def test_poisson(rnd, mu, loc=0):
# poisson random variate generator
poisson_dist = RVGs.Poisson(mu, loc)
# obtain samples
samples = get_samples(poisson_dist, rnd)
# report mean and variance
print_test_results('Poisson', samples, expectation=mu + loc, variance=mu)
def test_fitting_poisson():
print("\nTesting Poisson with mean=100 and loc = 10")
dist = RVGs.Poisson(mu=100, loc=10)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.Poisson.fit_mm(mean=np.average(data),
fixed_location=10)
dict_ml_results = RVGs.Poisson.fit_ml(data=data, fixed_location=10)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_poisson_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_poisson_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')