public static ISymbolicExpressionTree Prune(ISymbolicExpressionTree tree, SymbolicRegressionSolutionImpactValuesCalculator impactValuesCalculator, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IRegressionProblemData problemData, DoubleLimit estimationLimits, IEnumerable<int> rows, double nodeImpactThreshold = 0.0, bool pruneOnlyZeroImpactNodes = false) {
var clonedTree = (ISymbolicExpressionTree)tree.Clone();
var model = new SymbolicRegressionModel(problemData.TargetVariable, clonedTree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
var nodes = clonedTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList(); // skip the nodes corresponding to the ProgramRootSymbol and the StartSymbol
double qualityForImpactsCalculation = double.NaN; // pass a NaN value initially so the impact calculator will calculate the quality
for (int i = 0; i < nodes.Count; ++i) {
var node = nodes[i];
if (node is ConstantTreeNode) continue;
double impactValue, replacementValue;
double newQualityForImpactsCalculation;
impactValuesCalculator.CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, out newQualityForImpactsCalculation, qualityForImpactsCalculation);
if (pruneOnlyZeroImpactNodes && !impactValue.IsAlmost(0.0)) continue;
if (!pruneOnlyZeroImpactNodes && impactValue > nodeImpactThreshold) continue;
var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();
constantNode.Value = replacementValue;
ReplaceWithConstant(node, constantNode);
i += node.GetLength() - 1; // skip subtrees under the node that was folded
qualityForImpactsCalculation = newQualityForImpactsCalculation;
}
return model.SymbolicExpressionTree;
}
protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality)
{
var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
if (ApplyLinearScalingParameter.ActualValue.Value)
{
model.Scale(ProblemDataParameter.ActualValue);
}
return(new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone()));
}
public static ISymbolicExpressionTree Prune(ISymbolicExpressionTree tree, SymbolicRegressionSolutionImpactValuesCalculator impactValuesCalculator, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IRegressionProblemData problemData, DoubleLimit estimationLimits, IEnumerable <int> rows, double nodeImpactThreshold = 0.0, bool pruneOnlyZeroImpactNodes = false)
{
var clonedTree = (ISymbolicExpressionTree)tree.Clone();
var model = new SymbolicRegressionModel(problemData.TargetVariable, clonedTree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
var nodes = clonedTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList(); // skip the nodes corresponding to the ProgramRootSymbol and the StartSymbol
double qualityForImpactsCalculation = double.NaN; // pass a NaN value initially so the impact calculator will calculate the quality
for (int i = 0; i < nodes.Count; ++i)
{
var node = nodes[i];
if (node is ConstantTreeNode)
{
continue;
}
double impactValue, replacementValue;
double newQualityForImpactsCalculation;
impactValuesCalculator.CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, out newQualityForImpactsCalculation, qualityForImpactsCalculation);
if (pruneOnlyZeroImpactNodes && !impactValue.IsAlmost(0.0))
{
continue;
}
if (!pruneOnlyZeroImpactNodes && impactValue > nodeImpactThreshold)
{
continue;
}
var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();
constantNode.Value = replacementValue;
ReplaceWithConstant(node, constantNode);
i += node.GetLength() - 1; // skip subtrees under the node that was folded
qualityForImpactsCalculation = newQualityForImpactsCalculation;
}
return(model.SymbolicExpressionTree);
}
protected SymbolicRegressionModel(SymbolicRegressionModel original, Cloner cloner) : base(original, cloner)
{
}
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;
}
protected SymbolicRegressionModel(SymbolicRegressionModel original, Cloner cloner)
: base(original, cloner)
{
this.targetVariable = original.targetVariable;
}
protected SymbolicRegressionModel(SymbolicRegressionModel original, Cloner cloner) : base(original, cloner) { }
protected SymbolicRegressionModel(SymbolicRegressionModel original, Cloner cloner)
: base(original, cloner) {
this.targetVariable = original.targetVariable;
}
