def __init__(
self,
xs,
densities,
scale: Scale,
normalized=False,
traceable=False,
cumulative_normed_ps=None,
):
if scale is None:
raise ValueError
self.scale = scale
if normalized:
self.normed_xs = xs
self.normed_densities = densities
else:
self.normed_xs = scale.normalize_points(xs)
self.normed_densities = scale.normalize_densities(
self.normed_xs, densities)
self.cumulative_normed_ps = cumulative_normed_ps
if cumulative_normed_ps is None:
self.cumulative_normed_ps = np.append(np.array([0]),
np.cumsum(self.bin_probs))
def from_pairs(cls,
pairs,
scale: Scale,
normalized=False,
interpolate=True):
sorted_pairs = sorted([(v["x"], v["density"]) for v in pairs])
xs = np.array([x for (x, density) in sorted_pairs])
densities = np.array([density for (x, density) in sorted_pairs])
if not normalized:
xs = scale.normalize_points(xs)
densities = scale.normalize_densities(xs, densities)
if interpolate:
# interpolate ps at target_xs
if not (len(xs) == len(constants.target_xs)
and np.isclose(xs, constants.target_xs, rtol=1e-04).all()):
f = interp1d(xs, densities)
densities = f(constants.target_xs)
# Make sure AUC is 1
auc = np.sum(densities) / densities.size
densities /= auc
return cls(constants.target_xs,
densities,
scale=scale,
normalized=True)
def test_density_norm_denorm_roundtrip(scale: Scale):
rv = scipy.stats.logistic(loc=0.5, scale=0.15)
normed_xs = np.linspace(0.01, 1, 201)
normed_densities_truth_set = rv.pdf(normed_xs)
xs = scale.denormalize_points(normed_xs)
denormed_densities = scale.denormalize_densities(
xs, normed_densities_truth_set)
normed_densities = scale.normalize_densities(normed_xs, denormed_densities)
assert np.allclose(normed_densities_truth_set,
normed_densities) # type: ignore
def normalize(self, scale: Scale):
normed_xs = scale.normalize_points(self.xs)
normed_densities = scale.normalize_densities(normed_xs, self.densities)
return self.__class__(normed_xs, normed_densities, self.weight)
