def test_fitting_binomial():
print("\nTesting Binomial with n=100, p=0.3, loc=1:")
dist = RVGs.Binomial(n=100, p=0.3, loc=1)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
# method of moment
dict_mm_results = RVGs.Binomial.fit_mm(mean=np.mean(data),
st_dev=np.std(data),
fixed_location=1)
# maximum likelihood
dict_ml_results = RVGs.Binomial.fit_ml(data=data, fixed_location=1)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_binomial_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_binomial_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
def test_binomial(rnd, n, p, loc=0):
# bimonial random variate generator
binomial_dist = RVGs.Binomial(n, p, loc)
# obtain samples
samples = get_samples(binomial_dist, rnd)
# report mean and variance
print_test_results('Binomial',
samples,
expectation=n * p + loc,
variance=n * p * (1 - p))