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();
}
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();
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);
}
}
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 m = randn(2, num).sub(offs);
DoubleMatrix n = randn(2, num).add(offs);
DoubleMatrix testdata_real = concatHorizontally(m, n);
DoubleMatrix o = ones(1,num);
DoubleMatrix trainlab = concatHorizontally(o.neg(), o);
DoubleMatrix testlab = concatHorizontally(o.neg(), o);
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
GaussianKernel kernel = new GaussianKernel(feats_train, feats_train, width);
Labels labels = new Labels(trainlab);
LibSVM svm = new LibSVM(C, kernel, labels);
svm.train();
DoubleMatrix @out = svm.apply(feats_test).get_labels();
Console.WriteLine("Mean Error = " + signum(@out).ne(testlab).mean());
modshogun.exit_shogun();
}
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();
}
public virtual object run(IList para)
{
modshogun.init_shogun_with_defaults();
int cardinality = (int)((int?)para[0]);
int size_cache = (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");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
ANOVAKernel kernel = new ANOVAKernel(feats_train, feats_train, cardinality, size_cache);
DoubleMatrix km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
DoubleMatrix km_test = kernel.get_kernel_matrix();
ArrayList result = new ArrayList();
result.Add(km_train);
result.Add(km_test);
result.Add(kernel);
modshogun.exit_shogun();
return (object)result;
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
List<DoubleMatrix> result = new List<DoubleMatrix>(4);
DoubleMatrix inputRealMatrix = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures realFeatures = new RealFeatures(inputRealMatrix);
DoubleMatrix outputRealMatrix = realFeatures.get_feature_matrix();
result.Add(inputRealMatrix);
result.Add(outputRealMatrix);
DoubleMatrix inputByteMatrix = Load.load_numbers("../data/fm_train_byte.dat");
ByteFeatures byteFeatures = new ByteFeatures(inputByteMatrix);
DoubleMatrix outputByteMatrix = byteFeatures.get_feature_matrix();
result.Add(inputByteMatrix);
result.Add(outputByteMatrix);
DoubleMatrix inputLongMatrix = Load.load_numbers("../data/fm_train_byte.dat");
LongFeatures byteFeatures = new LongFeatures(inputLongMatrix);
DoubleMatrix outputLongMatrix = longFeatures.get_feature_matrix();
result.Add(inputByteMatrix);
result.Add(outputByteMatrix);
Console.WriteLine(result);
modshogun.exit_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;
}
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");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
distance.set_disable_sqrt(true);
double[,] dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
double[,] dm_test = distance.get_distance_matrix();
foreach(double item in dm_train) {
Console.Write(item);
}
foreach(double item in dm_test) {
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");
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();
}
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();
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
double sigma = 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(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
CauchyKernel kernel = new CauchyKernel(feats_train, feats_train, sigma, distance);
DoubleMatrix km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
DoubleMatrix km_test =kernel.get_kernel_matrix();
Console.WriteLine(km_train.ToString());
Console.WriteLine(km_test.ToString());
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);
Labels labels = new Labels(trainlab);
KNN knn = new KNN(k, distance, labels);
knn.train();
double[] out_labels = knn.apply(feats_test).get_labels();
foreach(double item in out_labels) {
Console.Write(item);
}
modshogun.exit_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();
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
double width = 0.8;
double tau = 1e-6;
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(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
GaussianKernel kernel = new GaussianKernel(feats_train, feats_train, width);
Labels labels = new Labels(trainlab);
KRR krr = new KRR(tau, kernel, labels);
krr.train(feats_train);
kernel.init(feats_train, feats_test);
DoubleMatrix out_labels = krr.apply().get_labels();
Console.WriteLine(out_labels.ToString());
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double width = 0.8;
double tau = 1e-6;
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(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
GaussianKernel kernel= new GaussianKernel(feats_train, feats_train, width);
Labels labels = new Labels(trainlab);
KRR krr = new KRR(tau, kernel, labels);
krr.train(feats_train);
kernel.init(feats_train, feats_test);
double[] out_labels = krr.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 width = 0.8;
int C = 1;
double epsilon = 1e-5;
double tube_epsilon = 1e-2;
