本文整理匯總了Java中weka.classifiers.Evaluation.pctIncorrect方法的典型用法代碼示例。如果您正苦於以下問題：Java Evaluation.pctIncorrect方法的具體用法？Java Evaluation.pctIncorrect怎麽用？Java Evaluation.pctIncorrect使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類weka.classifiers.Evaluation的用法示例。
在下文中一共展示了Evaluation.pctIncorrect方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。
示例1: getErrorPercent
import weka.classifiers.Evaluation; //導入方法依賴的package包/類
@Override
public double getErrorPercent() {
this.splitInstances();
try {
this.getClassifier().buildClassifier(getTrainInstances());
Evaluation eval = new Evaluation(getTestInstances());
eval.evaluateModel(getClassifier(), getTestInstances());
return eval.pctIncorrect();
} catch (Exception e) {
e.printStackTrace();
return -1;
}
}
示例2: getCrossValidation
import weka.classifiers.Evaluation; //導入方法依賴的package包/類
/**
* @param cls
* @param data
* @param folds
* @return [0] = pctCorrect, [1] = pctIncorrect
* @throws Exception
*/
public double[] getCrossValidation(Classifier cls, Instances data, int folds) throws Exception {
cls.buildClassifier(data);
Classifier copy = Classifier.makeCopy(cls);
double[] results = new double[2];
for (int n = 0; n < folds; n++) {
Instances train = data.trainCV(folds, n);
Instances test = data.testCV(folds, n);
// CSVSaver saver = new CSVSaver();
// saver.setInstances(train);
// saver.setFile(new File("../data.csv"));
// saver.writeBatch();
cls.buildClassifier(train);
Evaluation eval = new Evaluation(data);
eval.evaluateModel(cls, test);
results[0] = results[0] + (eval.pctCorrect() / 100);
results[1] = results[1] + (eval.pctIncorrect() / 100);
}
cls = copy;
results[0] = results[0] / folds;
results[1] = results[1] / folds;
return results;
}
示例3: getErrorPercent
import weka.classifiers.Evaluation; //導入方法依賴的package包/類
@Override
public double getErrorPercent() {
try {
Evaluation eval = new Evaluation(getInstances());
eval.crossValidateModel(getClassifier(), getInstances(),
getFolds(), new Random()
);
return eval.pctIncorrect();
} catch (Exception e) {
e.printStackTrace();
return 100;
}
}
示例4: getValidation
import weka.classifiers.Evaluation; //導入方法依賴的package包/類
/**
* @param cls
* @param trainingData
* @param testData
* @return [0] = pctCorrect, [1] = pctIncorrect
* @throws Exception
*/
public double[] getValidation(Classifier cls, Instances trainingData, Instances testData)
throws Exception {
cls.buildClassifier(trainingData);
Evaluation eval = new Evaluation(trainingData);
eval.evaluateModel(cls, testData);
double[] results = new double[2];
results[0] = eval.pctCorrect() / 100;
results[1] = eval.pctIncorrect() / 100;
return results;
}