PEP8

theano/tensor/tests/test_2nd_order_grads.py

@@ -10,6 +10,7 @@ import numpy
 
 utt.seed_rng()
 
+
 def test001_jacobian_vector():
     x = tensor.vector()
     y = x * 2
@@ -42,11 +43,11 @@ def test001_jacobian_vector():
     vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
     vz = rng.uniform(size=(10,)).astype(theano.config.floatX)
     vJs = f(vx, vz)
-    evx = numpy.zeros((10,10))
-    evz = numpy.zeros((10,10))
+    evx = numpy.zeros((10, 10))
+    evz = numpy.zeros((10, 10))
     for dx in xrange(10):
-        evx[dx,dx] = vx[dx]
-        evz[dx,dx] = vz[dx]
+        evx[dx, dx] = vx[dx]
+        evz[dx, dx] = vz[dx]
     assert numpy.allclose(vJs[0], evz)
     assert numpy.allclose(vJs[1], evx)
 
@@ -55,27 +56,27 @@ def test002_jacobian_matrix():
     x = tensor.matrix()
     y = 2 * x.sum(axis=0)
     rng = numpy.random.RandomState(seed=utt.fetch_seed())
-    ev = numpy.zeros((10,10,10))
+    ev = numpy.zeros((10, 10, 10))
     for dx in xrange(10):
-        ev[dx,:,dx] = 2.
+        ev[dx, :, dx] = 2.
 
     # test when the jacobian is called with a tensor as wrt
     Jx = tensor.jacobian(y, x)
     f = theano.function([x], Jx, allow_input_downcast=True)
-    vx = rng.uniform(size=(10,10)).astype(theano.config.floatX)
+    vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
     assert numpy.allclose(f(vx), ev)
 
     # test when the jacobian is called with a tuple as wrt
     Jx = tensor.jacobian(y, (x,))
     assert isinstance(Jx, tuple)
     f = theano.function([x], Jx[0], allow_input_downcast=True)
-    vx = rng.uniform(size=(10,10)).astype(theano.config.floatX)
+    vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
     assert numpy.allclose(f(vx), ev)
 
     # test when the jacobian is called with a list as wrt
     Jx = tensor.jacobian(y, [x])
     f = theano.function([x], Jx[0], allow_input_downcast=True)
-    vx = rng.uniform(size=(10,10)).astype(theano.config.floatX)
+    vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
     assert numpy.allclose(f(vx), ev)
 
     # test when the jacobian is called wit a list of two elements
@@ -83,14 +84,14 @@ def test002_jacobian_matrix():
     y = (x * z).sum(axis=1)
     Js = tensor.jacobian(y, [x, z])
     f = theano.function([x, z], Js, allow_input_downcast=True)
-    vx = rng.uniform(size=(10,10)).astype(theano.config.floatX)
-    vz = rng.uniform(size=(10,10)).astype(theano.config.floatX)
+    vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
+    vz = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
     vJs = f(vx, vz)
-    evx = numpy.zeros((10,10,10))
-    evz = numpy.zeros((10,10,10))
+    evx = numpy.zeros((10, 10, 10))
+    evz = numpy.zeros((10, 10, 10))
     for dx in xrange(10):
-        evx[dx,dx,:] = vx[dx,:]
-        evz[dx,dx,:] = vz[dx,:]
+        evx[dx, dx, :] = vx[dx, :]
+        evz[dx, dx, :] = vz[dx, :]
     assert numpy.allclose(vJs[0], evz)
     assert numpy.allclose(vJs[1], evx)
 
@@ -131,6 +132,7 @@ def test003_jacobian_scalar():
     assert numpy.allclose(vJx[0], vz)
     assert numpy.allclose(vJx[1], vx)
 
+
 def test004_hessian():
     x = tensor.vector()
     y = tensor.sum(x ** 2)