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')
# make a histogram
Hist.plot_histogram(data=cols[0], title='Weekly Number of Drinks', bin_width=1)
# mean and standard deviation
stat = Stat.SummaryStat(name='Weekly number of drinks', data=cols[0])
print('Mean = ', stat.get_mean())
print('StDev = ', stat.get_stdev())
# fit a Poisson distribution
fit_results = RVGs.Poisson.fit_ml(data=cols[0])
print('Fitting a Poisson distribution:', fit_results)
# plot the fitted Poisson distribution
Plot.plot_poisson_fit(data=cols[0],
fit_results=fit_results,
x_label='Weekly number of drinks',
x_range=(0, 40),
bin_width=1)
# fit a gamma-Poisson distribution
fit_results = RVGs.GammaPoisson.fit_ml(data=cols[0])
print('Fitting a gamma-Poisson distribution:', fit_results)
# plot the fitted gamma-Poisson distribution
Plot.plot_gamma_poisson_fit(data=cols[0],
fit_results=fit_results,
x_label='Weekly number of drinks',
x_range=(0, 40),
bin_width=1)
# fit a beta-binomial distribution