parameters: (self)
return_statement: return self._decision_function
docstring: Shifted opposite of the Local Outlier Factor of X.
  
          Bigger is better, i.e. large values correspond to inliers.
  
          The shift offset allows a zero threshold for being an outlier.
          Only available for novelty detection (when novelty is set to True).
          The argument X is supposed to contain *new data*: if X contains a
          point from training, it considers the later in its own neighborhood.
          Also, the samples in X are not considered in the neighborhood of any
          point.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              The query sample or samples to compute the Local Outlier Factor
              w.r.t. the training samples.
  
          Returns
          -------
          shifted_opposite_lof_scores : array, shape (n_samples,)
              The shifted opposite of the Local Outlier Factor of each input
              samples. The lower, the more abnormal. Negative scores represent
              outliers, positive scores represent inliers.
docstring_summary: Shifted opposite of the Local Outlier Factor of X.
docstring_tokens: Shifted opposite of the Local Outlier Factor of X .
function: def decision_function(self):
          """Shifted opposite of the Local Outlier Factor of X.
  
          Bigger is better, i.e. large values correspond to inliers.
  
          The shift offset allows a zero threshold for being an outlier.
          Only available for novelty detection (when novelty is set to True).
          The argument X is supposed to contain *new data*: if X contains a
          point from training, it considers the later in its own neighborhood.
          Also, the samples in X are not considered in the neighborhood of any
          point.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              The query sample or samples to compute the Local Outlier Factor
              w.r.t. the training samples.
  
          Returns
          -------
          shifted_opposite_lof_scores : array, shape (n_samples,)
              The shifted opposite of the Local Outlier Factor of each input
              samples. The lower, the more abnormal. Negative scores represent
              outliers, positive scores represent inliers.
          """
          if not self.novelty:
              msg = ('decision_function is not available when novelty=False. '
                     'Use novelty=True if you want to use LOF for novelty '
                     'detection and compute decision_function for new unseen '
                     'data. Note that the opposite LOF of the training samples '
                     'is always available by considering the '
                     'negative_outlier_factor_ attribute.')
              raise AttributeError(msg)
  
          return self._decision_function
function_tokens: def decision_function ( self ) : if not self . novelty : msg = ( 'decision_function is not available when novelty=False. ' 'Use novelty=True if you want to use LOF for novelty ' 'detection and compute decision_function for new unseen ' 'data. Note that the opposite LOF of the training samples ' 'is always available by considering the ' 'negative_outlier_factor_ attribute.' ) raise AttributeError ( msg ) return self . _decision_function