hi, indeed we could stick to indices and use np.take whenever possible.
In [33]: A = np.random.randn(500, 500) In [34]: idx = np.unique(np.random.randint(0, 499, 400)) In [35]: mask = np.zeros(500, dtype=np.bool) In [36]: mask[idx] = True In [37]: %timeit A[idx] 1000 loops, best of 3: 1.79 ms per loop In [38]: %timeit A[mask] 1000 loops, best of 3: 1.77 ms per loop In [39]: %timeit A.take(idx, axis=0) 10000 loops, best of 3: 103 us per loop Alex ------------------------------------------------------------------------------ Get your SQL database under version control now! Version control is standard for application code, but databases havent caught up. So what steps can you take to put your SQL databases under version control? Why should you start doing it? Read more to find out. http://pubads.g.doubleclick.net/gampad/clk?id=49501711&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
