Reverted changes with Scalars, added Props to few more place, fixed travis

theano/compile/builders.py

@@ -71,6 +71,7 @@ class OpFromGraph(gof.Op):
         fn = function([x, y, z], [e2])
 
     """
+
     def __init__(self, inputs, outputs, **kwargs):
         if not isinstance(outputs, list):
             raise TypeError('outputs must be list', outputs)

theano/gof/op.py

@@ -781,7 +781,6 @@ class Op(utils.object2, PureOp, CLinkerOp):
     Convenience class to bundle `PureOp` and `CLinkerOp`.
 
     """
-
     def prepare_node(self, node, storage_map, compute_map, impl):
         """
         Make any special modifications that the Op needs before doing

theano/sandbox/minimal.py

@@ -15,6 +15,8 @@ class Minimal(gof.Op):
     # If two Apply nodes have the same inputs and the ops compare equal...
     # then they will be MERGED so they had better have computed the same thing!
 
+    __props__ = ()
+
     def __init__(self):
         # If you put things here, think about whether they change the outputs
         # computed by # self.perform()
@@ -25,12 +27,6 @@ class Minimal(gof.Op):
 
         super(Minimal, self).__init__()
 
-    def __eq__(self, other):
-        return type(self) == type(other)
-
-    def __hash__(self):
-        return hash(type(self))
-
     def make_node(self, *args):
         # HERE `args` must be THEANO VARIABLES
         return gof.Apply(op=self, inputs=args, outputs=[tensor.lscalar()])

theano/sandbox/solve.py

@@ -27,11 +27,7 @@ class Solve(gof.Op):
     #      and keeps a memory workspace from call to call as a non-default Op
     #      output
 
-    def __eq__(self, other):
-        return type(self) == type(other)
-
-    def __hash__(self):
-        return hash(type(self))
+    __props__ = ()
 
     def make_node(self, A, b):
         A_ = tensor.as_tensor_variable(A)

theano/scalar/basic.py

@@ -852,7 +852,6 @@ class ScalarOp(Op):
 
     nin = -1
     nout = 1
-    __props__ = ("output_types_preference", "name")
 
     def __init__(self, output_types_preference=None, name=None):
         self.name = name
@@ -861,7 +860,7 @@ class ScalarOp(Op):
                 raise TypeError(
                     "Expected a callable for the 'output_types_preference' argument to %s. (got: %s)" %
                     (self.__class__, output_types_preference))
-        self.output_types_preference = output_types_preference
+            self.output_types_preference = output_types_preference
 
     def make_node(self, *inputs):
         if self.nin >= 0:
@@ -908,6 +907,16 @@ class ScalarOp(Op):
         raise utils.MethodNotDefined("grad", type(self),
                                      self.__class__.__name__)
 
+    def __eq__(self, other):
+        test = (type(self) == type(other) and
+                getattr(self, 'output_types_preference', None) ==
+                getattr(other, 'output_types_preference', None))
+        return test
+
+    def __hash__(self):
+        return hash(type(self).__name__) ^ hash(
+            getattr(self, 'output_types_preference', 0))
+
     def __str__(self):
         if hasattr(self, 'name') and self.name:
             return self.name
@@ -1174,7 +1183,6 @@ isinf = IsInf()
 
 class InRange(LogicalComparison):
     nin = 3
-    __props__ = ("openlow", "openhi")
 
     def __init__(self, openlow, openhi):
         self.openlow = openlow
@@ -2026,8 +2034,6 @@ identity = Identity(same_out, name='identity')
 
 # CASTING OPERATIONS
 class Cast(UnaryScalarOp):
-    __props__ = ("o_type", "name")
-
     def __init__(self, o_type, name=None):
         if not isinstance(o_type, Scalar):
             raise TypeError(o_type)
@@ -3453,7 +3459,7 @@ class Composite(ScalarOp):
     Composite depends on all the Ops in its graph having C code.
 
     """
-    __props__ = ('inputs', 'outputs')
+    init_param = ('inputs', 'outputs')
 
     def __str__(self):
         if self.name is None:
@@ -3467,7 +3473,7 @@ class Composite(ScalarOp):
         This fct allow fix patch this.
 
         """
-        d = dict([(k, getattr(self, k)) for k in self.__props__])
+        d = dict([(k, getattr(self, k)) for k in self.init_param])
         out = self.__class__(**d)
         if name:
             out.name = name