parameters: (self, X)
return_statement: return self._score_samples(X) - self.offset_
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, X):
          """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.
          """
  
          return self._score_samples(X) - self.offset_
function_tokens: def _decision_function ( self , X ) : return self . _score_samples ( X ) - self . offset_