本文整理匯總了Java中weka.classifiers.Evaluation.fMeasure方法的典型用法代碼示例。如果您正苦於以下問題：Java Evaluation.fMeasure方法的具體用法？Java Evaluation.fMeasure怎麽用？Java Evaluation.fMeasure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.classifiers.Evaluation的用法示例。
在下文中一共展示了Evaluation.fMeasure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: printMeasures
import weka.classifiers.Evaluation; //導入方法依賴的package包/類
public static void printMeasures(final Evaluation eval) {
final List<String> cat = EntityClassMap.entityClasses;
for (final String cl : EntityClassMap.entityClasses) {
final double f1 = eval.fMeasure(cat.indexOf(cl));
final double p = eval.precision(cat.indexOf(cl));
final double r = eval.recall(cat.indexOf(cl));
LOG.info("=== classes ===");
LOG.info("class: " + cl);
LOG.info("fMeasure: " + f1);
LOG.info("precision: " + p);
LOG.info("recall: " + r);
}
}
示例2: computeSingleRunResults
import weka.classifiers.Evaluation; //導入方法依賴的package包/類
public void computeSingleRunResults(MethodEvaluation methodEvaluation)
{
Evaluation evaluation = methodEvaluation.getEvaluation();
int hamIndex = HAM.ordinal();
int spamIndex = SPAM.ordinal();
Double hamPrecision = 100.0 * evaluation.precision(hamIndex);
Double spamPrecision = 100.0 * evaluation.precision(spamIndex);
Double weightedPrecision = 100.0 * evaluation.weightedPrecision();
Double hamRecall = 100.0 * evaluation.recall(hamIndex);
Double spamRecall = 100.0 * evaluation.recall(spamIndex);
Double weightedRecall = 100.0 * evaluation.weightedRecall();
Double hamAreaUnderPRC = 100.0 * evaluation.areaUnderPRC(hamIndex);
Double spamAreaUnderPRC = 100.0 * evaluation.areaUnderPRC(spamIndex);
Double weightedAreaUnderPRC = 100.0 * evaluation.weightedAreaUnderPRC();
Double hamAreaUnderROC = 100.0 * evaluation.areaUnderROC(hamIndex);
Double spamAreaUnderROC = 100.0 * evaluation.areaUnderROC(spamIndex);
Double weightedAreaUnderROC = 100.0 * evaluation.weightedAreaUnderROC();
Double hamFMeasure = 100.0 * evaluation.fMeasure(hamIndex);
Double spamFMeasure = 100.0 * evaluation.fMeasure(spamIndex);
Double weightedFMeasure = 100.0 * evaluation.weightedFMeasure();
Double trainTime = (double) (methodEvaluation.getTrainEnd() - methodEvaluation.getTrainStart());
Double testTime = (double) (methodEvaluation.getTestEnd() - methodEvaluation.getTestStart());
addSingleRunResult(Metric.HAM_PRECISION, hamPrecision);
addSingleRunResult(Metric.SPAM_PRECISION, spamPrecision);
addSingleRunResult(Metric.WEIGHTED_PRECISION, weightedPrecision);
addSingleRunResult(Metric.HAM_RECALL, hamRecall);
addSingleRunResult(Metric.SPAM_RECALL, spamRecall);
addSingleRunResult(Metric.WEIGHTED_RECALL, weightedRecall);
addSingleRunResult(Metric.HAM_AREA_UNDER_PRC, hamAreaUnderPRC);
addSingleRunResult(Metric.SPAM_AREA_UNDER_PRC, spamAreaUnderPRC);
addSingleRunResult(Metric.WEIGHTED_AREA_UNDER_PRC, weightedAreaUnderPRC);
addSingleRunResult(Metric.HAM_AREA_UNDER_ROC, hamAreaUnderROC);
addSingleRunResult(Metric.SPAM_AREA_UNDER_ROC, spamAreaUnderROC);
addSingleRunResult(Metric.WEIGHTED_AREA_UNDER_ROC, weightedAreaUnderROC);
addSingleRunResult(Metric.HAM_F_MEASURE, hamFMeasure);
addSingleRunResult(Metric.SPAM_F_MEASURE, spamFMeasure);
addSingleRunResult(Metric.WEIGHTED_F_MEASURE, weightedFMeasure);
addSingleRunResult(Metric.TRAIN_TIME, trainTime);
addSingleRunResult(Metric.TEST_TIME, testTime);
}