On 13/10/06, Charles R Harris <[EMAIL PROTECTED]> wrote: > You can also get *much* better results if you scale the x interval to [0,1] > as the problem will be better posed. For instance, with your data and a > degree 10 fit I get a condition number of about 2e7 when x is scaled to > [0,1], as opposed to about 1e36 when left as is. The former yields a > perfectly useable fit while the latter blows up. I suppose this could be > built into the polyfit routine if one were only interested in polynomial > fits of some sort, but the polynomial would have to carry around an offset > and scale factor to make evaluation work.
[-1,1] would probably be even better, no? > If Travis is interested in such a thing we could put together some variant > of the polynomials that includes the extra data. 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. A. M. Archibald ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
