def test_check_random_state():
# seed is None
rng_type = type(np.random.RandomState(10))
rng = _check_random_state(None)
assert type(rng) == rng_type
# seed is int
rng = _check_random_state(10)
assert type(rng) == rng_type
# seed is RandomState
rng_test = np.random.RandomState(10)
rng = _check_random_state(rng_test)
assert type(rng) == rng_type
# seed is none of the above : error
pytest.raises(ValueError, _check_random_state, 'random')
def test_sample_choose_weighted():
# make sure probabilities are factored in
rng = _check_random_state(0)
assert _sample_choose_weighted([0, 1, 2], [1, 0, 0], rng) == 0
assert _sample_choose_weighted([0, 1, 2], [0, 1, 0], rng) == 1
assert _sample_choose_weighted([0, 1, 2], [0, 0, 1], rng) == 2
samples = []
for _ in range(100000):
samples.append(_sample_choose_weighted([0, 1], [0.3, 0.7], rng))
samples = np.asarray(samples)
zero_ratio = (samples == 0).sum() / len(samples)
one_ratio = (samples == 1).sum() / len(samples)
assert np.allclose(zero_ratio, 0.3, atol=1e-2)
assert np.allclose(one_ratio, 0.7, atol=1e-2)
def test_sample_trunc_norm():
'''
Should return values from a truncated normal distribution.
'''
rng = _check_random_state(0)
# sample values from a distribution
mu, sigma, trunc_min, trunc_max = 2, 1, 0, 5
x = [_sample_trunc_norm(mu, sigma, trunc_min, trunc_max, random_state=rng) for _ in range(100000)]
x = np.asarray(x)
# simple check: values must be within truncated bounds
assert (x >= trunc_min).all() and (x <= trunc_max).all()
# trickier check: values must approximate distribution's PDF
hist, bins = np.histogram(x, bins=np.arange(0, 10.1, 0.2), density=True)
xticks = bins[:-1] + 0.1
a, b = (trunc_min - mu) / float(sigma), (trunc_max - mu) / float(sigma)
trunc_closed = truncnorm.pdf(xticks, a, b, mu, sigma)
assert np.allclose(hist, trunc_closed, atol=0.015)
def test_sample_choose():
# using choose with duplicates will issue a warning
rng = _check_random_state(0)
pytest.warns(ScaperWarning, _sample_choose, [0, 1, 2, 2, 2], rng)