def test_gamma_poisson(rnd, a, gamma_scale, loc=0, scale=1): # gamma-poisson random variate generator gamma_poisson_dist = RVGs.GammaPoisson(a, gamma_scale, loc, scale) # obtain samples samples = get_samples(gamma_poisson_dist, rnd) # report mean and variance print_test_results('GammaPoisson', samples, expectation=(a * gamma_scale) * scale + loc, variance=(a * gamma_scale + a * (gamma_scale**2)) * scale**2)
dictResults = Fit.fit_binomial(dat_bin, 'Data', fixed_location=1) # fit print("Fitting Binomial:", dictResults) # 5 Empirical (for int data) dat_em = np.random.poisson(30, 1000) dictResults = Fit.fit_empirical(dat_em, 'Data', bin_size=2.5) # fit print("Fitting Empirical:", dictResults) # 6 fitting a gamma distribution dist = RVGs.Gamma(10, 1, 2) dat_gamma = np.array(get_samples(dist, np.random)) # generate data dictResults = Fit.fit_gamma(dat_gamma, 'Data', fixed_location=1) # fit print("Fitting Gamma:", dictResults) # 7 GammaPoisson 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)