Add stuff so that scalar tests pass with the new bool type.

theano/gradient.py

@@ -477,7 +477,7 @@ def grad(cost, wrt, consider_constant=None,
         # function, sure, but nonetheless one we can and should support.
         # So before we try to cast it make sure it even has a dtype
         if (hasattr(g_cost.type, 'dtype') and
-                cost.type.dtype not in tensor.discrete_dtypes):
+                cost.type.dtype in tensor.continuous_dtypes):
                 # Here we enforce the constraint that floating point variables
                 # have the same dtype as their gradient.
                 g_cost = g_cost.astype(cost.type.dtype)
@@ -485,7 +485,7 @@ def grad(cost, wrt, consider_constant=None,
         # This is to be enforced by the Op.grad method for the
         # Op that outputs cost.
         if hasattr(g_cost.type, 'dtype'):
-            assert g_cost.type.dtype not in tensor.discrete_dtypes
+            assert g_cost.type.dtype in tensor.continuous_dtypes
 
         grad_dict[cost] = g_cost
 
@@ -1334,12 +1334,11 @@ def _float_ones_like(x):
     """ Like ones_like, but forces the object to have a
     floating point dtype """
 
-    rval = tensor.ones_like(x)
+    dtype = x.type.dtype
+    if 'float' not in dtype:
+        dtype = theano.config.floatX
 
-    if rval.type.dtype.find('float') != -1:
-        return rval
-
-    return rval.astype(theano.config.floatX)
+    return tensor.ones_like(x, dtype=dtype)
 
 
 class numeric_grad(object):

theano/tensor/basic.py

@@ -1246,6 +1246,10 @@ def _conversion(real_value, name):
 # what types you are casting to what.  That logic is implemented by the
 # `cast()` function below.
 
+_convert_to_bool = _conversion(
+    elemwise.Elemwise(scal.convert_to_bool), 'bool')
+"""Cast to boolean"""
+
 _convert_to_int8 = _conversion(
     elemwise.Elemwise(scal.convert_to_int8), 'int8')
 """Cast to 8-bit integer"""
@@ -1299,6 +1303,7 @@ _convert_to_complex128 = _conversion(
 """Cast to double-precision complex"""
 
 _cast_mapping = {
+    'bool': _convert_to_bool,
     'int8': _convert_to_int8,
     'int16': _convert_to_int16,
     'int32': _convert_to_int32,

theano/tensor/type.py

@@ -255,6 +255,7 @@ class TensorType(Type):
                 'float16': (float, 'npy_float16', 'NPY_FLOAT16'),
                 'float32': (float, 'npy_float32', 'NPY_FLOAT32'),
                 'float64': (float, 'npy_float64', 'NPY_FLOAT64'),
+                'bool': (bool, 'npy_bool', 'NPY_BOOL'),
                 'uint8': (int, 'npy_uint8', 'NPY_UINT8'),
                 'int8': (int, 'npy_int8', 'NPY_INT8'),
                 'uint16': (int, 'npy_uint16', 'NPY_UINT16'),