Data mining/pattern recogniton software in Python?
nelle.varoquaux at gmail.com
Fri Mar 23 18:01:42 CET 2012
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
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
scikit-learn is not image oriented. You can do timeseries with it: there is
probably already an example in the gallery.
Hope that helped,
On 23 March 2012 17:43, Grzegorz Staniak <gstaniak at gmail.com> wrote:
> 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,
> Grzegorz Staniak <gstaniak _at_ gmail [dot] com>
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