def test_uniform(rnd, loc=0, scale=1):
# uniform random variate generator
uniform_dist = RVGs.Uniform(loc, scale)
# obtain samples
samples = get_samples(uniform_dist, rnd)
# report mean and variance
print_test_results('Uniform',
samples,
expectation=(2 * loc + scale) / 2.0,
variance=scale**2 / 12.0)
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)
# 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)
