def test_blanket_changes(self):
pi = rvs.Dirichlet(tf.ones(3))
mu = rvs.Normal(0.0, 1.0)
z = rvs.Categorical(p=pi)
pi1_cond = ed.complete_conditional(pi, [z, pi])
pi2_cond = ed.complete_conditional(pi, [z, mu, pi])
self.assertIsInstance(pi1_cond, rvs.Dirichlet)
self.assertIsInstance(pi2_cond, rvs.Dirichlet)
with self.test_session() as sess:
alpha1_val, alpha2_val = sess.run([pi1_cond.alpha, pi2_cond.alpha])
self.assertAllClose(alpha1_val, alpha2_val)
def test_dirichlet_categorical(self):
x_data = np.array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3], np.int32)
N = x_data.shape[0]
D = x_data.max() + 1
alpha = np.zeros(D).astype(np.float32) + 2.0
theta = rvs.Dirichlet(alpha)
x = rvs.Categorical(p=theta, sample_shape=N)
theta_cond = ed.complete_conditional(theta, [theta, x])
with self.test_session() as sess:
alpha_val = sess.run(theta_cond.alpha, {x: x_data})
self.assertAllClose(alpha_val, np.array([6.0, 5.0, 4.0, 3.0], np.float32))
import tensorflow as tf
import edward as ed
import edward.models as edm
# node names in lexicographic (alphabetic) order
nd_names_lex_ord = ['Cloudy', 'Rain', 'Sprinkler', 'WetGrass']
# node names in topological (chronological) order
nd_names_topo_ord = ['Cloudy', 'Rain', 'Sprinkler', 'WetGrass']
with tf.name_scope('model'):
arr_Cloudy = np.array([ 0.5, 0.5])
ten_Cloudy = tf.convert_to_tensor(arr_Cloudy, dtype=tf.float32)
p_Cloudy = ten_Cloudy[:]
Cloudy = edm.Categorical(
probs=p_Cloudy, name='Cloudy')
arr_Rain = np.array([[ 0.4, 0.6],
[ 0.5, 0.5]])
ten_Rain = tf.convert_to_tensor(arr_Rain, dtype=tf.float32)
p_Rain = ten_Rain[Cloudy, :]
Rain = edm.Categorical(
probs=p_Rain, name='Rain')
arr_Sprinkler = np.array([[ 0.2, 0.8],
[ 0.7, 0.3]])
ten_Sprinkler = tf.convert_to_tensor(arr_Sprinkler, dtype=tf.float32)
p_Sprinkler = ten_Sprinkler[Cloudy, :]
Sprinkler = edm.Categorical(
probs=p_Sprinkler, name='Sprinkler')
import edward.models as edm
# node names in lexicographic (alphabetic) order
nd_names_lex_ord = ['Cloudy', 'Rain', 'Sprinkler', 'WetGrass']
# node names in topological (chronological) order
nd_names_topo_ord = ['Cloudy', 'Rain', 'Sprinkler', 'WetGrass']
with tf.name_scope('model'):
Cloudy = tf.placeholder(tf.int32, shape=[sam_size], name="Cloudy")
alpha_Rain = np.array([[1., 1.], [1., 1.]])
probs_Rain = edm.Dirichlet(alpha_Rain.astype(np.float32),
name='probs_Rain')
p_Rain = tf.stack([probs_Rain[Cloudy[j], :] for j in range(sam_size)])
Rain = edm.Categorical(probs=p_Rain, name='Rain')
alpha_Sprinkler = np.array([[1., 1.], [1., 1.]])
probs_Sprinkler = edm.Dirichlet(alpha_Sprinkler.astype(np.float32),
name='probs_Sprinkler')
p_Sprinkler = tf.stack(
[probs_Sprinkler[Cloudy[j], :] for j in range(sam_size)])
Sprinkler = edm.Categorical(probs=p_Sprinkler, name='Sprinkler')
arr_WetGrass = np.array([[[0.99, 0.01], [0.01, 0.99]],
[[0.01, 0.99], [0.01, 0.99]]])
ten_WetGrass = tf.convert_to_tensor(arr_WetGrass, dtype=tf.float32)
p_WetGrass = tf.stack(
[ten_WetGrass[Sprinkler[j], Rain[j], :] for j in range(sam_size)])
WetGrass = edm.Categorical(probs=p_WetGrass, name='WetGrass')