update examples

shogun-toolbox · Dec 23, 2014 · 28313eb · 28313eb
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diff --git a/examples/undocumented/libshogun/clustering_kmeans.cpp b/examples/undocumented/libshogun/clustering_kmeans.cpp
@@ -19,7 +19,6 @@
 #include <shogun/features/DenseFeatures.h>
 #include <shogun/clustering/KMeans.h>
 #include <shogun/clustering/KMeansMiniBatch.h>
-#include <shogun/clustering/KMeansLloyd.h>
 #include <shogun/distance/EuclideanDistance.h>
 #include <shogun/distance/MinkowskiMetric.h>
 

diff --git a/examples/undocumented/python_modular/clustering_kmeans_modular.py b/examples/undocumented/python_modular/clustering_kmeans_modular.py
@@ -4,13 +4,13 @@
 parameter_list = [[traindat,3],[traindat,4]]
 
 def clustering_kmeans_modular (fm_train=traindat,k=3):
-	from modshogun import EuclideanDistance, RealFeatures, KMeansLloyd, Math_init_random, CSVFile
+	from modshogun import EuclideanDistance, RealFeatures, KMeans, Math_init_random, CSVFile
 	Math_init_random(17)
 
 	feats_train=RealFeatures(CSVFile(fm_train))
 	distance=EuclideanDistance(feats_train, feats_train)
 
-	kmeans=KMeansLloyd(k, distance)
+	kmeans=KMeans(k, distance)
 	kmeans.train()
 
 	out_centers = kmeans.get_cluster_centers()