C# (CSharp) RealFeatures.set_feature_matrix Examples

Example #1

File: classifier_multiclasslibsvm_modular.cs Project: joseph-chan/rqpersonalsvn

    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();
    }

Example #2

File: classifier_averaged_perceptron_modular.cs Project: manantomar/test

    public static void Main()
    {
        modshogun.init_shogun_with_defaults();
        double learn_rate = 1.0;
        int    max_iter   = 1000;

        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_twoclass.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);
        BinaryLabels       labels     = new BinaryLabels(trainlab);
        AveragedPerceptron perceptron = new AveragedPerceptron(feats_train, labels);

        perceptron.set_learn_rate(learn_rate);
        perceptron.set_max_iter(max_iter);
        perceptron.train();

        perceptron.set_features(feats_test);
        double[] out_labels = LabelsFactory.to_binary(perceptron.apply()).get_labels();

        foreach (double item in out_labels)
        {
            Console.Write(item);
        }
    }

Example #3

File: classifier_gaussiannaivebayes_modular.cs Project: joseph-chan/rqpersonalsvn

    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);
        Labels labels = new Labels(trainlab);

        GaussianNaiveBayes gnb = new GaussianNaiveBayes(feats_train, labels);
        gnb.train();
        double[] out_labels = gnb.apply(feats_test).get_labels();

        foreach(double item in out_labels) {
            Console.Write(item);
        }

        modshogun.exit_shogun();
    }

Example #4

File: classifier_libsvmoneclass_modular.cs Project: orico/shogun

    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");

        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);

        LibSVMOneClass svm = new LibSVMOneClass(C, kernel);
        svm.set_epsilon(epsilon);
        svm.train();

        kernel.init(feats_train, feats_test);
        DoubleMatrix out_labels = svm.apply().get_labels();
        Console.WriteLine(out_labels.ToString());

        modshogun.exit_shogun();
    }

Example #5

File: classifier_larank_modular.cs Project: JackieXie168/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);
		}

	}

Example #6

File: classifier_perceptron_modular.cs Project: joseph-chan/rqpersonalsvn

    public static void Main()
    {
        modshogun.init_shogun_with_defaults();
        double learn_rate = 1.0;
        int max_iter = 1000;

        double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
        double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");

        // already tried double[][]
        double[] trainlab = Load.load_labels("../data/label_train_twoclass.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);

        Labels labels = new Labels(trainlab);

        Perceptron perceptron = new Perceptron(feats_train, labels);
        perceptron.set_learn_rate(learn_rate);
        perceptron.set_max_iter(max_iter);
        perceptron.train();

        perceptron.set_features(feats_test);
        //  already tried double[][]
        double[] out_labels = perceptron.apply().get_labels();

        foreach (double item in out_labels)
            Console.Write(item);

        modshogun.exit_shogun();
    }

Example #7

File: classifier_liblinear_modular.cs Project: orico/shogun

    static void Main(string[] argv)
    {
        modshogun.init_shogun_with_defaults();
        double C = 0.9;
        double epsilon = 1e-3;

        org.shogun.Math.init_random(17);
        DoubleMatrix traindata_real = Load.load_numbers(".../data/fm_train_real.dat");
        DoubleMatrix testdata_real = Load.load_numbers("../data/toy/fm_test_real.dat");

        DoubleMatrix trainlab = Load.load_labels("../data/label_train_twoclass.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);

        Labels labels = new Labels(trainlab);

        LibLinear svm = new LibLinear(C, feats_train, labels);
        svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL);
        svm.set_epsilon(epsilon);
        svm.set_bias_enabled(true);
        svm.train();
        svm.set_features(feats_test);
        DoubleMatrix out_labels = svm.apply().get_labels();
        Console.WriteLine(out_labels.ToString());

        modshogun.exit_shogun();
    }

Example #8

File: classifier_averaged_perceptron_modular.cs Project: orico/shogun

    public virtual Serializable run(IList para)
    {
        modshogun.init_shogun_with_defaults();
        double learn_rate = (double)((double?)para[0]);
        int max_iter = (int)((int?)para[1]);

        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_twoclass.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);
        Labels labels = new Labels(trainlab);
        AveragedPerceptron perceptron = new AveragedPerceptron(feats_train, labels);
        perceptron.set_learn_rate(learn_rate);
        perceptron.set_max_iter(max_iter);
        perceptron.train();

        perceptron.set_features(feats_test);
        DoubleMatrix out_labels = perceptron.apply().get_labels();
        ArrayList result = new ArrayList();
        result.Add(perceptron);
        result.Add(out_labels);

        modshogun.exit_shogun();
        return result;
    }

Example #9

    public static void Main()
    {
        modshogun.init_shogun_with_defaults();
        double C       = 0.9;
        double epsilon = 1e-3;

        Math.init_random(17);
        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_twoclass.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);

