It may be useful in a pipeline if you need to normalise between a preceding transformer and a following estimator.
On 28 June 2016 at 16:09, Michael Eickenberg <[email protected]> wrote: > well, true :) > > but you can put it in pipelines! :) > > (so, in that logic, is there any reason for keeping it in the package?) > > > On Tuesday, June 28, 2016, Joel Nothman <[email protected]> wrote: > >> (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 > >
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