fixed tests of shared_randomstreams and randomstreams

The test_default_shape function in both modules was coded incorrectly. It used the same generator for uniform and multinomial samples, whereas the modules being tested used different generators for the uniform an multinomial samples. It only worked for as long as it did because only a single multinomial sample was being tested, and by chance the different generators happened to draw the same bit.

fixed tests of shared_randomstreams and randomstreams
bb05e008 · James Bergstra · 4e0b9b66 · bb05e008 · bb05e008
--- a/theano/tensor/tests/test_randomstreams.py
+++ b/theano/tensor/tests/test_randomstreams.py
@@ -367,18 +367,22 @@ class T_RandomStreams(unittest.TestCase):
        made = m.make()
        made.random.initialize()

-        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
-        numpy_rng = numpy.random.RandomState(int(rng_seed))
+        #seed_rng is generator for generating *seeds* for RandomStates
+        seed_rng = numpy.random.RandomState(utt.fetch_seed())
+        uniform_rng = numpy.random.RandomState(int(seed_rng.randint(2**30)))
+        multinomial_rng = numpy.random.RandomState(int(seed_rng.randint(2**30)))
+
        val0 = made.f()
        val1 = made.f()
-        numpy_val0 = numpy_rng.uniform()
-        numpy_val1 = numpy_rng.uniform()
+        numpy_val0 = uniform_rng.uniform()
+        numpy_val1 = uniform_rng.uniform()
        assert numpy.allclose(val0, numpy_val0)
        assert numpy.allclose(val1, numpy_val1)

-        val2 = made.g()
-        numpy_val2 = numpy_rng.multinomial(n=1, pvals=[.5, .5])
-        assert numpy.all(val2 == numpy_val2)
+        for i in range(10): # every test has 50% chance of passing even with non-matching random states
+            val2 = made.g()
+            numpy_val2 = multinomial_rng.multinomial(n=1, pvals=[.5, .5])
+            assert numpy.all(val2 == numpy_val2)

    def test_vector_arguments(self):
        m = Module()

--- a/theano/tensor/tests/test_shared_randomstreams.py
+++ b/theano/tensor/tests/test_shared_randomstreams.py
@@ -336,19 +336,23 @@ class T_SharedRandomStreams(unittest.TestCase):
        random = RandomStreams(utt.fetch_seed())
        f = function([], random.uniform())
        g = function([], random.multinomial())
-        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
-        numpy_rng = numpy.random.RandomState(int(rng_seed))
+
+        #seed_rng is generator for generating *seeds* for RandomStates
+        seed_rng = numpy.random.RandomState(utt.fetch_seed())
+        uniform_rng = numpy.random.RandomState(int(seed_rng.randint(2**30)))
+        multinomial_rng = numpy.random.RandomState(int(seed_rng.randint(2**30)))

        val0 = f()
        val1 = f()
-        numpy_val0 = numpy_rng.uniform()
-        numpy_val1 = numpy_rng.uniform()
+        numpy_val0 = uniform_rng.uniform()
+        numpy_val1 = uniform_rng.uniform()
        assert numpy.allclose(val0, numpy_val0)
        assert numpy.allclose(val1, numpy_val1)

-        val2 = g()
-        numpy_val2 = numpy_rng.multinomial(n=1, pvals=[.5, .5])
-        assert numpy.all(val2 == numpy_val2)
+        for i in range(10): # every test has 50% chance of passing even with non-matching random states
+            val2 = g()
+            numpy_val2 = multinomial_rng.multinomial(n=1, pvals=[.5, .5])
+            assert numpy.all(val2 == numpy_val2)

    def test_vector_arguments(self):
        random = RandomStreams(utt.fetch_seed())