Use parameter 'p' instead of 'prob' in binomial(), as the latter is deprecated.

theano/tensor/tests/test_randomstreams.py

@@ -515,12 +515,12 @@ class T_RandomStreams(unittest.TestCase):
         m.random = RandomStreams(utt.fetch_seed())
         n = tensor.lvector()
         prob = tensor.vector()
-        out = m.random.binomial(n=n, prob=prob)
+        out = m.random.binomial(n=n, p=prob)
         assert out.ndim == 1
         m.f = Method([n, prob], out)
         # Specifying the size explicitly
         m.g = Method([n, prob],
-                m.random.binomial(n=n, prob=prob, size=(3,)))
+                m.random.binomial(n=n, p=prob, size=(3,)))
         made = m.make()
         made.random.initialize()
 

theano/tensor/tests/test_raw_random.py

@@ -592,7 +592,7 @@ class T_random_function(unittest.TestCase):
         rng_R = random_state_type()
         n = tensor.lvector()
         prob = tensor.vector()
-        post_r, out = binomial(rng_R, n=n, prob=prob)
+        post_r, out = binomial(rng_R, n=n, p=prob)
         assert out.ndim == 1
         f = compile.function([rng_R, n, prob], [post_r, out], accept_inplace=True)
 
@@ -613,7 +613,7 @@ class T_random_function(unittest.TestCase):
 
         # Specifying the size explicitly
         g = compile.function([rng_R, n, prob],
-                binomial(rng_R, n=n, prob=prob, size=(3,)),
+                binomial(rng_R, n=n, p=prob, size=(3,)),
                 accept_inplace=True)
         rng2, val2 = g(rng1, n_val, prob_val)
         numpy_val2 = numpy_rng.binomial(n=n_val, p=prob_val, size=(3,))

theano/tensor/tests/test_shared_randomstreams.py

@@ -465,7 +465,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         random = RandomStreams(utt.fetch_seed())
         n = tensor.lvector()
         prob = tensor.vector()
-        out = random.binomial(n=n, prob=prob)
+        out = random.binomial(n=n, p=prob)
         assert out.ndim == 1
         f = function([n, prob], out)
 
@@ -485,7 +485,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert numpy.all(val1 == numpy_val1)
 
         # Specifying the size explicitly
-        g = function([n, prob], random.binomial(n=n, prob=prob, size=(3,)))
+        g = function([n, prob], random.binomial(n=n, p=prob, size=(3,)))
         val2 = g(n_val, prob_val)
         numpy_rng = numpy.random.RandomState(int(seed_gen.randint(2**30)))
         numpy_val2 = numpy_rng.binomial(n=n_val, p=prob_val, size=(3,))