Changeset 8459

Timestamp:

08/09/12 14:36:46 (12 years ago)

Author:

mkommend

Message:

#1081: Code clean up in time series prognosis model and problem data.

Location:

branches/HeuristicLab.TimeSeries

Files:

• HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SymbolicTimeSeriesPrognosisModel.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblemData.cs (modified) (3 diffs)

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SymbolicTimeSeriesPrognosisModel.cs

@@ -49 +49 @@
     public SymbolicTimeSeriesPrognosisModel(ISymbolicExpressionTree tree, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double lowerLimit = double.MinValue, double upperLimit = double.MaxValue) : base(tree, interpreter, lowerLimit, upperLimit) { }
 
-
-
     public IEnumerable<IEnumerable<double>> GetPrognosedValues(Dataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
       var estimatedValues = Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows, horizons);
@@ -62 +60 @@
       return CreateTimeSeriesPrognosisSolution(problemData);
     }
-
-    //public static void Scale(SymbolicTimeSeriesPrognosisModel model, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
-    //  var dataset = problemData.Dataset;
-    //  var targetVariable = problemData.TargetVariable;
-    //  var estimatedValues = model.Interpreter.GetSymbolicExpressionTreeValues(model.SymbolicExpressionTree, dataset, rows);
-    //  var boundedEstimatedValues = estimatedValues.LimitToRange(model.lowerEstimationLimit, model.upperEstimationLimit);
-    //  var targetValues = problemData.Dataset.GetDoubleValues(targetVariable, rows);
-
-    //  double alpha, beta;
-    //  OnlineCalculatorError error;
-    //  OnlineLinearScalingParameterCalculator.Calculate(boundedEstimatedValues, targetValues, out alpha, out beta, out error);
-    //  if (error != OnlineCalculatorError.None) return;
-
-    //  ConstantTreeNode alphaTreeNode = null;
-    //  ConstantTreeNode betaTreeNode = null;
-    //  // check if model has been scaled previously by analyzing the structure of the tree
-    //  var startNode = model.SymbolicExpressionTree.Root.GetSubtree(0);
-    //  if (startNode.GetSubtree(0).Symbol is Addition) {
-    //    var addNode = startNode.GetSubtree(0);
-    //    if (addNode.SubtreeCount == 2 && addNode.GetSubtree(0).Symbol is Multiplication && addNode.GetSubtree(1).Symbol is Constant) {
-    //      alphaTreeNode = addNode.GetSubtree(1) as ConstantTreeNode;
-    //      var mulNode = addNode.GetSubtree(0);
-    //      if (mulNode.SubtreeCount == 2 && mulNode.GetSubtree(1).Symbol is Constant) {
-    //        betaTreeNode = mulNode.GetSubtree(1) as ConstantTreeNode;
-    //      }
-    //    }
-    //  }
-    //  // if tree structure matches the structure necessary for linear scaling then reuse the existing tree nodes
-    //  if (alphaTreeNode != null && betaTreeNode != null) {
-    //    betaTreeNode.Value *= beta;
-    //    alphaTreeNode.Value *= beta;
-    //    alphaTreeNode.Value += alpha;
-    //  } else {
-    //    var mainBranch = startNode.GetSubtree(0);
-    //    startNode.RemoveSubtree(0);
-    //    var scaledMainBranch = MakeSum(MakeProduct(mainBranch, beta), alpha);
-    //    startNode.AddSubtree(scaledMainBranch);
-    //  }
-    //}
-
-    //private static ISymbolicExpressionTreeNode MakeSum(ISymbolicExpressionTreeNode treeNode, double alpha) {
-    //  if (alpha.IsAlmost(0.0)) {
-    //    return treeNode;
-    //  } else {
-    //    var addition = new Addition();
-    //    var node = addition.CreateTreeNode();
-    //    var alphaConst = MakeConstant(alpha);
-    //    node.AddSubtree(treeNode);
-    //    node.AddSubtree(alphaConst);
-    //    return node;
-    //  }
-    //}
-
-    //private static ISymbolicExpressionTreeNode MakeProduct(ISymbolicExpressionTreeNode treeNode, double beta) {
-    //  if (beta.IsAlmost(1.0)) {
-    //    return treeNode;
-    //  } else {
-    //    var multipliciation = new Multiplication();
-    //    var node = multipliciation.CreateTreeNode();
-    //    var betaConst = MakeConstant(beta);
-    //    node.AddSubtree(treeNode);
-    //    node.AddSubtree(betaConst);
-    //    return node;
-    //  }
-    //}
-
-    //private static ISymbolicExpressionTreeNode MakeConstant(double c) {
-    //  var node = (ConstantTreeNode)(new Constant()).CreateTreeNode();
-    //  node.Value = c;
-    //  return node;
-    //}
   }
 }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblemData.cs

@@ -1542 +1542 @@
     private static readonly string defaultTargetVariable;
 
-    private static readonly TimeSeriesPrognosisProblemData emptyProblemData;
-    public static TimeSeriesPrognosisProblemData EmptyProblemData {
-      get { return emptyProblemData; }
-    }
-
     static TimeSeriesPrognosisProblemData() {
       defaultDataset = new Dataset(new string[] { "x" }, mackey_glass_17);
@@ -1552 +1547 @@
       defaultAllowedInputVariables = new List<string>() { "x" };
       defaultTargetVariable = "x";
-
-      var problemData = new TimeSeriesPrognosisProblemData();
-      problemData.Parameters.Clear();
-      problemData.Name = "Empty Time-Series Prognosis ProblemData";
-      problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
-      problemData.isEmpty = true;
-
-      problemData.Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", new Dataset()));
-      problemData.Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, ""));
-      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
-      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
-      problemData.Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>()));
-      emptyProblemData = problemData;
     }
     #endregion
@@ -1587 +1569 @@
     }
     public override IDeepCloneable Clone(Cloner cloner) {
-      if (this == emptyProblemData) return emptyProblemData;
       return new TimeSeriesPrognosisProblemData(this, cloner);
     }