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();
}
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_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);
Labels labels = new Labels(trainlab);
LibSVM svm = new LibSVM(C, kernel, labels);
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();
}
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);
LibSVM svm = new LibSVM(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);
}
modshogun.exit_shogun();
}
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 = LabelsFactory.to_binary(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);
}
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_twoclass.dat");
CombinedKernel kernel = new CombinedKernel();
CombinedFeatures feats_train = new CombinedFeatures();
RealFeatures tfeats = new RealFeatures(traindata_real);
PolyKernel tkernel = new PolyKernel(10,3);
tkernel.init(tfeats, tfeats);
DoubleMatrix K = tkernel.get_kernel_matrix();
kernel.append_kernel(new CustomKernel(K));
RealFeatures subkfeats_train = new RealFeatures(traindata_real);
feats_train.append_feature_obj(subkfeats_train);
PolyKernel subkernel = new PolyKernel(10,2);
kernel.append_kernel(subkernel);
kernel.init(feats_train, feats_train);
Labels labels = new Labels(trainlab);
LibSVM svm = new LibSVM(C, kernel, labels);
svm.train();
CombinedKernel kernel_pred = new CombinedKernel();
CombinedFeatures feats_pred = new CombinedFeatures();
RealFeatures pfeats = new RealFeatures(testdata_real);
PolyKernel tkernel_pred = new PolyKernel(10,3);
tkernel_pred.init(tfeats, pfeats);
DoubleMatrix KK = tkernel.get_kernel_matrix();
kernel_pred.append_kernel(new CustomKernel(KK));
RealFeatures subkfeats_test = new RealFeatures(testdata_real);
feats_pred.append_feature_obj(subkfeats_train);
PolyKernel subkernel_pred = new PolyKernel(10,2);
kernel_pred.append_kernel(subkernel_pred);
kernel_pred.init(feats_train, feats_pred);
svm.set_kernel(kernel_pred);
svm.apply();
DoubleMatrix km_train =kernel.get_kernel_matrix();
Console.WriteLine(km_train.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");
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);
LibSVM svm = new LibSVM(C, kernel, labels);
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();
}
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();
}
