Open Joshuaalbert opened 4 months ago
TruncatedCauchy quantile gives NaN for some parameter combinations. Similar to #1788
Numerical stability should be reinforced or it severely limits to usefulness of the distribution.
import jax import jax.numpy as jnp import pytest import tensorflow_probability.substrates.jax as tfp tfpd = tfp.distributions @pytest.mark.parametrize("scale", [0.1, 1.]) @pytest.mark.parametrize("low", [0.0]) @pytest.mark.parametrize("high", [1e6]) def test_truncated_cauchy(low, high, scale): dist = tfpd.TruncatedCauchy(1.0, scale, low=low, high=high) u = jnp.linspace(0., 1., 100) samples = jax.vmap(dist.quantile)(u) assert jnp.all(jnp.isfinite(samples)) assert jnp.all(samples >= low) assert jnp.all(samples <= high)
TruncatedCauchy quantile gives NaN for some parameter combinations. Similar to #1788
Numerical stability should be reinforced or it severely limits to usefulness of the distribution.
MVCE