def test_random_integers_vector(self):
rng_R = random_state_type()
low = tensor.lvector()
high = tensor.lvector()
post_r, out = random_integers(rng_R, low=low, high=high)
assert out.ndim == 1
f = compile.function([rng_R, low, high], [post_r, out], accept_inplace=True)
low_val = [100, 200, 300]
high_val = [110, 220, 330]
rng = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
rng0, val0 = f(rng, 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,)
rng1, val1 = f(rng0, 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 = compile.function(
[rng_R, low, high],
random_integers(rng_R, low=low, high=high, size=(3,)),
accept_inplace=True,
)
rng2, val2 = g(rng1, low_val, high_val)
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(rng2, low_val[:-1], high_val[:-1])
def test_random_integers(self):
# Test that raw_random.random_integers generates the same
# results as numpy. We use randint() for comparison since
# random_integers() is deprecated.
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
# Use non-default parameters, and larger dimensions because of
# the integer nature of the result
post_r, out = random_integers(rng_R, (11, 8), -3, 16)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[out],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.randint(-3, 17, size=(11, 8))
numpy_val1 = numpy_rng.randint(-3, 17, size=(11, 8))
assert np.allclose(val0, numpy_val0)
assert np.allclose(val1, numpy_val1)
def test_pkl(self):
# Test pickling of RandomFunction.
# binomial was created by calling RandomFunction on a string,
# random_integers by calling it on a function.
rng_r = random_state_type()
mode = None
if theano.config.mode in ["DEBUG_MODE", "DebugMode"]:
mode = "FAST_COMPILE"
post_bin_r, bin_sample = binomial(rng_r, (3, 5), 1, 0.3)
f = theano.function([rng_r], [post_bin_r, bin_sample], mode=mode)
pickle.dumps(f)
post_int_r, int_sample = random_integers(rng_r, (3, 5), -1, 8)
g = theano.function([rng_r], [post_int_r, int_sample], mode=mode)
pkl_g = pickle.dumps(g)
pickle.loads(pkl_g)
def test_dtype(self):
rng_R = random_state_type()
low = tensor.lscalar()
high = tensor.lscalar()
post_r, out = random_integers(
rng_R, low=low, high=high, size=(20,), dtype="int8"
)
assert out.dtype == "int8"
f = compile.function([rng_R, low, high], [post_r, out])
rng = np.random.RandomState(utt.fetch_seed())
rng0, val0 = f(rng, 0, 9)
assert val0.dtype == "int8"
rng1, val1 = f(rng0, 255, 257)
assert val1.dtype == "int8"
assert np.all(abs(val1) <= 1)
