提交 852114f3 authored 作者: Frederic's avatar Frederic

pep8

上级 ac850ee6
......@@ -1296,7 +1296,7 @@ class IncSubtensor(Op):
** helper_args
)
#Make a view on the output, as we will write into it.
# Make a view on the output, as we will write into it.
alloc_zview = self.make_view_array(z, view_ndim)
build_view = """
......@@ -1545,7 +1545,8 @@ class AdvancedSubtensor1(Op):
if not numpy.can_cast(i.dtype, numpy.intp):
# Check if there was actually an incorrect conversion
if numpy.any(i != i_):
raise IndexError('index contains values that are bigger '
raise IndexError(
'index contains values that are bigger '
'than the maximum array size on this system.', i)
i = i_
......@@ -1737,7 +1738,8 @@ class AdvancedIncSubtensor1(Op):
opname = 'set'
else:
opname = 'increment'
raise TypeError('cannot %s x subtensor with ndim=%s'
raise TypeError(
'cannot %s x subtensor with ndim=%s'
' by y with ndim=%s to x subtensor with ndim=%s ' % (
opname, x_.type.ndim, y_.type.ndim))
......@@ -1842,7 +1844,7 @@ def adv_index_broadcastable_pattern(a, idx):
newidx = tuple(map(replace_slice, idx))
#2 - True = 1; 2 - False = 2
# 2 - True = 1; 2 - False = 2
fakeshape = [2 - bc for bc in a.broadcastable]
retshape = numpy.empty(fakeshape)[newidx].shape
return tuple([dim == 1 for dim in retshape])
......@@ -1907,8 +1909,6 @@ class AdvancedSubtensor(Op):
'are too big (>= 2^32 elements). It is possible that '
'out[0] (%s), with shape %s, is not correctly filled.'
% (out[0], out[0].shape))
# return
#raise NotImplementedError()
def connection_pattern(self, node):
......@@ -1961,7 +1961,8 @@ class AdvancedIncSubtensor(Op):
def __str__(self):
return "%s{%s, %s}" % (self.__class__.__name__,
"inplace=" + str(self.inplace),
" set_instead_of_inc=" + str(self. set_instead_of_inc))
" set_instead_of_inc=" +
str(self. set_instead_of_inc))
def make_node(self, x, y, *inputs):
x = theano.tensor.as_tensor_variable(x)
......@@ -2004,7 +2005,8 @@ class AdvancedIncSubtensor(Op):
return gof.Apply(op,
(x, y) + inputs,
[theano.tensor.tensor(dtype=x.type.dtype,
[theano.tensor.tensor(
dtype=x.type.dtype,
broadcastable=x.type.broadcastable)])
def perform(self, node, inputs, out_):
......
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