.init_shogun_with_defaults(); double width = 2.1; double epsilon = 1e-5; double C = 1.0; double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat"); double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat"); double[] trainlab = Load.load_labels("../data/label_train_multiclass.dat"); RealFeatures feats_train = new RealFeatures(); feats_train.set_feature_matrix(traindata_real); RealFeatures feats_test = new RealFeatures(); feats_test.set_feature_matrix(testdata_real); GaussianKernel kernel = new GaussianKernel(feats_train, feats_train, width); MulticlassLabels labels = new MulticlassLabels(trainlab); GMNPSVM svm = new GMNPSVM(C, kernel, labels); svm.set_epsilon(epsilon); svm.train(); kernel.init(feats_train, feats_test); double[] out_labels = LabelsFactory.to_multiclass(svm.apply(feats_test)).get_labels(); foreach (double item in out_labels) { Console.Write(item); } }
internal static HandleRef getCPtr(GMNPSVM obj) { return (obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr; }
internal static HandleRef getCPtr(GMNPSVM obj) { return((obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr); }