本文整理汇总了Java中cc.mallet.types.Labeling.valueAtLocation方法的典型用法代码示例。如果您正苦于以下问题：Java Labeling.valueAtLocation方法的具体用法？Java Labeling.valueAtLocation怎么用？Java Labeling.valueAtLocation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cc.mallet.types.Labeling的用法示例。
在下文中一共展示了Labeling.valueAtLocation方法的1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: incorporateOneInstance
import cc.mallet.types.Labeling; //导入方法依赖的package包/类
private void incorporateOneInstance (Instance instance, double instanceWeight)
{
Labeling labeling = instance.getLabeling ();
if (labeling == null) return; // Handle unlabeled instances by skipping them
FeatureVector fv = (FeatureVector) instance.getData ();
double oneNorm = fv.oneNorm();
if (oneNorm <= 0) return; // Skip instances that have no features present
if (docLengthNormalization > 0)
// Make the document have counts that sum to docLengthNormalization
// I.e., if 20, it would be as if the document had 20 words.
instanceWeight *= docLengthNormalization / oneNorm;
assert (instanceWeight > 0 && !Double.isInfinite(instanceWeight));
for (int lpos = 0; lpos < labeling.numLocations(); lpos++) {
int li = labeling.indexAtLocation (lpos);
double labelWeight = labeling.valueAtLocation (lpos);
if (labelWeight == 0) continue;
//System.out.println ("NaiveBayesTrainer me.increment "+ labelWeight * instanceWeight);
me[li].increment (fv, labelWeight * instanceWeight);
// This relies on labelWeight summing to 1 over all labels
pe.increment (li, labelWeight * instanceWeight);
}
}