On Tue, Feb 8, 2011 at 10:44, Ben Gamari <bgamari.f...@gmail.com> wrote: > I have an array of (say, row) vectors, > > v = [ [ a1, a2, a3 ], > [ b1, b2, b3 ], > [ c1, c2, c3 ], > ... > ] > > What is the optimal way to compute the norm of each vector, > norm(v)**2 = [ > [ a1**2 + a2**2 + a3**2 ], > [ b1**2 + b2**2 + b3**2 ], > ... > ] > > It seems clear that numpy.norm does not handle the multidimensional case > as needed. The best thing I can think of is to do something like, > > sum(v**2, axis=0) * ones(3) > > but surely there must be a better way. Any ideas?
(v*v).sum(axis=1)[:,np.newaxis] You can leave off the newaxis bit if you don't really need a column vector. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
