class AutoRegressive(RandomVariable, Distribution):
# a 1-D AR(1) process
# a[t + 1] = a[t] + eps with eps ~ N(0, sig**2)
def __init__(self, T, a, sig, *args, **kwargs):
self.a = a
self.sig = sig
self.T = T
self.shocks = Normal(tf.zeros(T), scale=sig)
self.z = tf.scan(lambda acc, x: self.a * acc + x, self.shocks)
if 'dtype' not in kwargs:
kwargs['dtype'] = tf.float32
if 'allow_nan_stats' not in kwargs:
kwargs['allow_nan_stats'] = False
if 'reparameterization_type' not in kwargs:
kwargs['reparameterization_type'] = FULLY_REPARAMETERIZED
if 'validate_args' not in kwargs:
kwargs['validate_args'] = False
if 'name' not in kwargs:
kwargs['name'] = 'AutoRegressive'
super(AutoRegressive, self).__init__(*args, **kwargs)
self._args = (T, a, sig)
def _log_prob(self, value):
err = value - self.a * tf.pad(value[:-1], [[1, 0]], 'CONSTANT')
lpdf = self.shocks._log_prob(err)
return tf.reduce_sum(lpdf)
def _sample_n(self, n, seed=None):
return tf.scan(lambda acc, x: self.a * acc + x,
self.shocks._sample_n(n, seed))
import edward as ed
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import six
import tensorflow as tf
from edward.models import Categorical, Dirichlet, Empirical, InverseGamma, \
MultivariateNormalDiag, Normal, ParamMixture
D=3
K=10
x = Normal(tf.zeros(D),
tf.ones(D),
sample_shape=K)
print(x)
#s=ed.Gibbs({x:x})
#inference.initialize()
sess = ed.get_session()
#tf.global_variables_initializer().run()
print(sess.run(x._sample_n(10)))