Dear Ryan, Thank you very much for your help. I am using the modified version now and it scales much better.
Best regards, Anisa Llaveshi On Fri, Aug 24, 2018 at 4:09 AM Ryan Curtin <[email protected]> wrote: > On Thu, Aug 23, 2018 at 03:06:48PM +0200, Anisa Llaveshi wrote: > > Dear Ryan, > > > > Thank you for your quick response. I double checked and I am afraid that > is > > not the case. The matrix that I am providing as input to the model is a > 1xN > > matrix. I explicitly check the number of rows (1) and the number of > columns > > (N) of the matrix. The model would not even allow that because the labels > > provided to the model is a row vector (rowvec) of N columns so the > > dimensions would not match. > > > > Please let me know in case you think I am missing something or if you > have > > another idea of what might be going on. > > Hi Anisa, > > It turns out you aren't missing anything at all. I took a look at the > linear regression implementation and was surprised to see that it > actually is trying to form an NxN matrix, whereas for linear regression > it should only be necessary to form a dxd matrix. > > I spent a little time this evening reimplementing the code and have > opened a pull request to fix it: > > https://github.com/mlpack/mlpack/pull/1500 > > You can either work directly off that branch, wait for another release, > or copy the modified linear_regression.cpp file into your codebase. > > But now for your problem with 200k points, it should be blazing fast > (like 0.005s to solve the system, minus data loading time). So you > should be able to scale much more. > > Sorry for the error! I hope that this helps out, and if not, just let > me know. > > Thanks, > > Ryan > > -- > Ryan Curtin | "The enemy cannot press a button... if you have > [email protected] | disabled his hand." - Sgt. Zim >
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