本文整理了Java中org.apache.commons.math3.stat.descriptive.rank.Percentile.withNaNStrategy()方法的一些代码示例，展示了Percentile.withNaNStrategy()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Percentile.withNaNStrategy()方法的具体详情如下：
包路径：org.apache.commons.math3.stat.descriptive.rank.Percentile
类名称：Percentile
方法名：withNaNStrategy

Percentile.withNaNStrategy介绍

[英]Build a new instance similar to the current one except for the NaNStrategy strategy.

This method is intended to be used as part of a fluent-type builder pattern. Building finely tune instances should be done as follows:

Percentile customized = new Percentile(quantile). 
withEstimationType(estimationType). 
withNaNStrategy(nanStrategy). 
withKthSelector(kthSelector);

If any of the withXxx method is omitted, the default value for the corresponding customization parameter will be used.
[中]除了NansStrategy策略之外，创建一个与当前实例类似的新实例。
此方法旨在用作fluent type builder模式的一部分。构建微调实例应按以下步骤进行：

Percentile customized = new Percentile(quantile). 
withEstimationType(estimationType). 
withNaNStrategy(nanStrategy). 
withKthSelector(kthSelector);

如果省略了withXxx方法中的任何一个，则将使用相应自定义参数的默认值。

代码示例

代码示例来源：origin: zavtech/morpheus-core

@Override
public double getValue() {
  return new org.apache.commons.math3.stat.descriptive.rank.Percentile(nth * 100)
    .withEstimationType(org.apache.commons.math3.stat.descriptive.rank.Percentile.EstimationType.R_7)
    .withNaNStrategy(NaNStrategy.FIXED)
    .evaluate(values, 0, n);
}

代码示例来源：origin: stanford-futuredata/macrobase

@Override
public void consume(List<Datum> records) {
  List<DatumWithNorm> toClassify = new ArrayList<>();
  double[] scores = new double[records.size()];
  for(int i = 0; i < records.size(); i++) {
    Datum d = records.get(i);
    DatumWithNorm dwn = new DatumWithNorm(d);
    toClassify.add(dwn);
    scores[i] = dwn.getNorm();
  }
  Percentile pCalc = new Percentile().withNaNStrategy(NaNStrategy.MAXIMAL);
  pCalc.setData(scores);
  double cutoff = pCalc.evaluate(scores, targetPercentile * 100);
  log.debug("{} Percentile Cutoff: {}", targetPercentile, cutoff);
  log.debug("Median: {}", pCalc.evaluate(50));
  log.debug("Max: {}", pCalc.evaluate(100));
  for(DatumWithNorm dwn : toClassify) {
    results.add(new OutlierClassificationResult(dwn.getDatum(),
                          dwn.getNorm() >= cutoff || dwn.getNorm().isInfinite()));
  }
}