def eye_color(person):
random_variables = {x.name: x for x in
tf.get_collection('_random_variable_collection_')}
if person + '/' in random_variables:
return random_variables[person + '/']
else:
return Categorical(
logits=ed.logit(tf.constant([1.0 / 3] * 3)), name=person)
def eye_color(person):
random_variables = {
x.name: x
for x in tf.get_collection('_random_variable_collection_')
}
if person + '/' in random_variables:
return random_variables[person + '/']
else:
return Categorical(logits=ed.logit(tf.constant([1.0 / 3] * 3)),
name=person)
return x
N = 500 # num data points
K = 2 # num components
D = 2 # dimensionality of data
ed.set_seed(42)
# DATA
x_data = build_toy_dataset(N)
# MODEL
pi = Dirichlet(alpha=tf.constant([1.0] * K))
mu = Normal(mu=tf.zeros([K, D]), sigma=tf.ones([K, D]))
sigma = InverseGamma(alpha=tf.ones([K, D]), beta=tf.ones([K, D]))
c = Categorical(logits=ed.tile(ed.logit(pi), [N, 1]))
x = Normal(mu=tf.gather(mu, c), sigma=tf.gather(sigma, c))
# INFERENCE
T = 5000
qpi = Empirical(params=tf.Variable(tf.ones([T, K]) / K))
qmu = Empirical(params=tf.Variable(tf.zeros([T, K, D])))
qsigma = Empirical(params=tf.Variable(tf.ones([T, K, D])))
qc = Empirical(params=tf.Variable(tf.zeros([T, N], dtype=tf.int32)))
gpi = Dirichlet(alpha=tf.constant([1.4, 1.6]))
gmu = Normal(mu=tf.constant([[1.0, 1.0], [-1.0, -1.0]]),
sigma=tf.constant([[0.5, 0.5], [0.5, 0.5]]))
gsigma = InverseGamma(alpha=tf.constant([[1.1, 1.1], [1.1, 1.1]]),
beta=tf.constant([[1.0, 1.0], [1.0, 1.0]]))
gc = Categorical(logits=tf.zeros([N, K]))
return x
N = 500 # number of data points
K = 2 # number of components
D = 2 # dimensionality of data
ed.set_seed(42)
# DATA
x_data = build_toy_dataset(N)
# MODEL
pi = Dirichlet(alpha=tf.constant([1.0] * K))
mu = Normal(mu=tf.zeros([K, D]), sigma=tf.ones([K, D]))
sigma = InverseGamma(alpha=tf.ones([K, D]), beta=tf.ones([K, D]))
c = Categorical(logits=tf.tile(tf.reshape(ed.logit(pi), [1, K]), [N, 1]))
x = Normal(mu=tf.gather(mu, c), sigma=tf.gather(sigma, c))
# INFERENCE
T = 5000
qpi = Empirical(params=tf.Variable(tf.ones([T, K]) / K))
qmu = Empirical(params=tf.Variable(tf.zeros([T, K, D])))
qsigma = Empirical(params=tf.Variable(tf.ones([T, K, D])))
qc = Empirical(params=tf.Variable(tf.zeros([T, N], dtype=tf.int32)))
gpi = Dirichlet(alpha=tf.constant([1.4, 1.6]))
gmu = Normal(mu=tf.constant([[1.0, 1.0], [-1.0, -1.0]]),
sigma=tf.constant([[0.5, 0.5], [0.5, 0.5]]))
gsigma = InverseGamma(alpha=tf.constant([[1.1, 1.1], [1.1, 1.1]]),
beta=tf.constant([[1.0, 1.0], [1.0, 1.0]]))
gc = Categorical(logits=tf.zeros([N, K]))
