On Thu, Oct 28, 2010 at 06:38, Brennan Williams <brennan.willi...@visualreservoir.com> wrote: > I have used both linear least squares and radial basis functions as a > proxy equation, calculated from the results of computer simulations > which are calculating some objective function value based on a number of > varied input parameters. > > As an alternative option I want to add a quadratic function so if there > are parameters/variables x,y,z then rather than just having a linear > function f=a+bx+cy+dz I'll have f=a+bx+cx**2 + dxy + .... I'd like to > have the option not to include all the different second order terms.
A = np.column_stack([ np.ones_like(x), x, y, z, x*x, y*y, z*z, x*y, y*z, x*z, ]) x, res, rank, s = np.linalg.lstsq(A, f) -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion