def test_random_integers_vector(self):
random = RandomStreams(utt.fetch_seed())
low = tensor.lvector()
high = tensor.lvector()
out = random.random_integers(low=low, high=high)
assert out.ndim == 1
f = function([low, high], out)
low_val = [100, 200, 300]
high_val = [110, 220, 330]
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(low_val, high_val)
numpy_val0 = np.asarray([
numpy_rng.randint(low=lv, high=hv + 1)
for lv, hv in zip(low_val, high_val)
])
assert np.all(val0 == numpy_val0)
# arguments of size (2,)
val1 = f(low_val[:-1], high_val[:-1])
numpy_val1 = np.asarray([
numpy_rng.randint(low=lv, high=hv + 1)
for lv, hv in zip(low_val[:-1], high_val[:-1])
])
assert np.all(val1 == numpy_val1)
# Specifying the size explicitly
g = function([low, high],
random.random_integers(low=low, high=high, size=(3, )))
val2 = g(low_val, high_val)
numpy_rng = np.random.RandomState(int(seed_gen.randint(2**30)))
numpy_val2 = np.asarray([
numpy_rng.randint(low=lv, high=hv + 1)
for lv, hv in zip(low_val, high_val)
])
assert np.all(val2 == numpy_val2)
with pytest.raises(ValueError):
g(low_val[:-1], high_val[:-1])
def test_dtype(self):
random = RandomStreams(utt.fetch_seed())
low = tensor.lscalar()
high = tensor.lscalar()
out = random.random_integers(low=low,
high=high,
size=(20, ),
dtype="int8")
assert out.dtype == "int8"
f = function([low, high], out)
val0 = f(0, 9)
assert val0.dtype == "int8"
val1 = f(255, 257)
assert val1.dtype == "int8"
assert np.all(abs(val1) <= 1)
def test_random_integers(self):
# Test that RandomStreams.random_integers generates the same
# results as numpy. We use randint() for numpy since
# random_integers() is deprecated.
# Check over two calls to see if the random state is correctly updated.
random = RandomStreams(utt.fetch_seed())
fn = function([], random.random_integers((20, 20), -5, 5))
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.randint(-5, 6, size=(20, 20))
numpy_val1 = rng.randint(-5, 6, size=(20, 20))
assert np.all(fn_val0 == numpy_val0)
assert np.all(fn_val1 == numpy_val1)