univariate_ordered and multivariate_ordered

Author: TimOliverMaier

pymc/distributions/transforms.py

@@ -336,10 +336,17 @@ def extend_axis_rev(array, axis):
 Instantiation of :class:`pymc.distributions.transforms.LogExpM1`
 for use in the ``transform`` argument of a random variable."""
 
-ordered = Ordered()
-ordered.__doc__ = """
+univariate_ordered = Ordered(ndim_supp=0)
+univariate_ordered.__doc__ = """
 Instantiation of :class:`pymc.distributions.transforms.Ordered`
-for use in the ``transform`` argument of a random variable."""
+for use in the ``transform`` argument of a univariate random variable."""
+
+multivariate_ordered = Ordered(ndim_supp=1)
+multivariate_ordered.__doc__ = """
+Instantiation of :class:`pymc.distributions.transforms.Ordered`
+for use in the ``transform`` argument of a multivariate random variable."""
+
+ordered = univariate_ordered
 
 log = LogTransform()
 log.__doc__ = """

pymc/tests/distributions/test_transform.py

@@ -327,7 +327,7 @@ def check_vectortransform_elementwise_logp(self, model):
         jacob_det = transform.log_jac_det(test_array_transf, *x.owner.inputs)
         # Original distribution is univariate
         if x.owner.op.ndim_supp == 0:
-            assert model.logp(x, sum=False)[0].ndim == x.ndim == (jacob_det.ndim + 1)
+            assert model.logp(x, sum=False)[0].ndim == x.ndim == jacob_det.ndim
         # Original distribution is multivariate
         else:
             assert model.logp(x, sum=False)[0].ndim == (x.ndim - 1) == jacob_det.ndim
@@ -569,7 +569,11 @@ def test_uniform_other(self, lower, upper, size, transform):
     def test_mvnormal_ordered(self, mu, cov, size, shape):
         initval = np.sort(np.random.randn(*shape))
         model = self.build_model(
-            pm.MvNormal, {"mu": mu, "cov": cov}, size=size, initval=initval, transform=tr.ordered
+            pm.MvNormal,
+            {"mu": mu, "cov": cov},
+            size=size,
+            initval=initval,
+            transform=tr.multivariate_ordered,
         )
         self.check_vectortransform_elementwise_logp(model)