list(_.iter_all_coeffs()) does what I want. I will still take a look at
Mateusz's work.
Thanks for the help guys.
Bill
smichr wrote:
> If you use .iter_all_coeffs() I think you will get what you want:
>
> ###
Poly(x**2+1,x)
> Poly(x**2 + 1, x)
list(_.iter_all_coeffs())
> [1, 0, 1]
If you use .iter_all_coeffs() I think you will get what you want:
###
>>> Poly(x**2+1,x)
Poly(x**2 + 1, x)
>>> list(_.iter_all_coeffs())
[1, 0, 1]
###
Another option is to compare the coeffs zipped up with the monomials:
###
>>> p=Poly(x**2+1,x)
>>> zip(p.coeffs,p.monoms)
[(1, (2,)), (1, (0,))]
You might look at the new rewritten polys module on issue 1598. We
should hopefully have that merged in whenever Mateusz is fully
finished with it. Until then, you can pull his branch and check it out.
Aaron Meurer
On Sep 8, 2009, at 7:24 AM, William Purcell wrote:
>
> I am trying to subcl
On Thu, Apr 23, 2009 at 9:48 AM, Ondrej Certik wrote:
> On Tue, Apr 21, 2009 at 8:46 AM, dhruv wrote:
>> It seems like the above is the only thing I can do! The Poly class
>> can be instantiated using a few different calling schemes (lists,
>> tuples, dictionaries, &c.) and I'm going to have to
On Tue, Apr 21, 2009 at 8:46 AM, dhruv wrote:
>
> Thanks for the help, but I'm still stuck!
>
>> create GeneratingFunction.__new__() with your own arguments and then
>> call Poly.__new__() from it, with any preprocessing that you need.
>
> This is what I've got so far:
>
> == generating_funct
Thanks for the help, but I'm still stuck!
> create GeneratingFunction.__new__() with your own arguments and then
> call Poly.__new__() from it, with any preprocessing that you need.
This is what I've got so far:
== generating_functions.py
from sympy import Poly
X = Symbol('X')
c
Hi Dhruv!
On Sat, Apr 18, 2009 at 1:10 PM, dhruv wrote:
>
> Hello all,
>
> I want to build a class to describe a generating function (as used in
> the context of discrete probability distributions). It would have
> exactly the functionality of the Poly class, have a normalization
> constraint o
