Hi, Thanks for those tips Sebastian.That just saved my day. Regards, Rajkumar
On Tue, Feb 13, 2018 at 12:44 AM, Sebastian Raschka <se.rasc...@gmail.com> wrote: > [image: Boxbe] <https://www.boxbe.com/overview> This message is eligible > for Automatic Cleanup! (se.rasc...@gmail.com) Add cleanup rule > <https://www.boxbe.com/popup?url=https%3A%2F%2Fwww.boxbe.com%2Fcleanup%3Fkey%3D0a2mz6HiALxmseA8EtEa3hg8FtAfQyTwNzLAvbS3JOk%253D%26token%3D8qZlnKU2OJ%252BeTscNUfA9PjpDKa2%252FZO8i9dvKkAyr7bKz%252Bi2MdFTFnLILfmhv4s3s%252Bva0Dy7LpRz63wO18BlP48DNIu3aSb%252FmxAVjQq1fCD0tDxFcxxdH2mq9Otany%252FdER3CzXyokyLg%253D&tc_serial=36653890807&tc_rand=854549477&utm_source=stf&utm_medium=email&utm_campaign=ANNO_CLEANUP_ADD&utm_content=001> > | More info > <http://blog.boxbe.com/general/boxbe-automatic-cleanup?tc_serial=36653890807&tc_rand=854549477&utm_source=stf&utm_medium=email&utm_campaign=ANNO_CLEANUP_ADD&utm_content=001> > > Hi, > > by default, the clustering classes from sklearn, (e.g., DBSCAN), take an > [num_examples, num_features] array as input, but you can also provide the > distance matrix directly, e.g., by instantiating it with > metric='precomputed' > > my_dbscan = DBSCAN(..., metric='precomputed') > my_dbscan.fit(my_distance_matrix) > > Not sure if it helps in that particular case (depending on how many zero > elements you have), you can also use a sparse matrix in CSR format ( > https://docs.scipy.org/doc/scipy-1.0.0/reference/ > generated/scipy.sparse.csr_matrix.html). > > Also, you don't need to for-loop through the rows if you want to compute > the pair-wise distances, you can simply do that on the complete array. E.g., > > from sklearn.metrics.pairwise import cosine_distances > from scipy import sparse > > distance_matrix = cosine_distances(sparse.csr_matrix(X), > dense_output=False) > > where X is your "[num_examples, num_features]" array. > > Best, > Sebastian > > > > On Feb 12, 2018, at 1:10 PM, prince gosavi <princegosav...@gmail.com> > wrote: > > > > I have generated a cosine distance matrix and would like to apply > clustering algorithm to the given matrix. > > np.shape(distance_matrix)==(14000,14000) > > > > I would like to know which clustering suits better and is there any need > to process the data further to get it in the form so that a model can be > applied. > > Also any performance tip as the matrix takes around 3-4 hrs of > processing. > > You can find my code here https://github.com/ > maxyodedara5/BE_Project/blob/master/main.ipynb > > Code for READ ONLY PURPOSE. > > -- > > Regards > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- Regards
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