class_docstr: Example:
      >>> #The dataset to detect is y
      >>> y = numpy.array(...)
      >>> ad = AEDetector(roll_len=24)
      >>> ad.fit(y)
      >>> anomaly_scores = ad.score()
      >>> anomaly_indexes = ad.anomaly_indexes()
class_docstr_tok: ["Example",":",">",">",">","#","The","dataset","to","detect","is","y",">",">",">","y","=","numpy.array","(","...",")",">",">",">","ad","=","AEDetector","(","roll_len=24",")",">",">",">","ad.fit","(","y",")",">",">",">","anomaly_scores","=","ad.score","(",")",">",">",">","anomaly_indexes","=","ad.anomaly_indexes","(",")"]
docstr: Gets the indexes of N samples with the largest anomaly scores in y
  (N = size of input y * AEDetector.ratio)
  
  :return: the indexes of N samples
docstr_tok: ["Gets","the","indexes","of","N","samples","with","the","largest","anomaly","scores","in","y","(","N","=","size","of","input","y","*","AEDetector.ratio",")",":","return",":","the","indexes","of","N","samples"]
code: def anomaly_indexes(self):
      """
      Gets the indexes of N samples with the largest anomaly scores in y
      (N = size of input y * AEDetector.ratio)
  
      :return: the indexes of N samples
      """
      if self.anomaly_scores_ is None:
          self.score()
      num_anomalies = int(len(self.anomaly_scores_) * self.ratio)
      return self.anomaly_scores_.argsort()[-num_anomalies:]
  
file_path: BigDL/python/chronos/src/bigdl/chronos/detector/anomaly/ae_detector.py
imports: from bigdl.chronos.detector.anomaly.abstract import AnomalyDetector
  from bigdl.chronos.detector.anomaly.util import roll_arr, scale_arr
  import numpy
label_desc: Use image node_id 6 for calling the AEDetector obj's underlying member method code with example usage: obj.anomaly_indexes() and returns: self