Minimal documentation and example for MockRandomState

doc/extending/unittest.txt

@@ -390,6 +390,11 @@ is incompatible with the method of hard-coding the baseline variables
 determined "algorithmically". Although this represents more work, the
 test suite will be better because of it.
 
+To help you check that the boundaries provided to ``numpy.random`` are
+correct and your tests will pass those corner cases, you can check
+``utt.MockRandomState``. Code using ``utt.MockRandomState`` should not
+be committed, it is just a tool to help adjust the sampling range.
+
 
 Creating an Op UnitTest
 =======================

theano/tensor/tests/test_basic.py

@@ -79,6 +79,11 @@ else:
 # Use a seeded random number generator so that unittests are deterministic
 utt.seed_rng()
 test_rng = numpy.random.RandomState(seed=utt.fetch_seed())
+# In order to check random values close to the boundaries when designing
+# new tests, you can use utt.MockRandomState, for instance:
+# test_rng = MockRandomState(0)
+# test_rng = MockRandomState(0.99999982)
+# test_rng = MockRandomState(1)
 
 
 if PY3:

theano/tests/unittest_tools.py

@@ -120,10 +120,6 @@ class MockRandomState:
             return out + minval
         else:
             return out + maxval - 1
-# Examples of use:
-# test_rng = MockRandomState(0)
-# test_rng = MockRandomState(0.99999982)
-# test_rng = MockRandomState(1)
 
 
 class TestOptimizationMixin(object):