def test_cdf(self):
sim_model = EconDensity()
x = np.ones(shape=(2000, 1))
y = np.random.uniform(0.01, 5, size=(2000, 1))
p_sim = sim_model.cdf(x, y)
p_true = stats.norm.cdf(y, loc=1, scale=2)
diff = np.sum(np.abs(p_sim - p_true))
self.assertAlmostEquals(diff, 0.0, places=2)
def test_cdf_sample_consistency(self):
from statsmodels.distributions.empirical_distribution import ECDF
model = EconDensity()
x_cond = np.asarray([0.1 for _ in range(200000)])
_, y_sample = model.simulate_conditional(x_cond)
emp_cdf = ECDF(y_sample.flatten())
cdf = lambda y: model.cdf(x_cond, y)
mean_cdf_diff = np.mean(
np.abs(emp_cdf(y_sample).flatten() - cdf(y_sample).flatten()))
self.assertLessEqual(mean_cdf_diff, 0.01)