pep8

theano/tests/diverse_tests.py

@@ -8,24 +8,27 @@ import numpy.random
 from theano.tests import unittest_tools as utt
 
 '''
-  Different tests that are not connected to any particular Op, or functionality of
-  Theano. Here will go for example code that we will publish in papers, that we
-  should ensure that it will remain operational
+  Different tests that are not connected to any particular Op, or
+  functionality of Theano. Here will go for example code that we will
+  publish in papers, that we should ensure that it will remain
+  operational
+
 '''
 
+
 class T_scipy(unittest.TestCase):
     def setUp(self):
         utt.seed_rng()
         self.orig_floatX = theano.config.floatX
+
     def tearDown(self):
         theano.config.floatX = self.orig_floatX
 
-
     def test_scipy_paper_example1(self):
         a = theano.tensor.vector('a')  # declare variable
         b = a + a**10                  # build expression
         f = theano.function([a], b)    # compile function
-        assert numpy.all(f([0,1,2]) == numpy.array([0,2,1026]))
+        assert numpy.all(f([0, 1, 2]) == numpy.array([0, 2, 1026]))
 
     def test_scipy_paper_example2(self):
         ''' This just sees if things compile well and if they run '''
@@ -34,7 +37,7 @@ class T_scipy(unittest.TestCase):
         shared = theano.shared
         function = theano.function
         rng = numpy.random
-        theano.config.floatX='float64'
+        theano.config.floatX = 'float64'
 
         #
         # ACTUAL SCRIPT FROM PAPER
@@ -49,18 +52,18 @@ class T_scipy(unittest.TestCase):
         xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1)
         prediction = p_1 > 0.5
         cost = xent.mean() + 0.01*(w**2).sum()
-        gw,gb = T.grad(cost, [w,b])
+        gw, gb = T.grad(cost, [w, b])
 
         # Compile expressions to functions
         train = function(
-            inputs=[x,y],
+            inputs=[x, y],
             outputs=[prediction, xent],
             updates=[(w, w-0.1*gw), (b, b-0.1*gb)])
         predict = function(inputs=[x], outputs=prediction)
 
         N = 4
         feats = 100
-        D = (rng.randn(N, feats), rng.randint(size=4,low=0, high=2))
+        D = (rng.randn(N, feats), rng.randint(size=4, low=0, high=2))
         training_steps = 10
         for i in range(training_steps):
             pred, err = train(D[0], D[1])
@@ -68,4 +71,3 @@ class T_scipy(unittest.TestCase):
 
 if __name__ == '__main__':
     unittest.main()
-