public void PredictTest()
{
var svm = new Epsilon_SVR(prob, kernel, C, epsilon);
var predictions = new double[prob.l];
for (int i = 0; i < prob.l; i++)
{
var x = prob.x[i];
var y = prob.y[i];
predictions[i] = svm.Predict(x);
}
}
public void PredictTest()
{
//Train the svm with the training datatset
var svm = new Epsilon_SVR(training_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C, epsilon);
for (int i = 0; i < test_prob.l; i++)
{
var x = test_prob.x[i];
var expectedValue = test_prob.y[i];
var predictedValue = svm.Predict(x);
Console.WriteLine(
String.Format(
"Predicted value = {0} || Expected value = {1} || Error = {2}",
predictedValue,
expectedValue,
Math.Abs(predictedValue - expectedValue)));
}
}
public void GetMeanSquaredErrorTest()
{
var svm = new Epsilon_SVR(full_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C, epsilon);
double cms = svm.GetMeanSquaredError();
Assert.IsTrue(cms > 0);
}
public void GetCrossValidationSqsuaredCorrelationCoefficientTest()
{
var svm = new Epsilon_SVR(full_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C, epsilon);
double CVS = svm.GetCrossValidationSqsuaredCorrelationCoefficient();
}
public void GetMeanSquaredErrorTest()
{
var svm = new Epsilon_SVR(prob, kernel, C, epsilon);
double cms = svm.GetMeanSquaredError();
Assert.IsTrue(cms > 0);
}
public void GetCrossValidationSqsuaredCorrelationCoefficientTest()
{
var svm = new Epsilon_SVR(prob, kernel, C, epsilon);
double CVS = svm.GetCrossValidationSqsuaredCorrelationCoefficient();
}
public void Epsilon_SVRConstructorTest1()
{
var svm = new Epsilon_SVR(TRAINING_FILE, kernel, C, epsilon);
Assert.IsNotNull(svm);
}
public void Epsilon_SVRConstructorTest()
{
var svm = new Epsilon_SVR(prob, kernel, C, epsilon);
Assert.IsNotNull(svm);
}
