On Wed, Dec 03, 2014 at 04:13:06PM +0000, Илья Патрушев wrote: > Sure, I understand the rationale behind the requirement of 1000+ cites etc., > and as I mentioned above, I am quite happy to release it via PyPI.
And put it in a scikit-learn-contrib repo? That would be sweet. > Wang et al. 2008 claim that their approach improves correctness of Affinity > Propagation clustering (though it increases the running times). > Correct me if I am wrong, from your reply it looks like you are not > persuaded by the paper and do not recommend including the algorithm in > sklearn. Yes. But on the other hand, I do not hold the Truth. I would be very, very happy to be proven wrong, and if clearly proven wrong, integrate it in scikit-learn. You know, I have no horses in this race. The algorithms that I develop are not part of scikit-learn, and will never be, because of the requirements that we have. I just want scikit-learn to be something genuinely useful. Partly out of selfishness, because I have a research team that is relying on it to do the data analysis. Gaël ------------------------------------------------------------------------------ Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server from Actuate! Instantly Supercharge Your Business Reports and Dashboards with Interactivity, Sharing, Native Excel Exports, App Integration & more Get technology previously reserved for billion-dollar corporations, FREE http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
