private IContent CreateModel(int idx)
{
idx -= 1;
var rfModel = Content.Model as RandomForestModel;
if (rfModel == null)
{
return(null);
}
var regProblemData = Content.ProblemData as IRegressionProblemData;
var classProblemData = Content.ProblemData as IClassificationProblemData;
if (idx < 0 || idx >= rfModel.NumberOfTrees)
{
return(null);
}
if (regProblemData != null)
{
var syModel = new SymbolicRegressionModel(regProblemData.TargetVariable, rfModel.ExtractTree(idx),
new SymbolicDataAnalysisExpressionTreeLinearInterpreter());
return(syModel.CreateRegressionSolution(regProblemData));
}
else if (classProblemData != null)
{
var syModel = new SymbolicDiscriminantFunctionClassificationModel(classProblemData.TargetVariable, rfModel.ExtractTree(idx),
new SymbolicDataAnalysisExpressionTreeLinearInterpreter(), new NormalDistributionCutPointsThresholdCalculator());
syModel.RecalculateModelParameters(classProblemData, classProblemData.TrainingIndices);
return(syModel.CreateClassificationSolution(classProblemData));
}
else
{
throw new InvalidProgramException();
}
}
private static SymbolicDiscriminantFunctionClassificationModel CreateDiscriminantFunctionModel(ISymbolicExpressionTree tree,
ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
IClassificationProblemData problemData,
IEnumerable <int> rows)
{
var model = new SymbolicDiscriminantFunctionClassificationModel(problemData.TargetVariable, tree, interpreter, new AccuracyMaximizationThresholdCalculator());
model.RecalculateModelParameters(problemData, rows);
return(model);
}