def decision_function(self, X):
        """
        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).
        """
        predictions = self.predict_proba(X)
        out = np.zeros((predictions.shape[0], 1))
        out[:, 0] = 1 - predictions[:, -1]
        return out
def decision_function ( self , X ) : predictions = self . predict_proba ( X ) out = np . zeros ( ( predictions . shape [ 0 ] , 1 ) ) out [ : , 0 ] = 1 - predictions [ : , - 1 ] return out
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).