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fc329a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | """DGP with discrete heterogeneity aligned to coarse simplex groups."""
import numpy as np
from .base import BaseDGP, DGPSample
from .pure_scale import PureScaleDGP
from ..utils.simplex import aitchison_dist, ilr, ilr_inv
class DiscreteGroupsDGP(PureScaleDGP):
"""Step-function scale heterogeneity based on the predicted top class."""
def __init__(
self,
K: int = 10,
sigma_low: float = 0.08,
sigma_high: float = 0.30,
d_x: int = 5,
easy_classes: int = 5,
):
super().__init__(K=K, sigma_min=sigma_low, c=sigma_high - sigma_low, d_x=d_x)
self.sigma_low = sigma_low
self.sigma_high = sigma_high
self.easy_classes = easy_classes
def _sigma(self, u: np.ndarray) -> np.ndarray:
top_class = np.argmax(u, axis=1)
is_easy = top_class < self.easy_classes
return np.where(is_easy, self.sigma_low, self.sigma_high)
def sample(self, n: int, rng: np.random.Generator) -> DGPSample:
self._init_weights(rng)
X = rng.standard_normal((n, self.d_x))
mu = self._mu(X)
sigma = self._sigma(mu)
Z_mu = ilr(mu)
eps = rng.standard_normal((n, self.K - 1))
Y = ilr_inv(Z_mu + sigma[:, None] * eps, K=self.K)
U = mu
R = aitchison_dist(Y, U)
return DGPSample(X=X, Y=Y, U=U, R=R, sigma_true=sigma)
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