(Since Normalizer is applied to each sample independently, the Pipeline/Transformer mechanism doesn't actually provide any benefit over sklearn.preprocessing.normalize)
On 28 June 2016 at 09:20, Michael Eickenberg <[email protected]> wrote: > You could do > > from sklearn.pipeline import make_pipeline > from sklearn.preprocessing import Normalizer > from sklearn.cluster import KMeans # (or e.g. MiniBatchKMeans) > > spherical_kmeans = make_pipeline(Normalizer(), KMeans(n_clusters=5)) > > > > On Tue, Jun 28, 2016 at 12:28 AM, JAGANADH G <[email protected]> wrote: > >> Hi Fred and Michel, >> >> Thanks for the reply . I think I git this and am able to run it. >> >> >> Best >> Jagan >> >> >> On Mon, Jun 27, 2016 at 1:03 PM, Fred Mailhot <[email protected]> >> wrote: >> >>> Per the example here: >>> >>> >>> http://scikit-learn.org/stable/auto_examples/text/document_clustering.html >>> >>> if your inputs are normalized, sklearn's kmeans behaves like sperical >>> kmeans (unless I'm misunderstanding something, which is certainly possible, >>> caveat lector, &c )... >>> On Jun 27, 2016 12:13 PM, "Michael Eickenberg" < >>> [email protected]> wrote: >>> >>>> hmm, not an answer, and off the top of my head: >>>> if you normalize your data points to l2 norm equal 1, and then use >>>> standard kmeans with euclidean distance (which then amounts to 2 - 2 >>>> cos(angle between points)) would this be enough for your purposes? (with a >>>> bit of luck there may even be some sort of correspondence) >>>> >>>> Michael >>>> >>>> On Monday, June 27, 2016, JAGANADH G <[email protected]> wrote: >>>> >>>>> Hi , >>>>> is there any Python package available for experiment with Sperical >>>>> Kmeans ? >>>>> >>>>> >>>>> -- >>>>> ********************************** >>>>> JAGANADH G >>>>> http://jaganadhg.in >>>>> *ILUGCBE* >>>>> http://ilugcbe.org.in >>>>> >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> [email protected] >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> >>> _______________________________________________ >>> scikit-learn mailing list >>> [email protected] >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> >> >> -- >> ********************************** >> JAGANADH G >> http://jaganadhg.in >> *ILUGCBE* >> http://ilugcbe.org.in >> >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
_______________________________________________ scikit-learn mailing list [email protected] https://mail.python.org/mailman/listinfo/scikit-learn
