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();
}
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();
}
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);
}
}
private void ClassifierList_MouseUp(object sender, MouseEventArgs e)
{
if (e.Button == System.Windows.Forms.MouseButtons.Right && SelectedItem != null)
{
Classifier classifier = SelectedItem as Classifier;
if (classifier == null)
{
throw new NullReferenceException("Failed to cast classifier item from list");
}
string updateWindowTitle = "Updating " + classifier.GetType().Name + "...";
if (classifier is LibLinear)
{
LibLinear liblinear = classifier as LibLinear;
DynamicForm f = new DynamicForm("Set LibLinear parameters", DynamicForm.CloseButtons.OkCancel);
f.AddCheckBox("Run feature selection:", ContentAlignment.MiddleRight, liblinear.RunFeatureSelection, "run_feature_selection");
f.AddDropDown("Positive weighting:", Enum.GetValues(typeof(LibLinear.PositiveClassWeighting)), liblinear.Weighting, "positive_weighting", true);
if (f.ShowDialog() == DialogResult.OK)
{
liblinear.RunFeatureSelection = f.GetValue <bool>("run_feature_selection");
liblinear.Weighting = f.GetValue <LibLinear.PositiveClassWeighting>("positive_weighting");
}
}
else if (classifier is SvmRank)
{
SvmRank svmRank = classifier as SvmRank;
DynamicForm f = new DynamicForm("Set SvmRank parameters", DynamicForm.CloseButtons.OkCancel);
f.AddNumericUpdown("c:", (decimal)svmRank.C, 3, decimal.MinValue, decimal.MaxValue, (decimal)0.01, "c");
if (f.ShowDialog() == DialogResult.OK)
{
try { svmRank.C = Convert.ToSingle(f.GetValue <decimal>("c")); }
catch (Exception ex) { MessageBox.Show("Invalid value for C: " + ex.Message); }
}
}
else if (classifier is RandomForest)
{
RandomForest randomForest = classifier as RandomForest;
DynamicForm f = new DynamicForm("Set RandomForest parameters", DynamicForm.CloseButtons.OkCancel);
f.AddNumericUpdown("Number of trees:", randomForest.NumTrees, 0, 1, decimal.MaxValue, 1, "ntree");
if (f.ShowDialog() == DialogResult.OK)
{
randomForest.NumTrees = Convert.ToInt32(f.GetValue <decimal>("ntree"));
}
}
else if (classifier is AdaBoost)
{
AdaBoost adaBoost = classifier as AdaBoost;
DynamicForm f = new DynamicForm("Set AdaBoost parameters", DynamicForm.CloseButtons.OkCancel);
f.AddNumericUpdown("Number of iterations:", adaBoost.Iterations, 0, 1, decimal.MaxValue, 1, "iterations");
if (f.ShowDialog() == DialogResult.OK)
{
adaBoost.Iterations = Convert.ToInt32(f.GetValue <decimal>("iterations"));
}
}
}
}