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.
>>
>

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