On Sun, Sep 1, 2024 at 6:48 AM Rakshit Singh <rakshitsingh...@gmail.com>
wrote:

> Best Wishes
>
> I am really hesitant of changing the api, some packages might be dependent
> on it.
>
> Regards
> Rakshit Kr. Singh
>
> On Sun, Sep 1, 2024, 5:54 PM oc-spam66--- via NumPy-Discussion <
> numpy-discussion@python.org> wrote:
>
>> I can summarize the different possibilities/proposals:
>> (A) Create new properties: add a `P.coef_natural` property, with a
>> suitable documentation ; maybe also add a `P.coef_internal` property. There
>> would be no change to the existing code (only addition of properties).
>> (B) Change `P.coef` attribute into a property, with a suitable
>> documentation. Hide `P.coef` attribute into `P._coef` (change existing
>> code). Do not create more properties (unlike A).
>>
>> - About (A), I don't think that adding `P.coef_natural` would add a risk.
>> - About (B), it may be appreciated that the API does not change (does not
>> occupy more namespace)
>> - Both (A) and (B) would help basic users to get out of the `P.coef`
>> attribute confusion.
>>
>> Side remark (not important):
>> > "natural" coefficients make very little if any sense for some of the
>> other polynomial subclasses, such as Chebyshev -- for those, there's
>> nothing natural about them!
>> Are you sure? Can they not be the weights at different order of
>> approximation of a solution?
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Chebyshev polynomials have two important properties over the interval [-1,
1]:

   1. They are equiripple, consequently the coefficients of high power fits
   are a good approximation of the maximum error if the series is truncated at
   that point, i.e., they provide something close to an min-max fit.
   2.  High power fits are practical because the polynomials are more
   independent (in the L2 norm). The design matrix is generally
   well-conditioned.

Chebyshev polynomials are quite wonderful, but only if the domain of the
data is in the range [-1, 1]. Similar arguments apply to Legendre
polynomials, but in that case the coefficients approximate the L2 error
when the series is truncated and properly normalized. In both cases, the
coefficients are a good guide to the power needed to fit the underlying
data with minimum influence from noise.

Chuck
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