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); LaRank svm = new LaRank(C, kernel, labels); svm.set_batch_mode(false); svm.set_epsilon(epsilon); svm.train(); double[] out_labels = LabelsFactory.to_multiclass(svm.apply(feats_train)).get_labels(); foreach(double item in out_labels) { Console.Write(item); } }
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); MulticlassLibSVM svm = new MulticlassLibSVM(C, kernel, labels); svm.set_epsilon(epsilon); svm.train(); kernel.init(feats_train, feats_test); double[] out_labels = MulticlassLabels.obtain_from_generic(svm.apply()).get_labels(); foreach (double item in out_labels) Console.Write(item); modshogun.exit_shogun(); }
public static void Main() { modshogun.init_shogun_with_defaults(); 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); MulticlassLabels labels = new MulticlassLabels(trainlab); GaussianNaiveBayes gnb = new GaussianNaiveBayes(feats_train, labels); gnb.train(); double[] out_labels = MulticlassLabels.obtain_from_generic(gnb.apply(feats_test)).get_labels(); foreach (double item in out_labels) { Console.Write(item); } modshogun.exit_shogun(); }
public static void Main() { modshogun.init_shogun_with_defaults(); int k = 3; 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(traindata_real); RealFeatures feats_test = new RealFeatures(testdata_real); EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train); MulticlassLabels labels = new MulticlassLabels(trainlab); KNN knn = new KNN(k, distance, labels); knn.train(); double[] out_labels = MulticlassLabels.obtain_from_generic(knn.apply(feats_test)).get_labels(); foreach(double item in out_labels) { Console.Write(item); } modshogun.exit_shogun(); }
public static void Main() { modshogun.init_shogun_with_defaults(); int k = 3; 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(traindata_real); RealFeatures feats_test = new RealFeatures(testdata_real); EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train); MulticlassLabels labels = new MulticlassLabels(trainlab); KNN knn = new KNN(k, distance, labels); knn.train(); double[] out_labels = MulticlassLabels.obtain_from_generic(knn.apply(feats_test)).get_labels(); foreach (double item in out_labels) { Console.Write(item); } 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); LaRank svm = new LaRank(C, kernel, labels); svm.set_batch_mode(false); svm.set_epsilon(epsilon); svm.train(); double[] out_labels = LabelsFactory.to_multiclass(svm.apply(feats_train)).get_labels(); foreach (double item in out_labels) { Console.Write(item); } }
public static void Main() { modshogun.init_shogun_with_defaults(); 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); MulticlassLabels labels = new MulticlassLabels(trainlab); GaussianNaiveBayes gnb = new GaussianNaiveBayes(feats_train, labels); gnb.train(); double[] out_labels = MulticlassLabels.obtain_from_generic(gnb.apply(feats_test)).get_labels(); foreach(double item in out_labels) { Console.Write(item); } modshogun.exit_shogun(); }
public static void Main() { modshogun.init_shogun_with_defaults(); double width = 2.1; double epsilon = 1e-5; double C = 1.0; int mkl_norm = 2; 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"); 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); MulticlassLabels labels = new MulticlassLabels(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); double[] outMatrix = LabelsFactory.to_multiclass(mkl.apply()).get_labels(); }
public static void Main() { modshogun.init_shogun_with_defaults(); double width = 2.1; double epsilon = 1e-5; double C = 1.0; int mkl_norm = 2; 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"); 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); MulticlassLabels labels = new MulticlassLabels(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); double[] outMatrix = MulticlassLabels.obtain_from_generic(mkl.apply()).get_labels(); modshogun.exit_shogun(); }
public static void Main(string[] args) { modshogun.init_shogun_with_defaults(); double[] y = new double[5] {0, 1, 2, 3, 4,}; MulticlassLabels x = new MulticlassLabels(y); double[] r = x.get_labels(); for (int i = 0; i < 5; i++) { Console.WriteLine(r[i]); } }
public static void Main(string[] args) { modshogun.init_shogun_with_defaults(); double[] y = new double[5] { 0, 1, 2, 3, 4, }; MulticlassLabels x = new MulticlassLabels(y); double[] r = x.get_labels(); for (int i = 0; i < 5; i++) { Console.WriteLine(r[i]); } }
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); MulticlassLibSVM svm = new MulticlassLibSVM(C, kernel, labels); svm.set_epsilon(epsilon); svm.train(); kernel.init(feats_train, feats_test); double[] out_labels = MulticlassLabels.obtain_from_generic(svm.apply()).get_labels(); foreach (double item in out_labels) { Console.Write(item); } modshogun.exit_shogun(); }