def sample_indices(self, rng):
"""
:param rng: random number generator
:return: (list) indices of sampled categories
(in the example above, [1, 0] corresponds to age group 1 and sex group 0.
"""
probs = []
if self._ifOneDim:
probs = self._objs
else:
for df in self._objs:
probs.append(df.get_sum())
empirical_dist = RVGs.Empirical(probabilities=np.array(probs) /
sum(probs))
idx = empirical_dist.sample(rng=rng)
if self._ifOneDim:
return [idx]
else:
a = [idx]
a.extend(self._objs[idx].sample_indices(rng))
return a
def test_fitting_empirical():
print("\nTesting empirical with p=[0.1, 0.2, 0.7]")
dist = RVGs.Empirical(probabilities=[0.1, 0.2, 0.7])
data = np.array(get_samples(dist, np.random))
# method of moments
dict_mm_results = RVGs.Empirical.fit_mm(data=data, bin_size=1)
print(" Fit:")
print(" MM:", dict_mm_results)
def test_empirical(rnd, prob):
# empirical random variate generator
empirical_dist = RVGs.Empirical(prob)
# obtain samples
samples = get_samples(empirical_dist, rnd)
# report mean and variance
if type(prob) == list:
prob = np.array(prob)
outcome = np.array(range(len(prob)))
mean = sum(outcome * prob)
var = sum((outcome**2) * prob) - mean**2
print_test_results('Empirical', samples, expectation=mean, variance=var)