def test_negative_binomial(rnd, n, p, loc=0):
# negative bimonial random variate generator
negative_binomial_dist = RVGs.NegativeBinomial(n, p, loc)
# obtain samples
samples = get_samples(negative_binomial_dist, rnd)
# get theoretical mean and variance
mean = scipy.nbinom.stats(n, p, loc, moments='m')
mean = np.asarray(mean).item()
var = scipy.nbinom.stats(n, p, loc, moments='v')
var = np.asarray(var).item()
# report mean and variance
print_test_results('Negative Binomial',
samples,
expectation=mean,
variance=var)
def test_fitting_negbinomial():
print("\nTesting NegBinomial with n=10, p=0.2, loc=1")
dist = RVGs.NegativeBinomial(n=10, p=0.2, loc=1)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.NegativeBinomial.fit_mm(mean=np.average(data),
st_dev=np.std(data),
fixed_location=1)
dict_ml_results = RVGs.NegativeBinomial.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_negbinomial_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')