Buy Now. Awesome Open Source. I assume initially there was only a single Gaussian, and observations were separated into two groups to get two different Gaussians). README. When TRUE (the default is FALSE) the function checks if the covariance matrices are not degenerate (multivariate case) or if the variances are not zero (univariate case). The Wasserstein distance between the two Gaussian densities is computed by using the wassersteinpar function and the density parameters estimated from samples. We also used the R implementation of the Wasserstein distance provided in [40], and called it from the Python code using the rpy2 v3.4.5 bridge. Both the R wasserstein1d and Python scipy.stats.wasserstein_distance are intended solely for the 1D special case. kandi X-RAY | Wasserstein-Distance REVIEW AND RATINGS. * ModernPooch is OPEN by appointment ONLY. All Projects. Python Wasserstein Wasserstein Distance Using C# and Python. It could also be seen as an interpolation between Wasserstein and energy distances, more info in this paper. μ ¯ = argmin μ ∈ P 2 ( θ) ∑ t = 1 T α t W 2 2 ( μ, μ t), where α ∈ R T is a weight vector that sums to 1. Wasserstein GAN distance wasserstein distance python The 2-Wasserstein distance between two multivariate (p > 1) or univariate (p = 1) Gaussian densities given their parameters (mean vectors and covariance matrices if the densities are multivariate, or means and variances if univariate) (see Details). Optimal Transport for 1D distributions — POT Python Optimal …