disable broadcasting

Author: patel-zeel

pymc/distributions/multivariate.py

@@ -355,11 +355,10 @@ def dist(cls, nu, Sigma=None, mu=None, cov=None, tau=None, chol=None, lower=True
         return super().dist([nu, mu, cov], **kwargs)
 
     def get_moment(rv, size, nu, mu, cov):
-        if rv_size_is_none(size):
-            moment_size = at.concatenate([nu.shape, mu.shape])
-        else:
-            moment_size = at.concatenate([size, nu.shape, mu.shape])
-        moment = at.full(moment_size, mu)
+        moment = mu
+        if not rv_size_is_none(size):
+            moment_size = at.concatenate([size, moment.shape])
+            moment = at.full(moment_size, moment)
         return moment
 
     def logp(value, nu, mu, cov):
@@ -700,7 +699,7 @@ def dist(cls, eta, cutpoints, n, *args, **kwargs):
 
 
 class OrderedMultinomial:
-    R"""
+    r"""
     Wrapper class for Ordered Multinomial distributions.
 
     Useful for regression on ordinal data whose values range

pymc/tests/test_distributions_moments.py

@@ -842,10 +842,10 @@ def test_moyal_moment(mu, sigma, size, expected):
         (2, np.ones(1), np.eye(1), None, np.ones(1)),
         (2, rand1d, np.eye(2), None, rand1d),
         (2, rand1d, np.eye(2), 2, np.full((2, 2), rand1d)),
-        (2, rand2d, np.eye(3), 2, np.full((2, 2, 3), rand2d)),
         (2, rand1d, np.eye(2), (2, 5), np.full((2, 5, 2), rand1d)),
+        (2, rand2d, np.eye(3), None, rand2d),
+        (2, rand2d, np.eye(3), 2, np.full((2, 2, 3), rand2d)),
         (2, rand2d, np.eye(3), (2, 5), np.full((2, 5, 2, 3), rand2d)),
-        (np.array([3, 4]), np.ones(2), np.eye(2), None, np.ones((2, 2))),
     ],
 )
 def test_mvstudentt_moment(nu, mu, cov, size, expected):