def test_weibull(rnd, c, loc=0, scale=1):
# weibull random variate generator
weibull_dist = RVGs.Weibull(c, loc, scale)
# obtain samples
samples = get_samples(weibull_dist, rnd)
# get theoretical variance
var = scipy.weibull_min.stats(c, loc, scale, moments='v')
var = np.asarray(var).item()
# report mean and variance
print_test_results('Weibull',
samples,
expectation=math.gamma(1.0 + 1 / c) * scale + loc,
variance=var)
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)
# 15 Uniform
dist = RVGs.Uniform(0, 1)
dat_unif = np.array(get_samples(dist, np.random)) # mean, sigma
dictResults = Fit.fit_uniform(dat_unif, 'Data') # fit
print("Fitting Uniform:", dictResults)
# 16 UniformDiscrete
dist = RVGs.UniformDiscrete(0, 100)
dat_unifDis = np.array(get_samples(dist, np.random))
dictResults = Fit.fit_uniformDiscrete(dat_unifDis, 'Data') # fit
print("Fitting UniformDiscrete:", dictResults)
# 17 fitting a Weibull distribution
dist = RVGs.Weibull(5, 1, 2)
dat_weibull = np.array(get_samples(dist, np.random)) # generate data
dictResults = Fit.fit_weibull(dat_weibull, 'Data', fixed_location=1) # fit
print("Fitting Weibull:", dictResults)
# 18 fitting a Poisson distribution
dist = RVGs.Poisson(30, 1)
dat_poisson = np.array(get_samples(dist, np.random)) # generate data
dictResults = Fit.fit_poisson(dat_poisson, 'Data', fixed_location=1) # fit
print("Fitting Poisson:", dictResults)
