made code pep8 compatible

theano/tests/test_ifelse.py

@@ -3,7 +3,7 @@
 """
 
 __docformat__ = 'restructedtext en'
-__authors__ = ( "Razvan Pascanu " )
+__authors__ = ("Razvan Pascanu ")
 __copyright__ = "(c) 2010, Universite de Montreal"
 __contact__ = "Razvan Pascanu <r.pascanu@gmail>"
 
@@ -25,7 +25,7 @@ class test_ifelse(unittest.TestCase):
         x = tensor.vector('x')
         y = tensor.vector('y')
         c = tensor.iscalar('c')
-        f = theano.function([c,x,y], ifelse(c,x,y))
+        f = theano.function([c, x, y], ifelse(c, x, y))
         rng = numpy.random.RandomState(utt.fetch_seed())
 
         xlen = rng.randint(200)
@@ -34,18 +34,18 @@ class test_ifelse(unittest.TestCase):
         vx = numpy.asarray(rng.uniform(size=(xlen,)), theano.config.floatX)
         vy = numpy.asarray(rng.uniform(size=(ylen,)), theano.config.floatX)
 
-        assert numpy.allclose(vx, f(1,vx,vy))
-        assert numpy.allclose(vy, f(0,vx,vy))
+        assert numpy.allclose(vx, f(1, vx, vy))
+        assert numpy.allclose(vy, f(0, vx, vy))
 
     def test_grad_lazy_if(self):
         # Tests that we can compute the gradients through lazy if
         x = tensor.vector('x')
         y = tensor.vector('y')
         c = tensor.iscalar('c')
-        z = ifelse(c,x,y)
-        gx,gy = tensor.grad(z.sum(), [x,y])
+        z = ifelse(c, x, y)
+        gx, gy = tensor.grad(z.sum(), [x, y])
 
-        f = theano.function([c,x,y], [gx,gy])
+        f = theano.function([c, x, y], [gx, gy])
         rng = numpy.random.RandomState(utt.fetch_seed())
 
         xlen = rng.randint(200)
@@ -53,38 +53,35 @@ class test_ifelse(unittest.TestCase):
 
         vx = numpy.asarray(rng.uniform(size=(xlen,)), theano.config.floatX)
         vy = numpy.asarray(rng.uniform(size=(ylen,)), theano.config.floatX)
-        gx0,gy0 = f(1, vx,vy)
-        assert numpy.allclose( gx0.shape, vx.shape)
-        assert numpy.allclose( gy0.shape, vy.shape)
-        assert numpy.all( gx0 == 1.)
-        assert numpy.all( gy0 == 0.)
-
-
-        gx0,gy0 = f(0, vx,vy)
-        assert numpy.allclose( gx0.shape, vx.shape)
-        assert numpy.allclose( gy0.shape, vy.shape)
-        assert numpy.all( gx0 == 0.)
-        assert numpy.all( gy0 == 1.)
-
+        gx0, gy0 = f(1, vx, vy)
+        assert numpy.allclose(gx0.shape, vx.shape)
+        assert numpy.allclose(gy0.shape, vy.shape)
+        assert numpy.all(gx0 == 1.)
+        assert numpy.all(gy0 == 0.)
+
+        gx0, gy0 = f(0, vx, vy)
+        assert numpy.allclose(gx0.shape, vx.shape)
+        assert numpy.allclose(gy0.shape, vy.shape)
+        assert numpy.all(gx0 == 0.)
+        assert numpy.all(gy0 == 1.)
 
     def test_merge(self):
-        x  = tensor.vector('x')
-        y  = tensor.vector('y')
-        c  = tensor.iscalar('c')
-        z1 = ifelse(c, x+1, y+1)
-        z2 = ifelse(c, x+2, y+2)
-        z  = z1+z2
-        f = theano.function([c,x,y], z)
+        x = tensor.vector('x')
+        y = tensor.vector('y')
+        c = tensor.iscalar('c')
+        z1 = ifelse(c, x + 1, y + 1)
+        z2 = ifelse(c, x + 2, y + 2)
+        z = z1 + z2
+        f = theano.function([c, x, y], z)
         assert len([x for x in f.maker.env.toposort()
                     if isinstance(x.op, IfElse)]) == 1
 
