Online Algorithms for Statistics, Models, and Big Data Viz
⚡High-performance single-pass algorithms for statistics and data viz. ➕Updated one observation at a time. ✅Algorithms use O(1) memory. 📈Perfect for streaming and big data.
import Pkg Pkg.add("OnlineStats") using OnlineStats # Create several statistics o = Series(Mean(), Variance(), Extrema()) # Update with single data point fit!(o, 1.0) # Iterate through and update with lots of data fit!(o, randn(10^6)) # Get the values of the statistics value(o) # (value(mean), value(variance), value(extrema))
- Trivial PRs such as fixing typos are very welcome!
- For nontrivial changes, you'll probably want to first discuss the changes via issue/email/slack with
- Primary Author: Josh Day (@joshday)
- Significant early contributions from Tom Breloff (@tbreloff)
- Many algorithms developed under mentorship of Hua Zhou (@Hua-Zhou)
See also the list of contributors to OnlineStats.