MAINT Add back bound_elemwise. Port DiscreteUniform and Uniform to use it.

Author: twiecki

pymc3/distributions/continuous.py

@@ -13,7 +13,7 @@
 import warnings
 
 from . import transforms
-from .dist_math import bound, logpow, gammaln, betaln, std_cdf, i0, i1
+from .dist_math import bound, bound_elemwise, logpow, gammaln, betaln, std_cdf, i0, i1
 from .distribution import Continuous, draw_values, generate_samples
 
 __all__ = ['Uniform', 'Flat', 'Normal', 'Beta', 'Exponential', 'Laplace',
@@ -146,8 +146,8 @@ def random(self, point=None, size=None, repeat=None):
     def logp(self, value):
         lower = self.lower
         upper = self.upper
-        return bound(-tt.log(upper - lower),
-                     value >= lower, value <= upper)
+        return bound_elemwise(-tt.log(upper - lower),
+                              value >= lower, value <= upper)
 
 
 class Flat(Continuous):

pymc3/distributions/discrete.py

@@ -5,7 +5,7 @@
 import theano.tensor as tt
 from scipy import stats
 
-from .dist_math import bound, factln, binomln, betaln, logpow
+from .dist_math import bound, bound_elemwise, factln, binomln, betaln, logpow
 from .distribution import Discrete, draw_values, generate_samples
 
 __all__ = ['Binomial',  'BetaBinomial',  'Bernoulli',  'Poisson',
@@ -348,8 +348,8 @@ def random(self, point=None, size=None, repeat=None):
     def logp(self, value):
         upper = self.upper
         lower = self.lower
-        return bound(-tt.log(upper - lower + 1),
-                     lower <= value, value <= upper)
+        return bound_elemwise(-tt.log(upper - lower + 1) * tt.ones_like(value),
+                              lower <= value, value <= upper)
 
 
 class Categorical(Discrete):
@@ -454,7 +454,7 @@ class ZeroInflatedPoisson(Discrete):
     Often used to model the number of events occurring in a fixed period
     of time when the times at which events occur are independent.
 
-    .. math:: 
+    .. math::
 
         f(x \mid \theta, \psi) = \left\{ \begin{array}{l}
             (1-\psi) + \psi e^{-\theta}, \text{if } x = 0 \\
@@ -503,7 +503,7 @@ class ZeroInflatedNegativeBinomial(Discrete):
 
     The Zero-inflated version of the Negative Binomial (NB).
     The NB distribution describes a Poisson random variable
-    whose rate parameter is gamma distributed. 
+    whose rate parameter is gamma distributed.
 
     .. math::
 

pymc3/distributions/dist_math.py

@@ -11,6 +11,32 @@
 from .special import gammaln, multigammaln
 
 
+def bound_elemwise(logp, *conditions):
+    """
+    Bounds a log probability density with several conditions.
+
+    Respects shape of logp and performs broadcasting when
+    conditions.shape > logp.shape.
+
+    Parameters
+    ----------
+    logp : float
+    *conditions : booleans
+
+    Returns
+    -------
+    logp with elements set to -inf where any condition is False
+    """
+    return tt.switch(alltrue_elemwise(conditions), logp, -np.inf)
+
+
+def alltrue_elemwise(vals):
+    ret = 1
+    for c in vals:
+        ret = ret * (1 * c)
+    return ret
+
+
 def bound(logp, *conditions):
     """
     Bounds a log probability density with several conditions
@@ -27,7 +53,6 @@ def bound(logp, *conditions):
     """
     return tt.switch(alltrue(conditions), logp, -np.inf)
 
-
 def alltrue(vals):
     return tt.all([tt.all(1 * val) for val in vals])