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.
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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
@joshday.
- 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.