def test_uniform_discrete(rnd, l, u):
# uniform discrete random variate generator
uniformdiscrete_dist = RVGs.UniformDiscrete(l, u)
# obtain samples
samples = get_samples(uniformdiscrete_dist, rnd)
# report mean and variance
print_test_results('Uniform Discrete',
samples,
expectation=(l + u) / 2.0,
variance=((u - l + 1)**2 - 1) / 12.0)
def utility_sample_stat(utility, d_cost_samples, d_effect_samples,
wtp_random_variate, n_samples, rnd):
discrete_rnd = RVG.UniformDiscrete(l=0, u=len(d_cost_samples) - 1)
samples = []
for i in range(n_samples):
j = discrete_rnd.sample(rnd)
u = utility(d_effect=d_effect_samples[j], d_cost=d_cost_samples[j])
w = wtp_random_variate.sample(rnd)
samples.append(u(w))
return Stat.SummaryStat(name='', data=samples)
def test_fitting_uniform_discrete():
print("\nTesting uniform discrete with l=10, u=18")
dist = RVGs.UniformDiscrete(l=10, u=18)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.UniformDiscrete.fit_mm(mean=np.average(data),
st_dev=np.std(data))
dict_ml_results = RVGs.UniformDiscrete.fit_ml(data=data)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_uniform_discrete_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_uniform_discrete_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
