khyatimahendru · June 17, 2019 06:41
diff --git a/silhouette_score.py b/silhouette_score.py
 from sklearn.metrics import silhouette_score

 sil = []
 kmax = 10

 # dissimilarity would not be defined for a single cluster, thus, minimum number of clusters should be 2
 for k in range(2, kmax+1):
  kmeans = KMeans(n_clusters = k).fit(x)
  labels = kmeans.labels_
  sil.append(silhouette_score(x, labels, metric = 'euclidean'))
	from sklearn.metrics import silhouette_score

	sil = []
	kmax = 10

	# dissimilarity would not be defined for a single cluster, thus, minimum number of clusters should be 2
	for k in range(2, kmax+1):
	kmeans = KMeans(n_clusters = k).fit(x)
	labels = kmeans.labels_
	sil.append(silhouette_score(x, labels, metric = 'euclidean'))
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