Is it expected that all three linkages options should give the same result in my toy example?
Raphael On Thu, 26 Jul 2018 at 06:20 Gael Varoquaux <gael.varoqu...@normalesup.org> wrote: > FeatureAgglomeration uses the Ward, complete linkage, or average linkage, > algorithms, depending on the choice of "linkage". These are well > documented in the literature, or on wikipedia. > > Gaël > > On Thu, Jul 26, 2018 at 06:05:21AM +0100, Raphael C wrote: > > Hi, > > > I am trying to work out what, in precise mathematical terms, > > [FeatureAgglomeration][1] does and would love some help. Here is some > example > > code: > > > > import numpy as np > > from sklearn.cluster import FeatureAgglomeration > > for S in ['ward', 'average', 'complete']: > > FA = FeatureAgglomeration(linkage=S) > > print(FA.fit_transform(np.array([[-50,6,6,7,], [0,1,2,3]]))) > > > This outputs: > > > > > > [[ 6.33333333 -50. ] > > [ 2. 0. ]] > > [[ 6.33333333 -50. ] > > [ 2. 0. ]] > > [[ 6.33333333 -50. ] > > [ 2. 0. ]] > > > Is it possible to say mathematically how these values have been computed? > > > Also, what exactly does linkage do and why doesn't it seem to make any > > difference which option you choose? > > > Raphael > > > > [1]: http://scikit-learn.org/stable/modules/generated/ > > sklearn.cluster.FeatureAgglomeration.html > > > PS I also asked at > > https://stackoverflow.com/questions/51526616/ > > > what-does-featureagglomeration-compute-mathematically-and-when-does-linkage-make > > > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > > -- > Gael Varoquaux > Senior Researcher, INRIA Parietal > NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France > Phone: ++ 33-1-69-08-79-68 > http://gael-varoquaux.info http://twitter.com/GaelVaroquaux > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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