From e96139e10f5a236dd21fc5b5901f6629e1e71a1d Mon Sep 17 00:00:00 2001
From: Dustin Tran <dustinviettran@gmail.com>
Date: Sat, 7 Oct 2017 13:40:53 -0700
Subject: [PATCH] initial commit

---
 edward/inferences/conjugacy/conjugacy.py |  1 +
 edward/models/random_variables.py        | 16 --------
 edward/util/random_variables.py          | 48 ++++++++++++++----------
 3 files changed, 30 insertions(+), 35 deletions(-)

diff --git a/edward/inferences/conjugacy/conjugacy.py b/edward/inferences/conjugacy/conjugacy.py
index 329f61f02..af7cbd584 100644
--- a/edward/inferences/conjugacy/conjugacy.py
+++ b/edward/inferences/conjugacy/conjugacy.py
@@ -27,6 +27,7 @@ def normal_from_natural_params(p1, p2):
   return {'loc': loc, 'scale': tf.sqrt(sigmasq)}
 
 
+# TODO
 _suff_stat_to_dist = defaultdict(dict)
 _suff_stat_to_dist['binary'][(('#x',),)] = (
     Bernoulli, lambda p1: {'logits': p1})
diff --git a/edward/models/random_variables.py b/edward/models/random_variables.py
index be5cf8058..80c3e6382 100644
--- a/edward/models/random_variables.py
+++ b/edward/models/random_variables.py
@@ -26,22 +26,6 @@ def __init__(self, *args, **kwargs):
 
     del _candidate
 
-# Add supports; these are used, e.g., in conjugacy.
-Bernoulli.support = 'binary'
-Beta.support = '01'
-Binomial.support = 'onehot'
-Categorical.support = 'categorical'
-Chi2.support = 'nonnegative'
-Dirichlet.support = 'simplex'
-Exponential.support = 'nonnegative'
-Gamma.support = 'nonnegative'
-InverseGamma.support = 'nonnegative'
-Laplace.support = 'real'
-Multinomial.support = 'onehot'
-MultivariateNormalDiag.support = 'multivariate_real'
-Normal.support = 'real'
-Poisson.support = 'countable'
-
 del absolute_import
 del division
 del print_function
diff --git a/edward/util/random_variables.py b/edward/util/random_variables.py
index 5b5f3d137..19bcce819 100644
--- a/edward/util/random_variables.py
+++ b/edward/util/random_variables.py
@@ -753,28 +753,38 @@ def transform(x, *args, **kwargs):
   if len(args) != 0 or kwargs.get('bijector', None) is not None:
     return TransformedDistribution(x, *args, **kwargs)
 
-  try:
-    support = x.support
-  except AttributeError as e:
-    msg = """'{}' object has no 'support'
-             so cannot be transformed.""".format(type(x).__name__)
-    raise AttributeError(msg)
-
-  if support == '01':
-    bij = bijectors.Invert(bijectors.Sigmoid())
-    new_support = 'real'
-  elif support == 'nonnegative':
-    bij = bijectors.Invert(bijectors.Softplus())
-    new_support = 'real'
-  elif support == 'simplex':
-    bij = bijectors.Invert(bijectors.SoftmaxCentered(event_ndims=1))
-    new_support = 'multivariate_real'
-  elif support in ('real', 'multivariate_real'):
+  if isinstance(x, (Normal, MultivariateNormal..., ?)):
+    # TODO would like to do this one statically; hard-coded by
+    # distributions
     return x
-  else:
+
+  if x.support is None or len(x.support) > 1:
     msg = "'transform' does not handle supports of type '{}'".format(support)
     raise ValueError(msg)
 
+  interval, measure = x.support[0]
+  if measure is not 'real':
+    raise
+  # TODO simplex
+
+  # TODO get event_shape
+  # TODO compatible dtypes
+  # TODO tf.fill_like
+  is_real = tf.logical_and(tf.is_equal(interval[0], tf.constant(-np.inf)),
+                           tf.is_equal(interval[1], tf.constant(np.inf)))
+  is_01 = tf.logical_and(tf.is_equal(interval[0], tf.constant(0)),
+                         tf.is_equal(interval[1], tf.constant(1)))
+  is_nonnegative = tf.logical_and(tf.is_equal(interval[0], tf.constant(0)),
+                                  tf.is_equal(interval[1], tf.constant(np.inf)))
+  # TODO
+  tf.where(is_real, x, tf.where()...)
+  elif interval == '01':
+    bij = bijectors.Invert(bijectors.Sigmoid())
+  elif interval == 'nonnegative':
+    bij = bijectors.Invert(bijectors.Softplus())
+  elif interval == 'simplex':
+    bij = bijectors.Invert(bijectors.SoftmaxCentered(event_ndims=1))
+
   new_x = TransformedDistribution(x, bij, *args, **kwargs)
-  new_x.support = new_support
+  new_x.support = [([], 'real')]
   return new_x