        BinaryLabels labels = new BinaryLabels(trainlab);

        LibLinear svm = new LibLinear(C, feats_train, labels);

        svm.set_liblinear_solver_type(LIBLINEAR_SOLVER_TYPE.L2R_L2LOSS_SVC_DUAL);
        svm.set_epsilon(epsilon);
        svm.set_bias_enabled(true);
        svm.train();
        svm.set_features(feats_test);
        double[] out_labels = LabelsFactory.to_binary(svm.apply()).get_labels();

        foreach (double item in out_labels)
        {
            Console.Write(item);
        }
    }

Example #10

File: classifier_mpdsvm_modular.cs Project: JackieXie168/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");

		//  already tried double[,]
		double[] trainlab = Load.load_labels("../data/label_train_twoclass.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);

		BinaryLabels labels = new BinaryLabels(trainlab);

		MPDSVM svm = new MPDSVM(C, kernel, labels);
		svm.set_epsilon(epsilon);
		svm.train();

		kernel.init(feats_train, feats_test);
		//  already tried double[,]
		double[] out_labels = LabelsFactory.to_binary(svm.apply()).get_labels();

		foreach (double item in out_labels)
		      Console.Write(item);

	}

Example #11

File: classifier_lda_modular.cs Project: orico/shogun

    static void Main(string[] argv)
    {
        modshogun.init_shogun_with_defaults();
        int gamma = 3;

        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_twoclass.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);

        Labels labels = new Labels(trainlab);

        LDA lda = new LDA(gamma, feats_train, labels);
        lda.train();

        Console.WriteLine(lda.get_bias());
        Console.WriteLine(lda.get_w().ToString());
        lda.set_features(feats_test);
        DoubleMatrix out_labels = lda.apply().get_labels();
        Console.WriteLine(out_labels.ToString());

        modshogun.exit_shogun();
    }

Example #12

File: classifier_lda_modular.cs Project: JackieXie168/shogun

	public static void Main() {
		modshogun.init_shogun_with_defaults();
		int gamma = 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_twoclass.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);

		BinaryLabels labels = new BinaryLabels(trainlab);

		LDA lda = new LDA(gamma, feats_train, labels);
		lda.train();

		Console.WriteLine(lda.get_bias());

		//Console.WriteLine(lda.get_w().toString());
		foreach(double item in lda.get_w()) {
			Console.Write(item);
		}


		lda.set_features(feats_test);
		double[] out_labels = LabelsFactory.to_binary(lda.apply()).get_labels();

		foreach(double item in out_labels) {
			Console.Write(item);
		}

	}

Example #13

File: classifier_perceptron_modular.cs Project: orico/shogun

    static void Main(string[] argv)
    {
        modshogun.init_shogun_with_defaults();
        double learn_rate = 1.0;
        int max_iter = 1000;

        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_twoclass.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);

        Labels labels = new Labels(trainlab);

        Perceptron perceptron = new Perceptron(feats_train, labels);
        perceptron.set_learn_rate(learn_rate);
        perceptron.set_max_iter(max_iter);
        perceptron.train();

        perceptron.set_features(feats_test);
        DoubleMatrix out_labels = perceptron.apply().get_labels();
        Console.WriteLine(out_labels.ToString());

        modshogun.exit_shogun();
    }

Example #14

    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");

        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);

        LibSVMOneClass svm = new LibSVMOneClass(C, kernel);

        svm.set_epsilon(epsilon);
        svm.train();

        kernel.init(feats_train, feats_test);
        double[] out_labels = BinaryLabels.obtain_from_generic(svm.apply()).get_labels();

        foreach (double item in out_labels)
        {
            Console.Write(item);
        }

        modshogun.exit_shogun();
    }

Example #15

    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();
    }

Example #16

File: classifier_liblinear_modular.cs Project: behollis/muViewBranch

    public static void Main()
    {
        modshogun.init_shogun_with_defaults();
        double C = 0.9;
        double epsilon = 1e-3;

        Math.init_random(17);
        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_twoclass.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);

        BinaryLabels labels = new BinaryLabels(trainlab);

        LibLinear svm = new LibLinear(C, feats_train, labels);
        svm.set_liblinear_solver_type(LIBLINEAR_SOLVER_TYPE.L2R_L2LOSS_SVC_DUAL);
        svm.set_epsilon(epsilon);
        svm.set_bias_enabled(true);
        svm.train();
        svm.set_features(feats_test);
        double[] out_labels = BinaryLabels.obtain_from_generic(svm.apply()).get_labels();

        foreach(double item in out_labels) {
            Console.Write(item);
        }

        modshogun.exit_shogun();
    }

Example #17

File: classifier_gpbtsvm_modular.cs Project: manantomar/test

    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_twoclass.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);

        BinaryLabels labels = new BinaryLabels(trainlab);

