# ott.tools.gaussian_mixture.gaussian.Gaussian#

class ott.tools.gaussian_mixture.gaussian.Gaussian(loc, scale)[source]#

Normal distribution.

Parameters:

Methods

 Covariance of the Gaussian. 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(rng, n_dimensions[, stdev_mean, ...]) Construct a random Gaussian. from_samples(points[, weights]) Construct a Gaussian from weighted samples. Transform $$z$$ to $$x = loc + scale \cdot z$$. Log probability for a Gaussian with a diagonal covariance. sample(rng, size) Generate samples from the distribution. Transform $$x$$ to $$z = \frac{x - loc}{scale}$$. 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 Mean of the Gaussian. n_dimensions Dimensionality of the Gaussian. scale Scale of the Gaussian.