Ok I see what you mean. If people really want math-like symbolic representations for everything it’s probably better to use sympy or something On Mon, Jul 2, 2018 at 2:59 AM Eric Wieser <wieser.eric+numpy@gmail.com> wrote:
I think the `x` is just noise there, especially if it's ignored (that is, `T[0](x*2)` doesn't do anything reasonable).
Chebyshev.literal(lambda T: 1*T[0] + 2*T[1] + 3*T[2])
Would work, but honestly I don't think that provides much clarity. I think the value here is mainly for "simple" polynomials.
On Sun, 1 Jul 2018 at 23:42 Maxwell Aifer <maifer@haverford.edu> wrote:
Say we add a constructor to the polynomial base class that looks something like this:
------------------------------------------------------------------------------------------- @classmethod def literal(cls, f): def basis_function_getter(self, deg): coefs = [0]*deg + [1] return lambda _: cls(coefs) basis = type('',(object,),{'__getitem__': basis_function_getter})() return f(basis, None)
-------------------------------------------------------------------------------------------
Then the repr for, say, a Chebyshev polynomial could look like this:
Chebyshev.literal(lambda T,x: 1*T[0](x) + 2*T[1](x) + 3*T[2](x))
Does this sound like a good idea to anyone?
Max
On Sat, Jun 30, 2018 at 6:47 PM, Charles R Harris < charlesr.harris@gmail.com> wrote:
On Sat, Jun 30, 2018 at 4:42 PM, Charles R Harris < charlesr.harris@gmail.com> wrote:
On Sat, Jun 30, 2018 at 3:41 PM, Eric Wieser < wieser.eric+numpy@gmail.com> wrote:
Since the one of the arguments for the decreasing order seems to just be textual representation - do we want to tweak the repr to something like
Polynomial(lambda x: 2*x**3 + 3*x**2 + x + 0)
(And add a constructor that calls the lambda with Polynomial(1))
Eric
IIRC there was a proposal for that. There is the possibility of adding renderers for latex and html that could be used by Jupyter, and I think the ordering was an option.
See https://github.com/numpy/numpy/issues/8893 for the proposal. BTW, if someone would like to work on this, go for it.
Chuck
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