Weka分類：間違った+正しい<総インスタンス、どのように？

Question

私は有名なアイリスの花の問題のためにこのコードを実行し、10倍交差検証を行い、次に5つの異なる分類方法を使用して分類しました。

15.コード、および出力は次のとおり=

この

は15 10回の分類器135のインスタンス上の電車やテストをしなければならないので、私は間違って分類されたインスタンス+正しい分類されたインスタンスを持つことを期待しています。

public class WekaTest { 
    public static void main(String[] args) throws Exception {  
    // Comments are denoted by "//" at the beginning of the line. 

    BufferedReader datafile = readDataFile("C:\\Program Files\\Weka-3-8\\data\\iris.arff"); 
    //BufferedReader datafile = readDataFile("C:\\hwork\\titanic\\train.arff"); 

    Instances data = new Instances(datafile); 
    data.setClassIndex(data.numAttributes() - 1); 


    // Choose a type of validation split 
    Instances[][] split = crossValidationSplit(data, 10); 

    // Separate split into training and testing arrays 
    Instances[] trainingSplits = split[0]; 
    Instances[] testingSplits = split[1]; 

    // Choose a set of classifiers 
    Classifier[] models = {  new J48(), 
           new PART(), 
           new DecisionTable(), 
           new OneR(), 
           new DecisionStump() }; 

    // Run for each classifier model 
    double[][][] predictions = new double[100][100][2]; 
    for(int j = 0; j < models.length; j++) {   

     for(int i = 0; i < trainingSplits.length; i++) {     


      Evaluation validation = new Evaluation(trainingSplits[i]);   
      models[j].buildClassifier(trainingSplits[i]); 
      validation.evaluateModel(models[j], testingSplits[i]);          


      predictions[j][i][0] = validation.correct(); 
      predictions[j][i][1] = validation.incorrect(); 

      System.out.println("Classifier: "+models[j].getClass()+" : Correct: "+predictions[j][i][0]+", Wrong: "+predictions[i][j][1]);    
     }//training foreach fold. 
     System.out.println("==================================================================="); 
    }//training foreach classifier. 

}//main(). 





public static BufferedReader readDataFile(String filename) { 
    BufferedReader inputReader = null; 

    try { 
     inputReader = new BufferedReader(new FileReader(filename)); 
    } catch (FileNotFoundException ex) { 
     System.err.println("File not found: " + filename); 
    }   
    return inputReader; 
}//readDataFile(). 

public static Evaluation simpleClassify(Classifier model, Instances trainingSet, Instances testingSet) throws Exception { 
    Evaluation validation = new Evaluation(trainingSet);   
    model.buildClassifier(trainingSet); 
    validation.evaluateModel(model, testingSet);   
    return validation; 
}//simpleClassify(). 

public static double calculateAccuracy(FastVector predictions) { 
    double correct = 0; 

    for (int i = 0; i < predictions.size(); i++) { 
     NominalPrediction np = (NominalPrediction) predictions.elementAt(i); 
     if (np.predicted() == np.actual()) { 
      correct++; 
     } 
    } 

    return 100 * correct/predictions.size(); 
}//calculateAccuracy(). 

public static Instances[][] crossValidationSplit(Instances data, int numberOfFolds) { 
    Instances[][] split = new Instances[2][numberOfFolds]; 

    for (int i = 0; i < numberOfFolds; i++) { 
     split[0][i] = data.trainCV(numberOfFolds, i); 
     split[1][i] = data.testCV(numberOfFolds, i); 
    }   
    return split; 
}//corssValidationSplit(). 


}//class. 

====================

出力：

Classifier: class weka.classifiers.trees.J48 : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.J48 : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.J48 : Correct: 14.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.J48 : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.J48 : Correct: 14.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.J48 : Correct: 13.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.J48 : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.J48 : Correct: 13.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.J48 : Correct: 12.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.J48 : Correct: 15.0, Wrong: 0.0 
=================================================================== 
Classifier: class weka.classifiers.rules.PART : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.PART : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.PART : Correct: 14.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.PART : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.PART : Correct: 14.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.PART : Correct: 13.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.PART : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.PART : Correct: 13.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.PART : Correct: 9.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.PART : Correct: 13.0, Wrong: 0.0 
=================================================================== 
Classifier: class weka.classifiers.rules.DecisionTable : Correct: 15.0, Wrong: 1.0 
Classifier: class weka.classifiers.rules.DecisionTable : Correct: 15.0, Wrong: 1.0 
Classifier: class weka.classifiers.rules.DecisionTable : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.DecisionTable : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.DecisionTable : Correct: 13.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.DecisionTable : Correct: 13.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.DecisionTable : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.DecisionTable : Correct: 13.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.DecisionTable : Correct: 12.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.DecisionTable : Correct: 14.0, Wrong: 0.0 
=================================================================== 
Classifier: class weka.classifiers.rules.OneR : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.OneR : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.OneR : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.OneR : Correct: 14.0, Wrong: 1.0 
Classifier: class weka.classifiers.rules.OneR : Correct: 13.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.OneR : Correct: 12.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.OneR : Correct: 15.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.OneR : Correct: 14.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.OneR : Correct: 14.0, Wrong: 0.0 
Classifier: class weka.classifiers.rules.OneR : Correct: 14.0, Wrong: 0.0 
=================================================================== 
Classifier: class weka.classifiers.trees.DecisionStump : Correct: 15.0, Wrong: 1.0 
Classifier: class weka.classifiers.trees.DecisionStump : Correct: 15.0, Wrong: 1.0 
Classifier: class weka.classifiers.trees.DecisionStump : Correct: 15.0, Wrong: 2.0 
Classifier: class weka.classifiers.trees.DecisionStump : Correct: 5.0, Wrong: 2.0 
Classifier: class weka.classifiers.trees.DecisionStump : Correct: 0.0, Wrong: 15.0 
Classifier: class weka.classifiers.trees.DecisionStump : Correct: 0.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.DecisionStump : Correct: 5.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.DecisionStump : Correct: 0.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.DecisionStump : Correct: 0.0, Wrong: 0.0 
Classifier: class weka.classifiers.trees.DecisionStump : Correct: 0.0, Wrong: 0.0 
=================================================================== 

Answer 1

印刷ラインにおける

System.out.println("Classifier: "+models[j].getClass()+" : Correct: "+predictions[j][i][0]+", Wrong: "+predictions[i][j][1]);  

次の部分

Wrong: "+predictions[i][j][1]); 

はあなたがJと私をswiched

Wrong: "+predictions[j][i][1]); 

でなければなりません。