Adding support for Discrete distribution for max logprob

pymc/logprob/order.py

@@ -90,34 +90,17 @@ def find_measurable_max(fgraph: FunctionGraph, node: Node) -> Optional[List[Tens
     if not (isinstance(base_var.owner.op, RandomVariable) and base_var.owner.op.ndim_supp == 0):
         return None
 
-<<<<<<< HEAD
-    # TODO: We are currently only supporting continuous rvs
-    if isinstance(base_var.owner.op, RandomVariable) and base_var.owner.op.dtype.startswith("int"):
-        return None
-
-    # univariate i.i.d. test which also rules out other distributions
-    for params in base_var.owner.inputs[3:]:
-        if params.type.ndim != 0:
-            return None
-
-=======
     # univariate i.i.d. test which also rules out other distributions
     for params in base_var.owner.inputs[3:]:
         if params.type.ndim != 0:
             return None
 
->>>>>>> Adding support for Discrete distribution for max logprob
     # Check whether axis covers all dimensions
     axis = set(node.op.axis)
     base_var_dims = set(range(base_var.ndim))
     if axis != base_var_dims:
         return None
 
-<<<<<<< HEAD
-    measurable_max = MeasurableMax(list(axis))
-    max_rv_node = measurable_max.make_node(base_var)
-    max_rv = max_rv_node.outputs
-=======
     # logprob for discrete distribution
     if isinstance(base_var.owner.op, RandomVariable) and base_var.owner.op.dtype.startswith("int"):
         measurable_max = MeasurableMaxDiscrete(list(axis))
@@ -130,7 +113,6 @@ def find_measurable_max(fgraph: FunctionGraph, node: Node) -> Optional[List[Tens
         measurable_max = MeasurableMax(list(axis))
         max_rv_node = measurable_max.make_node(base_var)
         max_rv = max_rv_node.outputs
->>>>>>> Adding support for Discrete distribution for max logprob
 
         return max_rv
 
@@ -156,8 +138,6 @@ def max_logprob(op, values, base_rv, **kwargs):
     logprob = (n - 1) * logcdf + logprob + pt.math.log(n)
 
     return logprob
-<<<<<<< HEAD
-=======
 
 
 @_logprob.register(MeasurableMaxDiscrete)
@@ -178,4 +158,3 @@ def max_logprob_discrete(op, values, base_rv, **kwargs):
     logprob = pt.log((pt.exp(logcdf)) ** n - (pt.exp(logcdf_prev)) ** n)
 
     return logprob
->>>>>>> Adding support for Discrete distribution for max logprob

tests/logprob/test_order.py

@@ -147,8 +147,6 @@ def test_max_logprob(shape, value, axis):
         (x_max_logprob.eval({x_max_value: test_value})),
         rtol=1e-06,
     )
-<<<<<<< HEAD
-=======
 
 
 def test_max_discrete():
@@ -159,4 +157,3 @@ def test_max_discrete():
     x_max_logprob = logp(x_max, x_max_value)
 
     x_max_logprob.eval({x_max_value: 0.85})
->>>>>>> Adding support for Discrete distribution for max logprob