def test_basics(self, rng_ctor):
random = RandomStream(seed=utt.fetch_seed(), rng_ctor=rng_ctor)
with pytest.raises(ValueError):
random.uniform(0, 1, size=(2, 2), rng=np.random.default_rng(23))
with pytest.raises(AttributeError):
random.blah
assert hasattr(random, "standard_normal")
with pytest.raises(AttributeError):
np_random = RandomStream(namespace=np, rng_ctor=rng_ctor)
np_random.ndarray
fn = function([],
random.uniform(0, 1, size=(2, 2)),
updates=random.updates())
fn_val0 = fn()
fn_val1 = fn()
rng_seed = np.random.SeedSequence(utt.fetch_seed())
(rng_seed, ) = rng_seed.spawn(1)
rng = random.rng_ctor(rng_seed)
numpy_val0 = rng.uniform(0, 1, size=(2, 2))
numpy_val1 = rng.uniform(0, 1, size=(2, 2))
assert np.allclose(fn_val0, numpy_val0)
assert np.allclose(fn_val1, numpy_val1)
def test_basics(self):
random = RandomStream(seed=utt.fetch_seed())
with pytest.raises(TypeError):
random.uniform(0, 1, size=(2, 2), rng=np.random.RandomState(23))
with pytest.raises(AttributeError):
random.blah
with pytest.raises(AttributeError):
np_random = RandomStream(namespace=np)
np_random.ndarray
fn = function([],
random.uniform(0, 1, size=(2, 2)),
updates=random.updates())
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.uniform(0, 1, size=(2, 2))
numpy_val1 = rng.uniform(0, 1, size=(2, 2))
assert np.allclose(fn_val0, numpy_val0)
assert np.allclose(fn_val1, numpy_val1)
def test_default_updates(self, rng_ctor):
# Basic case: default_updates
random_a = RandomStream(utt.fetch_seed(), rng_ctor=rng_ctor)
out_a = random_a.uniform(0, 1, size=(2, 2))
fn_a = function([], out_a)
fn_a_val0 = fn_a()
fn_a_val1 = fn_a()
assert not np.all(fn_a_val0 == fn_a_val1)
nearly_zeros = function([], out_a + out_a - 2 * out_a)
assert np.all(abs(nearly_zeros()) < 1e-5)
# Explicit updates #1
random_b = RandomStream(utt.fetch_seed(), rng_ctor=rng_ctor)
out_b = random_b.uniform(0, 1, size=(2, 2))
fn_b = function([], out_b, updates=random_b.updates())
fn_b_val0 = fn_b()
fn_b_val1 = fn_b()
assert np.all(fn_b_val0 == fn_a_val0)
assert np.all(fn_b_val1 == fn_a_val1)
# Explicit updates #2
random_c = RandomStream(utt.fetch_seed(), rng_ctor=rng_ctor)
out_c = random_c.uniform(0, 1, size=(2, 2))
fn_c = function([], out_c, updates=random_c.state_updates)
fn_c_val0 = fn_c()
fn_c_val1 = fn_c()
assert np.all(fn_c_val0 == fn_a_val0)
assert np.all(fn_c_val1 == fn_a_val1)
# No updates at all
random_d = RandomStream(utt.fetch_seed(), rng_ctor=rng_ctor)
out_d = random_d.uniform(0, 1, size=(2, 2))
fn_d = function([], out_d, no_default_updates=True)
fn_d_val0 = fn_d()
fn_d_val1 = fn_d()
assert np.all(fn_d_val0 == fn_a_val0)
assert np.all(fn_d_val1 == fn_d_val0)
# No updates for out
random_e = RandomStream(utt.fetch_seed(), rng_ctor=rng_ctor)
out_e = random_e.uniform(0, 1, size=(2, 2))
fn_e = function([],
out_e,
no_default_updates=[random_e.state_updates[0][0]])
fn_e_val0 = fn_e()
fn_e_val1 = fn_e()
assert np.all(fn_e_val0 == fn_a_val0)
assert np.all(fn_e_val1 == fn_e_val0)
