On Fri, Nov 30, 2012 at 3:57 PM, Eviatar <eviatarb...@gmail.com> wrote:
> I think echelonize is a bit misleading, because it's not really doing
> Gaussian elimination. Besides, I think it would have to be rref to be
> consistent with matrices (it's reduced row echelon form, not simply row
> echelon form):
>
> sage: matrix([0,3,4]).echelon_form()
> [0 3 4]
> sage: matrix([0,3,4]).rref()
> [ 0 1 4/3]
>
> Even if "normal form" is used in some specific contexts, I don't think
> many people would associate it with that function, and it might create
> confusion with normalize().
>
> How about a vote?
>
> 1. reduce() (kind of ambiguous and might be mistaken for the Python
> function reduce)
> 2. echelonize() (probably echelon_form() is more appropriate, since
> it's not mutating the vector)
> 3. rref()
> 4. normal_form()
> 5. monicize()
> 6. divide_by_leading()
>
> I vote (6).
>
>
The name 'normal_form' refers to the property of the returned vector. From
that point of view, how about
7. monic()
I vote (7).
--
Benjamin Jones
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