smidmatej/RGP
Recursive Gaussian Process regression allows performing GP regression, while also being able to add train the model at runtime
This project helps engineers or scientists who need to model complex, continuous data relationships, like sensor readings or physical processes, over time. It takes in streaming data points and iteratively refines an underlying smooth curve or surface model, outputting an updated estimate with confidence bounds. This is ideal for individuals managing systems where data arrives continuously and an up-to-date, resource-efficient model is critical.
No commits in the last 6 months.
Use this if you need to continuously learn and update a smooth predictive model from a stream of new data, without needing to retrain on all historical data each time.
Not ideal if your data relationships are highly discontinuous or if you need to train a model only once on a fixed dataset.
Stars
8
Forks
4
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Apr 21, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/smidmatej/RGP"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
sbi-dev/sbi
sbi is a Python package for simulation-based inference, designed to meet the needs of both...
SMTorg/smt
Surrogate Modeling Toolbox
reservoirpy/reservoirpy
A simple and flexible code for Reservoir Computing architectures like Echo State Networks
GPflow/GPflow
Gaussian processes in TensorFlow
thousandbrainsproject/tbp.monty
Monty is a sensorimotor learning framework based on the thousand brains theory of the neocortex.