Changeset 15973 for branches/2522_RefactorPluginInfrastructure/HeuristicLab.Algorithms.DataAnalysis/3.4/TimeSeries

Timestamp:

06/28/18 11:13:37 (6 years ago)

Author:

gkronber

Message:

#2522: merged trunk changes from r13402:15972 to branch resolving conflicts where necessary

Location:

branches/2522_RefactorPluginInfrastructure

Files:

• . (modified) (2 props)
• HeuristicLab.Algorithms.DataAnalysis (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4 (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4/TimeSeries/AutoregressiveModeling.cs (modified) (5 diffs)

branches/2522_RefactorPluginInfrastructure

@@ -24 +24 @@
 protoc.exe
 obj
+.vs

@@ -24 +24 @@
 protoc.exe
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+.vs

branches/2522_RefactorPluginInfrastructure/HeuristicLab.Algorithms.DataAnalysis/3.4/TimeSeries/AutoregressiveModeling.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -22 +22 @@
 using System;
 using System.Linq;
+using System.Threading;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
-using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
@@ -63 +63 @@
     }
 
-    protected override void Run() {
+    protected override void Run(CancellationToken cancellationToken) {
       double rmsError, cvRmsError;
       var solution = CreateAutoRegressiveSolution(Problem.ProblemData, TimeOffset, out rmsError, out cvRmsError);
@@ -96 +96 @@
         inputMatrix[i, timeOffset] = targetValues[i + problemData.TrainingPartition.Start];
 
-      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+      if (inputMatrix.ContainsNanOrInfinity())
         throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset.");
 
@@ -114 +114 @@
       alglib.lrunpack(lm, out coefficients, out nFeatures);
 
-
-      ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
-      ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
-      tree.Root.AddSubtree(startNode);
-      ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();
-      startNode.AddSubtree(addition);
-
-      for (int i = 0; i < timeOffset; i++) {
-        LaggedVariableTreeNode node = (LaggedVariableTreeNode)new LaggedVariable().CreateTreeNode();
-        node.VariableName = targetVariable;
-        node.Weight = coefficients[i];
-        node.Lag = (i + 1) * -1;
-        addition.AddSubtree(node);
-      }
-
-      ConstantTreeNode cNode = (ConstantTreeNode)new Constant().CreateTreeNode();
-      cNode.Value = coefficients[coefficients.Length - 1];
-      addition.AddSubtree(cNode);
+      var tree = LinearModelToTreeConverter.CreateTree(
+        variableNames: Enumerable.Repeat(problemData.TargetVariable, nFeatures).ToArray(),
+        lags: Enumerable.Range(0, timeOffset).Select(i => (i + 1) * -1).ToArray(),
+        coefficients: coefficients.Take(nFeatures).ToArray(),
+        @const: coefficients[nFeatures]
+        );
 
       var interpreter = new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(problemData.TargetVariable);
-      var model = new SymbolicTimeSeriesPrognosisModel(tree, interpreter);
+      var model = new SymbolicTimeSeriesPrognosisModel(problemData.TargetVariable, tree, interpreter);
       var solution = model.CreateTimeSeriesPrognosisSolution((ITimeSeriesPrognosisProblemData)problemData.Clone());
       return solution;

@@ -24 +24 @@
 protoc.exe
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+.vs