def decision_function(self, X):
predictions = self.predict_proba(X)<EOL>out = np.zeros((predictions.shape[<NUM_LIT:0>], <NUM_LIT:1>))<EOL>out[:, <NUM_LIT:0>] = <NUM_LIT:1> - predictions[:, -<NUM_LIT:1>]<EOL>return out<EOL>
Generate an inlier score for each test data example.

Parameters
----------
X : array
    Test data, of dimension N times d (rows are examples, columns
    are data dimensions)

Returns:
-------
scores : array
    A vector of length N, where each element contains an inlier
    score in the range 0-1 (outliers have values close to zero,
    inliers have values close to one).