fixed test_mat_reciprocal unittest (seed issue) + merge

doc/basic_tutorial/module.txt

@@ -184,7 +184,6 @@ boilerplate code.
 All we need to do to use this mechanism is to give a method called
 ``_instance_print_state`` to our Module class.
 
-.. literalinclude:: ../examples/module/mechanism1.py
 
 Any method called like ``_instance_XXX`` will cause the object
 obtained through a call to ``make`` to have a method called ``XXX``.
@@ -204,7 +203,6 @@ If a number of instance methods are going to be defined, and especially if you
 will want to inherit from the kind of class that gets instantiated by make,
 you might prefer to consider using the InstanceType mechanism.
 
-.. literalinclude:: ../examples/module/mechanism2.py
 
 Adding custom initialization
 ============================
@@ -221,7 +219,6 @@ can override the default with your own method, which has to be called
 Here is an example where we take width and height arguments to
 initialize a state with a matrix of zeros:
 
-.. literalinclude:: ../examples/module/accumulator.py
 
 
 Nesting Modules
@@ -231,7 +228,6 @@ Probably the most powerful feature of theano's modules is that one can be
 included as an attribute to another so that the storage of each is available
 to both.
 
-.. literalinclude:: ../examples/module/nested.py
 
 As you read through examples of Theano code, you will probably see many
 instances of Modules being nested in this way.

doc/install.txt

@@ -108,13 +108,6 @@ automatic code generation, but that way is much, much slower.
 
     Omitting this variable defaults the mode to 'FAST_RUN'.
 
-- `THEANO_UNITTEST_SEED`:
-    An integer value specifying which seed should be used when
-    running unit tests. If this variable is not defined, the seed will default
-    to 666. Users wishing for non-deterministic behaviour can set this
-    variable to 'random'.
-
-
 Mac
 ---
 

theano/tensor/tests/test_basic.py

@@ -1305,6 +1305,9 @@ class test_matinv(unittest.TestCase):
         # and none of the dimensions are constrained to have length 1.
         # Note that TensorType's constructor does not actually allocate any memory.
         # TODO: Make TensorType syntax more explicit, and maybe give shape or number of dimensions.
+
+        unittest_tools.seed_rng()
+
         a, b = matrices('ab')
         ab = a*b
         # Here, as_tensor_variable actually uses the data allocated by numpy.
@@ -1334,7 +1337,7 @@ class test_matinv(unittest.TestCase):
         """Matrix reciprocal by gradient descent"""
         ssd0,ssd = self.mat_reciprocal(3)
 
-        numpy.random.seed(unittest_tools.fetch_seed(1))
+        unittest_tools.seed_rng()
         # hand-coded numpy implementation for verification
         x = numpy.random.rand(3,3)+0.1
         w = numpy.random.rand(3,3)