Make test_binomial yield for sub-tests to ease identifying the very long test.

theano/sandbox/test_rng_mrg.py

@@ -613,18 +613,15 @@ def test_binomial():
                 # test empty size (scalar)
                 ((), (), [], []),
                 ]:
+            yield (t_binomial, mean, size, const_size, var_input, input,
+                   steps, rtol)
 
-            # print ''
-            # print 'ON CPU with size=(%s) and mean(%d):' % (str(size), mean)
+
+def t_binomial(mean, size, const_size, var_input, input, steps, rtol):
     R = MRG_RandomStreams(234, use_cuda=False)
-            # Note: we specify `nstreams` to avoid a warning.
-            u = R.binomial(size=size, p=mean,
-                           nstreams=rng_mrg.guess_n_streams(size, warn=False))
+    u = R.binomial(size=size, p=mean)
     f = theano.function(var_input, u, mode=mode)
-            # theano.printing.debugprint(f)
     out = f(*input)
-            # print 'random?[:10]\n', out[0, 0:10]
-            # print 'random?[-1,-10:]\n', out[-1, -10:]
 
     # Increase the number of steps if sizes implies only a few samples
     if numpy.prod(const_size) < 10:
@@ -636,30 +633,21 @@ def test_binomial():
               target_avg=mean, mean_rtol=rtol)
 
     if mode != 'FAST_COMPILE' and cuda_available:
-                # print ''
-                # print 'ON GPU with size=(%s) and mean(%d):' % (str(size), mean)
         R = MRG_RandomStreams(234, use_cuda=True)
-                u = R.binomial(size=size, p=mean, dtype='float32',
-                               nstreams=rng_mrg.guess_n_streams(size,
-                                                                warn=False))
+        u = R.binomial(size=size, p=mean, dtype='float32')
         # well, it's really that this test w GPU doesn't make sense otw
         assert u.dtype == 'float32'
         f = theano.function(var_input, theano.Out(
                 theano.sandbox.cuda.basic_ops.gpu_from_host(u),
                 borrow=True), mode=mode_with_gpu)
-                # theano.printing.debugprint(f)
         gpu_out = numpy.asarray(f(*input))
-                # print 'random?[:10]\n', gpu_out[0, 0:10]
-                # print 'random?[-1,-10:]\n', gpu_out[-1, -10:]
+
         basictest(f, steps_, const_size, prefix='mrg  gpu',
                   inputs=input, allow_01=True,
                   target_avg=mean, mean_rtol=rtol)
         numpy.testing.assert_array_almost_equal(out, gpu_out,
                                                 decimal=6)
 
-            # print ''
-            # print 'ON CPU w NUMPY with size=(%s) and mean(%d):' % (str(size),
-            #                                                       mean)
     RR = theano.tensor.shared_randomstreams.RandomStreams(234)
 
     uu = RR.binomial(size=size, p=mean)