class TestDDT(unittest.TestCase):
def setUp(self):
self.N = 15
self.D = 2
self.df = Inverse(c=0.5)
self.lm = GaussianLikelihoodModel(mu0=np.zeros(self.D),
sigma0=np.eye(self.D),
sigma=np.eye(self.D)).compile()
self.ddt = DirichletDiffusionTree(self.df, self.lm)
self.sampler = MetropolisHastingsSampler(self.ddt, np.zeros((self.N, self.D)))
self.sampler.initialize_assignments()
def test_choice(self):
stay_prob = 1.0 / self.ddt.root.tree_size
self.assertEqual(self.ddt.uniform_index(stay_prob / 2, ignore_depth=0)[0], self.ddt.root)
left_size, right_size = self.ddt.root.left.tree_size, self.ddt.root.right.tree_size
remainder = 1 - stay_prob
left_prob, right_prob = left_size / float(left_size + right_size) * remainder, \
right_size / float(left_size + right_size) * remainder
self.assertEqual(stay_prob + left_prob + right_prob, 1)
p = 1.0 / left_size / 2
self.assertEqual(self.ddt.uniform_index(stay_prob + p * left_prob, ignore_depth=0)[0], self.ddt.root.left)
p = 1.0 / right_size / 2
self.assertEqual(self.ddt.uniform_index(stay_prob + left_prob + p * right_prob, ignore_depth=0)[0], self.ddt.root.right)
def test_get_node(self):
self.assertEqual(self.ddt[()], self.ddt.root)
def test_point_index(self):
def find_point(i):
for node in self.ddt.dfs():
if {i} == node.points():
return node
for i in xrange(self.N):
self.assertEqual(self.ddt.point_index(i), find_point(i))
def test_detach_node(self):
self.assertRaises(AssertionError, lambda: self.ddt.root.detach_node())
self.assertRaises(AssertionError, lambda: self.ddt.root.left.detach_node())
self.assertRaises(AssertionError, lambda: self.ddt.root.right.detach_node())
for i in xrange(self.N):
node = self.ddt.point_index(0)
if not node.parent is self.ddt.root:
break
old_parent = node.parent
sibling = node.parent.other_child(node)
old_grandparent = old_parent.parent
parent = node.detach_node()
self.assertEqual(old_parent, parent)
self.assertEqual(node.parent, parent)
self.assertEqual(node.parent, old_parent)
self.assertEqual(node.parent, old_parent)
self.assertEqual(node.parent.parent, None)
self.assertEqual(len(node.parent.children), 1)
self.assertTrue(sibling in old_grandparent.children)
def test_sample_assignment(self):
for _ in xrange(1000):
assignment, log_prob = self.ddt.sample_assignment()
self.assertAlmostEqual(log_prob, self.ddt.log_prob_assignment(assignment), places=5)
def test_gaussian(self):
mu = np.zeros(self.D)
x = np.ones(self.D)
for t in np.arange(0, 1, 0.01):
diff = 1 - t
self.assertAlmostEqual(self.lm.calculate_transition(x, mu, 1, t),
stats.multivariate_normal(mean=mu, cov=np.eye(self.D) * diff).logpdf(x))
