Greg Willden wrote: > On 10/13/06, *A. M. Archibald* <[EMAIL PROTECTED] > <mailto:[EMAIL PROTECTED]>> wrote: > > At this point you might as well use a polynomial class that can > accomodate a variety of bases for the space of polynomials - X^n, > (X-a)^n, orthogonal polynomials (translated and scaled as needed), > what have you. > > I think I vote for polyfit that is no more clever than it has to be > but which warns the user when the fit is bad. > > > > What about including multiple algorithms each returning a figure of fit? > Then I could try two or three different algorithms and then use the > one that works best for my data. A simple, "stupid" curve fitting algorithm may be appropriate for numpy, but once your getting into multiple algorithms it's time to move it to a package in scipy IMO (and it would be good to find someone who cares, and knows, about curve fitting to adopt it).
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