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); }