def decision_function(self, X):
    'Predict raw anomaly score of X using the fitted detector.\n        The anomaly score of an input sample is computed based on different\n        detector algorithms. For consistency, outliers are assigned with\n        larger anomaly scores.\n\n        Parameters\n        ----------\n        X : numpy array of shape (n_samples, n_features)\n            The training input samples. Sparse matrices are accepted only\n            if they are supported by the base estimator.\n\n        Returns\n        -------\n        anomaly_scores : numpy array of shape (n_samples,)\n            The anomaly score of the input samples.\n        '
    return self._sod(X)
Predict raw anomaly score of X using the fitted detector.
The anomaly score of an input sample is computed based on different
detector algorithms. 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.
def decision_function(self, X):
    'Predict raw anomaly score of X using the fitted detector.\n        The anomaly score of an input sample is computed based on different\n        detector algorithms. For consistency, outliers are assigned with\n        larger anomaly scores.\n\n        Parameters\n        ----------\n        X : numpy array of shape (n_samples, n_features)\n            The training input samples. Sparse matrices are accepted only\n            if they are supported by the base estimator.\n\n        Returns\n        -------\n        anomaly_scores : numpy array of shape (n_samples,)\n            The anomaly score of the input samples.\n        '
    return self._sod(X)