Update neighbors with latest changes to BallTree.

scikits/learn/neighbors.py

@@ -23,11 +23,10 @@ class NeighborsClassifier(BaseEstimator, ClassifierMixin):
     window_size : int, optional
         Window size passed to BallTree
 
-    algorithm : {'auto', 'ball_tree', 'brute', 'brute_inplace'}, optional
-        Algorithm used to compute the nearest neighbors. 'ball_tree'
-        will construct a BallTree, 'brute' and 'brute_inplace' will
-        perform brute-force search.'auto' will guess the most
-        appropriate based on current dataset.
+    algorithm : {'auto', 'ball_tree', 'brute'}, optional
+       Algorithm used to compute the nearest neighbors. 'ball_tree' will
+       construct a BallTree while 'brute'will perform brute-force
+       search. 'auto' will guess the most appropriate based on current dataset.
 
     Examples
     --------
@@ -136,9 +135,6 @@ class NeighborsClassifier(BaseEstimator, ClassifierMixin):
         self._set_params(**params)
         X = np.atleast_2d(X)
         if self.ball_tree is None:
-            if self.algorithm == 'brute_inplace' and not return_distance:
-                return knn_brute(self._fit_X, X, self.n_neighbors)
-            else:
             dist = euclidean_distances(X, self._fit_X, squared=True)
             # XXX: should be implemented with a partial sort
             neigh_ind = dist.argsort(axis=1)[:, :self.n_neighbors]
@@ -205,11 +201,11 @@ class NeighborsRegressor(NeighborsClassifier, RegressorMixin):
     mode : {'mean', 'barycenter'}, optional
         Weights to apply to labels.
 
-    algorithm : {'auto', 'ball_tree', 'brute', 'brute_inplace'}, optional
-        Algorithm used to compute the nearest neighbors. 'ball_tree'
-        will construct a BallTree, 'brute' and 'brute_inplace' will
-        perform brute-force search.'auto' will guess the most
-        appropriate based on current dataset.
+    algorithm : {'auto', 'ball_tree', 'brute'}, optional
+        Algorithm used to compute the nearest neighbors. 'ball_tree' will
+        construct a BallTree, while 'brute' will perform brute-force
+        search. 'auto' will guess the most appropriate based on current
+        dataset.
 
     Examples
     --------