On Wed, Jun 25, 2008 at 02:59:53PM +0200, Ondrej Certik wrote:
>
> On Sun, Jun 22, 2008 at 5:39 PM, Riccardo Gori <[EMAIL PROTECTED]> wrote:
> >
> > On Sunday 22 June 2008 12:47:38 physnut wrote:
> >> hello there, i'm only new to this project (and haven't started helping
> >> at all
> >> yet...) but wouldn't it be better to retain both versions and give
> >> them
> >> slightly different names (and explain there are two in docstrings)? I
> >> am
> >> presuming that the version which modifies the matrix in situ is faster
> >> and/or
> >> uses lower memory for lots of large matrices? From a CAS standpoint I
> >> agree
> >> with the convenience of making a new matrix, but I am probably not
> >> alone in
> >> wanting to use symbolic mathematics as part of the internals of my
> >> program
> >> and since I am running a home cluster the more efficient the code, the
> >> smaller
> >> my electricity bill is!!
> >>
> >
> > Hello,
> > I can suggest a solution:
> > we could implement expand (and the other functions as applyfunc, diff,
> > etc...)
> > in that way:
> >
> > def function(self,modint=False):
> > if modint:
> > modify(self)
> > else:
> > return new_modified
> >
> > The version that modify the matrix is 15-20% faster, and uses lot less
> > memory
> > (I don't know how to measure it, with a big matrix and using top I can see
> > 35-40% less memory used, but this is not a good method to measure)
>
> What others think about this? What does Sage/numpy and other CAS systems do?
I think inplace operations are important, especially for large matrices.
And also I think 'inplace' keyword is more appropriate for what we'are
try to do:
def function(self, inplace=False):
...
So I'm for 'inplace' :)
--
Всего хорошего, Кирилл.
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