class_docstr: A collection of simple detector combination methods.
  
  Parameters
  ----------
  base_estimators : list, length must be greater than 1
      Base unsupervised outlier detectors from PyOD. (Note: requires fit and
      decision_function methods)
  
  method : str, optional (default='average')
      Combination method: {'average', 'maximization',
      'median'}. Pass in weights of detector for weighted version.
  
  contamination : float in (0., 0.5), optional (default=0.1)
      The amount of contamination of the data set,
      i.e. the proportion of outliers in the data set. Used when fitting to
      define the threshold on the decision function.
  
  standardization : bool, optional (default=True)
      If True, perform standardization first to convert
      prediction score to zero mean and unit variance.
      See http://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html
  
  weights : numpy array of shape (1, n_detectors)
      detector weights.
  
  pre_fitted : bool, optional (default=False)
      Whether the base detectors are trained. If True, `fit`
      process may be skipped.
  
  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 threshold is based on ``contamination``. It is the
      ``n_samples * contamination`` most abnormal samples in
      ``decision_scores_``. The threshold is calculated for generating
      binary outlier labels.
  
  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_``.
docstr: Predict if a particular sample is an outlier or not.
  
  Parameters
  ----------
  X : numpy array of shape (n_samples, n_features)
      The input samples.
  
  Returns
  -------
  outlier_labels : numpy array of shape (n_samples,)
      For each observation, tells whether or not
      it should be considered as an outlier according to the
      fitted model. 0 stands for inliers and 1 for outliers.
docstr_tok: ["Predict","if","a","particular","sample","is","an","outlier","or","not",".","Parameters","--","--","--","--","--","X",":","numpy","array","of","shape","(","n_samples",",","n_features",")","The","input","samples",".","Returns","--","--","--","-","outlier_labels",":","numpy","array","of","shape","(","n_samples",",",")","For","each","observation",",","tells","whether","or","not","it","should","be","considered","as","an","outlier","according","to","the","fitted","model",".","0","stands","for","inliers","and","1","for","outliers","."]
code: def predict(self, X):
      """Predict if a particular sample is an outlier or not.
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      Returns
      -------
      outlier_labels : numpy array of shape (n_samples,)
          For each observation, tells whether or not
          it should be considered as an outlier according to the
          fitted model. 0 stands for inliers and 1 for outliers.
      """
      check_is_fitted(
          self, ["decision_scores_", "threshold_", "labels_"]
      )
      X = check_array(X)
      return self._detector_predict(X)
  
file_path: combo/combo/models/detector_comb.py
imports: import numpy
  from sklearn.utils import check_array
  from sklearn.utils import column_or_1d
  from sklearn.utils.validation import check_is_fitted
  from pyod.utils.utility import standardizer
  from .base import BaseAggregator
  from .score_comb import average, maximization, median
label_desc: Use image node_id 5 for calling the SimpleDetectorAggregator obj's underlying member method code with example usage: obj.predict(X) and returns: self