Merge pull request #1129 from delallea/minor

doc/index.txt

@@ -133,9 +133,11 @@ Community
 
 * Ask/view questions/answers at `metaoptimize/qa/tags/theano`_ (it's like stack overflow for machine learning)
 
-* We try to stay organized with `Assembla's tickets <http://www.assembla.com/spaces/theano/tickets>`__
+* We use `Github tickets <http://github.com/Theano/Theano/issues>`__ to keep track of issues
+  (however, some old tickets can still be found on
+  ``Assembla <http://www.assembla.com/spaces/theano/tickets>`__).
 
-* Come visit us in Montreal!  Most of the developers are students in the LISA_ group at the `University of Montreal`_.
+* Come visit us in Montreal! Most developers are students in the LISA_ group at the `University of Montreal`_.
 
 .. toctree::
    :maxdepth: 1

doc/library/tensor/basic.txt

@@ -1229,7 +1229,7 @@ Linear Algebra
     >>> result = batched_dot(first, second)
 
     :note:  This is a subset of numpy.einsum, but we do not provide it for now.
-    But numpy einsum is slower then dot or tensordot:
+    But numpy einsum is slower than dot or tensordot:
     http://mail.scipy.org/pipermail/numpy-discussion/2012-October/064259.html
 
     :param X: left term

theano/compile/profilemode.py

@@ -671,7 +671,7 @@ Test them first, as they are not guaranteed to always provide a speedup."""
         if not config.lib.amdlibm and any([exp_float32_op(a.op) and
                                            a.inputs[0].dtype == 'float32'
                                            for i, a in apply_time]):
-            print "  - With the default gcc libm, exp in float32 is slower then in float64! Try Theano flag floatX=float64, or install amdlibm and set the theano flags lib.amdlibm=True"
+            print "  - With the default gcc libm, exp in float32 is slower than in float64! Try Theano flag floatX=float64, or install amdlibm and set the theano flags lib.amdlibm=True"
             printed_tip = True
 
         #tip 4

theano/compile/profiling.py

@@ -824,7 +824,7 @@ if 0: # old code still to be ported from ProfileMode
 
         #tip 3
         if not config.lib.amdlibm and any([exp_float32_op(a.op) and a.inputs[0].dtype=='float32' for i,a in apply_time]):
-            print "  - With the default gcc libm, exp in float32 is slower then in float64! Try Theano flags floatX=float64 or install amdlibm and set the theano flags lib.amdlibm=True"
+            print "  - With the default gcc libm, exp in float32 is slower than in float64! Try Theano flags floatX=float64 or install amdlibm and set the theano flags lib.amdlibm=True"
 
         #tip 4
         for a, t in apply_time.iteritems():

theano/tensor/basic.py

@@ -736,7 +736,7 @@ class TensorType(Type):
             except AttributeError:
                 msg = ""
             raise TypeError("The numpy.ndarray object is not aligned."
-                            " Theano c code do not support that.",
+                            " Theano C code does not support that.",
                             msg,
                             "object shape", data.shape,
                             "object strides", data.strides)

theano/tensor/nnet/conv.py

@@ -162,7 +162,7 @@ class ConvOp(OpenMPOp):
 #It is an Intel(R) Xeon(R) CPU E5430 @ 2.66GHz. It is computer with theano/tensor/nnet/tests/speed_test_conv.py
 # and took 5 minutes to run.
 #TODO: we should compute this table for each computer/os as this can change.
-#      I saw on one computer that the speed with the shape can be slower then without!
+#      I saw on one computer that the speed with the shape can be slower than without!
 #      using the real shape and the same dtype could also help.
 
 #unroll_batch, unroll_kern, valid time, full time