Merge pull request #365 from delallea/minor

theano/gof/op.py

@@ -468,17 +468,17 @@ class PureOp(object):
 
 
         returns: a list of n elements
-                    rval[i] should be Rop(f = f_i(inputs),
-                                          wrt = inputs,
-                                          eval_points = eval_points)
+                    rval[i] should be Rop(f=f_i(inputs),
+                                          wrt=inputs,
+                                          eval_points=eval_points)
 
         """
-
-        raise NotImplementedError(str(self)+' of type '+str(self.__class__.__name__)
-                +" does not "
+        raise NotImplementedError(
+                "%s of class %s does not "
                 "implement R_op. If this is a theano op, write to the "
                 "theano-dev mailing list for assistance. If it is your "
-                "own op, implement the R_op method.")
+                "own op, implement the R_op method." %
+                (self, self.__class__.__name__))
 
 
     def perform(self, node, inputs, output_storage):
@@ -649,7 +649,7 @@ def debug_error_message(msg):
         warnings.warn(msg, stacklevel=2)
 
 
-def debug_assert(condition, msg = None):
+def debug_assert(condition, msg=None):
     if msg is None:
         msg = 'debug_assert failed'
     if not condition:

theano/ifelse.py

@@ -317,7 +317,7 @@ def ifelse(condition, then_branch, else_branch, name=None):
     # we will store them in these new_... lists.
     new_then_branch = []
     new_else_branch = []
-    for then_branch_elem, else_branch_elem in zip(then_branch, else_branch):
+    for then_branch_elem, else_branch_elem in izip(then_branch, else_branch):
         if not isinstance(then_branch_elem, theano.Variable):
             then_branch_elem = theano.tensor.as_tensor_variable(then_branch_elem)
         if not isinstance(else_branch_elem, theano.Variable):
@@ -341,12 +341,12 @@ def ifelse(condition, then_branch, else_branch, name=None):
             if then_branch_elem.type != else_branch_elem.type:
                 # If the types still don't match, there is a problem.
                 raise ValueError(
-                         ('The two branches should have identical types, '
-                          ' but they are '+str(then_branch_elem.type)+' and '+
-                          str(else_branch_elem.type)+' respectively. '
-                          'This error could be raised if for example '
-                          ' you provided a one element list on the then '
-                          ' branch but a tensor on the else branch'))
+                        'The two branches should have identical types, but '
+                        'they are %s and %s respectively. This error could be '
+                        'raised if for example you provided a one element '
+                        'list on the `then` branch but a tensor on the `else` '
+                        'branch.' %
+                        (then_branch_elem.type, else_branch_elem.type))
 
         new_then_branch.append(then_branch_elem)
         new_else_branch.append(else_branch_elem)

theano/printing.py

@@ -944,7 +944,7 @@ def min_informative_str(obj, indent_level=0,
     obj: the name to convert to a string
     indent_level: the number of tabs the tree should start printing at
                   (nested levels of the tree will get more tabs)
-    _prev_obs: should only be used to by min_informative_str
+    _prev_obs: should only be used by min_informative_str
                     a dictionary mapping previously converted
                     objects to short tags
 

theano/scan_module/scan_op.py

@@ -1298,11 +1298,12 @@ class Scan(PureOp):
                 else:
                     dim_offset = 0
                 if maxtap == mintap and maxtap != 0:
-                    nw_seq =seq[:abs(maxtap)]
+                    nw_seq = seq[:abs(maxtap)]
                 elif maxtap -k != 0 :
-                    nw_seq = seq[dim_offset +k -mintap - 1: -(maxtap -k + 1)][::-1]
+                    tmp = seq[dim_offset + k - mintap - 1:-(maxtap -k + 1)]
+                    nw_seq = tmp[::-1]
                 else:
-                    nw_seq = seq[dim_offset +k -mintap - 1: -1 ][::-1]
+                    nw_seq = seq[dim_offset + k - mintap - 1: -1][::-1]
                 if getattr(seq,'name', None) is not None:
                     nw_seq.name = seq.name + '[%d:]'%k
                 scan_seqs.append(nw_seq)
@@ -1593,7 +1594,7 @@ class Scan(PureOp):
         # Outputs
         n_mit_mot_outs = int(numpy.sum([len(x) for x in
                                         self.mit_mot_out_slices]))
-        info['n_mit_mot_outs'] = n_mit_mot_outs*2
+        info['n_mit_mot_outs'] = n_mit_mot_outs * 2
         b = 0
         e = n_mit_mot_outs
         inner_out_mit_mot = self_outputs[b:e] + rop_outs[b:e]

theano/tensor/elemwise.py

@@ -687,16 +687,17 @@ class Elemwise(Op):
                             msg2 += [str(d)]
                     msg.append('(%s)' % ", ".join(msg2))
 
