Correction: IndexedBase instead of IndexBase
On Wednesday, August 1, 2018 at 4:10:35 PM UTC+3, Kalevi Suominen wrote:
>
> I would first generate the list of monomial indices by using e.g.
> itertool.product, then create a dictionary containing the indexed
> coefficients, and finally create a Poly object with given variables from
> those coefficients. For example, to construct a polynomial of degree 3 or
> less in 3 variables this could be done:
>
> indices = [i for i in itertools.product(range(4), repeat=3) if sum(i) < 4]
> a = IndexBase('a')
> coeffs = {i: a[i] for i in indices}
> vars = symbols('x:3')
> Poly(coeffs, *vars)
>
> Kalevi Suominen
>
> On Wednesday, August 1, 2018 at 2:01:13 PM UTC+3, foadsf wrote:
>>
>> I want to use power series to approximate some PDEs. The first step I
>> need to generate symbolic multivariate polynomials, given a numpy ndarray.
>>
>> Consider the polynomial below:
>>
>> <https://i.stack.imgur.com/eBQVK.png>
>>
>>
>> I want to take a m dimensional ndarray of D=[d1,...,dm] where djs are
>> non-negative integers, and generate a symbolic multivariate polynomial in
>> the form of symbolic expression. The symbolic expression consists of
>> monomials of the form:
>>
>> <https://i.stack.imgur.com/pvDDT.png>
>>
>>
>> Fo example if D=[2,3] the output should be
>>
>> <https://i.stack.imgur.com/nDhGD.png>
>>
>>
>> For this specific case I could nest two for loops and add the
>> expressions. But I don't know what to do for Ds with arbitrary length.
>> If I could generate the D dimensional ndarrays of A and X without using
>> for loops, then I could use np.sum(np.multiply(A,X)) as Frobenius inner
>> product <https://en.wikipedia.org/wiki/Frobenius_inner_product> to get
>> what I need.
>>
>> I would appreciate if you could help me know how to do this in SymPy.
>> Thanks in advance.
>>
>
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