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Ignore:
Timestamp:
08/31/11 11:52:11 (13 years ago)
Author:
abeham
Message:

#1628

  • Updated branch from trunk
  • Changed ReferenceEqualityComparer<T> to become a non-generic class (generic implementation probably was only made because of lacking support for co- and contravariance in C# 3.5)
  • Added finished experiment from sample algorithms to the tests
  • Wrote a unit test to instantiate every IDeepCloneable type, clone it and compare the objects in the object graph for equal references
  • Wrote a unit test to load the experiment, clone it and compare again the objects in the object graph
  • Preliminary fix for a potential bug in ThreadSafeLog
  • Preliminary fix for a potential bug in OperatorGraphVisualizationInfo
  • Preliminary fix for a potential bug in Calculator (and added license headers)
  • Preliminary fix for a potential bug in ScrambleMove
Location:
branches/GeneralizedQAP
Files:
12 edited

Legend:

Unmodified
Added
Removed

  • branches/GeneralizedQAP

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs

    r6240 r6685  
    106106
    107107      var model = LinearDiscriminantAnalysis.CreateDiscriminantFunctionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter(), problemData, rows);
    108       SymbolicDiscriminantFunctionClassificationSolution solution = new SymbolicDiscriminantFunctionClassificationSolution(model, problemData);
     108      SymbolicDiscriminantFunctionClassificationSolution solution = new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)problemData.Clone());
    109109
    110110      return solution;

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs

    r6555 r6685  
    110110      addition.AddSubtree(cNode);
    111111
    112       SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), problemData);
     112      SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), (IRegressionProblemData)problemData.Clone());
    113113      solution.Model.Name = "Linear Regression Model";
    114114      return solution;

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs

    r6633 r6685  
    9595      relClassError = alglib.mnlrelclserror(lm, inputMatrix, nRows);
    9696
    97       MultinomialLogitClassificationSolution solution = new MultinomialLogitClassificationSolution(problemData, new MultinomialLogitModel(lm, targetVariable, allowedInputVariables, classValues));
     97      MultinomialLogitClassificationSolution solution = new MultinomialLogitClassificationSolution((IClassificationProblemData)problemData.Clone(), new MultinomialLogitModel(lm, targetVariable, allowedInputVariables, classValues));
    9898      return solution;
    9999    }

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourClassification.cs

    r6583 r6685  
    107107      }
    108108      alglib.nearestneighbor.kdtreebuild(inputMatrix, nRows, inputMatrix.GetLength(1) - 1, 1, 2, kdtree);
    109 
    110       return new NearestNeighbourClassificationSolution(problemData, new NearestNeighbourModel(kdtree, k, targetVariable, allowedInputVariables, problemData.ClassValues.ToArray()));
     109      var problemDataClone = (IClassificationProblemData) problemData.Clone();
     110      return new NearestNeighbourClassificationSolution(problemDataClone, new NearestNeighbourModel(kdtree, k, targetVariable, allowedInputVariables, problemDataClone.ClassValues.ToArray()));
    111111    }
    112112    #endregion

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs

    r6583 r6685  
    9898      alglib.nearestneighbor.kdtreebuild(inputMatrix, nRows, inputMatrix.GetLength(1) - 1, 1, 2, kdtree);
    9999
    100       return new NearestNeighbourRegressionSolution(problemData, new NearestNeighbourModel(kdtree, k, targetVariable, allowedInputVariables));
     100      return new NearestNeighbourRegressionSolution((IRegressionProblemData)problemData.Clone(), new NearestNeighbourModel(kdtree, k, targetVariable, allowedInputVariables));
    101101    }
    102102    #endregion

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkClassification.cs

    r6580 r6685  
    188188      relClassError = alglib.mlpclserror(multiLayerPerceptron, inputMatrix, nRows) / (double)nRows;
    189189
    190       return new NeuralNetworkClassificationSolution(problemData, new NeuralNetworkModel(multiLayerPerceptron, targetVariable, allowedInputVariables, problemData.ClassValues.ToArray()));
     190      var problemDataClone = (IClassificationProblemData)problemData.Clone();
     191      return new NeuralNetworkClassificationSolution(problemDataClone, new NeuralNetworkModel(multiLayerPerceptron, targetVariable, allowedInputVariables, problemDataClone.ClassValues.ToArray()));
    191192    }
    192193    #endregion

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs

    r6580 r6685  
    198198      avgRelError = alglib.mlpeavgrelerror(mlpEnsemble, inputMatrix, nRows);
    199199      relClassError = alglib.mlperelclserror(mlpEnsemble, inputMatrix, nRows);
    200 
    201       return new NeuralNetworkEnsembleClassificationSolution(problemData, new NeuralNetworkEnsembleModel(mlpEnsemble, targetVariable, allowedInputVariables, problemData.ClassValues.ToArray()));
     200      var problemDataClone = (IClassificationProblemData)problemData.Clone();
     201      return new NeuralNetworkEnsembleClassificationSolution(problemDataClone, new NeuralNetworkEnsembleModel(mlpEnsemble, targetVariable, allowedInputVariables, problemDataClone.ClassValues.ToArray()));
    202202    }
    203203    #endregion

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleRegression.cs

    r6580 r6685  
    185185      avgRelError = alglib.mlpeavgrelerror(mlpEnsemble, inputMatrix, nRows);
    186186
    187       return new NeuralNetworkEnsembleRegressionSolution(problemData, new NeuralNetworkEnsembleModel(mlpEnsemble, targetVariable, allowedInputVariables));
     187      return new NeuralNetworkEnsembleRegressionSolution((IRegressionProblemData)problemData.Clone(), new NeuralNetworkEnsembleModel(mlpEnsemble, targetVariable, allowedInputVariables));
    188188    }
    189189    #endregion

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkRegression.cs

    r6580 r6685  
    179179      avgRelError = alglib.mlpavgrelerror(multiLayerPerceptron, inputMatrix, nRows);     
    180180
    181       return new NeuralNetworkRegressionSolution(problemData, new NeuralNetworkModel(multiLayerPerceptron, targetVariable, allowedInputVariables));
     181      return new NeuralNetworkRegressionSolution((IRegressionProblemData)problemData.Clone(), new NeuralNetworkModel(multiLayerPerceptron, targetVariable, allowedInputVariables));
    182182    }
    183183    #endregion

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestClassification.cs

    r6241 r6685  
    126126      relClassificationError = rep.relclserror;
    127127      outOfBagRelClassificationError = rep.oobrelclserror;
    128       return new RandomForestClassificationSolution(problemData, new RandomForestModel(dforest, targetVariable, allowedInputVariables, classValues));
     128      return new RandomForestClassificationSolution((IClassificationProblemData)problemData.Clone(), new RandomForestModel(dforest, targetVariable, allowedInputVariables, classValues));
    129129    }
    130130    #endregion

  • branches/GeneralizedQAP/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegression.cs

    r6241 r6685  
    116116      outOfBagRmsError = rep.oobrmserror;
    117117
    118       return new RandomForestRegressionSolution(problemData, new RandomForestModel(dforest, targetVariable, allowedInputVariables));
     118      return new RandomForestRegressionSolution((IRegressionProblemData)problemData.Clone(), new RandomForestModel(dforest, targetVariable, allowedInputVariables));
    119119    }
    120120    #endregion
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