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);
BinaryLabels labels = new BinaryLabels(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 = LabelsFactory.to_binary(perceptron.apply()).get_labels();
foreach (double item in out_labels)
Console.Write(item);
}
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);
}
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();
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);
}
}
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 = 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();
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();
}
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);
}
}
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 static void Main()
{
modshogun.init_shogun_with_defaults();
bool reverse = false;
int order = 3;
int gap = 0;
String[] fm_train_dna = Load.load_dna("../data/fm_train_dna.dat");
String[] fm_test_dna = Load.load_dna("../data/fm_test_dna.dat");
StringCharFeatures charfeat = new StringCharFeatures(fm_train_dna, EAlphabet.DNA);
StringWordFeatures feats_train = new StringWordFeatures(charfeat.get_alphabet());
feats_train.obtain_from_char(charfeat, order-1, order, gap, false);
charfeat = new StringCharFeatures(fm_test_dna, EAlphabet.DNA);
StringWordFeatures feats_test = new StringWordFeatures(charfeat.get_alphabet());
feats_test.obtain_from_char(charfeat, order-1, order, gap, false);
BinaryLabels labels = new BinaryLabels(Load.load_labels("../data/label_train_dna.dat"));
PluginEstimate pie = new PluginEstimate();
pie.set_labels(labels);
pie.set_features(feats_train);
pie.train();
SalzbergWordStringKernel kernel = new SalzbergWordStringKernel(feats_train, feats_train, pie, labels);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
pie.set_features(feats_test);
BinaryLabels.obtain_from_generic(pie.apply()).get_labels();
double[,] km_test=kernel.get_kernel_matrix();
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);
BinaryLabels labels = new BinaryLabels(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");
}
}
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);
}
}
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);
}
}
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);
BinaryLabels labels = new BinaryLabels(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();
}
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_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(string[] argv)
{
modshogun.init_shogun_with_defaults();
double[] ground_truth = Load.load_labels("../data/label_train_twoclass.dat");
Random RandomNumber = new Random();
double[] predicted = new double[ground_truth.Length];
for (int i = 0; i < ground_truth.Length; i++)
{
predicted[i] = RandomNumber.NextDouble();
}
BinaryLabels ground_truth_labels = new BinaryLabels(ground_truth);
BinaryLabels predicted_labels = new BinaryLabels(predicted);
ContingencyTableEvaluation base_evaluator = new ContingencyTableEvaluation();
base_evaluator.evaluate(predicted_labels, ground_truth_labels);
AccuracyMeasure evaluator1 = new AccuracyMeasure();
double accuracy = evaluator1.evaluate(predicted_labels, ground_truth_labels);
ErrorRateMeasure evaluator2 = new ErrorRateMeasure();
double errorrate = evaluator2.evaluate(predicted_labels, ground_truth_labels);
BALMeasure evaluator3 = new BALMeasure();
double bal = evaluator3.evaluate(predicted_labels, ground_truth_labels);
WRACCMeasure evaluator4 = new WRACCMeasure();
double wracc = evaluator4.evaluate(predicted_labels, ground_truth_labels);
F1Measure evaluator5 = new F1Measure();
double f1 = evaluator5.evaluate(predicted_labels, ground_truth_labels);
CrossCorrelationMeasure evaluator6 = new CrossCorrelationMeasure();
double crosscorrelation = evaluator6.evaluate(predicted_labels, ground_truth_labels);
RecallMeasure evaluator7 = new RecallMeasure();
double recall = evaluator7.evaluate(predicted_labels, ground_truth_labels);
PrecisionMeasure evaluator8 = new PrecisionMeasure();
double precision = evaluator8.evaluate(predicted_labels, ground_truth_labels);
SpecificityMeasure evaluator9 = new SpecificityMeasure();
double specificity = evaluator9.evaluate(predicted_labels, ground_truth_labels);
Console.Write("{0}, {1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}\n", accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity);
modshogun.exit_shogun();
}
public static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
double[] ground_truth = Load.load_labels("../data/label_train_twoclass.dat");
Random RandomNumber = new Random();
double[] predicted = new double[ground_truth.Length];
for (int i = 0; i < ground_truth.Length; i++) {
predicted[i] = RandomNumber.NextDouble();
}
BinaryLabels ground_truth_labels = new BinaryLabels(ground_truth);
BinaryLabels predicted_labels = new BinaryLabels(predicted);
ContingencyTableEvaluation base_evaluator = new ContingencyTableEvaluation();
base_evaluator.evaluate(predicted_labels,ground_truth_labels);
AccuracyMeasure evaluator1 = new AccuracyMeasure();
double accuracy = evaluator1.evaluate(predicted_labels,ground_truth_labels);
ErrorRateMeasure evaluator2 = new ErrorRateMeasure();
double errorrate = evaluator2.evaluate(predicted_labels,ground_truth_labels);
BALMeasure evaluator3 = new BALMeasure();
double bal = evaluator3.evaluate(predicted_labels,ground_truth_labels);
WRACCMeasure evaluator4 = new WRACCMeasure();
double wracc = evaluator4.evaluate(predicted_labels,ground_truth_labels);
F1Measure evaluator5 = new F1Measure();
double f1 = evaluator5.evaluate(predicted_labels,ground_truth_labels);
CrossCorrelationMeasure evaluator6 = new CrossCorrelationMeasure();
double crosscorrelation = evaluator6.evaluate(predicted_labels,ground_truth_labels);
RecallMeasure evaluator7 = new RecallMeasure();
double recall = evaluator7.evaluate(predicted_labels,ground_truth_labels);
PrecisionMeasure evaluator8 = new PrecisionMeasure();
double precision = evaluator8.evaluate(predicted_labels,ground_truth_labels);
SpecificityMeasure evaluator9 = new SpecificityMeasure();
double specificity = evaluator9.evaluate(predicted_labels,ground_truth_labels);
Console.Write("{0}, {1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}\n", accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity);
modshogun.exit_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 = BinaryLabels.obtain_from_generic(lda.apply()).get_labels();
foreach (double item in out_labels)
{
Console.Write(item);
}
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
bool reverse = false;
int order = 3;
int gap = 0;
String[] fm_train_dna = Load.load_dna("../data/fm_train_dna.dat");
String[] fm_test_dna = Load.load_dna("../data/fm_test_dna.dat");
StringCharFeatures charfeat = new StringCharFeatures(fm_train_dna, EAlphabet.DNA);
StringWordFeatures feats_train = new StringWordFeatures(charfeat.get_alphabet());
feats_train.obtain_from_char(charfeat, order - 1, order, gap, false);
charfeat = new StringCharFeatures(fm_test_dna, EAlphabet.DNA);
StringWordFeatures feats_test = new StringWordFeatures(charfeat.get_alphabet());
feats_test.obtain_from_char(charfeat, order - 1, order, gap, false);
BinaryLabels labels = new BinaryLabels(Load.load_labels("../data/label_train_dna.dat"));
PluginEstimate pie = new PluginEstimate();
pie.set_labels(labels);
pie.set_features(feats_train);
pie.train();
SalzbergWordStringKernel kernel = new SalzbergWordStringKernel(feats_train, feats_train, pie, labels);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
pie.set_features(feats_test);
BinaryLabels.obtain_from_generic(pie.apply()).get_labels();
double[,] km_test = kernel.get_kernel_matrix();
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();
}