-
     def test_remove_useless_inputs1(self):
         x = tensor.vector('x')
         y = tensor.vector('y')
         c = tensor.iscalar('c')
-        z = ifelse(c, (x,x), (y,y))
-        f = theano.function([c,x,y],z)
+        z = ifelse(c, (x, x), (y, y))
+        f = theano.function([c, x, y], z)
 
         ifnode = [x for x in f.maker.env.toposort()
                   if isinstance(x.op, IfElse)][0]
@@ -96,14 +93,13 @@ class test_ifelse(unittest.TestCase):
         y1 = tensor.vector('y1')
         y2 = tensor.vector('y2')
         c = tensor.iscalar('c')
-        z = ifelse(c, (x1,x1,x1,x2,x2), (y1,y1,y2,y2,y2))
-        f = theano.function([c,x1,x2,y1,y2],z)
+        z = ifelse(c, (x1, x1, x1, x2, x2), (y1, y1, y2, y2, y2))
+        f = theano.function([c, x1, x2, y1, y2], z)
 
         ifnode = [x for x in f.maker.env.toposort()
                   if isinstance(x.op, IfElse)][0]
         assert len(ifnode.outputs) == 3
 
-
     def test_pushout1(self):
         x1 = tensor.scalar('x1')
         x2 = tensor.scalar('x2')
@@ -111,13 +107,13 @@ class test_ifelse(unittest.TestCase):
         y2 = tensor.scalar('y2')
         w1 = tensor.scalar('w1')
         w2 = tensor.scalar('w2')
-        c  = tensor.iscalar('c')
-        x,y = ifelse(c, (x1,y1), (x2,y2), name='f1')
-        z   = ifelse(c, w1,w2, name='f2')
-        out = x*z*y
+        c = tensor.iscalar('c')
+        x, y = ifelse(c, (x1, y1), (x2, y2), name='f1')
+        z = ifelse(c, w1, w2, name='f2')
+        out = x * z * y
 
-        f = theano.function([x1,x2,y1,y2,w1,w2,c], out,
-                            allow_input_downcast = True)
+        f = theano.function([x1, x2, y1, y2, w1, w2, c], out,
+                            allow_input_downcast=True)
         assert isinstance(f.maker.env.toposort()[-1].op, IfElse)
         rng = numpy.random.RandomState(utt.fetch_seed())
         vx1 = rng.uniform()
@@ -127,28 +123,33 @@ class test_ifelse(unittest.TestCase):
         vw1 = rng.uniform()
         vw2 = rng.uniform()
 
-        assert numpy.allclose(f(vx1,vx2,vy1,vy2,vw1,vw2,1), vx1*vy1*vw1)
-        assert numpy.allclose(f(vx1,vx2,vy1,vy2,vw1,vw2,0), vx2*vy2*vw2)
+        assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1),
+                              vx1 * vy1 * vw1)
+        assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0),
+                              vx2 * vy2 * vw2)
 
     def test_pushout3(self):
         x1 = tensor.scalar('x1')
         y1 = tensor.scalar('x2')
         y2 = tensor.scalar('y2')
-        c  = tensor.iscalar('c')
-        x,y = ifelse(c, (x1,y1), (numpy.asarray(2, dtype = theano.config.floatX),y2), name='f1')
-        z   = ifelse(c, numpy.asarray(0.3, dtype=theano.config.floatX),numpy.asarray(0.2,dtype=theano.config.floatX), name='f2')
-        out = x*z*y
-
-        f = theano.function([x1,y1,y2,c], out,
-                            allow_input_downcast = True)
+        c = tensor.iscalar('c')
+        two = numpy.asarray(2, dtype=theano.config.floatX)
+        x, y = ifelse(c, (x1, y1), (two, y2), name='f1')
+        o3 = numpy.asarray(0.3, dtype=theano.config.floatX)
+        o2 = numpy.asarray(0.2, dtype=theano.config.floatX)
+        z = ifelse(c, o3, o2, name='f2')
+        out = x * z * y
+
+        f = theano.function([x1, y1, y2, c], out,
+                            allow_input_downcast=True)
         assert isinstance(f.maker.env.toposort()[-1].op, IfElse)
         rng = numpy.random.RandomState(utt.fetch_seed())
         vx1 = rng.uniform()
         vy1 = rng.uniform()
         vy2 = rng.uniform()
 
