def test_geometric(rnd, p, loc=0):
# geometric random variate generator
geometric_dist = RVGs.Geometric(p, loc)
# obtain samples
samples = get_samples(geometric_dist, rnd)
# report mean and variance
print_test_results('Geometric',
samples,
expectation=1 / p + loc,
variance=(1 - p) / (p**2))
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)
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)
