Hey lampahome, I'm currently working on an online learning library called creme: https://creme-ml.github.io/. Each estimator and transformer has a fit_one(x, y) method so that you can learn from a stream of data. I've only been working on it for a bit less than a month now but it might be of interest to you nonetheless. Maybe it will give you some ideas. There's an introductory tutorial on GitHub.
Kind regards. On 13/02/2019, lampahome <pahome.c...@mirlab.org> wrote: > For example, I may have huge different regions and every regions have many > or less points. > > And I also want to real-time to analyze the newest data and older data, but > I don't want to put data into memory cuz I don't have enough memory. > > What I thought I can use is partial_fit to accept streaming data when new > data comes in. > > But the incoming data has hierarchical, it's hard to cluster them cuz I > don't have older and newer data together to cluster. > > How to design the system better? > > thx > -- Max Halford +336 28 25 13 38 _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
