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我爲着名的鳶尾花問題運行此代碼，進行10次交叉驗證，然後使用5種不同的分類方法對它們進行分類。Weka分類：錯誤+正確<全部實例，怎麼回事？
這應該使135個實例分類培訓和測試的15十次，所以我希望有錯誤的分類情況下+正確分類實例= 15
以下是代碼，並輸出。
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.
====================
輸出：
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
===================================================================