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)
def test_fitting_triangular():
print("\nTesting triangular with c=0.2, loc=6, scale=7")
dist = RVGs.Triangular(c=0.2, loc=6, scale=7)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_ml_results = RVGs.Triangular.fit_ml(data=data, fixed_location=6)
print(" Fit:")
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_triangular_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
