The Surrogate Gaussian Process (SGP) is a statistical model used in machine learning and Bayesian optimization for approximating complex, expensive-to-evaluate functions. It belongs to the family of surrogate models, which are trained on a limited set of data points to mimic the behavior of the true function.
active 2024-04-19 → 2024-05-06 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 18 days from 2024-04-19 to 2024-05-06. Pushes: 30 total, peak 15 in a day. Pull requests: 3 total, peak 2 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 1 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| Spinachboul | 28 | 27 | 1 | 0 |
| dependabot[bot] | 5 | 3 | 2 | 0 |
Recent activity
Latest issues, pull requests and releases
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 1 stars here means stars gained during the window, not the repo's star count.