(re)added poisson to RandomStreamsBase add added a unit test

theano/tensor/raw_random.py

@@ -629,6 +629,7 @@ def poisson_helper(random_state, lam, size):
     This is a generalization of numpy.random.poisson to the case where 
     `lam` is a tensor.
     """
+
     return random_state.poisson(lam, size)
 
 def poisson(random_state, size=None, lam=1.0, ndim=None, dtype='int64'):
@@ -637,8 +638,7 @@ def poisson(random_state, size=None, lam=1.0, ndim=None, dtype='int64'):
 
     The Poisson distribution is the limit of the Binomial distribution for large N.
     
-    :param lam: float
-
+    :param lam: float or ndarray-like of the same shape as size parameter
         Expectation of interval, should be >= 0.
 
     :param size: int or tuple of ints, optional
@@ -928,6 +928,18 @@ class RandomStreamsBase(object):
         """
         return self.gen(choice, size, a, replace, p, ndim=ndim, dtype=dtype)
 
+    def poisson(self, size=None, lam=None, ndim=None, dtype='int64'):
+        """
+        Draw samples from a Poisson distribution.
+  
+        The Poisson distribution is the limit of the Binomial distribution for large N.
+
+        If the size argument is ambiguous on the number of dimensions,
+        ndim may be a plain integer to supplement the missing
+        information.
+        """
+        return self.gen(poisson, size, lam, ndim=ndim, dtype=dtype)
+
     def permutation(self, size=None, n=1, ndim=None, dtype='int64'):
         """
         Returns permutations of the integers between 0 and n-1, as many times

theano/tensor/tests/test_shared_randomstreams.py

@@ -205,6 +205,23 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert numpy.all(fn_val0 == numpy_val0)
         assert numpy.all(fn_val1 == numpy_val1)
 
+    def test_poisson(self):
+        """Test that RandomStreams.poisson generates the same results as numpy"""
+        
+        # Check over two calls to see if the random state is correctly updated.
+        random = RandomStreams(utt.fetch_seed())
+        fn = function([], random.poisson(lam=5, size=(11, 8)))
+        fn_val0 = fn()
+        fn_val1 = fn()
+
+        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
+        rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
+        numpy_val0 = rng.poisson(lam=5, size=(11, 8))
+        numpy_val1 = rng.poisson(lam=5, size=(11, 8))
+
+        assert numpy.all(fn_val0 == numpy_val0)
+        assert numpy.all(fn_val1 == numpy_val1)
+
     def test_permutation(self):
         """Test that RandomStreams.permutation generates the same results as numpy"""
         # Check over two calls to see if the random state is correctly updated.