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R = 10; x_c = 5; y_c = 8; thetas = 0:pi/64:pi; xs = x_c + R*cos(thetas); ys = y_c + R*sin(thetas); % Now add some random noise to make the problem a bit more challenging: mult = 0.5; xs = xs+mult*randn(size(xs)); ys = ys+mult*randn(size(ys));Plot the points...
figure plot(xs,ys,'b.') axis equal...and then calculate and display the best-fit circle
[xfit,yfit,Rfit] = circfit(xs,ys); figure plot(xs,ys,'b.') hold on rectangle('position',[xfit-Rfit,yfit-Rfit,Rfit*2,Rfit*2],... 'curvature',[1,1],'linestyle','-','edgecolor','r'); title(sprintf('Best fit: R = %0.1f; Ctr = (%0.1f,%0.1f)',... Rfit,xfit,yfit)); plot(xfit,yfit,'g.') xlim([xfit-Rfit-2,xfit+Rfit+2]) ylim([yfit-Rfit-2,yfit+Rfit+2]) axis equalBy the way, if it seems odd to you to use a RECTANGLE command to plot a circle, stay tuned for next week's Pick of the Week! What other data-fitting problems do you commonly come up against, and how do you solve them?
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