def _snn(self, X):
    'This function is called internally to calculate the shared nearest\n        neighbors (SNN). SNN is reported to be more robust than k nearest\n        neighbors.\n\n        Returns\n        -------\n        snn_indices : numpy array of shape (n_shared_nearest_neighbors,)\n            The indices of top k shared nearest neighbors for each observation.\n        '
    knn = NearestNeighbors(n_neighbors=self.n_neighbors_)
    knn.fit(X)
    ind = knn.kneighbors(return_distance=False)
    return _snn_imp(ind, self.ref_set_)
This function is called internally to calculate the shared nearest
neighbors (SNN). SNN is reported to be more robust than k nearest
neighbors.

Returns
-------
snn_indices : numpy array of shape (n_shared_nearest_neighbors,)
    The indices of top k shared nearest neighbors for each observation.
def _snn(self, X):
    'This function is called internally to calculate the shared nearest\n        neighbors (SNN). SNN is reported to be more robust than k nearest\n        neighbors.\n\n        Returns\n        -------\n        snn_indices : numpy array of shape (n_shared_nearest_neighbors,)\n            The indices of top k shared nearest neighbors for each observation.\n        '
    knn = NearestNeighbors(n_neighbors=self.n_neighbors_)
    knn.fit(X)
    ind = knn.kneighbors(return_distance=False)
    return _snn_imp(ind, self.ref_set_)