README.md

In AnthonyEbert/EasyMMD: Allows users to compute the Maximum Mean Discrepancy (MMD) between two samples.

EasyMMD

Gretton et al. (2007) introduced Maximum Mean Discrepancy (MMD). EasyMMD in an R package which provides a simple (hopefully!) and computationally efficient way to compute the MMD between two datasets.

Installation

You can install EasyMMD from github with:

devtools::install_github("AnthonyEbert/EasyMMD")

Example

This is a basic example which shows you how to solve a common problem:

library(EasyMMD)

n <- 1e3
x <- rnorm(n)
y <- rnorm(n, 5)


MMD(y,x)

## [1] 1.120101

# For faster computation, precompute part of the MMD for the observed data y

y_kmmd <- kmmd(y)

MMD(y,x, y_kmmd)

## [1] 1.120101

microbenchmark::microbenchmark(MMD(y,x), MMD(y,x, y_kmmd), times = 10)

## Unit: milliseconds
##               expr      min       lq      mean   median       uq      max neval
##          MMD(y, x) 132.8372 133.4394 135.27005 133.6568 134.5340 142.3326    10
##  MMD(y, x, y_kmmd)  84.7363  85.0482  86.53848  85.5797  87.7038  91.0809    10

References

Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., & Smola, A. J. (2007). A kernel method for the two-sample-problem. In Advances in neural information processing systems (pp. 513-520).

AnthonyEbert/EasyMMD documentation built on March 29, 2022, 8:55 p.m.