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pythonnumpyleast-squaresgeometry-surface

fitting a linear surface with numpy least squares

So I want to solve the equation z= a + b*y +c*x,. getting a,b,c. ie: making a (plane) surface fit to a load of scatter points in 3D space.

But I can't seem to find anything! I thought there would be a simple module for such a simple problem.

I have tried, where x,y,z are arrays;

ys=zip(x,y)
(coeffs, residuals, rank, sing_vals) = np.linalg.lstsq(ys,z)

am I right in thinking coeffs = b,c? Or am I going completely in the wrong direction. I just can't seem to find anything else that will work in 3d...

Solution

  • I think you're on the right track. You could still try following the example of the scipy.linalg documentation, in particular the Solving least-squares...` section

    A = np.column_stack((np.ones(x.size), x, y))
c, resid,rank,sigma = np.linalg.lstsq(A,zi)

    (we added a column of 1 for the constant).