        GPBTSVM svm = new GPBTSVM(C, kernel, labels);

        svm.set_epsilon(epsilon);
        svm.train();
        kernel.init(feats_train, feats_test);
        double[] out_labels = LabelsFactory.to_binary(svm.apply()).get_labels();

        foreach (double item in out_labels)
        {
            Console.Write(item);
        }
    }

Example #18

File: classifier_libsvmoneclass_modular.cs Project: Anshul-Bansal/gsoc

	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");

		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);

		LibSVMOneClass svm = new LibSVMOneClass(C, kernel);
		svm.set_epsilon(epsilon);
		svm.train();

		kernel.init(feats_train, feats_test);
		double[] out_labels = svm.apply().get_labels();

		foreach (double item in out_labels)
		    Console.Write(item);

		modshogun.exit_shogun();
	}

Example #19

File: classifier_larank_modular.cs Project: orico/shogun

    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);

        LaRank svm = new LaRank(C, kernel, labels);
        svm.set_batch_mode(false);
        svm.set_epsilon(epsilon);
        svm.train();
        DoubleMatrix out_labels = svm.apply(feats_train).get_labels();
        Console.WriteLine(out_labels.ToString());

        modshogun.exit_shogun();
    }

Example #20

File: classifier_averaged_perceptron_modular.cs Project: behollis/muViewBranch

    public static void Main()
    {
        modshogun.init_shogun_with_defaults();
        double learn_rate = 1.0;
        int max_iter = 1000;

        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_twoclass.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);
        BinaryLabels labels = new BinaryLabels(trainlab);
        AveragedPerceptron perceptron = new AveragedPerceptron(feats_train, labels);
        perceptron.set_learn_rate(learn_rate);
        perceptron.set_max_iter(max_iter);
        perceptron.train();

        perceptron.set_features(feats_test);
        double[] out_labels = BinaryLabels.obtain_from_generic(perceptron.apply()).get_labels();

        foreach(double item in out_labels) {
            Console.Write(item);
        }

        modshogun.exit_shogun();
    }

Example #21

File: MatrixTest.cs Project: orico/shogun

 static void Main(string[] argv)
 {
     modshogun.init_shogun();
     Console.WriteLine("Test DoubleMatrix(jblas):");
     RealFeatures x = new RealFeatures();
     double[][] y = { new double[] { 1, 2 }, new double[] { 3, 4 }, new double[] { 5, 6 } };
     DoubleMatrix A = new DoubleMatrix(y);
     x.set_feature_matrix(A);
     DoubleMatrix B = x.get_feature_matrix();
     Console.WriteLine(B.ToString());
     modshogun.exit_shogun();
 }

Example #22

File: MatrixTest.cs Project: JackieXie168/shogun

	public static void Main(string[] args) {
		modshogun.init_shogun_with_defaults();

		RealFeatures x = new RealFeatures();
		double[,] y = new double[2,3] {{1, 2, 3}, {4, 5, 6}};

		x.set_feature_matrix(y);
		double [,] r = x.get_feature_matrix();
		foreach (int item in r) {
					Console.WriteLine(item);
		}

	}

Example #23

    public static void Main(string[] args)
    {
        modshogun.init_shogun_with_defaults();

        RealFeatures x = new RealFeatures();

        double[,] y = new double[2, 3] {
            { 1, 2, 3 }, { 4, 5, 6 }
        };

        x.set_feature_matrix(y);
        double [,] r = x.get_feature_matrix();
        foreach (int item in r)
        {
            Console.WriteLine(item);
        }
    }

Example #24

    public static void Main()
    {
        modshogun.init_shogun_with_defaults();
        int gamma = 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_twoclass.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);

        BinaryLabels labels = new BinaryLabels(trainlab);

        LDA lda = new LDA(gamma, feats_train, labels);

        lda.train();

        Console.WriteLine(lda.get_bias());

        //Console.WriteLine(lda.get_w().toString());
        foreach (double item in lda.get_w())
        {
            Console.Write(item);
        }


        lda.set_features(feats_test);
        double[] out_labels = BinaryLabels.obtain_from_generic(lda.apply()).get_labels();

        foreach (double item in out_labels)
        {
            Console.Write(item);
        }

        modshogun.exit_shogun();
    }

Example #25

File: classifier_gaussiannaivebayes_modular.cs Project: orico/shogun

    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");

        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);
        Labels labels = new Labels(trainlab);

        GaussianNaiveBayes gnb = new GaussianNaiveBayes(feats_train, labels);
        gnb.train();
        DoubleMatrix out_labels = gnb.apply(feats_test).get_labels();
        Console.WriteLine(out_labels.ToString());

        modshogun.exit_shogun();
    }

Example #26

    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 = MulticlassLabels.obtain_from_generic(svm.apply(feats_train)).get_labels();

        foreach (double item in out_labels)
        {
            Console.Write(item);
        }

        modshogun.exit_shogun();
    }