def test_random_no_limits(self):
# We begin by getting some data and building with no constraints.
data = np.random.randn(100, 3)
manifold = Manifold(data, 'euclidean').build()
# With no constraints, clusters should be singletons.
self.assertEqual(data.shape[0], manifold.graph.population)
self.assertEqual(1, len(manifold.find_clusters(data[0], 0., -1)))
self.assertEqual(1, len(manifold.find_points(data[0], 0.)))
self.assertEqual(data.shape[0], manifold.layers[-1].cardinality)
return
def test_random_large(self):
data = np.random.randn(1000, 3)
manifold = Manifold(data, 'euclidean').build(
criterion.MaxDepth(10),
criterion.LFDRange(60, 50),
)
for _ in range(10):
point = int(np.random.choice(3))
linear_results = linear_search(data[point], 0.5, data,
manifold.metric)
self.assertEqual(len(linear_results),
len(manifold.find_points(data[point], 0.5)))
return
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