def test_prunning(self):
pruned = hierarchical.prune(self.cluster, level=2)
depths = hierarchical.cluster_depths(pruned)
self.assertTrue(all(d <= 2 for d in depths.values()))
pruned = hierarchical.prune(self.cluster, height=10)
self.assertTrue(c.height >= 10 for c in hierarchical.preorder(pruned))
top = hierarchical.top_clusters(self.cluster, 3)
self.assertEqual(len(top), 3)
top = hierarchical.top_clusters(self.cluster, len(self.matrix))
self.assertEqual(len(top), len(self.matrix))
self.assertTrue(all(n.is_leaf for n in top))
top1 = hierarchical.top_clusters(self.cluster, len(self.matrix) + 1)
self.assertEqual(top1, top)
def test_prunning(self):
pruned = hierarchical.prune(self.cluster, level=2)
depths = hierarchical.cluster_depths(pruned)
self.assertTrue(all(d <= 2 for d in depths.values()))
pruned = hierarchical.prune(self.cluster, height=10)
self.assertTrue(c.height >= 10 for c in hierarchical.preorder(pruned))
top = hierarchical.top_clusters(self.cluster, 3)
self.assertEqual(len(top), 3)
top = hierarchical.top_clusters(self.cluster, len(self.matrix))
self.assertEqual(len(top), len(self.matrix))
self.assertTrue(all(n.is_leaf for n in top))
top1 = hierarchical.top_clusters(self.cluster, len(self.matrix) + 1)
self.assertEqual(top1, top)
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 select_top_n(self, n):
root = self._displayed_root
if root:
clusters = top_clusters(root, n)
self.dendrogram.set_selected_clusters(clusters)
def select_top_n(self, n):
root = self._displayed_root
if root:
clusters = top_clusters(root, n)
self.dendrogram.set_selected_clusters(clusters)