def test_example_clustering_on(self, data):
constructors = [Euclidean, Manhattan]
for distance_constructor in constructors:
clust = clustering(data, distance_constructor, HierarchicalClustering.Single)
clust = clustering(data, distance_constructor, HierarchicalClustering.Average)
clust = clustering(data, distance_constructor, HierarchicalClustering.Complete)
clust = clustering(data, distance_constructor, HierarchicalClustering.Ward)
top_clust = top_clusters(clust, 5)
cluster_list = cluster_to_list(clust, 5)
def test_example_clustering_on(self, data):
constructors = [Euclidean, Manhattan]
for distance_constructor in constructors:
clust = clustering(data, distance_constructor,
HierarchicalClustering.Single)
clust = clustering(data, distance_constructor,
HierarchicalClustering.Average)
clust = clustering(data, distance_constructor,
HierarchicalClustering.Complete)
clust = clustering(data, distance_constructor,
HierarchicalClustering.Ward)
top_clust = top_clusters(clust, 5)
cluster_list = cluster_to_list(clust, 5)
def test_attribute_clustering_on(self, data):
clust = clustering_features(data)
cluster_list = cluster_to_list(clust, 5)
def test_attribute_clustering_on(self, data):
clust = clustering_features(data)
cluster_list = cluster_to_list(clust, 5)
