Jaime, If you are going to work on this, you should also take a look at the recent thread http://mail.scipy.org/pipermail/numpy-discussion/2013-February/065649.html, which is about the weighting function, which is in a confused state in the current version of polyfit. By the way, Numerical Recipes has a nice discussion both about fixing parameters and about weighting the data in different ways in polynomial least squares fitting.
David On Mon, Mar 4, 2013 at 7:23 PM, Jaime Fernández del Río < [email protected]> wrote: > A couple of days back, answering a question in StackExchange ( > http://stackoverflow.com/a/15196628/110026), I found myself using > Lagrange multipliers to fit a polynomial with least squares to data, making > sure it went through some fixed points. This time it was relatively easy, > because some 5 years ago I came across the same problem in real life, and > spent the better part of a week banging my head against it. Even knowing > what you are doing, it is far from simple, and in my own experience very > useful: I think the only time ever I have fitted a polynomial to data with > a definite purpose, it required that some points were fixed. > > Seeing that polyfit is entirely coded in python, it would be relatively > straightforward to add support for fixed points. It is also something I > feel capable, and willing, of doing. > > * Is such an additional feature something worthy of investigating, or > will it never find its way into numpy.polyfit? > * Any ideas on the best syntax for the extra parameters? > > Thanks, > > Jaime > > -- > (\__/) > ( O.o) > ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes > de dominación mundial. > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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