本文整理匯總了Java中edu.stanford.nlp.classify.LinearClassifierFactory.setVerbose方法的典型用法代碼示例。如果您正苦於以下問題：Java LinearClassifierFactory.setVerbose方法的具體用法？Java LinearClassifierFactory.setVerbose怎麽用？Java LinearClassifierFactory.setVerbose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edu.stanford.nlp.classify.LinearClassifierFactory的用法示例。
在下文中一共展示了LinearClassifierFactory.setVerbose方法的2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: trainRVF
import edu.stanford.nlp.classify.LinearClassifierFactory; //導入方法依賴的package包/類
public LinearClassifier trainRVF(List<HashMap<String, Double>> list_feature2values,
List<String> list_labels) {
List<Datum<String, String>> trainingData = new ArrayList<Datum<String, String>>();
for (int i = 0; i < list_feature2values.size(); i++) {
HashMap<String, Double> feature2values = list_feature2values.get(i);
String label = list_labels.get(i);
Datum<String, String> d = new RVFDatum(Counters.fromMap(feature2values), label);
trainingData.add(d);
}
// Build a classifier factory
LinearClassifierFactory<String, String> factory = new LinearClassifierFactory<String, String>();
factory.setSigma(3);
factory.setEpsilon(15);
factory.useQuasiNewton();
factory.setVerbose(true);
LinearClassifier<String, String> classifier = factory.trainClassifier(trainingData);
// {
// ArrayList<String> temp = new ArrayList<String>();
// temp.add("NS=" + GREEN);
// System.out.println(classifier.scoreOf(new BasicDatum<String,
// String>(temp, BROKEN), BROKEN));
// }
this.classifier = classifier;
return classifier;
}
示例2: trainBasic
import edu.stanford.nlp.classify.LinearClassifierFactory; //導入方法依賴的package包/類
public LinearClassifier trainBasic(
List<List<String>> list_features, List<String> list_labels) {
List<Datum<String, String>> trainingData = new ArrayList<Datum<String, String>>();
for (int i = 0; i < list_features.size(); i++) {
List<String> features = list_features.get(i);
String label = list_labels.get(i);
Datum<String, String> d = new BasicDatum<String, String>(features, label);
trainingData.add(d);
}
// Build a classifier factory
LinearClassifierFactory<String, String> factory = new LinearClassifierFactory<String, String>();
// factory.setTol(tol);
// factory.setSigma(1);
// factory.setEpsilon(0.01);
// factory.useQuasiNewton();
factory.setVerbose(true);
LinearClassifier<String, String> classifier = factory.trainClassifier(trainingData);
// {
// ArrayList<String> temp = new ArrayList<String>();
// temp.add("NS=" + GREEN);
// System.out.println(classifier.scoreOf(new BasicDatum<String,
// String>(temp, BROKEN), BROKEN));
// }
this.classifier = classifier;
return classifier;
}