本文整理匯總了Java中weka.core.Instances.stratify方法的典型用法代碼示例。如果您正苦於以下問題：Java Instances.stratify方法的具體用法？Java Instances.stratify怎麽用？Java Instances.stratify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.core.Instances的用法示例。
在下文中一共展示了Instances.stratify方法的1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: showFolds
import weka.core.Instances; //導入方法依賴的package包/類
/***
* <p>Using Feature Selection method to get 10 folds results in the given project</p>
* @param path project path
* @param sel label means we use the Feature Selection method
* @throws Exception
*/
public static void showFolds(String path, int k, int flag) throws Exception{
Instances data1 = DataSource.read(path);
data1.setClassIndex(data1.numAttributes()-1);
if(!data1.classAttribute().isNominal()) // in case of noisy data, return
return;
/** Feature Selection: Correlation */
CorrelationAttributeEval evall = new CorrelationAttributeEval();
Ranker ranker = new Ranker();
AttributeSelection selector = new AttributeSelection();
selector.setEvaluator(evall);
selector.setSearch(ranker);
selector.setInputFormat(data1);
data1 = Filter.useFilter(data1, selector);
/** Randomize and stratify the dataset*/
data1.randomize(new Random(1));
data1.stratify(10); // 10 folds
double[][] matrix = new double[10][7];
for(int i=0; i<10; i++){ // To calculate the results in each fold
Instances test = data1.testCV(10, i);
Instances train = data1.trainCV(10, i);
/** SMOTE */
SMOTE smote = new SMOTE();
smote.setInputFormat(train);
train = Filter.useFilter(train, smote);
/** C4.5 */
J48 rf = new J48();
// RandomForest rf = new RandomForest();
// rf.setNumIterations(300);
rf.buildClassifier(train);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(rf, test);
matrix[i][6] = 1-eval.errorRate();
matrix[i][0] = eval.precision(0);
matrix[i][1] = eval.recall(0);
matrix[i][2] = eval.fMeasure(0);
matrix[i][3] = eval.precision(1);
matrix[i][4] = eval.recall(1);
matrix[i][5] = eval.fMeasure(1);
}
for(int i=0;i<10;i++){
for(int j=0;j<7;j++){
System.out.printf("%15.8f", matrix[i][j]);
}
System.out.println("");
}
}