def test_cdf(xscale: Scale):
scipydist_normed = scipy.stats.logistic(0.5, 0.05)
true_loc = xscale.denormalize_point(0.5)
true_s = 0.05 * xscale.width
ergodist = Logistic(loc=true_loc, s=true_s, scale=xscale)
for x in np.linspace(0, 1, 10):
assert scipydist_normed.cdf(x) == pytest.approx(float(
ergodist.cdf(xscale.denormalize_point(x))),
rel=1e-3)
# TODO: consider a better approach for log scale
if isinstance(xscale, LogScale):
for x in np.linspace(xscale.low, xscale.high, 10):
assert scipydist_normed.cdf(
xscale.normalize_point(x)) == pytest.approx(float(
ergodist.cdf(x)),
rel=1e-3)
else:
scipydist_true = scipy.stats.logistic(true_loc, true_s)
for x in np.linspace(xscale.low, xscale.high, 10):
assert scipydist_true.cdf(x) == pytest.approx(float(
ergodist.cdf(x)),
rel=1e-3)
def test_cdf():
jax_dist = Logistic(loc=10, scale=1)
original_dist = jax_dist.rv()
for x in range(5, 15):
assert jax_dist.cdf(x) == pytest.approx(original_dist.cdf(x), rel=0.1)