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HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis

Timestamp:

06/28/16 13:33:17 (8 years ago)

Author:

mkommend

Message:

#2604:

Location:

Files:

r12012	r13941
72	72	mse = mseCalculator.MeanSquaredError;
73	73	} else if (applyLinearScaling) { //first create model to perform linear scaling and afterwards calculate fitness for the scaled model
74		var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)solution.Clone(), interpreter, lowerEstimationLimit, upperEstimationLimit);
	74	var model = new SymbolicTimeSeriesPrognosisModel(problemData.TargetVariable, (ISymbolicExpressionTree)solution.Clone(), interpreter, lowerEstimationLimit, upperEstimationLimit);
75	75	model.Scale(problemData);
76	76	var scaledSolution = model.SymbolicExpressionTree;

r12012	r13941
64	64
65	65	protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
66		var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue as ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
	66	var model = new SymbolicTimeSeriesPrognosisModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue as ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
67	67	if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue);
68	68	return new SymbolicTimeSeriesPrognosisSolution(model, (ITimeSeriesPrognosisProblemData)ProblemDataParameter.ActualValue.Clone());

r12012	r13941
52	52
53	53	protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
54		var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue as ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
	54	var model = new SymbolicTimeSeriesPrognosisModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue as ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
55	55	if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue);
56	56

r12509	r13941
47	47	}
48	48
49		public SymbolicTimeSeriesPrognosisModel(~~ISymbolicExpressionTree tree, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double lowerLimit = double.MinValue, double upperLimit = double.MaxValue) : base(~~tree, interpreter, lowerLimit, upperLimit) { }
	49	public SymbolicTimeSeriesPrognosisModel(string targetVariable, ISymbolicExpressionTree tree, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double lowerLimit = double.MinValue, double upperLimit = double.MaxValue) : base(targetVariable, tree, interpreter, lowerLimit, upperLimit) { }
50	50
51	51	public IEnumerable<IEnumerable<double>> GetPrognosedValues(IDataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {

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