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