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)
# 10 fitting a JohnsonSu distribution
dist = RVGs.JohnsonSu(a=10, b=3, loc=1, scale=2)
dat_JohnsonSu = np.array(get_samples(dist, np.random)) # generate data
dictResults = Fit.fit_johnsonSu(dat_JohnsonSu, 'Data', fixed_location=1) # fit
print("Fitting johnsonSu:", dictResults)
# 11 LogNormal
dist = RVGs.LogNormal(s=1, loc=1, scale=2)
dat_lognorm = np.array(get_samples(dist, np.random)) # mean, sigma
dictResults = Fit.fit_lognorm(dat_lognorm, 'Data',
fixed_location=1) # fit (scale=exp(mean))
print("Fitting LogNormal:", dictResults)
# 12 NegativeBinomial
dist = RVGs.NegativeBinomial(3, 0.3, 1)
dat_neg_bin = np.array(get_samples(dist, np.random)) # mean, sigma
dictResults = Fit.fit_negative_binomial(dat_neg_bin, 'Data', fixed_location=1)
print("Fitting NegativeBinomial:", dictResults)
# 13 Normal
dist = RVGs.Normal(0, 1)
dat_norm = np.array(get_samples(dist, np.random)) # mean, sigma
dictResults = Fit.fit_normal(dat_norm, 'Data') # fit
print("Fitting Normal:", dictResults)
# 14 Triangular
dist = RVGs.Triangular(0.5, loc=1, scale=2)
dat_tri = np.array(get_samples(dist, np.random))
dictResults = Fit.fit_triang(dat_tri, 'Data', fixed_location=1) # fit
print("Fitting Triangular:", dictResults)