/// <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); }