a lot of pep8

theano/tensor/nnet/sigm.py

@@ -42,7 +42,7 @@ class ScalarSigmoid(scalar.UnaryScalarOp):
 
         assert rval.type.dtype.find('float') != -1
 
-        return [ rval ]
+        return [rval]
 
     def c_code(self, node, name, inp, out, sub):
         x, = inp
@@ -69,7 +69,7 @@ sigmoid = elemwise.Elemwise(scalar_sigmoid, name='sigmoid')
 
 sigmoid_inplace = elemwise.Elemwise(
         ScalarSigmoid(scalar.transfer_type(0)),
-        inplace_pattern={0:0},
+        inplace_pattern={0: 0},
         name='sigmoid_inplace',
         )
 
@@ -84,12 +84,15 @@ class ScalarSoftplus(scalar.UnaryScalarOp):
         if x > 30.0:
             return x
         return numpy.log1p(numpy.exp(x))
+
     def impl(self, x):
         return ScalarSoftplus.static_impl(x)
+
     def grad(self, inp, grads):
         x, = inp
         gz, = grads
         return [gz * scalar_sigmoid(x)]
+
     def c_code(self, node, name, inp, out, sub):
         x, = inp
         z, = out
@@ -103,27 +106,29 @@ class ScalarSoftplus(scalar.UnaryScalarOp):
             return """%(z)s = %(x)s < -745.0 ? 0.0 : %(x)s > 16.0 ? %(x)s : log1p(exp(%(x)s));""" % locals()
         else:
             raise NotImplementedError('only floatingpoint is implemented')
+
     def c_code_cache_version(self):
         v = super(ScalarSoftplus, self).c_code_cache_version()
         if v:
             return (2,) + v
         else:
             return v
-scalar_softplus = ScalarSoftplus(scalar.upgrade_to_float, name='scalar_softplus')
+scalar_softplus = ScalarSoftplus(scalar.upgrade_to_float, name=                                                                                                                                                                                                        'scalar_softplus')
 softplus = elemwise.Elemwise(scalar_softplus, name='softplus')
 
 pprint.assign(softplus, printing.FunctionPrinter('softplus'))
 
+
 def _skip_mul_1(r):
     if r.owner and r.owner.op == tensor.mul:
-        not_is_1 = [i for i in r.owner.inputs if not _is_1(i) ]
-        if len(not_is_1)==1:
+        not_is_1 = [i for i in r.owner.inputs if not _is_1(i)]
+        if len(not_is_1) == 1:
             return not_is_1[0]
 
 logsigm_to_softplus = gof.PatternSub(
     (tensor.log, (sigmoid, 'x')),
     (tensor.neg, (softplus, (tensor.neg, 'x'))),
-    allow_multiple_clients = True,
+    allow_multiple_clients=True,
     skip_identities_fn=_skip_mul_1)
 
 
@@ -139,21 +144,22 @@ def _is_1(expr):
 log1msigm_to_softplus = gof.PatternSub(
     (tensor.log,
         (tensor.sub,
-            dict(pattern='y', constraint = _is_1),
+            dict(pattern='y', constraint=_is_1),
             (sigmoid, 'x'))),
     (tensor.neg, (softplus, 'x')),
-    allow_multiple_clients = True,
+    allow_multiple_clients=True,
     skip_identities_fn=_skip_mul_1)
 
 log1pexp_to_softplus = gof.PatternSub(
     (tensor.log1p,
      (tensor.exp, 'x')),
     (softplus, 'x'),
-    allow_multiple_clients = True)
+    allow_multiple_clients=True)
+
+opt.register_stabilize(logsigm_to_softplus, name='logsigm_to_softplus')
+opt.register_stabilize(log1msigm_to_softplus, name='log1msigm_to_softplus')
+opt.register_stabilize(log1pexp_to_softplus, name='log1pexp_to_softplus')
 
-opt.register_stabilize(logsigm_to_softplus, name = 'logsigm_to_softplus')
-opt.register_stabilize(log1msigm_to_softplus, name = 'log1msigm_to_softplus')
-opt.register_stabilize(log1pexp_to_softplus, name = 'log1pexp_to_softplus')
 
 def is_1pexp(t):
     """
@@ -247,7 +253,7 @@ def partition_num_or_denom(r, f):
         else:
             neg_t, f_t = f_t
             f_terms.append(f_t)
-            neg ^= neg_t #bit flip if neg_t is true
+            neg ^= neg_t  # bit flip if neg_t is true
     return f_terms, rest, neg
 
