def test_log_pdf(xscale: Scale):
normed_test_loc = 0.5
normed_test_s = 0.1
test_loc = xscale.denormalize_point(normed_test_loc)
test_s = normed_test_s * xscale.width
ergoLogisticMixture = LogisticMixture(
components=[
Logistic(
loc=xscale.denormalize_point(0.2),
s=0.5 * xscale.width,
scale=xscale,
),
Logistic(loc=test_loc, s=test_s, scale=xscale),
],
probs=[1.8629593e-29, 1.0],
)
ergoLogistic = Logistic(loc=test_loc, s=test_s, scale=xscale)
## Test PDF
normed_scipydist = scipy.stats.logistic(normed_test_loc, normed_test_s)
for x in np.linspace(0, 1, 10):
denormalized_x = xscale.denormalize_point(x)
assert (normed_scipydist.pdf(x) / xscale.width == pytest.approx(
float(ergoLogistic.pdf(denormalized_x)), rel=1e-3) ==
pytest.approx(float(ergoLogisticMixture.pdf(denormalized_x)),
rel=1e-3))
def test_pdf(xscale: Scale):
normed_test_loc = 0.5
normed_test_s = 0.1
test_loc = xscale.denormalize_point(normed_test_loc)
test_s = normed_test_s * xscale.width
ergoLogisticMixture = LogisticMixture(
components=[
Logistic(
loc=xscale.denormalize_point(0.2),
s=0.5 * xscale.width,
scale=xscale,
),
Logistic(loc=test_loc, s=test_s, scale=xscale),
],
probs=[1.8629593e-29, 1.0],
)
ergoLogistic = Logistic(loc=test_loc, s=test_s, scale=xscale)
## Make sure it integrates to 1
_xs = xscale.denormalize_points(np.linspace(0, 1, 100))
densities_logistic = np.array([float(ergoLogistic.pdf(x)) for x in _xs])
densities_mixture = np.array(
[float(ergoLogisticMixture.pdf(x)) for x in _xs])
auc_logistic = float(trapz(densities_logistic, x=_xs))
auc_mixture = float(trapz(densities_mixture, x=_xs))
assert (1 == pytest.approx(auc_logistic, abs=0.03) == pytest.approx(
auc_mixture, abs=0.03))
if not isinstance(xscale, LogScale):
scipydist = scipy.stats.logistic(test_loc, test_s)
for x in np.linspace(xscale.denormalize_point(0),
xscale.denormalize_point(1), 10):
assert (scipydist.pdf(x) == pytest.approx(
float(ergoLogistic.pdf(x)), rel=1e-3) == pytest.approx(
float(ergoLogisticMixture.pdf(x)), rel=1e-3))