Renamed ConstantDist to Constant

Author: fonnesbeck

pymc3/distributions/__init__.py

@@ -31,6 +31,7 @@
 from .discrete import Poisson
 from .discrete import NegativeBinomial
 from .discrete import ConstantDist
+from .discrete import Constant
 from .discrete import ZeroInflatedPoisson
 from .discrete import ZeroInflatedNegativeBinomial
 from .discrete import DiscreteUniform
@@ -94,6 +95,7 @@
            'Poisson',
            'NegativeBinomial',
            'ConstantDist',
+           'Constant',
            'ZeroInflatedPoisson',
            'ZeroInflatedNegativeBinomial',
            'DiscreteUniform',

pymc3/distributions/discrete.py

@@ -9,7 +9,7 @@
 from .distribution import Discrete, draw_values, generate_samples
 
 __all__ = ['Binomial',  'BetaBinomial',  'Bernoulli',  'Poisson',
-           'NegativeBinomial', 'ConstantDist', 'ZeroInflatedPoisson',
+           'NegativeBinomial', 'ConstantDist', 'Constant', 'ZeroInflatedPoisson',
            'ZeroInflatedNegativeBinomial', 'DiscreteUniform', 'Geometric',
            'Categorical']
 
@@ -413,7 +413,7 @@ def logp(self, value):
                      sumto1)
 
 
-class ConstantDist(Discrete):
+class Constant(Discrete):
     """
     Constant log-likelihood.
 
@@ -424,7 +424,7 @@ class ConstantDist(Discrete):
     """
 
     def __init__(self, c, *args, **kwargs):
-        super(ConstantDist, self).__init__(*args, **kwargs)
+        super(Constant, self).__init__(*args, **kwargs)
         self.mean = self.median = self.mode = self.c = c
 
     def random(self, point=None, size=None, repeat=None):
@@ -441,6 +441,11 @@ def logp(self, value):
         c = self.c
         return bound(0, tt.eq(value, c))
 
+def ConstantDist(*args, **kwargs):
+    warnings.warn("ConstantDist has been deprecated. In future, use Constant instead.",
+                DeprecationWarning)
+    return Constant(*args, **kwargs)
+
 
 class ZeroInflatedPoisson(Discrete):
     R"""

pymc3/tests/test_distributions.py

@@ -7,7 +7,7 @@
 from ..blocking import DictToVarBijection, DictToArrayBijection, ArrayOrdering
 from ..distributions import (DensityDist, Categorical, Multinomial, VonMises, Dirichlet,
                              MvStudentT, MvNormal, ZeroInflatedPoisson,
-                             ZeroInflatedNegativeBinomial, ConstantDist, Poisson, Bernoulli, Beta,
+                             ZeroInflatedNegativeBinomial, ConstantDist, Constant, Poisson, Bernoulli, Beta,
                              BetaBinomial, HalfStudentT, StudentT, Weibull, Pareto, InverseGamma,
                              Gamma, Cauchy, HalfCauchy, Lognormal, Laplace, NegativeBinomial,
                              Geometric, Exponential, ExGaussian, Normal, Flat, LKJCorr, Wald,
@@ -479,7 +479,7 @@ def test_poisson(self):
                                  lambda value, mu: sp.poisson.logpmf(value, mu))
 
     def test_constantdist(self):
-        self.pymc3_matches_scipy(ConstantDist, I, {'c': I},
+        self.pymc3_matches_scipy(Constant, I, {'c': I},
                                  lambda value, c: np.log(c == value))
 
     def test_zeroinflatedpoisson(self):

pymc3/tests/test_distributions_random.py

@@ -281,8 +281,8 @@ class TestNegativeBinomial(BaseTestCases.BaseTestCase):
     params = {'mu': 1., 'alpha': 1.}
 
 
-class TestConstantDist(BaseTestCases.BaseTestCase):
-    distribution = pm.ConstantDist
+class TestConstant(BaseTestCases.BaseTestCase):
+    distribution = pm.Constant
     params = {'c': 3}
 
 
@@ -471,7 +471,7 @@ def ref_rand(size, p):
     def test_constant_dist(self):
         def ref_rand(size, c):
             return c * np.ones(size, dtype=int)
-        pymc3_random_discrete(pm.ConstantDist, {'c': I}, ref_rand=ref_rand)
+        pymc3_random_discrete(pm.Constant, {'c': I}, ref_rand=ref_rand)
 
     def test_mv_normal(self):
         def ref_rand(size, mu, cov):