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_twoclass.dat");
RealFeatures tfeats = new RealFeatures(traindata_real);
PolyKernel tkernel = new PolyKernel(10, 3);
tkernel.init(tfeats, tfeats);
double[,] K_train = tkernel.get_kernel_matrix();
RealFeatures pfeats = new RealFeatures(testdata_real);
tkernel.init(tfeats, pfeats);
double[,] K_test = tkernel.get_kernel_matrix();
CombinedFeatures feats_train = new CombinedFeatures();
feats_train.append_feature_obj(new RealFeatures(traindata_real));
CombinedKernel kernel = new CombinedKernel();
kernel.append_kernel(new CustomKernel(K_train));
kernel.append_kernel(new PolyKernel(10, 2));
kernel.init(feats_train, feats_train);
BinaryLabels labels = new BinaryLabels(trainlab);
MKLClassification mkl = new MKLClassification();
mkl.set_mkl_norm(1);
mkl.set_kernel(kernel);
mkl.set_labels(labels);
mkl.train();
CombinedFeatures feats_pred = new CombinedFeatures();
feats_pred.append_feature_obj(new RealFeatures(testdata_real));
CombinedKernel kernel2 = new CombinedKernel();
kernel2.append_kernel(new CustomKernel(K_test));
kernel2.append_kernel(new PolyKernel(10, 2));
kernel2.init(feats_train, feats_pred);
mkl.set_kernel(kernel2);
mkl.apply();
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();
}
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 CombinedKernel KernelToCombinedKernel(Kernel n)
{
IntPtr cPtr = modshogunPINVOKE.CombinedKernel_KernelToCombinedKernel(swigCPtr, Kernel.getCPtr(n));
CombinedKernel ret = (cPtr == IntPtr.Zero) ? null : new CombinedKernel(cPtr, false);
if (modshogunPINVOKE.SWIGPendingException.Pending)
{
throw modshogunPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
double width = 2.1;
double epsilon = 1e-5;
double C = 1.0;
int mkl_norm = 2;
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 tfeats = new RealFeatures(traindata_real);
PolyKernel tkernel = new PolyKernel(10,3);
tkernel.init(tfeats, tfeats);
DoubleMatrix K_train = tkernel.get_kernel_matrix();
RealFeatures pfeats = new RealFeatures(testdata_real);
tkernel.init(tfeats, pfeats);
DoubleMatrix K_test = tkernel.get_kernel_matrix();
CombinedFeatures feats_train = new CombinedFeatures();
feats_train.append_feature_obj(new RealFeatures(traindata_real));
CombinedKernel kernel = new CombinedKernel();
kernel.append_kernel(new CustomKernel(K_train));
kernel.append_kernel(new PolyKernel(10,2));
kernel.init(feats_train, feats_train);
Labels labels = new Labels(trainlab);
MKLClassification mkl = new MKLClassification();
mkl.set_mkl_norm(1);
mkl.set_kernel(kernel);
mkl.set_labels(labels);
mkl.train();
CombinedFeatures feats_pred = new CombinedFeatures();
feats_pred.append_feature_obj(new RealFeatures(testdata_real));
CombinedKernel kernel2 = new CombinedKernel();
kernel2.append_kernel(new CustomKernel(K_test));
kernel2.append_kernel(new PolyKernel(10, 2));
kernel2.init(feats_train, feats_pred);
mkl.set_kernel(kernel2);
mkl.apply();
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int cardinality = 2;
int cache = 10;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
String[] fm_train_dna = Load.load_dna("../data/fm_train_dna.dat");
String[] fm_test_dna = Load.load_dna("../data/fm_test_dna.dat");
RealFeatures subfeats_train = new RealFeatures(traindata_real);
RealFeatures subfeats_test = new RealFeatures(testdata_real);
CombinedKernel kernel = new CombinedKernel();
CombinedFeatures feats_train = new CombinedFeatures();
CombinedFeatures feats_test = new CombinedFeatures();
GaussianKernel subkernel = new GaussianKernel(cache, 1.1);
feats_train.append_feature_obj(subfeats_train);
feats_test.append_feature_obj(subfeats_test);
kernel.append_kernel(subkernel);
StringCharFeatures subkfeats_train = new StringCharFeatures(fm_train_dna, EAlphabet.DNA);
StringCharFeatures subkfeats_test = new StringCharFeatures(fm_test_dna, EAlphabet.DNA);
int degree = 3;
FixedDegreeStringKernel subkernel2 = new FixedDegreeStringKernel(10, degree);
feats_train.append_feature_obj(subkfeats_train);
feats_test.append_feature_obj(subkfeats_test);
kernel.append_kernel(subkernel2);
subkfeats_train = new StringCharFeatures(fm_train_dna, EAlphabet.DNA);
subkfeats_test = new StringCharFeatures(fm_test_dna, EAlphabet.DNA);
LocalAlignmentStringKernel subkernel3 = new LocalAlignmentStringKernel(10);
feats_train.append_feature_obj(subkfeats_train);
feats_test.append_feature_obj(subkfeats_test);
kernel.append_kernel(subkernel3);
