stable-trunc-gaussian 1.0.1

A numerically-stable and differentiable implementation of the Truncated Gaussian distribution in Pytorch.

Stable Truncated Gaussian

A differentiable implementation of the Truncated Gaussian (Normal) distribution using Python and Pytorch, which is numerically stable even when the μ parameter lies outside the interval [a,b] given by the bounds of the distribution. In this situation, a naive evaluation of the mean, variance and log-probability of the distribution could otherwise result in catastrophic cancellation. Our code is inspired by TruncatedNormal.jl and torch_truncnorm. Currently, we only provide functionality for calculating the mean, variance and log-probability, but not for calculating the entropy or sampling from the distribution.

Installation

Simply install with pip:

pip install stable-trunc-gaussian

Example

Run the following code in Python:

from stable_trunc_gaussian import TruncatedGaussian as TG
from torch import tensor as t

# Create a Truncated Gaussian with mu=0, sigma=1, a=10, b=11
# Notice how mu is outside the interval [a,b]
dist = TG(t(0),t(1),t(10),t(11))

print("Mean:", dist.mean)
print("Variance:", dist.variance)
print("Log-prob(10.5):", dist.log_prob(t(10.5)))

Result:

Mean: tensor(10.0981)
Variance: tensor(0.0094)
Log-prob(10.5): tensor(-2.8126)