def test_johnsonsb(rnd, a, b, loc, scale): # johnsonSb random variate generator johnsonsb_dist = RVGs.JohnsonSb(a, b, loc, scale) # obtain samples samples = get_samples(johnsonsb_dist, rnd) # report mean and variance mean = scipy.johnsonsb.mean(a, b, loc, scale) var = scipy.johnsonsb.var(a, b, loc, scale) print_test_results('JohnsonSb', samples, expectation=mean, variance=var)
dist = RVGs.GammaPoisson(a=2, gamma_scale=4, loc=1, scale=2) dat_gamma_poisson = np.array(get_samples(dist, np.random)) dictResults = Fit.fit_gamma_poisson(dat_gamma_poisson, 'Data', fixed_location=1, fixed_scale=2) # fit print("Fitting GammaPoisson:", dictResults) # 8 Geometric dist = RVGs.Geometric(0.3, 1) dat_geom = np.array(get_samples(dist, np.random)) # generate data dictResults = Fit.fit_geometric(dat_geom, 'Data', fixed_location=1) # fit print("Fitting Geometric:", dictResults) # 9 fitting a JohnsonSb distribution dist = RVGs.JohnsonSb(a=10, b=3, loc=1, scale=2) dat_JohnsonSb = np.array(get_samples(dist, np.random)) # generate data dictResults = Fit.fit_johnsonSb(dat_JohnsonSb, 'Data', fixed_location=1) # fit print("Fitting johnsonSb:", dictResults) # 10 fitting a JohnsonSu distribution dist = RVGs.JohnsonSu(a=10, b=3, loc=1, scale=2) dat_JohnsonSu = np.array(get_samples(dist, np.random)) # generate data dictResults = Fit.fit_johnsonSu(dat_JohnsonSu, 'Data', fixed_location=1) # fit print("Fitting johnsonSu:", dictResults) # 11 LogNormal dist = RVGs.LogNormal(s=1, loc=1, scale=2) dat_lognorm = np.array(get_samples(dist, np.random)) # mean, sigma dictResults = Fit.fit_lognorm(dat_lognorm, 'Data', fixed_location=1) # fit (scale=exp(mean))