int num_threads = 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(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
GaussianKernel kernel= new GaussianKernel(feats_train, feats_train, width);
RegressionLabels labels = new RegressionLabels(trainlab);
SVRLight svr = new SVRLight(C, epsilon, kernel, labels);
svr.set_tube_epsilon(tube_epsilon);
//svr.parallel.set_num_threads(num_threads);
svr.train();
kernel.init(feats_train, feats_test);
double[] out_labels = RegressionLabels.obtain_from_generic(svr.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 width = 1.4;
int size_cache = 10;
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(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
LogPlusOne preproc = new LogPlusOne();
preproc.init(feats_train);
feats_train.add_preprocessor(preproc);
feats_train.apply_preprocessor();
feats_test.add_preprocessor(preproc);
feats_test.apply_preprocessor();
Chi2Kernel kernel = new Chi2Kernel(feats_train, feats_train, width, size_cache);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
foreach (double item in km_train)
Console.Write(item);
foreach (double item in km_test)
Console.Write(item);
modshogun.exit_shogun();
}
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();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double shift_coef = 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(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
MultiquadricKernel kernel = new MultiquadricKernel(feats_train, feats_test, shift_coef, distance);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
foreach(double item in km_train) {
Console.Write(item);
}
foreach(double item in km_test) {
Console.Write(item);
}
modshogun.exit_shogun();
}
public static void Main() {
modshogun.init_shogun_with_defaults();
double width = 0.8;
int C = 1;
double epsilon = 1e-5;
double tube_epsilon = 1e-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_twoclass.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
GaussianKernel kernel= new GaussianKernel(feats_train, feats_train, width);
Labels labels = new Labels(trainlab);
LibSVR svr = new LibSVR(C, epsilon, kernel, labels);
svr.set_tube_epsilon(tube_epsilon);
svr.train();
kernel.init(feats_train, feats_test);
double[] out_labels = svr.apply().get_labels();
foreach (double item in out_labels)
Console.Write(out_labels);
modshogun.exit_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();
}
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();
}
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");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
JensenMetric distance = new JensenMetric(feats_train, feats_train);
double[,] dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
double[,] dm_test = distance.get_distance_matrix();
foreach(double item in dm_train) {
Console.Write(item);
}
foreach(double item in dm_test) {
Console.Write(item);
}
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double width = 1.3;
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(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
GaussianKernel kernel = new GaussianKernel(feats_train, feats_train, width);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
foreach(double item in km_train) {
Console.Write(item);
}
foreach(double item in km_test) {
Console.Write(item);
}
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double degree = 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(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
PowerKernel kernel = new PowerKernel(feats_train, feats_test, degree, distance);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
foreach (double item in km_train)
Console.Write(item);
foreach (double item in km_test)
Console.Write(item);
modshogun.exit_shogun();
}
public static void Main() {
modshogun.init_shogun_with_defaults();
double width = 1.6;
double[,] train_real = Load.load_numbers("../data/fm_train_real.dat");
double[] trainlab = Load.load_labels("../data/label_train_twoclass.dat");
RealFeatures feats_train = new RealFeatures(train_real);
GaussianKernel subkernel = new GaussianKernel(feats_train, feats_train, width);
Labels labels = new Labels(trainlab);
AUCKernel kernel = new AUCKernel(0, subkernel);
kernel.setup_auc_maximization(labels);
double[,] km_train = kernel.get_kernel_matrix();
int numRows = km_train.GetLength(0);
int numCols = km_train.GetLength(1);
Console.Write("km_train:\n");
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(km_train[i,j] +" ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
double width = 1.4;
int size_cache = 10;
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(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
RandomFourierGaussPreproc preproc = new RandomFourierGaussPreproc();
preproc.init(feats_train);
feats_train.add_preprocessor(preproc);
feats_train.apply_preprocessor();
feats_test.add_preprocessor(preproc);
feats_test.apply_preprocessor();
