Changeset 8762 for branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/TimeSeries/AutoregressiveModeling.cs

Timestamp:

10/08/12 16:11:03 (12 years ago)

Author:

mkommend

Message:

#1081: Fixed serialization and corrected AR(n) modeling.

File:

• branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/TimeSeries/AutoregressiveModeling.cs (modified) (2 diffs)

branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/TimeSeries/AutoregressiveModeling.cs

@@ -77 +77 @@
     /// <param name="timeOffset">The parameter p of the AR(p) specifying the maximum time offset [1,infinity] </param>
     /// <returns>The times series autoregressive solution </returns>
-    public ITimeSeriesPrognosisSolution CreateAutoRegressiveSolution(ITimeSeriesPrognosisProblemData problemData, int timeOffset) {
+    public static ITimeSeriesPrognosisSolution CreateAutoRegressiveSolution(ITimeSeriesPrognosisProblemData problemData, int timeOffset) {
       double rmsError, cvRmsError;
       return CreateAutoRegressiveSolution(problemData, timeOffset, out rmsError, out cvRmsError);
     }
 
-    internal ITimeSeriesPrognosisSolution CreateAutoRegressiveSolution(ITimeSeriesPrognosisProblemData problemData, int timeOffset, out double rmsError, out double cvRmsError) {
+    private static ITimeSeriesPrognosisSolution CreateAutoRegressiveSolution(ITimeSeriesPrognosisProblemData problemData, int timeOffset, out double rmsError, out double cvRmsError) {
       string targetVariable = problemData.TargetVariable;
 
       double[,] inputMatrix = new double[problemData.TrainingPartition.Size, timeOffset + 1];
-      var targetValues = problemData.Dataset.GetReadOnlyDoubleValues(targetVariable);
+      var targetValues = problemData.Dataset.GetDoubleValues(targetVariable).ToList();
       for (int i = 0, row = problemData.TrainingPartition.Start; i < problemData.TrainingPartition.Size; i++, row++) {
-        for (int col = 0; col < timeOffset + 1; col++) {
-          inputMatrix[i, col] = targetValues[row - col];
+        for (int col = 0; col < timeOffset; col++) {
+          inputMatrix[i, col] = targetValues[row - col - 1];
         }
       }
+      // set target values in last column
+      for (int i = 0; i < inputMatrix.GetLength(0); i++)
+        inputMatrix[i, timeOffset] = targetValues[i + problemData.TrainingPartition.Start];
 
       if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
@@ -119 +122 @@
 
       for (int i = 0; i < timeOffset; i++) {
-        LaggedVariableTreeNode node = (LaggedVariableTreeNode)new HeuristicLab.Problems.DataAnalysis.Symbolic.LaggedVariable().CreateTreeNode();
+        LaggedVariableTreeNode node = (LaggedVariableTreeNode)new LaggedVariable().CreateTreeNode();
         node.VariableName = targetVariable;
         node.Weight = coefficients[i];