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)
