The paper builds upon an intuitive idea: the family of Wasserstein distances is a nice distance between probability distributions, that is well grounded in theory. This appears to be normalized in some way—adding non-moving elements will reduce the distance, and doubling all pixel values doesn’t affect the distance. Compilers 60. python The q-Wasserstein distance is defined as the minimal value achieved by a perfect matching between the points of the two diagrams (+ all diagonal points), where the value of a matching is defined as the q-th root of the sum of all edge lengths to the power q. Wasserstein - Python Package Health Analysis | Snyk The Wasserstein distance between the two Gaussian densities is computed by using the wassersteinpar function and the density parameters estimated from samples. Returns the 2- Wasserstein distance between the two probability densities. Description The 2-Wasserstein distance between two multivariate ( p > 1) or univariate ( p = 1) Gaussian densities (see Details). Advertising 9. PyPI ... Python package wrapping C++ code for computing Wasserstein distances. A common metric to use is the Wasserstein metric, also known as the Earth Movers distance or Mallows distance. distance gabsens / Learning-Embeddings-into-Entropic-Wasserstein-Spaces-ENSAE. wasserstein_dist has no issues reported. Wasserstein distance user manual — gudhi documentation