[Numpy-discussion] polyfit with fixed points
Jaime Fernández del Río
Wed Mar 6 17:52:11 CST 2013
On Tue, Mar 5, 2013 at 5:23 AM, Charles R Harris
> On Tue, Mar 5, 2013 at 12:41 AM, Jaime Fernández del Río <
> email@example.com> wrote:
>> On Mon, Mar 4, 2013 at 8:37 PM, Charles R Harris <
>> firstname.lastname@example.org> wrote:
>>> There are actually seven versions of polynomial fit, two for the usual
>>> polynomial basis, and one each for Legendre, Chebyshev, Hermite, Hermite_e,
>>> and Laguerre ;)
>> Correct me if I am wrong, but the fitted function is the same regardless
>> of the polynomial basis used. I don't know if there can be numerical
>> stability issues, but chebfit(x, y, n) returns the same as
>> poly2cheb(polyfit(x, y, n)).
>> In any case, with all the already existing support for these special
>> polynomials, it wouldn't be too hard to set the problem up to calculate the
>> right coefficients directly for each case.
>>> How do you propose to implement it? I think Lagrange multipliers is
>>> overkill, I'd rather see using the weights (approximate) or change of
>>> variable -- a permutation in this case -- followed by qr and lstsq.
>> The weights method is already in place, but I find it rather inelegant
>> and unsatisfactory as a solution to this problem. But if it is deemed
>> sufficient, then there is of course no need to go any further.
>> I hadn't thought of any other way than using Lagrange multipliers, but
>> looking at it in more detail, I am not sure it will be possible to
>> formulate it in a manner that can be fed to lstsq, as polyfit does today.
>> And if it can't, it probably wouldn't make much sense to have two different
>> methods which cannot produce the same full output running under the same
>> I can't figure out your "change of variable" method from the succinct
>> description, could you elaborate a little more?
> I think the place to add this is to lstsq as linear constraints. That is,
> the coefficients must satisfy B * c = y_c for some set of equations B. In
> the polynomial case the rows of B would be the powers of x at the points
> you want to constrain. Then do an svd on B, B = u * d * v. Apply v to the
> design matrix of the unconstrained points A' = A * v.T so that B' becomes
> u * d. The coefficients are now replaced by new variables c' with the
> contraints in the first two columns. If there are, say, 2 constraints, u *
> d will be 2x2. Solve that equation for the first two constraints then
> multiply the first two columns of the design matrix A' by the result and
> put them on the rhs, i.e.,
> y = y - A'[:, :2] * c'[:2]
> then solve the usual l least squares thing with
> A[:, 2:] * c'[2:] = y
> to get the rest of the transformed coefficients c'. Put the coefficients
> altogether and multiply with v^T to get
> c = v^T * c'
Very nice, and works beautifully! I have tried the method you describe, and
there are a few relevant observations:
1. It gives the exact same result as the Lagrange multiplier approach,
which is probably expected, but I wasn't all that sure it would be the case.
2. The result also seems to be to what the sequence of fits giving
increasing weights to the fixed points converges to. This image
http://i1092.photobucket.com/albums/i412/jfrio/image.png is an example. In
* blue crosses are the data points to fit to
* red points are the fixed points
* blue line is the standard polyfit
* red line is the constrained polyfit
* cyan, magenta, yellow and black are polyfits with weights of 2, 4,
8, 16 for the fixed points, 1 for the rest
Seeing this last point, probably the cleanest, least disruptive
implementation of this, would be to allow np.inf values in the weights
parameter, which would get filtered out, and dealt with in the above manner.
So I have two questions:
1. Does this make sense? Or will it be better to make it more explicit,
with a 'fixed_points' keyword argument defaulting to None?
2. Once I have this implemented, documented and tested... How do I go
about submitting it for consideration? Would a patch be the way to go, or
should I fork?
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