def func(alpha, nClusters=nClusters):
alpha1, alpha2 = alpha[:self.nVars], alpha[self.nVars:]
alpha1 = alpha1.reshape((self.nVars, 1))
alpha2 = alpha2.reshape(self.nVars, 1)
alpha = numpy.append(alpha1, alpha2, axis=1)
smpl = numpy.dot(self.Z, alpha)
smpl = orange.ExampleTable(smpl)
km = orngClustering.KMeans(smpl, centroids=nClusters)
score = orngClustering.score_silhouette(km)
return -score
def func(alpha, nClusters=nClusters):
alpha1, alpha2 = alpha[:self.nVars], alpha[self.nVars:]
alpha1 = alpha1.reshape((self.nVars,1))
alpha2 = alpha2.reshape(self.nVars,1)
alpha = numpy.append(alpha1, alpha2, axis=1)
smpl = numpy.dot(self.Z, alpha)
smpl = orange.ExampleTable(smpl)
km = orngClustering.KMeans(smpl, centroids=nClusters)
score = orngClustering.score_silhouette(km)
return -score
import orange
import orngClustering
data = orange.ExampleTable("voting")
# data = orange.ExampleTable("iris")
for k in range(2, 5):
km = orngClustering.KMeans(data, k, initialization=orngClustering.kmeans_init_diversity)
score = orngClustering.score_silhouette(km)
print k, score
km = orngClustering.KMeans(data, 3, initialization=orngClustering.kmeans_init_diversity)
orngClustering.plot_silhouette(km, "kmeans-silhouette.png")
import orange
import orngClustering
import random
data = orange.ExampleTable("iris")
# data = orange.ExampleTable("lung-cancer")
bestscore = 0
for k in range(2, 10):
random.seed(42)
km = orngClustering.KMeans(
data,
k,
initialization=orngClustering.KMeans_init_hierarchicalClustering(n=50),
nstart=10)
score = orngClustering.score_silhouette(km)
print "%d: %.3f" % (k, score)
if score > bestscore:
best_km = km
bestscore = score
orngClustering.plot_silhouette(best_km, filename='tmp.png')