 
@@ -299,7 +305,8 @@ def local_exp_over_1_plus_exp(node):
         #find all the exp() terms in the numerator
         num, denom = node.inputs
         num_exp_x, num_rest, num_neg = partition_num_or_denom(num, is_exp)
-        denom_1pexp, denom_rest, denom_neg = partition_num_or_denom(denom, is_1pexp)
+        denom_1pexp, denom_rest,
+            denom_neg = partition_num_or_denom(denom, is_1pexp)
 
         sigmoids = []
         for t in denom_1pexp:
@@ -311,7 +318,7 @@ def local_exp_over_1_plus_exp(node):
                 # case: 1/(1+exp(x))
                 sigmoids.append(sigmoid(-t))
 
-        if not sigmoids: # we didn't find any.  abort
+        if not sigmoids:  # we didn't find any.  abort
             return
         # put the new numerator together
         new_num = sigmoids + [tensor.exp(t) for t in num_exp_x] + num_rest
@@ -330,6 +337,7 @@ def local_exp_over_1_plus_exp(node):
         else:
             return [new_num / tensor.mul(*denom_rest)]
 
+
 def parse_mul_tree(root):
     """
     Parse a tree of multiplications starting at the given root.
@@ -512,7 +520,7 @@ def perform_sigm_times_exp(tree, exp_x=None, exp_minus_x=None, sigm_x=None,
         sigm_minus_x = []
     if full_tree is None:
         full_tree = tree
-    if False: # Debug code.
+    if False:  # Debug code.
         print '<perform_sigm_times_exp>'
         print '  full_tree   = %s' % full_tree
         print '  tree        = %s' % tree
@@ -621,10 +629,13 @@ def local_inv_1_plus_exp(node):
                 if nonconsts[0].owner and nonconsts[0].owner.op == tensor.exp:
                     if scalars and numpy.allclose(numpy.sum(scalars), 1):
                         return opt._fill_chain(
-                                sigmoid(tensor.neg(nonconsts[0].owner.inputs[0])),
+                                sigmoid(
+                                    tensor.neg(nonconsts[0].owner.inputs[0])),
                                 scalar_inputs)
 
 # Registration is below, and conditional.
+
+
 @gof.local_optimizer([tensor.sub])
 def local_1msigmoid(node):
     """
@@ -633,7 +644,7 @@ def local_1msigmoid(node):
     if node.op == tensor.sub:
         sub_l, sub_r = node.inputs
         if len(sub_r.clients) > 1:
-            return # graph is using both sigm and 1-sigm
+            return  # graph is using both sigm and 1-sigm
         if sub_r.owner and sub_r.owner.op == sigmoid:
             try:
                 val_l = opt.get_constant_value(sub_l)
@@ -686,13 +697,14 @@ if 0:
                         assert t0.owner.op == div
                         t0top, t0bot = t0.owner.inputs
                         t1top, t1bot = t1.owner.inputs
-                        rval.append(div(mul(*(t0top+t1top)), mul(*(t0bot+t1bot))))
+                        rval.append(div(mul(*(
+                            t0top + t1top)), mul(*(t0bot + t1bot))))
 
                         if len(rval) > 100:
                             # This loop can be exponentially long.
                             # aborting
                             return []
-        elif len(node.outputs)>1:
+        elif len(node.outputs) > 1:
             return []
         else:
             return [node.outputs[0]]

theano/tensor/randomstreams.py

@@ -136,7 +136,7 @@ class RandomStreams(Component, raw_random.RandomStreamsBase):
 
     """
 
-    def __init__(self, seed=None, no_warn = False):
+    def __init__(self, seed=None, no_warn=False):
         """:type seed: None or int
 
         :param seed: a default seed to initialize the RandomState
@@ -146,7 +146,7 @@ class RandomStreams(Component, raw_random.RandomStreamsBase):
         """
         if not no_warn:
             deprecation_warning()
-        super(RandomStreams, self).__init__(no_warn = True)
+        super(RandomStreams, self).__init__(no_warn=True)
         self.random_state_variables = []
         self.default_instance_seed = seed
 

theano/tensor/tests/test_elemwise.py

@@ -47,7 +47,8 @@ class test_DimShuffle(unittest_tools.InferShapeTester):
             #test that DimShuffle.infer_shape work correctly
             x = TensorType('float64', ib)('x')
             e = DimShuffle(ib, shuffle)(x)
-            f = copy(linker).accept(FunctionGraph([x], [e.shape])).make_function()
+            f = copy(linker).accept(FunctionGraph([x], [e.
+                shape])).make_function()
             assert all(f(numpy.ones(xsh))) == all(zsh)
 
