Remove unused sigmoid import

Author: AlexAndorra

pymc3/distributions/multivariate.py

@@ -25,7 +25,12 @@
 from aesara.graph.basic import Apply
 from aesara.graph.op import Op
 from aesara.tensor.nlinalg import det, eigh, matrix_inverse, trace
-from aesara.tensor.random.basic import MultinomialRV, dirichlet, multivariate_normal, RandomVariable
+from aesara.tensor.random.basic import (
+    MultinomialRV,
+    RandomVariable,
+    dirichlet,
+    multivariate_normal,
+)
 from aesara.tensor.random.op import default_shape_from_params
 from aesara.tensor.random.utils import broadcast_params
 from aesara.tensor.slinalg import (
@@ -45,7 +50,7 @@
 from pymc3.distributions.dist_math import bound, factln, logpow
 from pymc3.distributions.distribution import Continuous, Discrete
 from pymc3.distributions.special import gammaln, multigammaln
-from pymc3.math import kron_diag, kron_dot, kron_solve_lower, kronecker, sigmoid
+from pymc3.math import kron_diag, kron_dot, kron_solve_lower, kronecker
 
 __all__ = [
     "MvNormal",
@@ -155,7 +160,7 @@ def quaddist_tau(delta, chol_mat):
 
 
 class MvNormal(Continuous):
-    R"""
+    r"""
     Multivariate normal log-likelihood.
 
     .. math::
@@ -250,7 +255,7 @@ def _distr_parameters_for_repr(self):
 
 
 class MvStudentT(Continuous):
-    R"""
+    r"""
     Multivariate Student-T log-likelihood.
 
     .. math::
@@ -363,7 +368,7 @@ def _distr_parameters_for_repr(self):
 
 
 class Dirichlet(Continuous):
-    R"""
+    r"""
     Dirichlet log-likelihood.
 
     .. math::
@@ -452,7 +457,7 @@ def rng_fn(cls, rng, n, p, size):
 
 
 class Multinomial(Discrete):
-    R"""
+    r"""
     Multinomial log-likelihood.
 
     Generalizes binomial distribution, but instead of each trial resulting
@@ -527,9 +532,7 @@ class DirichletMultinomialRV(RandomVariable):
     _print_name = ("DirichletMN", "\\operatorname{DirichletMN}")
 
     def _shape_from_params(self, dist_params, rep_param_idx=1, param_shapes=None):
-        return default_shape_from_params(
-            self.ndim_supp, dist_params, rep_param_idx, param_shapes
-        )
+        return default_shape_from_params(self.ndim_supp, dist_params, rep_param_idx, param_shapes)
 
     @classmethod
     def rng_fn(cls, rng, n, a, size):
@@ -561,7 +564,7 @@ def rng_fn(cls, rng, n, a, size):
 
 
 class DirichletMultinomial(Discrete):
-    R"""Dirichlet Multinomial log-likelihood.
+    r"""Dirichlet Multinomial log-likelihood.
 
     Dirichlet mixture of Multinomials distribution, with a marginalized PMF.
 
@@ -616,10 +619,18 @@ def logp(value, n, a):
         -------
         TensorVariable
         """
+        n = intX(n)
+        a = floatX(a)
+        if value.ndim >= 1:
+            n = at.shape_padright(n)
+            if a.ndim > 1:
+                a = at.shape_padleft(a)
+
         sum_a = a.sum(axis=-1, keepdims=True)
         const = (gammaln(n + 1) + gammaln(sum_a)) - gammaln(n + sum_a)
         series = gammaln(value + a) - (gammaln(value + 1) + gammaln(a))
         result = const + series.sum(axis=-1, keepdims=True)
+
         # Bounds checking to confirm parameters and data meet all constraints
         # and that each observation value_i sums to n_i.
         return bound(
@@ -686,7 +697,7 @@ def __str__(self):
 
 
 class Wishart(Continuous):
-    R"""
+    r"""
     Wishart log-likelihood.
 
     The Wishart distribution is the probability distribution of the
@@ -798,7 +809,7 @@ def logp(self, X):
 
 
 def WishartBartlett(name, S, nu, is_cholesky=False, return_cholesky=False, initval=None):
-    R"""
+    r"""
     Bartlett decomposition of the Wishart distribution. As the Wishart
     distribution requires the matrix to be symmetric positive semi-definite
     it is impossible for MCMC to ever propose acceptable matrices.
@@ -903,7 +914,7 @@ def _lkj_normalizing_constant(eta, n):
 
 
 class _LKJCholeskyCov(Continuous):
-    R"""Underlying class for covariance matrix with LKJ distributed correlations.
+    r"""Underlying class for covariance matrix with LKJ distributed correlations.
     See docs for LKJCholeskyCov function for more details on how to use it in models.
     """
 
@@ -1083,7 +1094,7 @@ def _distr_parameters_for_repr(self):
 
 
 def LKJCholeskyCov(name, eta, n, sd_dist, compute_corr=False, store_in_trace=True, *args, **kwargs):
-    R"""Wrapper function for covariance matrix with LKJ distributed correlations.
+    r"""Wrapper function for covariance matrix with LKJ distributed correlations.
 
     This defines a distribution over Cholesky decomposed covariance
     matrices, such that the underlying correlation matrices follow an
@@ -1236,7 +1247,7 @@ def LKJCholeskyCov(name, eta, n, sd_dist, compute_corr=False, store_in_trace=Tru
 
 
 class LKJCorr(Continuous):
-    R"""
+    r"""
     The LKJ (Lewandowski, Kurowicka and Joe) log-likelihood.
 
     The LKJ distribution is a prior distribution for correlation matrices.
@@ -1379,20 +1390,15 @@ def logp(self, x):
         result = _lkj_normalizing_constant(eta, n)
         result += (eta - 1.0) * at.log(det(X))
         return bound(
-            result,
-            X >= -1,
-            X <= 1,
-            matrix_pos_def(X),
-            eta > 0,
-            broadcast_conditions=False,
+            result, X >= -1, X <= 1, matrix_pos_def(X), eta > 0, broadcast_conditions=False
         )
 
     def _distr_parameters_for_repr(self):
         return ["eta", "n"]
 
 
 class MatrixNormal(Continuous):
-    R"""
+    r"""
     Matrix-valued normal log-likelihood.
 
     .. math::
@@ -1651,7 +1657,7 @@ def _distr_parameters_for_repr(self):
 
 
 class KroneckerNormal(Continuous):
-    R"""
+    r"""
     Multivariate normal log-likelihood with Kronecker-structured covariance.
 
     .. math::
@@ -1898,7 +1904,7 @@ def _distr_parameters_for_repr(self):
 
 
 class CAR(Continuous):
-    R"""
+    r"""
     Likelihood for a conditional autoregression. This is a special case of the
     multivariate normal with an adjacency-structured covariance matrix.