/// <summary>
/// //we need here to provide loading all class which are derived from IFitness interface
/// on that way we hav complete customization of th fitness functions
/// </summary>
private void loadFitnessFunsInCombo(ColumnType problemType, CategoryEncoding encoding)
{
cmbFitnessFuncs.Items.Clear();
//
if (problemType == ColumnType.Numeric)
{
cmbFitnessFuncs.Items.Add("AE -Absolute error (regression) ");
cmbFitnessFuncs.Items.Add("MAE -Mean absolute error (regression) ");
cmbFitnessFuncs.Items.Add("RMSE -Root mean square error (regression) ");
cmbFitnessFuncs.Items.Add("RSE -Root square error (regression) ");
cmbFitnessFuncs.Items.Add("SE -Square error (regression) ");
//cmbFitnessFuncs.Items.Add("MSE -Mean square error (regression) ");
//cmbFitnessFuncs.Items.Add("RRSE -Relative root square error (regression) ");
//cmbFitnessFuncs.Items.Add("RAE -Root absolute error (regression) ");
}
else
{
if (encoding == CategoryEncoding.OnevsAll || encoding == CategoryEncoding.OnevsAll_1)
{
cmbFitnessFuncs.Items.Add("SRMS - Softmax root mean square error (classification) ");
cmbFitnessFuncs.Items.Add("LSRF - Logarithmic scoring rule (classification) ");
cmbFitnessFuncs.Items.Add("MAHD - Mahanalobis Distance (classification) ");
}
else
{
cmbFitnessFuncs.Items.Add("ACC -Total accuracy (classification) ");
cmbFitnessFuncs.Items.Add("HSS -Heidke skill score (classification) ");
cmbFitnessFuncs.Items.Add("PSS -Peirce skill score (classification) ");
}
}
}
private void loadRootNodeFunction(ColumnType problemType, CategoryEncoding encoding)
{
//
cb_rootNodeFunction.Items.Clear();
if (problemType == ColumnType.Numeric)
{
cb_rootNodeFunction.Items.Add("None ");
}
else if (problemType == ColumnType.Binary)
{
cb_rootNodeFunction.Items.Add("Sigmoid(two class ) ");
cb_rootNodeFunction.Items.Add("Step(two class) ");
cb_rootNodeFunction.Items.Add("Scaled Sigmoid[0, numClasses] (multi class) ");
cb_rootNodeFunction.Items.Add("Softmax function(multi class) ");
}
else
{
if (encoding == CategoryEncoding.OnevsAll || encoding == CategoryEncoding.OnevsAll_1)
{
cb_rootNodeFunction.Items.Add("Softmax function(multi class) ");
}
else
{
cb_rootNodeFunction.Items.Add("Scaled Sigmoid[0, numClasses] (multi class) ");
cb_rootNodeFunction.Items.Add("Softmax function(multi class) ");
}
}
}
double[][] m_EncodedValues; // before apply to the solver column has to be normalized
public ColumnData(bool isOutput = false, CategoryEncoding encoding = CategoryEncoding.None)
{
m_Encoding = encoding;
if (isOutput)
{
m_ParamType = ParameterType.Output;
}
m_ColType = ColumnType.Numeric;
}
public void InitializeControls(ColumnType problemType, CategoryEncoding encoding)
{
loadFitnessFunsInCombo(problemType, encoding);
loadRootNodeFunction(problemType, encoding);
}
