class_docstr: Example:
      >>> #The dataset to detect is y
      >>> y = numpy.array(...)
      >>> ad = DBScanDetector(eps=0.1, min_samples=6)
      >>> ad.fit(y)
      >>> anomaly_scores = ad.score()
      >>> anomaly_indexes = ad.anomaly_indexes()
docstr: Initialize a DBScanDetector.
  
  :param eps: The maximum distance between two samples for one to be considered
      as the neighborhood of the other.
      It is a parameter of DBSCAN, refer to sklearn.cluster.DBSCAN docs for more details.
  :param min_samples: The number of samples (or total weight) in a neighborhood
      for a point to be considered as a core point.
      It is a parameter of DBSCAN, refer to sklearn.cluster.DBSCAN docs for more details.
  :param argv: Other parameters used in DBSCAN.
      Refer to sklearn.cluster.DBSCAN docs for more details.
code: def __init__(self, eps=0.01, min_samples=6, **argv):
      """
      Initialize a DBScanDetector.
  
      :param eps: The maximum distance between two samples for one to be considered
          as the neighborhood of the other.
          It is a parameter of DBSCAN, refer to sklearn.cluster.DBSCAN docs for more details.
      :param min_samples: The number of samples (or total weight) in a neighborhood
          for a point to be considered as a core point.
          It is a parameter of DBSCAN, refer to sklearn.cluster.DBSCAN docs for more details.
      :param argv: Other parameters used in DBSCAN.
          Refer to sklearn.cluster.DBSCAN docs for more details.
      """
      self.eps = eps
      self.min_samples = min_samples
      self.argv = argv
      self.anomaly_indexes_ = None
      self.anomaly_scores_ = None
  
file_path: analytics-zoo/pyzoo/zoo/chronos/detector/anomaly/dbscan_detector.py
imports: from zoo.chronos.detector.anomaly.abstract import AnomalyDetector
  import numpy
  from sklearn.cluster import DBSCAN
label_desc: Use image node_id 1 to create a new DBScanDetector object from inherited base classes: AnomalyDetector with example: obj = DBScanDetector(eps, min_samples)