Cosmetic changes

theano/sparse/tests/test_basic.py

@@ -169,15 +169,17 @@ class test_structureddot(unittest.TestCase):
                 # iterate for a few different random graph patterns
                 for i in range(10):
                     spmat = sp.csc_matrix((4,6), dtype=sparse_dtype)
-                    for i in range(5):
+                    for k in range(5):
                         # set non-zeros in random locations (row x, col y)
                         x = numpy.floor(numpy.random.rand()*spmat.shape[0])
                         y = numpy.floor(numpy.random.rand()*spmat.shape[1])
                         spmat[x,y] = numpy.random.rand()*10
                     spmat = sp.csc_matrix(spmat)
                
-                    kerns = tensor.Tensor(broadcastable=[False], dtype=sparse_dtype)('kerns')
-                    images = tensor.Tensor(broadcastable=[False, False], dtype=dense_dtype)('images')
+                    kerns = tensor.Tensor(broadcastable=[False],
+                            dtype=sparse_dtype)('kerns')
+                    images = tensor.Tensor(broadcastable=[False, False],
+                            dtype=dense_dtype)('images')
 
                     output_dtype = theano.scalar.upcast(sparse_dtype, dense_dtype)
                     ##
@@ -186,7 +188,8 @@ class test_structureddot(unittest.TestCase):
 
                     # build symbolic theano graph
                     def buildgraphCSC(kerns,images):
-                        csc = CSC(kerns, spmat.indices[:spmat.size], spmat.indptr, spmat.shape)
+                        csc = CSC(kerns, spmat.indices[:spmat.size],
+                                spmat.indptr, spmat.shape)
                         assert csc.type.dtype == sparse_dtype
                         rval = structured_dot(csc, images.T)
                         assert rval.type.dtype == output_dtype
@@ -197,8 +200,12 @@ class test_structureddot(unittest.TestCase):
 
                     # compute theano outputs
                     kernvals = spmat.data[:spmat.size]
-                    imvals = 1.0 + 1.0 * numpy.array(numpy.arange(bsize*spmat.shape[1]).\
+                    imvals = 1.0 + 1.0 * numpy.array(
+                            numpy.arange(bsize*spmat.shape[1]).\
                             reshape(bsize,spmat.shape[1]), dtype=dense_dtype)
+                    #print('dense_dtype=%s' % dense_dtype)
+                    #print('sparse_dtype=%s' % sparse_dtype)
+                    #print('i=%s' % i)
                     print 'kerntype', str(kernvals.dtype), kernvals.dtype.num
                     outvals = f(kernvals,imvals)
                     print 'YAY'
@@ -210,9 +217,10 @@ class test_structureddot(unittest.TestCase):
                     assert _is_dense(c)
                     assert str(outvals.dtype) == output_dtype
                     assert numpy.all(numpy.abs(outvals - 
-                                     numpy.array(c, dtype=output_dtype)) < 1e-4)
+                        numpy.array(c, dtype=output_dtype)) < 1e-4)
 
-                    if sparse_dtype.startswith('float') and dense_dtype.startswith('float'):
+                    if (sparse_dtype.startswith('float') and
+                            dense_dtype.startswith('float')):
                         utt.verify_grad(buildgraphCSC, 
                                 [kernvals, imvals])