My mistake there, I meant the L1 norm, re-typed: ----------------------------- X= [[1 2 3],[4 5 6]]
# now, X[1,:] is 1x3 array, containing 1 2 3 # but let's peek at its L1-norm: norm( X[1,:], 1 ) # --> we get 3, where I would expect 6 (1+2+3) ----------------------------- can you try that on v0.2? I am on 0.3 from upstream. ------------------------------------------ Carlos On Tue, Mar 4, 2014 at 1:19 AM, Patrick O'Leary <patrick.ole...@gmail.com>wrote: > This is odd, as I get norm() working just fine with any of a row, column, > or vector, and all getting exactly the same result of 3.741... (v0.2.0, on > julia.forio.com, since it's quick for me to get to). Note that it will > return the L2 norm by default, exactly as MATLAB does. Supplying a second > argument with p in it (norm([1 2 3], 1)) will return the p-norm, exactly > like MATLAB. > > > On Monday, March 3, 2014 6:12:53 PM UTC-6, Carlos Becker wrote: >> >> Hello all, >> >> today I fought for an hour with a very simple piece of code, of the kind: >> >> ----------------------------- >> X= [[1 2 3],[4 5 6]] >> >> # now, X[1,:] is 1x3 array, containing 1 2 3 >> >> # but let's peek at its L1-norm: >> norm( X[1,:] ) # --> we get 3, where I would expect 6 (1+2+3) >> ----------------------------- >> >> I believe this comes back to the 'how 1xN matrices should be handled'. >> The point is that the current behaviour is totally non-intuitive for >> someone coming from Matlab, >> and having matrix and vector norms in the same function hides this (in >> this case) unwanted behavior. >> >> I am not sure what is the right way to deal with this, but seems like a >> hard wall that more than one >> will hit when coming from matlab-like backgrounds. >> >> Cheers. >> >
