def test_exponential_distribution(scale, n_kolmogorov_smirnov):
u = cn.random.exponential(scale, n_kolmogorov_smirnov)
reject = perform_ks_test(u,
alpha=0.01,
distribution='expon',
args=(0.0, scale),
verbose=True)
assert not reject
def test_wald_distribution(mean, scale, n_kolmogorov_smirnov):
u = cn.random.wald(mean, scale, n_kolmogorov_smirnov)
reject = perform_ks_test(u,
alpha=0.01,
distribution='invgauss',
args=(mean, scale, 0),
verbose=True)
assert not reject
def test_lognormal_distribution(mu, sigma, n_kolmogorov_smirnov):
u = cn.random.lognormal(mu, sigma, n_kolmogorov_smirnov)
reject = perform_ks_test(u,
alpha=0.01,
distribution='lognorm',
args=(sigma, 0.0, np.exp(mu)),
verbose=True)
assert not reject
def test_logistic_distribution(loc, scale, n_kolmogorov_smirnov):
u = cn.random.logistic(loc, scale, n_kolmogorov_smirnov)
reject = perform_ks_test(u,
alpha=0.01,
distribution='logistic',
args=(loc, scale),
verbose=True)
assert not reject
def test_beta_distribution(a, b, n_kolmogorov_smirnov):
u = cn.random.beta(a, b, n_kolmogorov_smirnov)
print(u.shape)
reject = perform_ks_test(u,
alpha=0.01,
distribution='beta',
args=(a, b),
verbose=True)
assert not reject