[Scipy-svn] r4189 - trunk/scipy/cluster

scipy-svn@scip... scipy-svn@scip...
Sun Apr 27 07:53:46 CDT 2008


Author: damian.eads
Date: 2008-04-27 07:53:43 -0500 (Sun, 27 Apr 2008)
New Revision: 4189

Modified:
   trunk/scipy/cluster/vq.py
Log:
Tightening up the language of vq's module summary.

Modified: trunk/scipy/cluster/vq.py
===================================================================
--- trunk/scipy/cluster/vq.py	2008-04-27 12:52:45 UTC (rev 4188)
+++ trunk/scipy/cluster/vq.py	2008-04-27 12:53:43 UTC (rev 4189)
@@ -12,13 +12,13 @@
 
     A vector v belongs to cluster i if it is closer to centroid i than
     the other centroids. If v belongs to i, we say centroid i is the
-    dominating centroid of v. Most variants of k-means try to minimize
-    distortion, which is defined as the sum of the distances between
-    each observation vector and its dominating centroid.  Each step of
-    the k-means algorithm refines the choices of centroids to reduce
-    distortion. The change in distortion is often used as a stopping
-    criterion: when the change is lower than a threshold, the k-means
-    algorithm is not making progress and terminates.
+    dominating centroid of v. Common variants of k-means try to
+    minimize distortion, which is defined as the sum of the distances
+    between each observation vector and its dominating centroid.  Each
+    step of the k-means algorithm refines the choices of centroids to
+    reduce distortion. The change in distortion is often used as a
+    stopping criterion: when the change is lower than a threshold, the
+    k-means algorithm is not making progress and terminates.
 
     Since vector quantization is a natural application for k-means,
     information theory terminology is often used.  The centroid index



More information about the Scipy-svn mailing list