/// <summary>
///Show how to get the Mean Squared Error
///</summary>
//[TestMethod()]
public void GetMeanSquaredErrorTest()
{
var svm = new Epsilon_SVR(training_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C, epsilon);
double cms = svm.GetMeanSquaredError();
Assert.IsTrue(cms > 0);
}
private void buttonSVM_Click(object sender, EventArgs e)
{
List <double> values = new List <double>();
foreach (var column in checkedListBoxVariableRellenar.SelectedItems)
{
for (int i = 0; i < dt.Rows.Count; i++)
{
values.Add(double.Parse(dt.Rows[i][column.ToString()].ToString()));
}
var dataTraining = ProblemHelper.ReadAndScaleProblem(new List <List <double> >()
{
values
});
var svm = new Epsilon_SVR(DataProblem, KernelHelper.RadialBasisFunctionKernel(Gamma), C, Elipson);
double mse = svm.GetMeanSquaredError();
var prediction = svm.Predict(dataTraining.x[0]);
}
// 1. primero se debe armar una subtabla con los atributos que se van a utilizar.
// que serian los que estan en el checkbox.
// 2. elegir la columna sobre la que se quiere rellenar valores
// 3. se quitan los registros que contengan datos faltantes de las variables predictoras, para este caso son los que tengan valor de -200
// 4. Aplicar el algoritmo de VSM
// 5. Generar Vista con valores resultado
// 6. Generar resumen de resultados: en tal fila, cambie tal por tal.
}
/// <summary>
///Show how predict values for regression problems
///</summary>
//[TestMethod()]
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)));
}
}
/// <summary>
///Show how to get the sqsuared correlation coefficient using cross validation method
///Note : cros validation use the full dataset to increase the accuracy
///</summary>
//[TestMethod()]
public void GetCrossValidationSqsuaredCorrelationCoefficientTest()
{
var svm = new Epsilon_SVR(training_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C, epsilon);
double CVS = svm.GetCrossValidationSqsuaredCorrelationCoefficient();
}
