def test_triangular(rnd, c, loc=0, scale=1):
# triangular random variate generator
triangular_dist = RVGs.Triangular(c, loc, scale)
# obtain samples
samples = get_samples(triangular_dist, rnd)
# get theoretical variance
var = scipy.triang.stats(c, loc, scale, moments='v')
var = np.asarray(var).item()
# report mean and variance
print_test_results('Triangular',
samples,
expectation=(3 * loc + scale + c * scale) / 3.0,
variance=var)
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)
# 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)
