syntax change to respect pep8.

theano/tensor/tests/test_blas.py

@@ -660,9 +660,8 @@ def test_dot_w_self():
 
 def test_dot_vm():
     ''' Test vector dot matrix '''
-    v = theano.shared( numpy.array(numpy.random.rand(2), dtype='float32'))
-    m = theano.shared( numpy.array(numpy.random.rand(2,2),
-                                   dtype='float32'))
+    v = theano.shared(numpy.array(numpy.random.rand(2), dtype='float32'))
+    m = theano.shared(numpy.array(numpy.random.rand(2,2), dtype='float32'))
     f = theano.function([], theano.dot(v,m), mode = mode_blas_opt)
 
     # Assert they produce the same output
@@ -673,8 +672,8 @@ def test_dot_vm():
 
 def test_dot_mv():
     ''' Test matrix dot vector '''
-    v = theano.shared( numpy.array(numpy.random.rand(2), dtype='float32'))
-    m = theano.shared( numpy.array(numpy.random.rand(2,2),
+    v = theano.shared(numpy.array(numpy.random.rand(2), dtype='float32'))
+    m = theano.shared(numpy.array(numpy.random.rand(2,2),
                                    dtype='float32'))
     f = theano.function([], theano.dot(m,v), mode = mode_blas_opt)
 
@@ -686,10 +685,10 @@ def test_dot_mv():
 
 def test_gemv1():
     ''' test vector1+dot(matrix,vector2) '''
-    v1 = theano.shared( numpy.array(numpy.random.rand(2)  , dtype='float32'))
+    v1 = theano.shared(numpy.array(numpy.random.rand(2), dtype='float32'))
     v2_orig = numpy.array(numpy.random.rand(2), dtype='float32')
-    v2 = theano.shared( v2_orig )
-    m  = theano.shared( numpy.array(numpy.random.rand(2,2), dtype='float32'))
+    v2 = theano.shared(v2_orig)
+    m  = theano.shared(numpy.array(numpy.random.rand(2,2), dtype='float32'))
 
     f = theano.function([], v2+theano.dot(m,v1), mode = mode_blas_opt)
 
@@ -716,10 +715,10 @@ def test_gemv1():
 
 def test_gemv2():
     ''' test vector1+dot(vector2,matrix) '''
-    v1 = theano.shared( numpy.array(numpy.random.rand(2)  , dtype='float32'))
+    v1 = theano.shared(numpy.array(numpy.random.rand(2), dtype='float32'))
     v2_orig = numpy.array(numpy.random.rand(2), dtype='float32')
-    v2 = theano.shared( v2_orig )
-    m  = theano.shared( numpy.array(numpy.random.rand(2,2), dtype='float32'))
+    v2 = theano.shared(v2_orig )
+    m  = theano.shared(numpy.array(numpy.random.rand(2,2), dtype='float32'))
 
     f = theano.function([], v2+theano.dot(v1,m), mode = mode_blas_opt)