Python 3 compatibility fixes

edward/inferences/conjugacy/conjugacy.py

@@ -98,7 +98,7 @@ def complete_conditional(rv, blanket, log_joint=None):
 
     # Simplify those nodes, and extract any new linear terms into multipliers_i.
     s_stat_exprs = defaultdict(list)
-    for i in xrange(len(s_stats)):
+    for i in range(len(s_stats)):
       expr = symbolic_suff_stat(s_stats[i], rv.value(), stop_nodes)
       expr = full_simplify(expr)
       multipliers_i, s_stats_i = extract_s_stat_multipliers(expr)
@@ -152,7 +152,7 @@ def complete_conditional(rv, blanket, log_joint=None):
     nat_params = tf.gradients(log_joint_copy, s_stat_placeholders)
 
     # Removes any dependencies on those old placeholders.
-    for i in xrange(len(nat_params)):
+    for i in range(len(nat_params)):
       nat_params[i] = edward.util.copy(nat_params[i], swap_back,
                                        scope=scope + 'swapback')
     nat_params = [nat_params[i] for i in order]

edward/inferences/conjugacy/simplify.py

@@ -202,7 +202,7 @@ def mul_add_simplify(expr):
   '''Turns Mul(Add(.), .) into Add(Mul(.), Mul(.),...)'''
   if expr[0] != '#Mul':
     return None
-  for i in xrange(1, len(expr)):
+  for i in range(1, len(expr)):
     if expr[i][0] == '#Add':
       other_args = expr[1:i] + expr[i + 1:]
       return ('#Add',) + tuple((('#Mul',) + other_args + (j,)
@@ -277,9 +277,9 @@ def square_add_simplify(expr):
   if not (expr[0] == '#CPow2.0000e+00' and expr[1][0] == '#Add'):
     return None
   terms = []
-  for i in xrange(1, len(expr[1])):
+  for i in range(1, len(expr[1])):
     terms.append(('#CPow2.0000e+00', expr[1][i]))
-    for j in xrange(i + 1, len(expr[1])):
+    for j in range(i + 1, len(expr[1])):
       terms.append(('#Mul', ('2.0',), expr[1][i], expr[1][j]))
   return ('#Add',) + tuple(terms)
 
@@ -300,7 +300,7 @@ def add_const_simplify(expr):
     return None
   did_something = False
   new_args = []
-  for i in xrange(1, len(expr)):
+  for i in range(1, len(expr)):
     if expr_contains(expr[i], '#x'):
       new_args.append(expr[i])
     else:

edward/models/param_mixture.py

@@ -19,8 +19,8 @@ def __init__(self,
                allow_nan_stats=True,
                name="ParamMixture"):
     parameters = locals()
-    with tf.name_scope(name, values=[mixing_weights] +
-                       component_params.values()) as name:
+    values = [mixing_weights] + [i for i in component_params.values()]
+    with tf.name_scope(name, values=values) as name:
       self._mixing_weights = mixing_weights
       self._component_params = component_params
       self._cat = Categorical(p=self._mixing_weights,

examples/mixture_gaussian_gibbs.py

@@ -62,7 +62,7 @@
 cond_dict = {pi: pi_est, mu: mu_est, sigmasq: sigmasq_est, z: z_est, x: x_val}
 t0 = time()
 T = 500
-for t in xrange(T):
+for t in range(T):
   z_est = sess.run(z_cond, cond_dict)
   cond_dict[z] = z_est
   pi_est, mu_est = sess.run([pi_cond, mu_cond], cond_dict)

tests/test-inferences/test_conjugacy.py

@@ -197,7 +197,7 @@ def test_mog(self):
 
     true_pi = pi_alpha + np.unique(z_val, return_counts=True)[1]
     self.assertAllClose(pi_cond_alpha, true_pi)
-    for k in xrange(K):
+    for k in range(K):
       sigmasq_true = (1. / 4**2 + 1. / sigmasq * (z_val == k).sum())**-1
       mu_true = sigmasq_true * (1. / sigmasq * x_val[z_val == k].sum())
       self.assertAllClose(np.sqrt(sigmasq_true), mu_cond_sigma[k])