private ISymbolicExpressionTreeNode GenTreeShiftStdDist(string variable, double m_org, double s_org, double m_tar, double s_tar)
{
// x - m_org
var substractionNode = new Subtraction().CreateTreeNode();
var xNode = CreateVariableTreeNode(variable, "x");
var m_orgNode = CreateConstantTreeNode("m_org", m_org);
substractionNode.AddSubtree(xNode);
substractionNode.AddSubtree(m_orgNode);
// (x - m_org) / s_org
var divisionNode = new Division().CreateTreeNode();
var s_orgNode = CreateConstantTreeNode("s_org", s_org);
divisionNode.AddSubtree(substractionNode);
divisionNode.AddSubtree(s_orgNode);
// ((x - m_org) / s_org ) * s_tar
var multiplicationNode = new Multiplication().CreateTreeNode();
var s_tarNode = CreateConstantTreeNode("s_tar", s_tar);
multiplicationNode.AddSubtree(divisionNode);
multiplicationNode.AddSubtree(s_tarNode);
// ((x - m_org) / s_org ) * s_tar + m_tar
var additionNode = new Addition().CreateTreeNode();
var m_tarNode = CreateConstantTreeNode("m_tar", m_tar);
additionNode.AddSubtree(multiplicationNode);
additionNode.AddSubtree(m_tarNode);
return(additionNode);
}
// helper
private ISymbolicExpressionTreeNode GenerateModelForLinearTransformation()
{
var linearTransformation = (LinearTransformation)transformation;
var kValue = linearTransformation.Multiplier;
var dValue = linearTransformation.Addend;
// k * x
var multiplicationNode = new Multiplication().CreateTreeNode();
var kNode = CreateConstantTreeNode("k", kValue);
var xNode = CreateVariableTreeNode(column, "x");
multiplicationNode.AddSubtree(kNode);
multiplicationNode.AddSubtree(xNode);
// ( k * x ) + d
var additionNode = new Addition().CreateTreeNode();
var dNode = CreateConstantTreeNode("d", dValue);
additionNode.AddSubtree(multiplicationNode);
additionNode.AddSubtree(dNode);
return(additionNode);
}
private static ISymbolicRegressionSolution CreateSymbolicSolution(List<IRegressionModel> models, double nu, IRegressionProblemData problemData) {
var symbModels = models.OfType<ISymbolicRegressionModel>();
var lowerLimit = symbModels.Min(m => m.LowerEstimationLimit);
var upperLimit = symbModels.Max(m => m.UpperEstimationLimit);
var interpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter();
var progRootNode = new ProgramRootSymbol().CreateTreeNode();
var startNode = new StartSymbol().CreateTreeNode();
var addNode = new Addition().CreateTreeNode();
var mulNode = new Multiplication().CreateTreeNode();
var scaleNode = (ConstantTreeNode)new Constant().CreateTreeNode(); // all models are scaled using the same nu
scaleNode.Value = nu;
foreach (var m in symbModels) {
var relevantPart = m.SymbolicExpressionTree.Root.GetSubtree(0).GetSubtree(0); // skip root and start
addNode.AddSubtree((ISymbolicExpressionTreeNode)relevantPart.Clone());
}
mulNode.AddSubtree(addNode);
mulNode.AddSubtree(scaleNode);
startNode.AddSubtree(mulNode);
progRootNode.AddSubtree(startNode);
var t = new SymbolicExpressionTree(progRootNode);
var combinedModel = new SymbolicRegressionModel(problemData.TargetVariable, t, interpreter, lowerLimit, upperLimit);
var sol = new SymbolicRegressionSolution(combinedModel, problemData);
return sol;
}