Dear R community members, please find here a short presentation of our new package.
We introduce GuessCompx, a new R package that makes an empirical guess on the time and memory complexities of an algorithm or a function. It will test multiple, increasing-sizes samples of the user’s data and try to fit one of seven complexity functions : O(n), O(n2), O(log(n)), etc. Based on a best fit procedure using LOO-MSE error, it also predicts the full computation time and memory usage on the whole dataset. It relies on the base R functions `system.time` and `memory.size `, the latter being only suitable for Windows users. Together with this result, a plot and a significance test are also returned. Complexity is assessed with regard to the user’s actual dataset through its size (and no other parameter). We provide several examples showing some use case (distance function, time series, custom function) and how to best tune the parameters. The subject of empirical computational complexity has been relatively little studied in computer sciences, and such a package provides a convenient and simple procedure to estimate it, thus preventing the user to run any computation for an unknown amount of time. Empirical fit does not guarantee to find the true complexity function but approaches it in an acceptable way. The package does not require to have the code of the target function. https://cran.r-project.org/package=GuessCompx Regards, Marc Agenis Neeraj Bokde marc.age...@gmail.com +33 (0)6 19 60 91 23 [[alternative HTML version deleted]] _______________________________________________ R-packages mailing list r-packa...@r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
