# Least-squares circle fitting

 var brd = JXG.JSXGraph.initBoard('jxgbox',{boundingbox:[-5,5,5,-5], keepaspectratio:true}); var i, p = [], angle, co, si, delta = 0.8; 
// Random points are constructed which lie roughly on a circle // of radius 4 having the origin as center. // delta*0.5 is the maximal distance in x- and y- direction of the random // points from the circle line. brd.suspendUpdate(); for (i=0;i<100;i++) { 
 angle = Math.random()*2*Math.PI;  co = 4*Math.cos(angle)+delta*(Math.random()-0.5); si = 4*Math.sin(angle)+delta*(Math.random()-0.5); p.push(brd.create('point',[co, si], {withLabel:false})); } brd.unsuspendUpdate(); // Having constructed the points, we can fit a circle // through the point set, consisting of n points. // The (n times 3) matrix consists of // x_1, y_1, 1 // x_2, y_2, 1 // ... // x_n, y_n, 1 // where x_i, y_i is the position of point p_i // The vector y of length n consists of // x_i*x_i+y_i*y_i // for i=1,...n. var M = [], y = [], MT, B, c, z, n; n = p.length; for (i=0;i<n;i++) {  M.push([p[i].X(), p[i].Y(), 1.0]); y.push(p[i].X()*p[i].X() + p[i].Y()*p[i].Y()); } // Now, the general linear least-square fitting problem // min_z || M*z - y||_2^2 // is solved by solving the system of linear equations // (M^T*M) * z = (M^T*y) // with Gauss elimination. MT = JXG.Math.transpose(M); B = JXG.Math.matMatMult(MT, M); c = JXG.Math.matVecMult(MT, y); z = JXG.Math.Numerics.Gauss(B, c); // Finally, we can read from the solution vector z the coordinates [xm, ym] of the center // and the radius r and draw the circle. var xm = z[0]*0.5; var ym = z[1]*0.5; var r = Math.sqrt(z[2]+xm*xm+ym*ym); brd.create('circle',[ [xm,ym], r]); </source> ReferencesCoope, I.D., Circle fitting by linear and nonlinear least squares, Journal of Optimization Theory and Applications Volume 76, Issue 2, New York: Plenum Press, February 1993