DOC: Explain why the centroid of the manhattan metric is the median

TamChen · Oct 21, 2014 · 3e7b816 · 3e7b816
1 parent 9622e85
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diff --git a/sklearn/neighbors/nearest_centroid.py b/sklearn/neighbors/nearest_centroid.py
@@ -32,6  32,11 @@ class NearestCentroid(BaseEstimator, ClassifierMixin):
         feature array. If metric is a string or callable, it must be one of
         the options allowed by metrics.pairwise.pairwise_distances for its
         metric parameter.
         The centroids for the samples corresponding to each class is the point
         from which the sum of the distances (according to the metric) of all
         samples that belong to that particular class are minimized.
         If the "manhattan" metric is provided, this centroid is the median and
         for all other metrics, the centroid is now set to be the mean.
     shrink_threshold : float, optional (default = None)
         Threshold for shrinking centroids to remove features.
 

diff --git a/sklearn/utils/sparsefuncs.py b/sklearn/utils/sparsefuncs.py
@@ -384,7  384,7 @@ def csc_row_median(X):
 
     Returns
     -------
-    median : ndarray, shape = (n_features,)
     median : ndarray, shape (n_features,)
         Median. 
 
     """
-Original file line number
+Diff line change
@@ Expand Up / @@ -384,7 384,7 @@ def csc_row_median(X): @@
         Returns
         -------
-        median : ndarray, shape = (n_features,)
+        median : ndarray, shape (n_features,)
             Median.
         """
@@ Expand Down @@