A .NET Core implementation of T-Digest. This is a nearly line-for-line port of the original Java reference implementation by Ted Dunning.
The AVL Tree version and histogram classes have not been ported as I do not need them for my own project, but I will welcome any PRs to add missing classes or keep this repo up to date. This version includes the MergingDigest class and most of the relevant unit tests.
dotnet add package T-Digest --version 1.0.0
A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means. The t-digest algorithm is also very friendly to parallel programs making it useful in map-reduce and parallel streaming applications implemented using, say, Apache Spark.
The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to produce a very compact data structure that allows accurate estimation of quantiles. This t-digest data structure can be used to estimate quantiles, compute other rank statistics or even to estimate related measures like trimmed means. The advantage of the t-digest over previous digests for this purpose is that the t-digest handles data with full floating point resolution. With small changes, the t-digest can handle values from any ordered set for which we can compute something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by previous digest algorithms in spite of the fact that t-digests are much more compact when stored on disk.
In summary, the particularly interesting characteristics of the t-digest are that it
has smaller summaries when serialized works on double precision floating point as well as integers. provides part per million accuracy for extreme quantiles and typically <1000 ppm accuracy for middle quantiles is very fast (~ 140 ns per add) is very simple (~ 5000 lines of code total, <1000 for the most advanced implementation alone) has a reference implementation that has > 90% test coverage can be used with map-reduce very easily because digests can be merged requires no dynamic allocation after initial creation (MergingDigest only) has no runtime dependencies
var digest = new MergingDigest(100);
for (var i = 0; i < 1000; i++) {
digest.Add(i);
}
digest.Cdf(244); // To get the percentile of any value.
digest.Quantile(99); // To get the value of a specific percentile.