def test_normal_vector(self):
random = RandomStreams(utt.fetch_seed())
avg = tensor.dvector()
std = tensor.dvector()
out = random.normal(avg=avg, std=std)
assert out.ndim == 1
f = function([avg, std], out)
avg_val = [1, 2, 3]
std_val = [0.1, 0.2, 0.3]
seed_gen = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(int(seed_gen.randint(2**30)))
# Arguments of size (3,)
val0 = f(avg_val, std_val)
numpy_val0 = numpy_rng.normal(loc=avg_val, scale=std_val)
assert np.allclose(val0, numpy_val0)
# arguments of size (2,)
val1 = f(avg_val[:-1], std_val[:-1])
numpy_val1 = numpy_rng.normal(loc=avg_val[:-1], scale=std_val[:-1])
assert np.allclose(val1, numpy_val1)
# Specifying the size explicitly
g = function([avg, std], random.normal(avg=avg, std=std, size=(3, )))
val2 = g(avg_val, std_val)
numpy_rng = np.random.RandomState(int(seed_gen.randint(2**30)))
numpy_val2 = numpy_rng.normal(loc=avg_val, scale=std_val, size=(3, ))
assert np.allclose(val2, numpy_val2)
with pytest.raises(ValueError):
g(avg_val[:-1], std_val[:-1])
def test_tutorial(self):
srng = RandomStreams(seed=234)
rv_u = srng.uniform((2, 2))
rv_n = srng.normal((2, 2))
f = function([], rv_u)
g = function([], rv_n,
no_default_updates=True) # Not updating rv_n.rng
nearly_zeros = function([], rv_u + rv_u - 2 * rv_u)
assert np.all(f() != f())
assert np.all(g() == g())
assert np.all(abs(nearly_zeros()) < 1e-5)
assert isinstance(rv_u.rng.get_value(borrow=True),
np.random.RandomState)
def test_normal(self):
# Test that RandomStreams.normal 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.normal((2, 2), -1, 2))
fn_val0 = fn()
fn_val1 = fn()
rng_seed = np.random.RandomState(utt.fetch_seed()).randint(2**30)
rng = np.random.RandomState(int(rng_seed)) # int() is for 32bit
numpy_val0 = rng.normal(-1, 2, size=(2, 2))
numpy_val1 = rng.normal(-1, 2, size=(2, 2))
assert np.allclose(fn_val0, numpy_val0)
assert np.allclose(fn_val1, numpy_val1)