Hi Deepak, Then, there might be a problem (i.e. wrong dimension) with the matrix passed to initalise LMNN. I suggest you to try setting use_pca to True. That will make Shogun's LMNN to start using a matrix obtained by applying PCA.
Modifying line 55 in your gist to lmnn = LMNN(k=3, learn_rate=1e-3, use_pca=True) should do it. Let me know how it goes. Cheers, Fernando. On 12 April 2016 at 18:52, Deepak Rishi <[email protected]> wrote: > Hi Fernando, > > The gist is at > https://gist.github.com/deerishi/e4ea6257e88ea924cc0fb091a5af1670 . > > I did not use the parameter use_pca. Nor did I call the PCA function. > > @Chintak , have you used LMNN before? > > > Regards, > Deepak > > On Tue, Apr 12, 2016 at 4:33 AM, Fernando J. Iglesias García < > [email protected]> wrote: > >> Dear Deepak, >> >> Can you share in a gist, pastebin, or the like, a snippet with your >> relevant code? Particularly at this moment I am interested to know whether >> you are using the parameter use_pca. >> >> Cheers, >> Fernando. >> >> On 12 April 2016 at 08:39, Deepak Rishi <[email protected]> wrote: >> >>> Hi everyone, >>> >>> I am using the python package metric_learn >>> http://all-umass.github.io/metric-learn/metric_learn.lmnn.html for >>> Large Margin Nearest Neighbour. It uses LMNN from Shogun if it is >>> installed. Since Shogun has a faster implementation for LMNN I decied to >>> use it. >>> >>> When I run my code for LMNN in python I get the error "SystemError: >>> [ERROR] In file /home/drishi/shogun-4.1.0/src/shogun/preprocessor/PCA.cpp >>> line 102: target dimension should be less or equal to than minimum of N and >>> D >>> " >>> >>> Any advice on why is this error occurring ? >>> >>> >>> Regards, >>> Deepak >>> >> >> >
