Least-squares line fitting

This little JXSGraph application finds the line or the circle which is the best fit for given set of points.

The underlying JavaScript code

var brd = JXG.JSXGraph.initBoard('jxgbox',{boundingbox:[-5,5,5,-5], keepaspectratio:true, axis:true});
brd.suspendUpdate();

// Experiments with lines and circles:

// 1) Plot random points on a line disturbed by a random factor
    var i, p1 = [], angle, xr, yr, delta = 0.1;

    // Random points are constructed which lie roughly on a line
    // defined by y = 0.3*x+1.
    // delta*0.5 is the maximal distance in y-direction of the random
    // points from the line.
    brd.suspendUpdate();
    for (i=0;i<100;i++) {
        yr = 10*(Math.random()-0.5);
        xr = 0.*yr+delta*(Math.random()-0.5);
        p1.push(brd.create('point',[xr, yr], {withLabel:false}));
    }

// 2) Plot random points on a circle disturbed by a random factor
    var i, p2 = [], angle, co, si, delta = 0.2;
 
    // 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.
    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);
        p2.push(brd.create('point',[co+2, si-1], {withLabel:false}));
    }
brd.unsuspendUpdate();

var bestFit = function(p) {
//
// From here on, the best-fitting circle or line is found by least-squares fitting.
//
var i, j, r = [], rbar = [], x = [], y = [], z = [], A = [[0,0,0],[0,0,0],[0,0,0]], n, d,
    eigen, minIndex, minE, ev, c, xm, ym, zm, radius;
n = p.length;
for (i=0;i<n;i++) {
    r.push([1.0, p[i].X(), p[i].Y()]);
    d = r[i][0]*r[i][0] + r[i][1]*r[i][1] + r[i][2]*r[i][2];
    r[i][0] = 1.0 - r[i][0]/d;
    r[i][1] /= d;
    r[i][2] /= d;
}

for (j=0;j<3;j++) {
    for (i=0,d=0;i<n;i++) {
        d += r[i][j];
    }
    d /= n;
    rbar[j] = d;
    for (i=0;i<n;i++) {
        r[i][j] -= d;
    }
}
for (i=0;i<n;i++) {
    A[0][0] += r[i][0]*r[i][0];
    A[0][1] += r[i][0]*r[i][1];
    A[0][2] += r[i][0]*r[i][2];
    A[1][0] += r[i][1]*r[i][0];
    A[1][1] += r[i][1]*r[i][1];
    A[1][2] += r[i][1]*r[i][2];
    A[2][0] += r[i][2]*r[i][0];
    A[2][1] += r[i][2]*r[i][1];
    A[2][2] += r[i][2]*r[i][2];
}

eigen = JXG.Math.Numerics.Jacobi(A);
minIndex = 0;
minE = eigen[0][0][0];
for (j=1;j<3;j++) {
    if (eigen[0][j][j]<minE) {
        minIndex = j;
        minE = eigen[0][j][j];
    }
}
ev = [eigen[1][0][minIndex],eigen[1][1][minIndex],eigen[1][2][minIndex]];
c = -(rbar[0]*ev[0]+rbar[1]*ev[1]+rbar[2]*ev[2]);

xm = -ev[1];
ym = -ev[2];
zm = 2.0*(c+ev[0]);
//console.log(c, c+ev[0]);

// If c is close to zero, the best fittting object is a line.
// The best threshold parameter has yet to be determined.
// At the moment it is set to 0.01.
if (Math.abs(c)<0.01) {
    brd.create('line',[zm,xm,ym], {strokeColor:'green'});
    
}  else {
    var radius = Math.sqrt((xm*xm+ym*ym-2*c*zm)/(zm*zm));
    brd.create('circle',[[zm,xm,ym],radius]);
}

}; // end of bestFit()

bestFit(p1);
bestFit(p2);