On 2013-10-19 17:36, Lars Buitinck wrote: > 2013/10/19 Thomas Unterthiner <[email protected]>: >> I've noticed that sklearn uses numpy.linalg instead of scipy.linalg for its >> linear algebra implementations (e.g. dot or svd). However I found that >> scipy.linalg consistently performs better on my machines. Since sklearn >> requires scipy anyways, I was wondering what the reason is for sticking with >> the numpy.linalg versions in sklearn? > Not always: in performance-critical code, we use our own > sklearn.utils.extmath.{norm,row_norms,randomized_svd} or even > specialized implementations. But there are indeed some places where > np.linalg is still used. > > dot is not replaced by SciPy, btw., and the test you ran seems to > actually measure inv's performance, not dot's. > >
do'h, I didn't even notice the 'inv' in that code =) ------------------------------------------------------------------------------ October Webinars: Code for Performance Free Intel webinars can help you accelerate application performance. Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most from the latest Intel processors and coprocessors. See abstracts and register > http://pubads.g.doubleclick.net/gampad/clk?id=60135031&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
