ott.tools.gaussian_mixture.gaussian.Gaussian

class ott.tools.gaussian_mixture.gaussian.Gaussian(loc, scale)

PyTree for a normal distribution.

Methods

covariance()	rtype Array
f_potential(dest, points)	Optimal potential for W2 distance between Gaussians.
from_mean_and_cov(mean, cov)	Construct a Gaussian from a mean and covariance.
from_random(key, n_dimensions[, stdev_mean, ...])	Construct a random Gaussian.
from_samples(points[, weights])	Construct a Gaussian from weighted samples.
from_z(z)	rtype Array
log_prob(x)	Log probability for a gaussian with a diagonal covariance.
sample(key, size)	Generate samples from the distribution.
to_z(x)	rtype Array
transport(dest, points)	Transport points according to map between two Gaussian measures.
w2_dist(other)	Wasserstein distance W_2^2 to another Gaussian.

Attributes

loc	rtype Array
n_dimensions	rtype int
scale	rtype ScaleTriL

Parameters

• loc (jax.Array) –

• scale (ott.tools.gaussian_mixture.scale_tril.ScaleTriL) –