tdigest: On-line accumulation of rank-based statistics
A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means.
See original paper: "Computing extremely accurate quantiles using t-digest" by Ted Dunning and Otmar Ertl for more details https://github.com/tdunning/t-digest/blob/master/docs/t-digest-paper/histo.pdf.
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- tdigest-0.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
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Candidates
| Versions [RSS] | 0, 0.1, 0.2, 0.2.1, 0.2.1.1, 0.3, 0.3.1 |
|---|---|
| Dependencies | base (>=4.7 && <4.10), base-compat (>=0.9.1 && <0.10), binary (>=0.7.1.0 && <0.9), deepseq (>=1.3.0.2 && <1.5), reducers (>=3.12.1 && <3.13), semigroups (>=0.18.2 && <0.19), vector (>=0.11 && <0.13), vector-algorithms (>=0.7.0.1 && <0.8) [details] |
| Tested with | ghc ==7.8.4, ghc ==7.10.3, ghc ==8.0.1, ghc ==8.0.2 |
| License | BSD-3-Clause |
| Author | Oleg Grenrus <oleg.grenrus@iki.fi> |
| Maintainer | Oleg Grenrus <oleg.grenrus@iki.fi> |
| Category | Numeric |
| Home page | https://github.com/futurice/haskell-tdigest#readme |
| Bug tracker | https://github.com/futurice/haskell-tdigest/issues |
| Source repo | head: git clone https://github.com/futurice/haskell-tdigest |
| Uploaded | by phadej at 2017-02-16T15:28:40Z |
| Distributions | Arch:0.3.1, LTSHaskell:0.3.1, NixOS:0.3.1, Stackage:0.3.1 |
| Reverse Dependencies | 11 direct, 19 indirect [details] |
| Downloads | 12245 total (154 in the last 30 days) |
| Rating | 2.0 (votes: 1) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs available [build log] Last success reported on 2017-02-16 [all 1 reports] |
Readme for tdigest-0
[back to package description]tdigest
A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means.
See original paper: "Computing extremely accurate quantiles using t-digest" by Ted Dunning and Otmar Ertl
Synopsis
λ *Data.TDigest > median (tdigest [1..1000] :: TDigest 3)
Just 499.0090729817737
Benchmarks
Using 50M exponentially distributed numbers:
- average: 16s; incorrect approximation of median, mostly to measure prng speed
- sorting using
vector-algorithms: 33s; using 1000MB of memory - sparking t-digest (using some
par): 53s - buffered t-digest: 68s
- sequential t-digest: 65s
Example histogram
tdigest-simple -m tdigest -d standard -s 100000 -c 10 -o output.svg -i 34
inkscape --export-png=example.png --export-dpi=80 --export-background-opacity=0 --without-gui example.svg
