本文整理匯總了Java中weka.core.Instance.setClassValue方法的典型用法代碼示例。如果您正苦於以下問題：Java Instance.setClassValue方法的具體用法？Java Instance.setClassValue怎麽用？Java Instance.setClassValue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.Instance的用法示例。
在下文中一共展示了Instance.setClassValue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: generateDecisionTree
import weka.core.Instance; //導入方法依賴的package包/類
protected Classifier generateDecisionTree(AbstractClusterer clusterer, MarkovAttributeSet aset, Instances data) throws Exception {
// We need to create a new Attribute that has the ClusterId
Instances newData = data; // new Instances(data);
newData.insertAttributeAt(new Attribute("ClusterId"), newData.numAttributes());
Attribute cluster_attr = newData.attribute(newData.numAttributes()-1);
assert(cluster_attr != null);
assert(cluster_attr.index() > 0);
newData.setClass(cluster_attr);
// We will then tell the Classifier to predict that ClusterId based on the MarkovAttributeSet
ObjectHistogram<Integer> cluster_h = new ObjectHistogram<Integer>();
for (int i = 0, cnt = newData.numInstances(); i < cnt; i++) {
// Grab the Instance and throw it at the the clusterer to get the target cluster
Instance inst = newData.instance(i);
int c = (int)clusterer.clusterInstance(inst);
inst.setClassValue(c);
cluster_h.put(c);
} // FOR
System.err.println("Number of Elements: " + cluster_h.getValueCount());
System.err.println(cluster_h);
NumericToNominal filter = new NumericToNominal();
filter.setInputFormat(newData);
newData = Filter.useFilter(newData, filter);
String output = this.catalog_proc.getName() + "-labeled.arff";
FileUtil.writeStringToFile(output, newData.toString());
LOG.info("Wrote labeled data set to " + output);
// Decision Tree
J48 j48 = new J48();
String options[] = {
"-S", Integer.toString(this.rand.nextInt()),
};
j48.setOptions(options);
// Make sure we add the ClusterId attribute to a new MarkovAttributeSet so that
// we can tell the Classifier to classify that!
FilteredClassifier fc = new FilteredClassifier();
MarkovAttributeSet classifier_aset = new MarkovAttributeSet(aset);
classifier_aset.add(cluster_attr);
fc.setFilter(classifier_aset.createFilter(newData));
fc.setClassifier(j48);
// Bombs away!
fc.buildClassifier(newData);
return (fc);
}