-        assert numpy.allclose(f(vx1,vy1,vy2,1), vx1*vy1*0.3)
-        assert numpy.allclose(f(vx1,vy1,vy2,0), 2*vy2*0.2)
+        assert numpy.allclose(f(vx1, vy1, vy2, 1), vx1 * vy1 * 0.3)
+        assert numpy.allclose(f(vx1, vy1, vy2, 0), 2 * vy2 * 0.2)
 
     def test_pushout2(self):
         x1 = tensor.scalar('x1')
@@ -157,13 +158,13 @@ class test_ifelse(unittest.TestCase):
         y2 = tensor.scalar('y2')
         w1 = tensor.scalar('w1')
         w2 = tensor.scalar('w2')
-        c  = tensor.iscalar('c')
-        x,y = ifelse(c, (x1,y1), (x2,y2), name='f1')
-        z   = ifelse(x>y, w1,w2, name='f2')
-        out = x*z*y
+        c = tensor.iscalar('c')
+        x, y = ifelse(c, (x1, y1), (x2, y2), name='f1')
+        z = ifelse(x > y, w1, w2, name='f2')
+        out = x * z * y
 
-        f = theano.function([x1,x2,y1,y2,w1,w2,c], out,
-                            allow_input_downcast = True)
+        f = theano.function([x1, x2, y1, y2, w1, w2, c], out,
+                            allow_input_downcast=True)
         assert isinstance(f.maker.env.toposort()[-1].op, IfElse)
         rng = numpy.random.RandomState(utt.fetch_seed())
         vx1 = rng.uniform()
@@ -176,13 +177,15 @@ class test_ifelse(unittest.TestCase):
             vw = vw1
         else:
             vw = vw2
-        assert numpy.allclose(f(vx1,vx2,vy1,vy2,vw1,vw2,1), vx1*vy1*vw)
+        assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1),
+                              vx1 * vy1 * vw)
 
         if vx2 > vy2:
             vw = vw1
         else:
             vw = vw2
-        assert numpy.allclose(f(vx1,vx2,vy1,vy2,vw1,vw2,0), vx2*vy2*vw)
+        assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0),
+                              vx2 * vy2 * vw)
 
     def test_merge_ifs_true_false(self):
         x1 = tensor.scalar('x1')
@@ -191,15 +194,15 @@ class test_ifelse(unittest.TestCase):
         y2 = tensor.scalar('y2')
         w1 = tensor.scalar('w1')
         w2 = tensor.scalar('w2')
-        c  = tensor.iscalar('c')
+        c = tensor.iscalar('c')
 
         out = ifelse(c,
-            ifelse(c, x1,x2) + ifelse(c,y1,y2) + w1,
-            ifelse(c, x1,x2) + ifelse(c,y1,y2) + w2)
-        f = theano.function([x1,x2,y1,y2,w1,w2,c], out,
-                            allow_input_downcast = True)
+            ifelse(c, x1, x2) + ifelse(c, y1, y2) + w1,
+            ifelse(c, x1, x2) + ifelse(c, y1, y2) + w2)
+        f = theano.function([x1, x2, y1, y2, w1, w2, c], out,
+                            allow_input_downcast=True)
         assert len([x for x in f.maker.env.toposort()
-                if isinstance(x.op, IfElse)])==1
+                if isinstance(x.op, IfElse)]) == 1
 
         rng = numpy.random.RandomState(utt.fetch_seed())
         vx1 = rng.uniform()
@@ -208,13 +211,11 @@ class test_ifelse(unittest.TestCase):
         vy2 = rng.uniform()
         vw1 = rng.uniform()
         vw2 = rng.uniform()
-        assert numpy.allclose(f(vx1,vx2,vy1,vy2,vw1,vw2,1), vx1+vy1+vw1)
-        assert numpy.allclose(f(vx1,vx2,vy1,vy2,vw1,vw2,0), vx2+vy2+vw2)
-
-
+        assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1),
+                              vx1 + vy1 + vw1)
+        assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0),
+                              vx2 + vy2 + vw2)
 
 
 if __name__ == '__main__':
     print ' Use nosetests to run these tests '
-
-