-                base_exc_str = 'Dimension mismatch; shapes are %s' % (', '.join(msg))
+                base_exc_str = 'Dimension mismatch; shapes are %s' % (
+                               ', '.join(msg))
                 if config.exception_verbosity == 'high':
-                    msg_chunks = [ base_exc_str ]
+                    msg_chunks = [base_exc_str]
                     for i, ipt in enumerate(node.inputs):
-                        msg_chunks.append('input %d: %s' % (i, min_informative_str(ipt)))
+                        msg_chunks.append('input %d: %s' %
+                                          (i, min_informative_str(ipt)))
                     raise ValueError('\n'.join(msg_chunks))
                 else:
                     raise ValueError(base_exc_str)
 
-                                 #', '.join('(%s)' % ', '.join(msg)))
                 #backport
                 #raise ValueError('Dimension mismatch; shapes are %s' %
                 #                 ', '.join('(%s)' % ', '.join('*' if b else str(d)

theano/tensor/tensor_grad.py

@@ -28,13 +28,13 @@ def format_as(use_list, use_tuple, outputs):
     """
     Formats the outputs according to the flags `use_list` and `use_tuple`.
     If `use_list` is True, `outputs` is returned as a list (if `outputs`
-    is not a list or a tuple then it is converted in a one element list)
+    is not a list or a tuple then it is converted in a one element list).
     If `use_tuple` is True, `outputs` is returned as a tuple (if `outputs`
-    is not a list or a tuple then it is converted into a one element tuple)
+    is not a list or a tuple then it is converted into a one element tuple).
     Otherwise (if both flags are false), `outputs` is returned.
     """
     assert not (use_list and use_tuple), \
-            "Both flags can not be simultaneously True"
+            "Both flags cannot be simultaneously True"
     if (use_list or use_tuple) and not isinstance(outputs, (list, tuple)):
         if use_list:
             return [outputs]
@@ -94,8 +94,8 @@ def Rop(f, wrt, eval_points):
 
     assert len(wrt) == len(eval_points)
 
-    #check that each element of wrt corresponds to an element
-    #of eval_points with the same dimensionality
+    # Check that each element of wrt corresponds to an element
+    # of eval_points with the same dimensionality.
     for pack in enumerate(zip(wrt, eval_points)):
         i = pack[0]
         wrt_elem, eval_point = pack[1]
@@ -701,14 +701,15 @@ class GradientError(Exception):
     def __str__(self):
         # args may have been inserted by e.g. makeTester
         args_msg = ", ".join(str(a) for a in self.args)
-        return """GradientError: numeric gradient and analytic gradient exceed tolerance:
+        return """\
+GradientError: numeric gradient and analytic gradient exceed tolerance:
         At position %i of argument %i,
             abs. error = %f,  abs. tolerance = %f
-            rel. error = %f,  rel. tolerance = %f\nException args: %s
-        """ %(self.err_pos, self.arg,
-              self.abs_err, self.abs_tol,
-              self.rel_err, self.rel_tol,
-              args_msg)
+            rel. error = %f,  rel. tolerance = %f
+Exception args: %s""" % (self.err_pos, self.arg,
+                         self.abs_err, self.abs_tol,
+                         self.rel_err, self.rel_tol,
+                         args_msg)
 
 verify_grad.E_grad = GradientError
 
@@ -789,8 +790,8 @@ def hessian(cost, wrt, consider_constant=None, warn_type=False,
              disconnected_inputs='raise'):
     """
     :type cost: Scalar (0-dimensional) `Variable`
-    :type wrt: Vector (1-dimensional tensors) 'Variable' or list of
-            vectors (1-dimensional tensors) `Variables`s
+    :type wrt: Vector (1-dimensional tensor) 'Variable' or list of
+            vectors (1-dimensional tensors) `Variable`s
 
     :param consider_constant: a list of expressions not to backpropagate
         through

theano/tensor/tests/test_2nd_order_grads.py

@@ -94,7 +94,7 @@ def test002_jacobian_matrix():
         evx[dx, dx, :] = vx[dx, :]
         evz[dx, dx, :] = vz[dx, :]
     assert numpy.allclose(vJs[0], evz)
-    assert numpy.allclose(vJs[1 ], evx)
+    assert numpy.allclose(vJs[1], evx)
 
 
 def test003_jacobian_scalar():
@@ -117,7 +117,7 @@ def test003_jacobian_scalar():
 
     # test when the jacobian is called with a list as wrt
     Jx = tensor.jacobian(y, [x])
-    assert isinstance(Jx,list)
+    assert isinstance(Jx, list)
     f = theano.function([x], Jx[0])
     vx = numpy.cast[theano.config.floatX](rng.uniform())
     assert numpy.allclose(f(vx), 2)