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I got this error:
```
julia> Pl(1.2,3)
DomainError with 1.2:
Legendre Polynomials are defined for arguments lying in -1 ⩽ x ⩽ 1
```
I am somewhat perplexed. A polynomial defined on -1 ⩽ x ⩽ …
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One thing that bugs me slightly as the 1.0 release is coming up is the `PiecewiseLegendrePoly` and `PiecewiseLegendreFT` classes.
If, at any point in the future, we want to switch to a different se…
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There are sooo much _polynomial_ KANs, beside this there are Chebyshev, Legendre, etc...
After implementing several of these KANs, I wonder
- is this really meaningful?
- are these types of _poly…
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With mpmath as comparison:
```
sage: import mpmath
sage: from sage.libs.mpmath.all import call as mpcall
sage: mpcall(mpmath.legenp, 29/2, 0, 0)
-0.145632776315886
sage: mpcall(mpmath.legenp, 31…
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The Legendre polynomials, returned by `legendre_P()`, of the first kind are orthogonal over [-1,1] and are normalized to have value +-1 at the endpoints.
When solving least-squares problems, it's c…
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(bug discovered by making few cleanings in `basic_fun_cl.cpp`)
e.g. :
```
IDL> print, LEGENDRE('.2', [12b,1b],/dou)
-0.18513690 0.20000000
IIDL> print, LEGENDRE(.2, [12b,1b],[1,0],/dou…
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It appears there is no _legendre function in legendre_ext.c. Can I use legendre_old instead of legendre in utils.py? What's the difference?
Please respond! Very important!
Thank you much
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Would really love your feedback: https://github.com/1ssb/torchkan
Using Legendre Polynomials instead; ~98% on MNIST.
1ssb updated
4 months ago
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```Julia
> using ApproxFun
> @time Fun(BigFloat(1),Legendre())
0.000463 seconds (316 allocations: 23.984 KiB)
Fun(Legendre(),BigFloat[1.0])
> @time Fun(1,Chebyshev(BigFloat(-1)..1))
0.000060…
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Interpolation functions have not been implemented yet for the new discretizations introduced in #149. They will be needed eventually to support moment-kinetic simulations, or collisions between differ…