NaNType -> NullType following code review. Made NullType raise

theano/gof/fg.py

@@ -11,8 +11,7 @@ import toolbox
 from python25 import all
 from theano import config
 import warnings
-NaNType = None
-
+NullType = None
 
 class InconsistencyError(Exception):
     """
@@ -212,9 +211,9 @@ class FunctionGraph(utils.object2):
     ### import ###
 
     def __import_r__(self, variables):
-        global NaNType
-        if NaNType is None:
-            from nan_type import NaNType
+        global NullType
+        if NullType is None:
+            from null_type import NullType
         # Imports the owners of the variables
         r_owner_done = set(self.nodes)
         for node in [r.owner for r in variables if r.owner is not None]:
@@ -223,8 +222,8 @@ class FunctionGraph(utils.object2):
                 self.__import__(node)
         for r in variables:
             if r.owner is None and not isinstance(r, graph.Constant) and r not in self.inputs:
-                if isinstance(r.type,NaNType):
-                    raise TypeError("Computation graph contains a NaN. "+r.type.why_nan)
+                if isinstance(r.type,NullType):
+                    raise TypeError("Computation graph contains a NaN. "+r.type.why_null)
                 raise MissingInputError("Undeclared input", r)
             if not getattr(r, 'fgraph', None) is self:
                 self.__setup_r__(r)

theano/gof/nan_type.py → theano/gof/null_type.py

 from type import Type
 from graph import Variable
 
-class NaNType(Type):
-    def __init__(self, why_nan = '(no explanation given)'):
+class NullType(Type):
+    def __init__(self, why_null = '(no explanation given)'):
         """
-            why_nan: A string explaining why this variable is NaN
+            why_null: A string explaining why this variable
+                    can't take on any values
         """
-        self.why_nan = why_nan
+        self.why_null = why_null
 
     def filter(self, data, strict=False, allow_downcast=None):
-       raise
+       raise ValueError("No values may be assigned to a NullType")
     def filter_variable(self, other):
-       raise
+       raise ValueError("No values may be assigned to a NullType")
     def may_share_memory(a, b):
        return False
     def values_eq(a, b, force_same_dtype=True):
-       raise
+       raise ValueError("NullType has no values to compare")
 
 
 
-class NaNVariable(Variable):
+class NullVariable(Variable):
 
     pass

theano/gradient.py

@@ -20,7 +20,7 @@ from theano import gof
 from theano.gof import Variable
 from theano.gof.python25 import all
 import theano.gof.utils
-from theano.gof.nan_type import NaNType
+from theano.gof.null_type import NullType
 from theano.printing import min_informative_str
 tensor = None
 
@@ -68,7 +68,7 @@ def grad_not_implemented(op, x_pos, x, comment = ""):
     gradient is not implemented.
     """
 
-    return NaNType("This variable is NaN because the grad method for " + \
+    return NullType("This variable is NaN because the grad method for " + \
             "input "+str(x_pos)+" ("+str(x)+") of the "+str(op)+" op is" + \
             " not implemented."+comment)()
 
@@ -86,7 +86,7 @@ def grad_undefined(op, x_pos, x, comment = ""):
     gradient is not defined.
     """
 
-    return NaNType("This variable is NaN because the gradient for " + \
+    return NullType("This variable is NaN because the gradient for " + \
             "input "+str(x_pos)+" ("+str(x)+") of the "+str(op)+" op is" + \
              " mathematically undefined."+comment)()
 
@@ -375,9 +375,9 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = False,
         raise TypeError("cost must be a scalar.")
 
 
-    if isinstance(cost.type, NaNType):
+    if isinstance(cost.type, NullType):
         raise ValueError("Can't differentiate a NaN cost. cost is NaN because "+\
-                cost.type.why_nan)
+                cost.type.why_null)
 
     if consider_constant is None:
         consider_constant = []
@@ -609,9 +609,9 @@ def _populate_grad_dict(var_to_node_to_idx,\
                                     " Variable instance." % (str(node.op),
                                         type(term)))
 
-                        if isinstance(term.type,NaNType):
+                        if isinstance(term.type,NullType):
                             raise TypeError("tensor.grad encountered a NaN. "+\
-                                    term.type.why_nan)
+                                    term.type.why_null)
 
                         terms.append( term)
                 grad_dict[var] = nonempty_sum(terms)