kernel.init(feats_train, feats_train);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
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");
string[] fm_train_dna = Load.load_dna("../data/fm_train_dna.dat");
string[] fm_test_dna = Load.load_dna("../data/fm_test_dna.dat");
RealFeatures subfeats_train = new RealFeatures(traindata_real);
RealFeatures subfeats_test = new RealFeatures(testdata_real);
CombinedKernel kernel = new CombinedKernel();
CombinedFeatures feats_train = new CombinedFeatures();
CombinedFeatures feats_test = new CombinedFeatures();
GaussianKernel subkernel = new GaussianKernel(10, 1.1);
feats_train.append_feature_obj(subfeats_train);
feats_test.append_feature_obj(subfeats_test);
kernel.append_kernel(subkernel);
StringCharFeatures subkfeats_train = new StringCharFeatures(fm_train_dna, DNA);
StringCharFeatures subkfeats_test = new StringCharFeatures(fm_test_dna, DNA);
int degree = 3;
FixedDegreeStringKernel subkernel2 = new FixedDegreeStringKernel(10, degree);
feats_train.append_feature_obj(subkfeats_train);
feats_test.append_feature_obj(subkfeats_test);
kernel.append_kernel(subkernel2);
subkfeats_train = new StringCharFeatures(fm_train_dna, DNA);
subkfeats_test = new StringCharFeatures(fm_test_dna, DNA);
LocalAlignmentStringKernel subkernel3 = new LocalAlignmentStringKernel(10);
feats_train.append_feature_obj(subkfeats_train);
feats_test.append_feature_obj(subkfeats_test);
kernel.append_kernel(subkernel3);
kernel.init(feats_train, feats_train);
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();
double width = 2.1;
double epsilon = 1e-5;
double C = 1.0;
int mkl_norm = 2;
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();
CombinedFeatures feats_test = new CombinedFeatures();
RealFeatures subkfeats_train = new RealFeatures(traindata_real);
RealFeatures subkfeats_test = new RealFeatures(testdata_real);
GaussianKernel subkernel = new GaussianKernel(10, width);
feats_train.append_feature_obj(subkfeats_train);
feats_test.append_feature_obj(subkfeats_test);
kernel.append_kernel(subkernel);
LinearKernel subkernel2 = new LinearKernel();
feats_train.append_feature_obj(subkfeats_train);
feats_test.append_feature_obj(subkfeats_test);
kernel.append_kernel(subkernel2);
PolyKernel subkernel3 = new PolyKernel(10, 2);
feats_train.append_feature_obj(subkfeats_train);
feats_test.append_feature_obj(subkfeats_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);
DoubleMatrix @out = mkl.apply().get_labels();
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int cardinality = 2;
int cache = 10;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
String[] fm_train_dna = Load.load_dna("../data/fm_train_dna.dat");
String[] fm_test_dna = Load.load_dna("../data/fm_test_dna.dat");
RealFeatures subfeats_train = new RealFeatures(traindata_real);
RealFeatures subfeats_test = new RealFeatures(testdata_real);
CombinedKernel kernel= new CombinedKernel();
CombinedFeatures feats_train = new CombinedFeatures();
CombinedFeatures feats_test = new CombinedFeatures();
GaussianKernel subkernel = new GaussianKernel(cache, 1.1);
feats_train.append_feature_obj(subfeats_train);
feats_test.append_feature_obj(subfeats_test);
kernel.append_kernel(subkernel);
StringCharFeatures subkfeats_train = new StringCharFeatures(fm_train_dna, EAlphabet.DNA);
StringCharFeatures subkfeats_test = new StringCharFeatures(fm_test_dna, EAlphabet.DNA);
int degree = 3;
FixedDegreeStringKernel subkernel2= new FixedDegreeStringKernel(10, degree);
feats_train.append_feature_obj(subkfeats_train);
feats_test.append_feature_obj(subkfeats_test);
kernel.append_kernel(subkernel2);
subkfeats_train = new StringCharFeatures(fm_train_dna, EAlphabet.DNA);
subkfeats_test = new StringCharFeatures(fm_test_dna, EAlphabet.DNA);
LocalAlignmentStringKernel subkernel3 = new LocalAlignmentStringKernel(10);
feats_train.append_feature_obj(subkfeats_train);
feats_test.append_feature_obj(subkfeats_test);
kernel.append_kernel(subkernel3);
kernel.init(feats_train, feats_train);
double[,] km_train=kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test=kernel.get_kernel_matrix();
modshogun.exit_shogun();
}
internal static HandleRef getCPtr(CombinedKernel obj)
{
return((obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr);
}
internal static HandleRef getCPtr(CombinedKernel obj) {
return (obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr;
}
