def main(_):
# DATA
pi_true = np.random.dirichlet(np.array([20.0, 30.0, 10.0, 10.0]))
z_data = np.array([np.random.choice(FLAGS.K, 1, p=pi_true)[0]
for n in range(FLAGS.N)])
print("pi: {}".format(pi_true))
# MODEL
pi = Dirichlet(tf.ones(4))
z = Categorical(probs=pi, sample_shape=FLAGS.N)
# INFERENCE
qpi = Dirichlet(tf.nn.softplus(
tf.get_variable("qpi/concentration", [FLAGS.K])))
inference = ed.KLqp({pi: qpi}, data={z: z_data})
inference.run(n_iter=1500, n_samples=30)
sess = ed.get_session()
print("Inferred pi: {}".format(sess.run(qpi.mean())))
def main(_):
# DATA
pi_true = np.random.dirichlet(np.array([20.0, 30.0, 10.0, 10.0]))
z_data = np.array(
[np.random.choice(FLAGS.K, 1, p=pi_true)[0] for n in range(FLAGS.N)])
print("pi: {}".format(pi_true))
# MODEL
pi = Dirichlet(tf.ones(4))
z = Categorical(probs=pi, sample_shape=FLAGS.N)
# INFERENCE
qpi = Dirichlet(
tf.nn.softplus(tf.get_variable("qpi/concentration", [FLAGS.K])))
inference = ed.KLqp({pi: qpi}, data={z: z_data})
inference.run(n_iter=1500, n_samples=30)
sess = ed.get_session()
print("Inferred pi: {}".format(sess.run(qpi.mean())))
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import edward as ed
import numpy as np
import tensorflow as tf
from edward.models import Categorical, Dirichlet
N = 1000
K = 4
# DATA
pi_true = np.random.dirichlet(np.array([20.0, 30.0, 10.0, 10.0]))
z_data = np.array([np.random.choice(K, 1, p=pi_true)[0] for n in range(N)])
print('pi={}'.format(pi_true))
# MODEL
pi = Dirichlet(alpha=tf.ones(4))
z = Categorical(p=tf.ones([N, 1]) * pi)
# INFERENCE
qpi = Dirichlet(alpha=tf.nn.softplus(tf.Variable(tf.random_normal([K]))))
inference = ed.KLqp({pi: qpi}, data={z: z_data})
inference.run(n_iter=1500, n_samples=30)
sess = ed.get_session()
print('Inferred pi={}'.format(sess.run(qpi.mean())))
scale=tf.nn.softplus(tf.Variable(tf.random_normal([K, D]))))
qsigma = InverseGamma(concentration=tf.nn.softplus(
tf.Variable(tf.random_normal([K, D]))),
rate=tf.nn.softplus(tf.Variable(tf.random_normal([K,
D]))))
qc = Categorical(logits=tf.Variable(tf.zeros([N, K])))
inference = ed.KLqp(latent_vars={
pi: qpi,
mu: qmu,
sigma: qsigma,
c: qc
},
data={x: x_data})
inference.initialize(n_iter=10000, n_samples=200)
sess = ed.get_session()
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
info_dict = inference.update()
inference.print_progress(info_dict)
t = info_dict['t']
if t == 1 or t % inference.n_print == 0:
qpi_mean, qmu_mean, qsigma_mean = \
sess.run([qpi.mean(), qmu.mean(), qsigma.mean()])
print('\nInferred membership probabilities: {}'.format(qpi_mean))
print('Inferred cluster means: {}'.format(qmu_mean))
print('Inferred sigmas: {}'.format(qsigma_mean))