def test_tensor(self):
with self.test_session():
a = Normal(0.0, 1.0)
b = tf.constant(2.0)
c = a + b
d = Normal(c, 1.0)
self.assertEqual(get_ancestors(a), [])
self.assertEqual(get_ancestors(b), [])
self.assertEqual(get_ancestors(c), [a])
self.assertEqual(get_ancestors(d), [a])
def test_tensor(self):
with self.test_session():
a = Normal(mu=0.0, sigma=1.0)
b = tf.constant(2.0)
c = a + b
d = Normal(mu=c, sigma=1.0)
self.assertEqual(get_ancestors(a), [])
self.assertEqual(get_ancestors(b), [])
self.assertEqual(get_ancestors(c), [a])
self.assertEqual(get_ancestors(d), [a])
def test_control_flow(self):
with self.test_session():
a = Bernoulli(p=0.5)
b = Normal(mu=0.0, sigma=1.0)
c = tf.constant(0.0)
d = tf.cond(tf.cast(a, tf.bool), lambda: b, lambda: c)
e = Normal(mu=d, sigma=1.0)
self.assertEqual(get_ancestors(a), [])
self.assertEqual(get_ancestors(b), [])
self.assertEqual(get_ancestors(c), [])
self.assertEqual(set(get_ancestors(d)), set([a, b]))
self.assertEqual(set(get_ancestors(e)), set([a, b]))
def test_chain_structure(self):
with self.test_session():
a = Normal(mu=0.0, sigma=1.0)
b = Normal(mu=a, sigma=1.0)
c = Normal(mu=b, sigma=1.0)
d = Normal(mu=c, sigma=1.0)
e = Normal(mu=d, sigma=1.0)
self.assertEqual(get_ancestors(a), [])
self.assertEqual(get_ancestors(b), [a])
self.assertEqual(set(get_ancestors(c)), set([a, b]))
self.assertEqual(set(get_ancestors(d)), set([a, b, c]))
self.assertEqual(set(get_ancestors(e)), set([a, b, c, d]))
def test_v_structure(self):
with self.test_session():
a = Normal(mu=0.0, sigma=1.0)
b = Normal(mu=a, sigma=1.0)
c = Normal(mu=0.0, sigma=1.0)
d = Normal(mu=c, sigma=1.0)
e = Normal(mu=tf.multiply(b, d), sigma=1.0)
self.assertEqual(get_ancestors(a), [])
self.assertEqual(get_ancestors(b), [a])
self.assertEqual(get_ancestors(c), [])
self.assertEqual(get_ancestors(d), [c])
self.assertEqual(set(get_ancestors(e)), set([a, b, c, d]))
def test_control_flow(self):
with self.test_session():
a = Bernoulli(0.5)
b = Normal(0.0, 1.0)
c = tf.constant(0.0)
d = tf.cond(tf.cast(a, tf.bool), lambda: b, lambda: c)
e = Normal(d, 1.0)
self.assertEqual(get_ancestors(a), [])
self.assertEqual(get_ancestors(b), [])
self.assertEqual(get_ancestors(c), [])
self.assertEqual(set(get_ancestors(d)), set([a, b]))
self.assertEqual(set(get_ancestors(e)), set([a, b]))
def test_chain_structure(self):
"""a -> b -> c -> d -> e"""
with self.test_session():
a = Normal(0.0, 1.0)
b = Normal(a, 1.0)
c = Normal(b, 1.0)
d = Normal(c, 1.0)
e = Normal(d, 1.0)
self.assertEqual(get_ancestors(a), [])
self.assertEqual(get_ancestors(b), [a])
self.assertEqual(set(get_ancestors(c)), set([a, b]))
self.assertEqual(set(get_ancestors(d)), set([a, b, c]))
self.assertEqual(set(get_ancestors(e)), set([a, b, c, d]))
def test_a_structure(self):
"""e <- d <- a -> b -> c"""
with self.test_session():
a = Normal(0.0, 1.0)
b = Normal(a, 1.0)
c = Normal(b, 1.0)
d = Normal(a, 1.0)
e = Normal(d, 1.0)
self.assertEqual(get_ancestors(a), [])
self.assertEqual(get_ancestors(b), [a])
self.assertEqual(set(get_ancestors(c)), set([a, b]))
self.assertEqual(get_ancestors(d), [a])
self.assertEqual(set(get_ancestors(e)), set([a, d]))
def test_v_structure(self):
"""a -> b -> e <- d <- c"""
with self.test_session():
a = Normal(0.0, 1.0)
b = Normal(a, 1.0)
c = Normal(0.0, 1.0)
d = Normal(c, 1.0)
e = Normal(b * d, 1.0)
self.assertEqual(get_ancestors(a), [])
self.assertEqual(get_ancestors(b), [a])
self.assertEqual(get_ancestors(c), [])
self.assertEqual(get_ancestors(d), [c])
self.assertEqual(set(get_ancestors(e)), set([a, b, c, d]))
def test_scan(self):
"""copied from test_chain_structure"""
def cumsum(x):
return tf.scan(lambda a, x: a + x, x)
with self.test_session():
a = Normal(tf.ones([3]), tf.ones([3]))
b = Normal(cumsum(a), tf.ones([3]))
c = Normal(cumsum(b), tf.ones([3]))
d = Normal(cumsum(c), tf.ones([3]))
e = Normal(cumsum(d), tf.ones([3]))
self.assertEqual(get_ancestors(a), [])
self.assertEqual(get_ancestors(b), [a])
self.assertEqual(set(get_ancestors(c)), set([a, b]))
self.assertEqual(set(get_ancestors(d)), set([a, b, c]))
self.assertEqual(set(get_ancestors(e)), set([a, b, c, d]))
