private void trainModelButton_Click(object sender, EventArgs e)
{
var model = (Model)modelsListBox.SelectedItem;
if (model == null)
{
MessageBox.Show("Debe seleccionar un modelo de la lista", "Error", MessageBoxButtons.OK,
MessageBoxIcon.Error);
return;
}
var features = ExtractFeaturesFromCheckBoxList();
if (features.Length == 0)
{
MessageBox.Show("Debe seleccionar las features de entrenamiento", "Error", MessageBoxButtons.OK,
MessageBoxIcon.Error);
return;
}
var target = (string)targetComboBoxList.SelectedItem;
if (target == null)
{
MessageBox.Show("Debe seleccionar el target de entrenamiento", "Error", MessageBoxButtons.OK,
MessageBoxIcon.Error);
return;
}
var trainingPercentage = (int)trainingPercentageNumericUpDown.Value;
var job = new TrainingJob
{
Model = model,
Features = features,
Target = target,
DataHandler = dataHandler,
TrainingPercentage = trainingPercentage
};
using (var m = new TrainingDialog())
{
m.Job = job;
switch (m.ShowDialog())
{
case DialogResult.OK:
DisplayResults(job);
break;
case DialogResult.Cancel:
MessageBox.Show("Entrenamiento cancelado.");
break;
}
}
}
private void DisplayResults(TrainingJob job)
{
job.Predict();
var validator = new Validator(job);
PropulatePredictionsListBox(validator.TestY, validator.PredY, validator.PercentualError.Values);
PopulateResumeGroupBox(validator.PercentualError);
// change tab to display results.
tabControl.SelectedTab = tabControl.TabPages[1];
}
public Validator(TrainingJob job)
{
this.job = job;
TestX = job.TestData.AsArray(job.Features);
TestX = job.Scaler.Transform(TestX);
TestY = MathUtils.Flatten(job.TestData.AsArray(job.Target));
PredY = MathUtils.Flatten(job.Predictions);
PerformCalculations();
}
public ANOVA(TrainingJob job)
{
this.job = job;
Calculate();
}