         # Test when we drop a axis that is not broadcastable
@@ -125,7 +126,8 @@ class test_Broadcast(unittest.TestCase):
                 x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
                 y = TensorType('float64', [(entry == 1) for entry in ysh])('y')
                 e = Elemwise(scalar.add)(x, y)
-                f = copy(linker).accept(FunctionGraph([x, y], [e.shape])).make_function()
+                f = copy(linker).accept(FunctionGraph([x,
+                     y], [e.shape])).make_function()
                 assert tuple(f(xv, yv)) == tuple(zv.shape)
 
     def with_linker_inplace(self, linker):
@@ -154,7 +156,8 @@ class test_Broadcast(unittest.TestCase):
                 x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
                 y = TensorType('float64', [(entry == 1) for entry in ysh])('y')
                 e = Elemwise(scalar.Add(scalar.transfer_type(0)), {0: 0})(x, y)
-                f = copy(linker).accept(FunctionGraph([x, y], [e.shape])).make_function()
+                f = copy(linker).accept(FunctionGraph([x,
+                     y], [e.shape])).make_function()
                 xv = numpy.asarray(numpy.random.rand(*xsh))
                 yv = numpy.asarray(numpy.random.rand(*ysh))
                 zv = xv + yv
@@ -349,7 +352,8 @@ class test_CAReduce(unittest_tools.InferShapeTester):
                     e = tensor_op(x, axis=tosum)
                 if tosum is None:
                     tosum = range(len(xsh))
-                f = copy(linker).accept(FunctionGraph([x], [e.shape])).make_function()
+                f = copy(linker).accept(FunctionGraph([x],
+                     [e.shape])).make_function()
                 if not(scalar_op in [scalar.maximum, scalar.minimum] and
                        ((xsh == () or numpy.prod(xsh) == 0))):
                     assert all(f(xv) == zv.shape)
@@ -459,7 +463,8 @@ class test_Prod(unittest.TestCase):
 
         # including zeros, as the case with zeros is important
         # (and special cases: 1 zero in the row, more than 1 zero in the row)
-        x_val = numpy.asarray([[1,2,3],[4,5,6],[7,8,9]], dtype='float32')
+        x_val = numpy.asarray([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+             dtype='float32')
         x = theano.tensor.dmatrix()
         # now with verify_grad
         unittest_tools.verify_grad(Prod(axis=1), [x_val], mode=self.mode)
@@ -471,26 +476,28 @@ class test_Prod(unittest.TestCase):
 
         unittest_tools.verify_grad(fn, [x_val], mode=self.mode)
 
-
     def test_verify_grad_with_zeros(self):
         # including zeros, as the case with zeros is important
         # (and special cases: 1 zero in the row, more than 1 zero in the row)
-        x_val = numpy.asarray([[1.,2.,3.],[0.,5.,6.],[0.,0.,9.]], dtype='float32')
+        x_val = numpy.asarray([[1., 2., 3.], [0., 5., 6.], [0., 0., 9.]],
+             dtype='float32')
         x = theano.tensor.dmatrix()
 
         # sanity check
         x2 = theano.tensor.dmatrix()
         p = Prod(axis=1)(x)
         p2 = Prod(axis=1)(x2)
-        fn = theano.function([x,x2],[p-p2], mode=self.mode)
+        fn = theano.function([x, x2], [p - p2], mode=self.mode)
         #print "hand computed diff for each row"
-        x2_val = numpy.asarray([[1., 2., 3.003], [0.003,5.,6], [0.,0.,9.01]])
+        x2_val = numpy.asarray([[1., 2., 3.003], [0.003, 5., 6], [
+            0., 0., 9.01]])
         #print fn(x_val, x2_val)
-        fn2 = theano.function([x],[theano.tensor.grad(p.sum(),x)], mode=self.mode)
+        fn2 = theano.function([x], [theano.tensor.grad(p.sum(), x)],
+             mode=self.mode)
         #print "real grad"
         #print fn2(x_val)
-        fn3 = theano.function([x],[p], mode=self.mode)
-        assert numpy.allclose(fn3(x_val), [6.,0.,0.])
+        fn3 = theano.function([x], [p], mode=self.mode)
+        assert numpy.allclose(fn3(x_val), [6., 0., 0.])
 
