输出：

Question

我为着名的Iris花问题运行此代码，进行10倍交叉验证，然后使用5种不同的分类方法对它们进行分类。

这应该使分类器训练在135个实例上并且在15次上进行十次测试，所以我希望错误的分类实例+正确的分类实例= 15。

以下是代码和输出。

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.

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

输出：

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

在印刷线上

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

以下部分

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

应该是

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

你开始 j 和我。

Weka分类：错误+正确＆lt;总实例，怎么样？

输出：

1 个答案: