Hello, There are two steps in using a supervised learning algorithm: fitting the classifier on data labeled, and predicting on new data. If you are looking to fit with incoming data, you are looking for online algorithms: algorithms that take chunks of data to fit the classifier on the fly. scikit-learn have a couple of algorithms that are online (k-means for example) If you are looking to predict with chunks of data, it can easily be done with any kind of already fitted classifier. Hence, you only need to find a way to retrieve the data. twisted may come in handy for that, or any other asynchronous framework.
scikit-learn is not image oriented. You can do timeseries with it: there is probably already an example in the gallery. Hope that helped, N On 23 March 2012 17:43, Grzegorz Staniak <gstan...@gmail.com> wrote: > Hello, > > I've been asked by a colleague for help in a small educational > project, which would involve the recognition of patterns in a live > feed of data points (readings from a measuring appliance), and then > a more general search for patterns on archival data. The language > of preference is Python, since the lab uses software written in > Python already. I can see there are packages like Open CV, > scikit-learn, Orange that could perhaps be of use for the mining > phase -- and even if they are slanted towards image pattern > recognition, I think I'd be able to find an appropriate package > for the timeseries analyses. But I'm wondering about the "live" > phase -- what approach would you suggest? I wouldn't want to > force an open door, perhaps there are already packages/modules that > could be used to read data in a loop i.e. every 10 seconds, > maintain a a buffer of 15 readings and ring a bell when the data > in buffer form a specific pattern (a spike, a trough, whatever)? > > I'll be grateful for a push in the right direction. Thanks, > > GS > -- > Grzegorz Staniak <gstaniak _at_ gmail [dot] com> > -- > http://mail.python.org/mailman/listinfo/python-list >
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