Chi2Kernel kernel = new Chi2Kernel(feats_train, feats_train, width, size_cache);
DoubleMatrix km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
DoubleMatrix km_test = kernel.get_kernel_matrix();
Console.WriteLine(km_train.ToString());
Console.WriteLine(km_test.ToString());
modshogun.exit_shogun();
}
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();
}
public HistogramIntersectionKernel(RealFeatures l, RealFeatures r) : this(modshogunPINVOKE.new_HistogramIntersectionKernel__SWIG_4(RealFeatures.getCPtr(l), RealFeatures.getCPtr(r)), true)
{
if (modshogunPINVOKE.SWIGPendingException.Pending)
{
throw modshogunPINVOKE.SWIGPendingException.Retrieve();
}
}
public MinkowskiMetric(RealFeatures l, RealFeatures r, double k) : this(modshogunPINVOKE.new_MinkowskiMetric__SWIG_2(RealFeatures.getCPtr(l), RealFeatures.getCPtr(r), k), true)
{
if (modshogunPINVOKE.SWIGPendingException.Pending)
{
throw modshogunPINVOKE.SWIGPendingException.Retrieve();
}
}
public RealFeatures(RealFeatures orig) : this(modshogunPINVOKE.new_RealFeatures__SWIG_2(RealFeatures.getCPtr(orig)), true)
{
if (modshogunPINVOKE.SWIGPendingException.Pending)
{
throw modshogunPINVOKE.SWIGPendingException.Retrieve();
}
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int num = 1000;
double dist = 1.0;
double width = 2.1;
double C = 1.0;
Random RandomNumber = new Random();
double[,] traindata_real = new double[2, num * 2];
for (int i = 0; i < num; i++)
{
traindata_real[0, i] = RandomNumber.NextDouble() - dist;
traindata_real[0, i + num] = RandomNumber.NextDouble() + dist;
traindata_real[1, i] = RandomNumber.NextDouble() - dist;
traindata_real[1, i + num] = RandomNumber.NextDouble() + dist;
}
double[,] testdata_real = new double[2, num * 2];
for (int i = 0; i < num; i++)
{
testdata_real[0, i] = RandomNumber.NextDouble() - dist;
testdata_real[0, i + num] = RandomNumber.NextDouble() + dist;
testdata_real[1, i] = RandomNumber.NextDouble() - dist;
testdata_real[1, i + num] = RandomNumber.NextDouble() + dist;
}
double[] trainlab = new double[num * 2];
for (int i = 0; i < num; i++)
{
trainlab[i] = -1;
trainlab[i + num] = 1;
}
double[] testlab = new double[num * 2];
for (int i = 0; i < num; i++)
{
testlab[i] = -1;
testlab[i + num] = 1;
}
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
GaussianKernel kernel = new GaussianKernel(feats_train, feats_train, width);
BinaryLabels labels = new BinaryLabels(trainlab);
LibSVM svm = new LibSVM(C, kernel, labels);
svm.train();
double[] result = BinaryLabels.obtain_from_generic(svm.apply(feats_test)).get_labels();
int err_num = 0;
for (int i = 0; i < num; i++)
{
if (result[i] > 0)
{
err_num += 1;
}
if (result[i + num] < 0)
{
err_num += 1;
}
}
double testerr = err_num / (2 * num);
Console.WriteLine(testerr);
modshogun.exit_shogun();
}
public ManhattanMetric(RealFeatures l, RealFeatures r) : this(modshogunPINVOKE.new_ManhattanMetric__SWIG_1(RealFeatures.getCPtr(l), RealFeatures.getCPtr(r)), true)
{
if (modshogunPINVOKE.SWIGPendingException.Pending)
{
throw modshogunPINVOKE.SWIGPendingException.Retrieve();
}
}
public ChiSquareDistance(RealFeatures l, RealFeatures r) : this(modshogunPINVOKE.new_ChiSquareDistance__SWIG_1(RealFeatures.getCPtr(l), RealFeatures.getCPtr(r)), true)
{
if (modshogunPINVOKE.SWIGPendingException.Pending)
{
throw modshogunPINVOKE.SWIGPendingException.Retrieve();
}
}
public GaussianShiftKernel(RealFeatures l, RealFeatures r, double width, int max_shift, int shift_step) : this(modshogunPINVOKE.new_GaussianShiftKernel__SWIG_3(RealFeatures.getCPtr(l), RealFeatures.getCPtr(r), width, max_shift, shift_step), true)
{
if (modshogunPINVOKE.SWIGPendingException.Pending)
{
throw modshogunPINVOKE.SWIGPendingException.Retrieve();
}
}
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);
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);
double[] outMatrix = mkl.apply().get_labels();
modshogun.exit_shogun();
}
public StreamingRealFeatures(RealFeatures simple_features) : this(modshogunPINVOKE.new_StreamingRealFeatures__SWIG_3(RealFeatures.getCPtr(simple_features)), true)
{
if (modshogunPINVOKE.SWIGPendingException.Pending)
{
throw modshogunPINVOKE.SWIGPendingException.Retrieve();
}
}
internal static HandleRef getCPtr(RealFeatures obj)
{
return((obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr);
}
public StreamingRealFeatures(RealFeatures simple_features, SWIGTYPE_p_double lab) : this(modshogunPINVOKE.new_StreamingRealFeatures__SWIG_2(RealFeatures.getCPtr(simple_features), SWIGTYPE_p_double.getCPtr(lab)), true)
{
if (modshogunPINVOKE.SWIGPendingException.Pending)
{
throw modshogunPINVOKE.SWIGPendingException.Retrieve();
}
}
public WeightedDegreeRBFKernel(RealFeatures l, RealFeatures r, double width, int degree, int nof_properties) : this(modshogunPINVOKE.new_WeightedDegreeRBFKernel__SWIG_3(RealFeatures.getCPtr(l), RealFeatures.getCPtr(r), width, degree, nof_properties), true)
{
if (modshogunPINVOKE.SWIGPendingException.Pending)
{
throw modshogunPINVOKE.SWIGPendingException.Retrieve();
}
}
