[Numpy-discussion] quadratic function
Thu Oct 28 12:35:46 CDT 2010
On Thu, Oct 28, 2010 at 12:33, Brennan Williams
> On 29/10/2010 2:34 a.m., Robert Kern wrote:
>> On Thu, Oct 28, 2010 at 06:38, Brennan Williams
>> <email@example.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([
>> 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.
> On a related topic I also use the Rbf radial basis function as a proxy
> equation. I have one set of data that it fails to return an Rbf for and
> I've just realised that in my set of simulations that are used to build
> the proxy equation I have some duplicate equations. I'm wondering if Rbf
> doesn't like duplicate points? It obviously doesn't affect linalg.lstsq.
Rbf doesn't like duplicate points. :-)
"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
More information about the NumPy-Discussion