| Mathematica:
(1)
xy = ReadList["xyL.dat",{Number,Number}];
Fit[ xy, {x^5, x^4, x^3, x^2, x^1, 1}, x]
(2)
xy = ReadList["xyN.dat",{Number,Number}];
Needs["Statistics`NonlinearFit`"]
YofX[x_] = a Exp[b x] Sin[c x + d]
initialguesses = {{a,0.5},
{b,-0.5},{c,1.5},{d,0.5}}
ft = NonlinearFit[xy, YofX[x], x,
initialguesses]
Plot[ft, {x,0,20}];
|
Matlab:
(1)
load xyL.dat % assuming OK format
a = polyfit(xyl(:,1),xyL(:,2),5)
(2)
|
| Maple:
xxx
| IDL:
(1)
xy = fltarr(2,21)
OpenR, 19, 'xyL.dat'
ReadF, 19, xy
Close, 19
x=reform(xy[0,*]) & y=reform(xy[1,*])
print, Poly_Fit(x,y,5)
(2)
xy = fltarr(2,41)
OpenR, 23, 'xyN.dat'
ReadF, 23, xy
Close, 23
PRO YofX, x, A, f
(first define the nonlinear function)
f = A[0] * exp(A[1] * x) * sin(A[2]*x + A[3])
END
x=reform(xy[0,*]) & y=reform(xy[1,*])
A = [ 0.5, -0.5, 1.5, 0.5 ]
w = x * 0.0 + 1.0
fit = CurveFit(x,y,w,A,sd, $
Function_Name='YofX', /noderivative)
print, A & plot,x,fit
|