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