def test_johnsonsu(rnd, a, b, loc, scale): # johnsonSu random variate generator johnsonsu_dist = RVGs.JohnsonSu(a, b, loc, scale) # obtain samples samples = get_samples(johnsonsu_dist, rnd) # report mean and variance mean = scipy.johnsonsu.mean(a, b, loc, scale) var = scipy.johnsonsu.var(a, b, loc, scale) print_test_results('JohnsonSu', samples, expectation=mean, variance=var)
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)) print("Fitting LogNormal:", dictResults) # 12 NegativeBinomial dist = RVGs.NegativeBinomial(3, 0.3, 1) dat_neg_bin = np.array(get_samples(dist, np.random)) # mean, sigma dictResults = Fit.fit_negative_binomial(dat_neg_bin, 'Data', fixed_location=1)