#13273: extension of sage.numerical.optimize.find_fit
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Reporter: hackstein | Owner: jason, jkantor
Type: enhancement | Status: new
Priority: major | Milestone: sage-5.3
Component: numerical | Keywords: fitting, interpolation,
two-dimensional
Work issues: enhancement | Report Upstream: N/A
Reviewers: tba | Authors: hackstein
Merged in: | Dependencies:
Stopgaps: |
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There is no routine in Sage to interpolate points
((x,y),(f_1,f_2))\in\mathbb{R}^{2} \times \mathbb{R}^{2} by an arbitrary
class of functions f\colon\mathbb{R}^{2}\to \mathbb{R}^{2} other than
splines. Until now, a similar routine exists only for interpolating
functions \mathbb{R}^{k} \to \mathbb{R}, called
sage.numerical.optimize.find_fit. It seems to me as one could extend the
routine "find_fit" to the two-dimensional case with not too much work as
the underlying routine scipy.optimize.leastsq (see line 645/655) is not
restricted to the one-dimensional case. The new "model" (cp. line 527 ff.)
should be a pair of symbolic expressions, of symbolic functions, or of
python functions. model has to be a function of the variables '(x_1, x_2)`
and free parameters `(a_1, a_2, \ldots, a_l)'. Thus I want to propose to
extend the routine find_fit to the two-dimensional case.
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Ticket URL: <http://trac.sagemath.org/sage_trac/ticket/13273>
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