2013/11/7 Vlad Niculae <zephy...@gmail.com>: >> This is a known problem with np.linalg.norm, and so is the memory >> consumption. You should use sklearn.utils.extmath.norm for the >> Frobenius norm. > > Hmm. Indeed I missed that, but still, this is a bit odd. > sklearn.utils.extmath.norm is slower than raveling on my anaconda with > MKL accelerate setup:
Apologies, I was mistaken. For a squared norm, ravel+dot is actually the way to go. nrm2 typically wastes time to ensure numerical stability. ------------------------------------------------------------------------------ November Webinars for C, C++, Fortran Developers Accelerate application performance with scalable programming models. Explore techniques for threading, error checking, porting, and tuning. Get the most from the latest Intel processors and coprocessors. See abstracts and register http://pubads.g.doubleclick.net/gampad/clk?id=60136231&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
