def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
        """Finds the K-neighbors of a point.

        Returns indices of and distances to the neighbors of each point.

        Parameters
        ----------
        X : array-like, shape (n_query, n_features), \
                or (n_query, n_indexed) if metric == 'precomputed'
            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.

        n_neighbors : int
            Number of neighbors to get (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
            Array representing the lengths to points, only present if
            return_distance=True

        ind : array
            Indices of the nearest points in the population matrix.

        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]

        >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
        >>> from sklearn.neighbors import NearestNeighbors
        >>> neigh = NearestNeighbors(n_neighbors=1)
        >>> neigh.fit(samples) # doctest: +ELLIPSIS
        NearestNeighbors(algorithm='auto', leaf_size=30, ...)
        >>> print(neigh.kneighbors([[1., 1., 1.]])) # doctest: +ELLIPSIS
        (array([[ 0.5]]), array([[2]]...))

        As you can see, it returns [[0.5]], and [[2]], which means that the
        element is at distance 0.5 and is the third element of samples
        (indexes start at 0). You can also query for multiple points:

        >>> X = [[0., 1., 0.], [1., 0., 1.]]
        >>> neigh.kneighbors(X, return_distance=False) # doctest: +ELLIPSIS
        array([[1],
               [2]]...)

        """
        if self._fit_method is None:
            raise NotFittedError("Must fit neighbors before querying.")

        if n_neighbors is None:
            n_neighbors = self.n_neighbors

        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
            # Include an extra neighbor to account for the sample itself being
            # returned, which is removed later
            n_neighbors += 1

        train_size = self._fit_X.shape[0]
        if n_neighbors > train_size:
            raise ValueError(
                "Expected n_neighbors <= n_samples, "
                " but n_samples = %d, n_neighbors = %d" %
                (train_size, n_neighbors)
            )
        n_samples, _ = X.shape
        sample_range = np.arange(n_samples)[:, None]

        n_jobs = _get_n_jobs(self.n_jobs)
        if self._fit_method == 'brute':
            # for efficiency, use squared euclidean distances
            if self.effective_metric_ == 'euclidean':
                dist = pairwise_distances(X, self._fit_X, 'euclidean',
                                          n_jobs=n_jobs, squared=True)
            else:
                dist = pairwise_distances(
                    X, self._fit_X, self.effective_metric_, n_jobs=n_jobs,
                    **self.effective_metric_params_)

            neigh_ind = argpartition(dist, n_neighbors - 1, axis=1)
            neigh_ind = neigh_ind[:, :n_neighbors]
            # argpartition doesn't guarantee sorted order, so we sort again
            neigh_ind = neigh_ind[
                sample_range, np.argsort(dist[sample_range, neigh_ind])]

            if return_distance:
                if self.effective_metric_ == 'euclidean':
                    result = np.sqrt(dist[sample_range, neigh_ind]), neigh_ind
                else:
                    result = dist[sample_range, neigh_ind], neigh_ind
            else:
                result = 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)
            result = Parallel(n_jobs, backend='threading')(
                delayed(self._tree.query, check_pickle=False)(
                    X[s], n_neighbors, return_distance)
                for s in gen_even_slices(X.shape[0], n_jobs)
            )
            if return_distance:
                dist, neigh_ind = tuple(zip(*result))
                result = np.vstack(dist), np.vstack(neigh_ind)
            else:
                result = np.vstack(result)
        else:
            raise ValueError("internal: _fit_method not recognized")

        if not query_is_train:
            return result
        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 = result
            else:
                neigh_ind = result

            sample_mask = neigh_ind != sample_range

            # Corner case: When the number of duplicates are more
            # than the number of neighbors, the first NN will not
            # be the sample, but a duplicate.
            # In that case mask the first duplicate.
            dup_gr_nbrs = np.all(sample_mask, axis=1)
            sample_mask[:, 0][dup_gr_nbrs] = False

            neigh_ind = np.reshape(
                neigh_ind[sample_mask], (n_samples, n_neighbors - 1))

            if return_distance:
                dist = np.reshape(
                    dist[sample_mask], (n_samples, n_neighbors - 1))
                return dist, neigh_ind
            return neigh_ind
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neighbors/base.py/KNeighborsMixin.kneighbors/original