         # now with verify_grad
         unittest_tools.verify_grad(Prod(axis=1), [x_val], mode=self.mode)
@@ -511,10 +518,10 @@ class test_Prod(unittest.TestCase):
 
     def test_prod_without_zeros(self):
         x = theano.tensor.dmatrix()
-        x_val = numpy.array([[1,2,3],[0,5,6],[0,0,9]], dtype='float32')
+        x_val = numpy.array([[1, 2, 3], [0, 5, 6], [0, 0, 9]], dtype='float32')
         pwz = ProdWithoutZeros(axis=1)(x)
         fn = theano.function([x], pwz, mode=self.mode)
-        assert numpy.allclose(fn(x_val), [6,30,9])
+        assert numpy.allclose(fn(x_val), [6, 30, 9])
 
         pwz_a0 = ProdWithoutZeros(axis=0)(x)
         fn_a0 = theano.function([x], pwz_a0, mode=self.mode)
@@ -522,25 +529,30 @@ class test_Prod(unittest.TestCase):
 
     def test_other_grad_tests(self):
         x = theano.tensor.dmatrix()
-        x_val1 = numpy.array([[1,2,3],[0,5,6],[0,0,9]], dtype='float32')
-        x_val2 = numpy.array([[1,2,0],[0,5,6],[7,8,9],[9,10,0]], dtype='float32')
+        x_val1 = numpy.array([[1, 2, 3], [0, 5, 6], [0, 0, 9]],
+             dtype='float32')
+        x_val2 = numpy.array([[1, 2, 0], [0, 5, 6], [7, 8, 9], [9, 10, 0]],
+             dtype='float32')
         rng = rng = numpy.random.RandomState(43)
 
         p = Prod(axis=1)
         grad_p = theano.tensor.grad(p(x).sum(), x)
         grad_fn = theano.function([x], grad_p, mode=self.mode)
-        assert numpy.allclose(grad_fn(x_val1), [[6.,3.,2.],[30.,0.,0.],[0.,0.,0.]])
-        assert numpy.allclose(grad_fn(x_val2), [[0., 0., 2.], [30., 0., 0.], [72., 63., 56.], [0., 0., 90.]])
+        assert numpy.allclose(grad_fn(x_val1), [[6., 3., 2.], [30., 0.,
+            0.], [0., 0., 0.]])
+        assert numpy.allclose(grad_fn(x_val2), [[0., 0., 2.], [30.,
+             0., 0.], [72., 63., 56.], [0., 0., 90.]])
 
         p_axis0 = Prod(axis=0)
         grad_p_axis0 = theano.tensor.grad(p_axis0(x).sum(), x)
         grad_fn_axis0 = theano.function([x], grad_p_axis0, mode=self.mode)
-        assert numpy.allclose(grad_fn_axis0(x_val2), [[0., 400., 0.],[63., 160., 0.], [0., 100., 0.], [0., 80., 0.]])
+        assert numpy.allclose(grad_fn_axis0(x_val2), [[0., 400.,
+             0.], [63., 160., 0.], [0., 100., 0.], [0., 80., 0.]])
 
         tensor.verify_grad(p, [x_val1], rng=rng, mode=self.mode)
 
     def test_mul_without_zeros_zeros(self):
-        a = numpy.zeros((3,3))
+        a = numpy.zeros((3, 3))
 
         x = theano.tensor.dmatrix()
 
@@ -655,6 +667,7 @@ class T_sum_dtype(unittest.TestCase):
 
                 idx += 1
 
+
 class T_mean_dtype(unittest.TestCase):
     def test_mean_default_dtype(self):
         """
@@ -710,6 +723,7 @@ class T_mean_dtype(unittest.TestCase):
 
                 idx += 1
 
+
 class T_prod_dtype(unittest.TestCase):
     def test_prod_default_dtype(self):
         """
@@ -761,6 +775,7 @@ class T_prod_dtype(unittest.TestCase):
 
                 idx += 1
 
+
 class T_prod_without_zeros_dtype(unittest.TestCase):
     def test_prod_without_zeros_default_dtype(self):
         """
@@ -844,11 +859,8 @@ if __name__ == '__main__':
 """
 
 
-
 if __name__ == '__main__':
 
     t = TestElemwise('setUp')
     t.setUp()
     t.test_infer_shape()
-
-