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author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
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mc a = 2; b = 3; x = -1:0.01:1; xa = ao(plist('dtype', 'cdata', 'yvals', x)); n = 1*randn(size(x)); sn = 2; na = sn.*ao(plist('dtype', 'cdata', 'yvals', n)); y0 = a.*(xa + b).^3; ya = y0 + na; %% linearisation % ylin = dyda.*da + dydb.*db + y0; da = 0.2; db = -0.3; ag = a+da; bg = b+db; y0 = ag.*(xa + bg).^3; dyda = (xa+bg).^3; dydb = ag.*(xa+bg).^2; tgtData = ya-y0; fitData = lscov(dyda, dydb, tgtData); dfit = fitData - tgtData; iplot(dfit) iplot(hist(dfit)) params = find(fitData.procinfo, 'P') af = ag + params(1) bf = bg + params(2) errs = find(fitData.procinfo, 'STD'); afe = 100*errs(1)./af bfe = 100*errs(2)./bf %% Non-linear fit xy = ao(plist('xvals', x, 'yvals', ya.y)); pl = plist('Function', 'P(1).*(Xdata + P(2)).^3', 'P0', [ag bg]); nlfit = curvefit(xy, pl);