def METHOD_NAME(self, X):
    """Predict raw anomaly score of X using the fitted detector.
    For consistency, outliers are assigned with larger anomaly scores.
    Parameters
    ----------
    X : numpy array of shape (n_samples, n_features)
        The training input samples. Sparse matrices are accepted only
        if they are supported by the base estimator.
    Returns
    -------
    anomaly_scores : numpy array of shape (n_samples,)
        The anomaly score of the input samples.
    """
    check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
    # Validate input X
    X = check_array(X)
    # Get Cook's distance
    distances_ = _process_distances(X, self.model)
    return distances_