C# ISymbolicExpressionTree.Clone方法代码示例

    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;
    }

    public static double Calculate(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows, IntRange evaluationPartition, int horizon, bool applyLinearScaling) {
      var horizions = rows.Select(r => Math.Min(horizon, evaluationPartition.End - r));
      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows.Zip(horizions, Enumerable.Range).SelectMany(r => r));
      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows, horizions).SelectMany(x => x);
      OnlineCalculatorError errorState;

      double mse;
      if (applyLinearScaling && horizon == 1) { //perform normal evaluation and afterwards scale the solution and calculate the fitness value        
        var mseCalculator = new OnlineMeanSquaredErrorCalculator();
        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, mseCalculator, problemData.Dataset.Rows * horizon);
        errorState = mseCalculator.ErrorState;
        mse = mseCalculator.MeanSquaredError;
      } else if (applyLinearScaling) { //first create model to perform linear scaling and afterwards calculate fitness for the scaled model
        var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)solution.Clone(), interpreter, lowerEstimationLimit, upperEstimationLimit);
        model.Scale(problemData);
        var scaledSolution = model.SymbolicExpressionTree;
        estimatedValues = interpreter.GetSymbolicExpressionTreeValues(scaledSolution, problemData.Dataset, rows, horizions).SelectMany(x => x);
        var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
        mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
      } else {
        var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
        mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
      }

      if (errorState != OnlineCalculatorError.None) return Double.NaN;
      else return mse;
    }

    public static bool CreateNewArgument(
      IRandom random,
      ISymbolicExpressionTree symbolicExpressionTree,
      int maxTreeLength, int maxTreeDepth,
      int maxFunctionDefinitions, int maxFunctionArguments) {
      // work on a copy in case we find out later that the tree would be too big
      // in this case it's easiest to simply return the original tree.
      ISymbolicExpressionTree clonedTree = (ISymbolicExpressionTree)symbolicExpressionTree.Clone();

      var functionDefiningBranches = clonedTree.IterateNodesPrefix().OfType<DefunTreeNode>().ToList();
      if (!functionDefiningBranches.Any())
        // no function defining branch found => abort
        return false;

      // select a random function defining branch
      var selectedDefunBranch = functionDefiningBranches.SampleRandom(random);

      var definedArguments = (from symbol in selectedDefunBranch.Grammar.Symbols.OfType<Argument>()
                              select symbol.ArgumentIndex).Distinct();
      if (definedArguments.Count() >= maxFunctionArguments)
        // max number of arguments reached => abort
        return false;

      var allowedArgumentIndexes = Enumerable.Range(0, maxFunctionArguments);
      var newArgumentIndex = allowedArgumentIndexes.Except(definedArguments).First();
      ArgumentTreeNode newArgumentNode = MakeArgumentNode(newArgumentIndex);

      // this operation potentially creates very big trees so the access to the length property might throw overflow exception
      try {
        if (CreateNewArgumentForDefun(random, clonedTree, selectedDefunBranch, newArgumentNode) && clonedTree.Length <= maxTreeLength && clonedTree.Depth <= maxTreeDepth) {

          // size constraints are fulfilled 
          // replace root of original tree with root of manipulated tree
          symbolicExpressionTree.Root = clonedTree.Root;
          return true;
        } else {
          // keep originalTree
          return false;
        }
      }
      catch (OverflowException) {
        // keep original tree
        return false;
      }
    }