[SciPy-dev] Proposal for more generic optimizers (posted before on scipy-user)

Matthieu Brucher matthieu.brucher@gmail....
Thu Mar 22 14:19:55 CDT 2007

Thanks for this intel ;)
I think that what I want to do is more general that what LOESS proposes, as
it is not only about modelling. But there surely is stuff to use :) - and on
other topic, there are some good ideas in LOESS that I could use in my PhD
thesis -


2007/3/22, Pierre GM <pgmdevlist@gmail.com>:
> On Thursday 22 March 2007 13:37:59 Matthieu Brucher wrote:
> > > Right now you require an object that implements methods with
> > > certain names, which is ok but I think not perfect.  Here is
> > > a possibly crazy thought, just to explore.  How about making
> > > all arguments optional, and allowing passing either such an
> > > object or the needed components?  Finally, to accmmodate
> > > existing objects with a different name structure, allow
> > > passing a dictionary of attribute names.
> >
> > Indeed, that could be a solution. The only question remaining is how to
> use
> > the dictionnary, perhaps creating a fake object with the correct
> interface
> Just 2c:
> I just ported the loess package to numpy (the package is available in the
> scipy SVN sandbox as 'pyloess'). The loess part has a C-based structure
> that
> looks like what you're trying to reproduce.
> Basically, the package implements a new class, loess. The attributes of a
> loess object are themselves classes: one for inputs, one for outputs, and
> several others controlling different aspects of the estimation.
> The __init__method of a loess object requires two mandatory arguments for
> the
> independent variables (x) and the observed response (y). These arguments
> are
> used to initialize the inputs subclass. The __init__ method also accepts
> optional parameters, that modiify the values of the different parameters.
> However, one can also modify specific arguments directly.
> The outputs section is created when a new loess object is instantiated,
> but
> with empty arrays. The arrays are filled when the .fit() method of the
> loess
> object is called.
> Maybe you would like to check the tests/test_pyloess.py file to get a
> better
> idea. I'm currently updating the docs and writing a small example.
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