本文整理汇总了Java中weka.core.Instances.trainCV方法的典型用法代码示例。如果您正苦于以下问题：Java Instances.trainCV方法的具体用法？Java Instances.trainCV怎么用？Java Instances.trainCV使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weka.core.Instances的用法示例。
在下文中一共展示了Instances.trainCV方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: getTrainSet
import weka.core.Instances; //导入方法依赖的package包/类
public Instances getTrainSet(int foldNumber, int foldTotal, String fnData) throws Exception {
// load instances
Instances data = new Instances(new BufferedReader(new FileReader(fnData)));
data.setClassIndex(data.numAttributes() - 1);
Instances trainSet = data.trainCV(foldTotal, foldNumber);
return trainSet;
}
示例2: getTestSet
import weka.core.Instances; //导入方法依赖的package包/类
public Instances getTestSet(int foldNumber, int foldTotal, String fnData) throws Exception {
// load instances
Instances data = new Instances(new BufferedReader(new FileReader(fnData)));
data.setClassIndex(data.numAttributes() - 1);
Instances testSet = data.trainCV(foldTotal, foldNumber);
return testSet;
}
示例3: showFolds
import weka.core.Instances; //导入方法依赖的package包/类
/***
* <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("");
}
}