def test_binomial_to_normal():
n, p = 10000, 0.1
mu, sd = binomial_to_normal(n, p)
binomial = [binom.cdf(x, n, p) for x in range(0, 10000, 500)]
normal = [norm(mu, sd).cdf(x) for x in range(0, 10000, 500)]
assert np.allclose(binomial, normal, atol=0.01)
def test_normal_neg_log_pvalue():
sds = range(1, 10)
neg_log_pvalue = [normal_neg_log_pvalue(0, sd, 0) for sd in sds]
assert np.allclose(neg_log_pvalue, [-log(0.5)] * len(sds))
def _check_subst_probs(subst_probs, msg):
assert len(subst_probs) == len(A)
for i in range(len(A)):
correct = [.7 if i == j else .1 for j in range(len(A))]
assert np.allclose(M.subst_probs[i], correct), msg
