Merge pull request #1867 from nouiz/fix_test

theano/gof/type.py

@@ -401,10 +401,17 @@ class SingletonType(Type):
     It saves having to implement __eq__ and __hash__
     """
     __instance = None
+
     def __new__(cls):
-        if cls.__instance is None:
+        # If sub-subclass of SingletonType don't redeclare __instance
+        # when we look for it, we will find it in the subclass.  We
+        # don't want that, so we check the class.  When we add one, we
+        # add one only to the current class, so all is working
+        # correctly.
+        if cls.__instance is None or not isinstance(cls.__instance, cls):
             cls.__instance = Type.__new__(cls)
         return cls.__instance
+
     def __str__(self):
         return self.__class__.__name__
 

theano/sandbox/linalg/ops.py

@@ -1119,11 +1119,22 @@ class Eigvalsh(Op):
     def make_node(self, a, b):
         assert imported_scipy, (
             "Scipy not available. Scipy is needed for the Eigvalsh op")
-        a, b = map(as_tensor_variable, (a, b))
+        a = as_tensor_variable(a)
         assert a.ndim == 2
+        if not isinstance(b, (theano.Variable)):
+            if b is None:
+                b = theano.tensor.NoneConst
+                out_dtype = a.dtype
+            else:
+                b = as_tensor_variable(b)
+                out_dtype = theano.scalar.upcast(a.dtype, b.dtype)
+        elif not isinstance(b.type, theano.tensor.NoneTypeT):
+            b = as_tensor_variable(b)
+            out_dtype = theano.scalar.upcast(a.dtype, b.dtype)
             assert b.ndim == 2
+        else:
+            out_dtype = a.dtype
 
-        out_dtype = theano.scalar.upcast(a.dtype, b.dtype)
         w = theano.tensor.vector(dtype=out_dtype)
         return Apply(self, [a, b], [w])
 

theano/sandbox/linalg/tests/test_linalg.py

@@ -587,11 +587,20 @@ def test_eigvalsh():
     rng = numpy.random.RandomState(utt.fetch_seed())
     a = rng.randn(5, 5)
     a = a + a.T
-    for b in [10 * numpy.eye(5, 5) + rng.randn(5, 5), None]:
+    for b in [10 * numpy.eye(5, 5) + rng.randn(5, 5)]:
         w = f(a, b)
         refw = scipy.linalg.eigvalsh(a, b)
         numpy.testing.assert_array_almost_equal(w, refw)
 
+    # We need to test None separatly, as otherwise DebugMode will
+    # complain, as this isn't a valid ndarray.
+    b = None
+    B = theano.tensor.NoneConst
+    f = function([A], eigvalsh(A, B))
+    w = f(a)
+    refw = scipy.linalg.eigvalsh(a, b)
+    numpy.testing.assert_array_almost_equal(w, refw)
+
 
 def test_eigvalsh_grad():
     rng = numpy.random.RandomState(utt.fetch_seed())

theano/tensor/type_other.py

@@ -72,10 +72,9 @@ class NoneTypeT(Generic):
         else:
             raise TypeError('Expected None!')
 
-    def __str__(self):
-        return "None"
+none_type_t = NoneTypeT()
 
 # This is a variable instance. It can be used only once per fgraph.
 # So use NoneConst.clone() before using it in a Theano graph.
 # Use NoneConst.equal(x) to check if two variable are NoneConst.
-NoneConst = Constant(NoneTypeT(), None, name='None')
+NoneConst = Constant(NoneTypeT(), None, name='NoneConst')