wnet 0.9.9

Tools for calculation of Wasserstein metric between distributions based on Network Flow algorithm

wnet

Wasserstein Network (wnet) is a Python/C++ library for working with Wasserstein distances. It uses the Min Cost Flow algorithm as implemented by the LEMON library, exposed to Python via the pylmcf module, enabling efficient computation and manipulation of Wasserstein distances between multidimensional distributions.

Features

• Wasserstein and Truncated Wasserstein distance calculations between multidimensional distributions
• Calculation of derivatives with respect to deltas in flow or position (in progress)
• Python and C++ integration
• Support for distribution mixtures, and efficient recalculation of distance with changed mixture proportions

Installation

You can install the Python package using pip:

pip install wnet

Usage

Simple usage:

import numpy as np
from wnet import WassersteinDistance, Distribution
from wnet.distances import DistanceMetric

positions1 = np.array(
    [[0, 1, 5, 10],
     [0, 0, 0, 3]]
)
intensities1 = np.array([10, 5, 5, 5])

positions2 = np.array(
    [[1, 10],
     [0, 0]]
)
intensities2 = np.array([20, 5])

S1 = Distribution(positions1, intensities1)
S2 = Distribution(positions2, intensities2)

print(WassersteinDistance(S1, S2, DistanceMetric.L1))
# 45

Licence

MIT Licence

Related Projects

• pylmcf - Python bindings for Min Cost Flow algorithms from LEMON library.
• wnetalign - Alignment of MS/NMR spectra using Truncated Wasserstein Distance