initial commit

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})

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

edward/util/random_variables.py

@@ -753,28 +753,52 @@ 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'):
+  real = (Gumbel,
+          Laplace,
+          Logistic,
+          Normal,
+          StudentT,
+          MultivariateNormalDiag,
+          MultivariateNormalFullCovariance,
+          MultivariateNormalTriL,
+          MultivariateNormalDiagPlusLowRank)
+  if isinstance(x, real):
+    # Determine if distribution has real support at construction time
+    # via hard-coded distributions. This prevents adding unnecessary
+    # ops via a transformation with identity bijector.
     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 == 'simplex':
+    # TODO
+    pass
+  elif measure != 'real':
+    raise
+
+  # 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))
+  # TODO identity
+
   new_x = TransformedDistribution(x, bij, *args, **kwargs)
-  new_x.support = new_support
+  # TODO
+  new_x.support = [([], 'real')]
   return new_x