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| author | Charles Harris <charlesr.harris@gmail.com> | 2006-10-13 19:37:48 +0000 |
|---|---|---|
| committer | Charles Harris <charlesr.harris@gmail.com> | 2006-10-13 19:37:48 +0000 |
| commit | 58541aa666e7363c3313bd886e1eafbe07d77cc6 (patch) | |
| tree | 8306f11720853e445fe3067c1b724cf38cf9cda9 /numpy/lib/polynomial.py | |
| parent | 10f2827029febe95d6e72a2f8568b0595f83c66b (diff) | |
| download | numpy-58541aa666e7363c3313bd886e1eafbe07d77cc6.tar.gz | |
Mention scaling in the polyfit docstring.
Diffstat (limited to 'numpy/lib/polynomial.py')
| -rw-r--r-- | numpy/lib/polynomial.py | 21 |
1 files changed, 13 insertions, 8 deletions
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py index c87d60f51..405e005f2 100644 --- a/numpy/lib/polynomial.py +++ b/numpy/lib/polynomial.py @@ -201,16 +201,21 @@ def polyfit(x, y, N, rcond=-1): value method takes a paramenter, 'rcond', which sets a limit on the relative size of the smallest singular value to be used in solving the equation. The default value of rcond is the double precision machine - precision as the actual solution is carried out in double precision. + precision as the actual solution is carried out in double precision. If you + are simply interested in a polynomial line drawn through the data points + and *not* in a true power series expansion about zero, then the best bet is + to scale the x sample points to the interval [0,1] as the problem will + probably be much better posed. WARNING: Power series fits are full of pitfalls for the unwary once the - degree of the fit get up around 4 or 5. Computation of the polynomial - values are sensitive to coefficient errors and the Vandermonde matrix is - ill conditioned. The knobs available to tune the fit are degree and rcond. - The rcond knob is a bit flaky and it can be useful to use values of rcond - less than the machine precision, 1e-24 for instance, but the quality of the - resulting fit needs to be checked against the data. The quality of - polynomial fits *can not* be taken for granted. + degree of the fit get up around 4 or 5 and the interval of sample points + gets large. Computation of the polynomial values are sensitive to + coefficient errors and the Vandermonde matrix is ill conditioned. The knobs + available to tune the fit are degree and rcond. The rcond knob is a bit + flaky and it can be useful to use values of rcond less than the machine + precision, 1e-24 for instance, but the quality of the resulting fit needs + to be checked against the data. The quality of polynomial fits *can not* be + taken for granted. For more info, see http://mathworld.wolfram.com/LeastSquaresFittingPolynomial.html, |