class_docstr: The Wrap-around Quasi-Monte Carlo discrepancy is a uniformity criterion 
     which is used to assess the space filling of a number of samples in a hypercube. 
     It quantifies the distance between the continuous uniform distribution on a 
     hypercube and the discrete uniform distribution on distinct sample points. 
     Therefore, lower discrepancy values for a sample point indicates that it provides 
     better coverage of the parameter space with regard to the rest of the samples. This
     method is kernel based and a higher discrepancy score is relative to the
     rest of the samples, the higher the likelihood of it being an outlier. 
     Read more in the :cite:`fang2001wrap`.
  
  Parameters
  ----------          
  
  Attributes
  ----------
  decision_scores_ : numpy array of shape (n_samples,)
      The outlier scores of the training data.
      The higher, the more abnormal. Outliers tend to have higher
      scores. This value is available once the detector is
      fitted.
  
  threshold_ : float
     The modified z-score to use as a threshold. Observations with
     a modified z-score (based on the median absolute deviation) greater
     than this value will be classified as outliers.
  
  labels_ : int, either 0 or 1
      The binary labels of the training data. 0 stands for inliers
      and 1 for outliers/anomalies. It is generated by applying
      ``threshold_`` on ``decision_scores_``.
      
code: def __init__(self, contamination=0.1):
      super(QMCD, self).__init__(contamination=contamination)
  
file_path: pyod/pyod/models/qmcd.py
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  from numba import njit, prange
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
label_desc: Use image node_id 1 to create a new QMCD object from inherited base classes: BaseDetector with example: obj = QMCD(contamination)