def test_all_same(self):
# A bit simpler, every point is the same.
data = np.ones((1000, 3))
manifold = Manifold(data, 'euclidean').build()
# There should only ever be one cluster here.
self.assertEqual(1, len(manifold.layers))
manifold.build_tree()
# Even after explicit deepen calls.
self.assertEqual(1, len(manifold.layers))
self.assertEqual(
1, len(manifold.find_clusters(np.asarray([1, 1, 1]), 0.0, -1)))
# And, we should get all 1000 points back for any of the data.
self.assertEqual(1000, len(manifold.find_points(data[0], 0.0)))
return
def test_build_tree(self):
m = Manifold(self.data, 'euclidean')
self.assertEqual(1, len(m.layers))
m.build_tree(criterion.AddLevels(2))
self.assertEqual(3, len(m.layers))
# MaxDepth shouldn't do anything in build_tree if we're beyond that depth already.
m.build_tree(criterion.MaxDepth(1))
self.assertEqual(3, len(m.layers))
m.build_tree()
self.assertEqual(len(self.data), m.layers[-1].cardinality)
return