seckcoder
diff --git a/‎doc/modules/neighbors.rst
Lines changed: 8 additions & 8 deletions b/‎doc/modules/neighbors.rst
Lines changed: 8 additions & 8 deletions
diff --git a/‎scikits/learn/neighbors.py
Lines changed: 10 additions & 10 deletions b/‎scikits/learn/neighbors.py
Lines changed: 10 additions & 10 deletions
@@ -22,15 +22,15 @@ in the k nearest neighbors of a point is assigned to this point.
 
 It is possible to use different nearest neighbor search strategies by
 using the keyword ``strategy``. Possible values are ``'auto'``,
-``'ball_tree'``, ``'brute'`` and ``'inplace'``. ``'ball_tree'`` will
-create an instance of :class:`BallTree` to conduct the search, which
-is usually very efficient in low-dimensional spaces. In higher
+``'ball_tree'``, ``'brute'`` and ``'brute_inplace'``. ``'ball_tree'``
+will create an instance of :class:`BallTree` to conduct the search,
+which is usually very efficient in low-dimensional spaces. In higher
 dimension, a brute-force approach is is prefered thus parameters
-``'brute'`` and ``'inplace'`` can be used . Both conduct a brute-force
-search, the difference being that ``'inplace'`` does not perform any
-precomputations, and thus is better suited for low-memory settings.
-Finally, ``'auto'`` is a simple heuristic that will guess the best
-approach based on the current dataset.
+``'brute'`` and ``'brute_inplace'`` can be used . Both conduct a
+brute-force search, the difference being that ``'brute_inplace'`` does
+not perform any precomputations, and thus is better suited for
+low-memory settings.  Finally, ``'auto'`` is a simple heuristic that
+will guess the best approach based on the current dataset.
 
 
 .. figure:: ../auto_examples/images/plot_neighbors.png
 
@@ -22,11 +22,11 @@ class NeighborsClassifier(BaseEstimator, ClassifierMixin):
     window_size : int, optional
         Window size passed to BallTree
 
-    strategy : {'auto', 'ball_tree', 'brute', 'inplace'}, optional
+    strategy : {'auto', 'ball_tree', 'brute', 'brute_inplace'}, optional
         Strategy used to compute the nearest neighbors. 'ball_tree'
-        will construct a BallTree, 'brute' and 'inplace' will perform
-        brute-force search.'auto' will guess the most appropriate based
-        on current dataset.
+        will construct a BallTree, 'brute' and 'brute_inplace' will
+        perform brute-force search.'auto' will guess the most
+        appropriate based on current dataset.
 
     Examples
     --------
@@ -160,7 +160,7 @@ def predict(self, X, **params):
 
         # .. get neighbors ..
         if self.ball_tree is None:
-            if self.strategy == 'inplace':
+            if self.strategy == 'brute_inplace':
                 neigh_ind = knn_brute(self._fit_X, X, self.n_neighbors)
             else:
                 from .metrics import euclidean_distances
@@ -203,11 +203,11 @@ class NeighborsRegressor(NeighborsClassifier, RegressorMixin):
     mode : {'mean', 'barycenter'}, optional
         Weights to apply to labels.
 
-    strategy : {'auto', 'ball_tree', 'brute', 'inplace'}, optional
+    strategy : {'auto', 'ball_tree', 'brute', 'brute_inplace'}, optional
         Strategy used to compute the nearest neighbors. 'ball_tree'
-        will construct a BallTree, 'brute' and 'inplace' will perform
-        brute-force search.'auto' will guess the most appropriate based
-        on current dataset.
+        will construct a BallTree, 'brute' and 'brute_inplace' will
+        perform brute-force search.'auto' will guess the most
+        appropriate based on current dataset.
 
     Examples
     --------
@@ -256,7 +256,7 @@ def predict(self, X, **params):
 
         # .. get neighbors ..
         if self.ball_tree is None:
-            if self.strategy == 'inplace':
+            if self.strategy == 'brute_inplace':
                 neigh_ind = knn_brute(self._fit_X, X, self.n_neighbors)
             else:
                 from .metrics.pairwise import euclidean_distances