class_docstr: A collection of simple classifier combination methods.
  
  Parameters
  ----------
  base_estimators: list or numpy array (n_estimators,)
      A list of base classifiers.
  
  method : str, optional (default='average')
      Combination method: {'average', 'maximization', 'majority vote',
      'median'}. Pass in weights of classifier for weighted version.
  
  threshold : float in (0, 1), optional (default=0.5)
      Cut-off value to convert scores into binary labels.
  
  weights : numpy array of shape (1, n_classifiers)
      Classifier weights.
  
  pre_fitted : bool, optional (default=False)
      Whether the base classifiers are trained. If True, `fit`
      process may be skipped.
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docstr: Predict the class labels for the provided data.
  
  Parameters
  ----------
  X : numpy array of shape (n_samples, n_features)
      The input samples.
  
  Returns
  -------
  labels : numpy array of shape (n_samples,)
      Class labels for each data sample.
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code: def predict(self, X):
      """Predict the class labels for the provided data.
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      Returns
      -------
      labels : numpy array of shape (n_samples,)
          Class labels for each data sample.
      """
      X = check_array(X)
  
      all_scores = np.zeros([X.shape[0], self.n_base_estimators_])
  
      for i, clf in enumerate(self.base_estimators):
          if clf.fitted_ is not True and self.pre_fitted == False:
              ValueError("Classifier should be fitted first!")
          else:
              if hasattr(clf, "predict"):
                  all_scores[:, i] = clf.predict(X)
              else:
                  raise ValueError(
                      "{clf} does not have predict.".format(clf=clf)
                  )
  
      if self.method == "average":
          agg_score = average(
              all_scores, estimator_weights=self.weights
          )
      if self.method == "maximization":
          agg_score = maximization(all_scores)
      if self.method == "majority_vote":
          agg_score = majority_vote(all_scores, weights=self.weights)
      if self.method == "median":
          agg_score = median(all_scores)
  
      return (agg_score >= self.threshold).astype("int").ravel()
  
file_path: combo/combo/models/classifier_comb.py
imports: import numpy
  from sklearn.utils import check_array
  from sklearn.utils import check_X_y
  from sklearn.utils import column_or_1d
  from pyod.utils.utility import check_parameter
  from .base import BaseAggregator
  from .score_comb import average, maximization, majority_vote, median
  from ..utils.utility import score_to_proba
label_desc: Use image node_id 3 for calling the SimpleClassifierAggregator obj's underlying member method code with example usage: obj.predict(X) and returns: unknown