On Thu, Oct 28, 2010 at 12:47, Brennan Williams <brennan.williams@visualreservoir.com> wrote:
On 29/10/2010 6:35 a.m., Robert Kern wrote:
On Thu, Oct 28, 2010 at 12:33, Brennan Williams <brennan.williams@visualreservoir.com> wrote:
On 29/10/2010 2:34 a.m., Robert Kern wrote:
On Thu, Oct 28, 2010 at 06:38, Brennan Williams <brennan.williams@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)
OK, so in other words, you can use linalg.lstsq for whatever higher order terms you want to include or exclude. Very nice. Thanks. Right. Just as long as the problem is linear in the coefficients, the design matrix can be derived however you like.
So I could optionally put log terms in if I thought it was linear in log(x) for example?
Yup! -- 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