def test_uniform(rnd, loc=0, scale=1):
# uniform random variate generator
uniform_dist = RVGs.Uniform(scale=scale, loc=loc)
# 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)
def sample_values(self, rng):
"""
:param rng: random number generator
:return: (list) values of categories
(in the example above, [1.4, 0] corresponds to age 1.4 and sex group 0.
"""
values = []
idx = self.sample_indices(rng=rng)
for i, deltaX in enumerate(self.listXDelta):
if deltaX == 'int':
values.append(idx[i] + self._list_x_min[i])
else:
unif_dist = RVGs.Uniform(loc=0, scale=deltaX)
values.append(idx[i] * deltaX + self._list_x_min[i] +
unif_dist.sample(rng=rng))
return values
def test_fitting_uniform():
print("\nTesting triangular with scale=10, loc=2")
dist = RVGs.Uniform(scale=10, loc=2)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.Uniform.fit_mm(mean=np.average(data),
st_dev=np.std(data))
dict_ml_results = RVGs.Uniform.fit_ml(data=data)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_uniform_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_uniform_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')