def radius_neighbors(self, X=None, radius=None, return_distance=True):
        """Finds the neighbors within a given radius of a point or points.

        Return the indices and distances of each point from the dataset
        lying in a ball with size ``radius`` around the points of the query
        array. Points lying on the boundary are included in the results.

        The result points are *not* necessarily sorted by distance to their
        query point.

        Parameters
        ----------
        X : array-like, (n_samples, n_features), optional
            The query point or points.
            If not provided, neighbors of each indexed point are returned.
            In this case, the query point is not considered its own neighbor.

        radius : float
            Limiting distance of neighbors to return.
            (default is the value passed to the constructor).

        return_distance : boolean, optional. Defaults to True.
            If False, distances will not be returned

        Returns
        -------
        dist : array, shape (n_samples,) of arrays
            Array representing the distances to each point, only present if
            return_distance=True. The distance values are computed according
            to the ``metric`` constructor parameter.

        ind : array, shape (n_samples,) of arrays
            An array of arrays of indices of the approximate nearest points
            from the population matrix that lie within a ball of size
            ``radius`` around the query points.

        Examples
        --------
        In the following example, we construct a NeighborsClassifier
        class from an array representing our data set and ask who's
        the closest point to [1, 1, 1]:

        >>> import numpy as np
        >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
        >>> from sklearn.neighbors import NearestNeighbors
        >>> neigh = NearestNeighbors(radius=1.6)
        >>> neigh.fit(samples) # doctest: +ELLIPSIS
        NearestNeighbors(algorithm='auto', leaf_size=30, ...)
        >>> rng = neigh.radius_neighbors([[1., 1., 1.]])
        >>> print(np.asarray(rng[0][0])) # doctest: +ELLIPSIS
        [ 1.5  0.5]
        >>> print(np.asarray(rng[1][0])) # doctest: +ELLIPSIS
        [1 2]

        The first array returned contains the distances to all points which
        are closer than 1.6, while the second array returned contains their
        indices.  In general, multiple points can be queried at the same time.

        Notes
        -----
        Because the number of neighbors of each point is not necessarily
        equal, the results for multiple query points cannot be fit in a
        standard data array.
        For efficiency, `radius_neighbors` returns arrays of objects, where
        each object is a 1D array of indices or distances.
        """
        if self._fit_method is None:
            raise NotFittedError("Must fit neighbors before querying.")

        if X is not None:
            query_is_train = False
            X = check_array(X, accept_sparse='csr')
        else:
            query_is_train = True
            X = self._fit_X

        if radius is None:
            radius = self.radius

        n_samples = X.shape[0]
        if self._fit_method == 'brute':
            # for efficiency, use squared euclidean distances
            if self.effective_metric_ == 'euclidean':
                dist = pairwise_distances(X, self._fit_X, 'euclidean',
                                          squared=True)
                radius *= radius
            else:
                dist = pairwise_distances(X, self._fit_X,
                                          self.effective_metric_,
                                          **self.effective_metric_params_)

            neigh_ind_list = [np.where(radius <= d)[0] for d in dist]

            # See https://github.com/numpy/numpy/issues/5456
            # if you want to understand why this is initialized this way.
            neigh_ind = np.empty(n_samples, dtype='object')
            neigh_ind[:] = neigh_ind_list

            if return_distance:
                dist_array = np.empty(n_samples, dtype='object')
                if self.effective_metric_ == 'euclidean':
                    dist_list = [np.sqrt(d[neigh_ind[i]])
                                 for i, d in enumerate(dist)]
                else:
                    dist_list = [d[neigh_ind[i]]
                                 for i, d in enumerate(dist)]
                dist_array[:] = dist_list

                results = dist_array, neigh_ind
            else:
                results = neigh_ind

        elif self._fit_method in ['ball_tree', 'kd_tree']:
            if issparse(X):
                raise ValueError(
                    "%s does not work with sparse matrices. Densify the data, "
                    "or set algorithm='brute'" % self._fit_method)
            results = self._tree.query_radius(X, radius,
                                              return_distance=return_distance)
            if return_distance:
                results = results[::-1]
        else:
            raise ValueError("internal: _fit_method not recognized")

        if not query_is_train:
            return results
        else:
            # If the query data is the same as the indexed data, we would like
            # to ignore the first nearest neighbor of every sample, i.e
            # the sample itself.
            if return_distance:
                dist, neigh_ind = results
            else:
                neigh_ind = results

            for ind, ind_neighbor in enumerate(neigh_ind):
                mask = ind_neighbor != ind

                neigh_ind[ind] = ind_neighbor[mask]
                if return_distance:
                    dist[ind] = dist[ind][mask]

            if return_distance:
                return dist, neigh_ind
            return neigh_ind
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neighbors/base.py/RadiusNeighborsMixin.radius_neighbors/swapped operands of `<=`