Add testcode for typed_list and get rid of test_tutorial.py

doc/library/typed_list.txt

@@ -15,32 +15,29 @@
     tensor.
 
 This is a type that represents a list in Theano. All elements must have
-the same Theano type. Here is an example::
+the same Theano type. Here is an example:
 
-    import theano.typed_list
-
-    tl = theano.typed_list.TypedListType(theano.tensor.fvector)()
-    v = theano.tensor.fvector()
-    o = theano.typed_list.append(tl, v)
-    f = theano.function([tl, v], o)
-    print f([[1, 2, 3], [4, 5]], [2])
-    #[array([ 1.,  2.,  3.], dtype=float32), array([ 4.,  5.], dtype=float32), array([ 2.], dtype=float32)]
+>>> import theano.typed_list
+>>> tl = theano.typed_list.TypedListType(theano.tensor.fvector)()
+>>> v = theano.tensor.fvector()
+>>> o = theano.typed_list.append(tl, v)
+>>> f = theano.function([tl, v], o)
+>>> f([[1, 2, 3], [4, 5]], [2])
+[array([ 1.,  2.,  3.], dtype=float32), array([ 4.,  5.], dtype=float32), array([ 2.], dtype=float32)]
 
 A second example with Scan. Scan doesn't yet have direct support of
 TypedList, so you can only use it as non_sequences (not in sequences or
-as outputs)::
-
-    import theano.typed_list
-
-    a = theano.typed_list.TypedListType(theano.tensor.fvector)()
-    l = theano.typed_list.length(a)
-    s, _ = theano.scan(fn=lambda i, tl: tl[i].sum(),
-                       non_sequences=[a],
-                       sequences=[theano.tensor.arange(l, dtype='int64')])
-
-    f = theano.function([a], s)
-    f([[1, 2, 3], [4, 5]])
-    #array([ 6.,  9.], dtype=float32)
+as outputs):
+
+>>> import theano.typed_list
+>>> a = theano.typed_list.TypedListType(theano.tensor.fvector)()
+>>> l = theano.typed_list.length(a)
+>>> s, _ = theano.scan(fn=lambda i, tl: tl[i].sum(),
+...                    non_sequences=[a],
+...                    sequences=[theano.tensor.arange(l, dtype='int64')])
+>>> f = theano.function([a], s)
+>>> f([[1, 2, 3], [4, 5]])
+array([ 6.,  9.], dtype=float32)
 
 .. automodule:: theano.typed_list.basic
     :members:

theano/tests/test_flake8.py

@@ -37,7 +37,6 @@ whitelist_flake8 = [
     "tests/test_pickle_unpickle_theano_fn.py",
     "tests/test_determinism.py",
     "tests/record.py",
-    "tests/test_tutorial.py",
     "tests/unittest_tools.py",
     "compile/__init__.py",
     "compile/profiling.py",

theano/tests/test_tutorial.py

-""" test code snippet in the Theano tutorials.
-"""
-from __future__ import print_function
-
-import os
-import shutil
-import unittest
-
-from nose.plugins.attrib import attr
-from nose.plugins.skip import SkipTest
-import numpy
-from numpy import array
-
-import theano
-import theano.tensor as T
-from theano import function, compat
-
-from six.moves import xrange
-from theano import config
-from theano.tests import unittest_tools as utt
-from theano.sandbox.rng_mrg import MRG_RandomStreams
-from theano.tensor.shared_randomstreams import RandomStreams
-
-class T_typedlist(unittest.TestCase):
-    # All tests here belong to
-    # http://deeplearning.net/software/theano/library/typed_list.html
-    # Theano/doc/library/typed_list.txt
-    # Any change you do here must also be done in the documentation !
-
-    def test_typedlist_basic(self):
-        import theano.typed_list
-
-        tl = theano.typed_list.TypedListType(theano.tensor.fvector)()
-        v = theano.tensor.fvector()
-        o = theano.typed_list.append(tl, v)
-        f = theano.function([tl, v], o)
-        output = f([[1, 2, 3], [4, 5]], [2])
-
-        # Validate ouput is as expected
-        expected_output = [numpy.array([1, 2, 3], dtype="float32"),
-                           numpy.array([4, 5], dtype="float32"),
-                           numpy.array([2], dtype="float32")]
-
-        assert len(output) == len(expected_output)
-        for i in range(len(output)):
-            utt.assert_allclose(output[i], expected_output[i])
-
-    def test_typedlist_with_scan(self):
-        import theano.typed_list
-
-        a = theano.typed_list.TypedListType(theano.tensor.fvector)()
-        l = theano.typed_list.length(a)
-        s, _ = theano.scan(fn=lambda i, tl: tl[i].sum(),
-                        non_sequences=[a],
-                        sequences=[theano.tensor.arange(l, dtype='int64')])
-
-        f = theano.function([a], s)
-        output = f([[1, 2, 3], [4, 5]])
-
-        # Validate ouput is as expected
-        expected_output = numpy.array([6, 9], dtype="float32")
-        utt.assert_allclose(output, expected_output)