[Numpy-discussion] poly class question
josef.pktd@gmai...
josef.pktd@gmai...
Fri Oct 2 15:40:03 CDT 2009
On Fri, Oct 2, 2009 at 3:38 PM, Charles R Harris
<charlesr.harris@gmail.com> wrote:
>
>
> On Fri, Oct 2, 2009 at 12:30 PM, <josef.pktd@gmail.com> wrote:
>>
>> On Fri, Oct 2, 2009 at 2:09 PM, Charles R Harris
>> <charlesr.harris@gmail.com> wrote:
>> >
>> >
>> > On Fri, Oct 2, 2009 at 11:35 AM, Charles R Harris
>> > <charlesr.harris@gmail.com> wrote:
>> >>
>> >>
>> >> On Fri, Oct 2, 2009 at 11:33 AM, Charles R Harris
>> >> <charlesr.harris@gmail.com> wrote:
>> >>>
>> >>>
>> >>> On Fri, Oct 2, 2009 at 11:30 AM, Charles R Harris
>> >>> <charlesr.harris@gmail.com> wrote:
>> >>>>
>> >>>>
>> >>>> On Fri, Oct 2, 2009 at 11:08 AM, <josef.pktd@gmail.com> wrote:
>> >>>>>
>> >>>>> Is there a way in numpy (or scipy) to get an infinite expansion for
>> >>>>> the inverse of a polynomial (for a finite number of terms)
>> >>>>>
>> >>>>> np.poly1d([ -0.8, 1])**(-1)
>> >>>>>
>> >>>>> application for example the MA representation of an AR(1)
>> >>>>>
>> >>>>
>> >>>> Hmm, I've been working on a chebyshev class and division of a scalar
>> >>>> by
>> >>>> a chebyshev series is
>> >>>> expressly forbidden, but it could be included if a good interface is
>> >>>> proposed. Same would go for polynomials.
>> >>>
>> >>> In fact is isn't hard to get, for poly1d you should be able to
>> >>> multiply
>> >>> the series by a power of x to shift it left, then divide.
>> >>>
>> >>
>> >> That is, divide a power of x by the polynomial.
>> >>
>> >
>> > You will also need to reverse the denominator coefficients...Chuck
>>
>> That's the hint I needed. However the polynomial coefficients are then
>> reversed and not consistent with other polynomial operations, aren't
>> they?
>>
>> >>> from scipy.signal import lfilter
>>
>> >>> (np.poly1d([1, 0])**10)/np.poly1d([1, -0.8])
>> (poly1d([ 1. , 0.8 , 0.64 , 0.512 , 0.4096 ,
>> 0.32768 , 0.262144 , 0.2097152 , 0.16777216,
>> 0.13421773]), poly1d([ 0.10737418]))
>>
>> >>> lfilter([1], [1,-0.8], [1] + [0]*9)
>> array([ 1. , 0.8 , 0.64 , 0.512 , 0.4096 ,
>> 0.32768 , 0.262144 , 0.2097152 , 0.16777216, 0.13421773])
>>
>> >>> (np.poly1d([1, 0])**10)/np.poly1d([1, -0.8, 0.2])
>> (poly1d([ 1. , 0.8 , 0.44 , 0.192 , 0.0656 ,
>> 0.01408 , -0.001856 , -0.0043008 , -0.00306944]),
>> poly1d([-0.00159539, 0.00061389]))
>> >>> lfilter([1], [1,-0.8, 0.2], [1] + [0]*9)
>> array([ 1. , 0.8 , 0.44 , 0.192 , 0.0656 ,
>> 0.01408 , -0.001856 , -0.0043008 , -0.00306944, -0.00159539])
>>
>>
>> What I meant initally doesn't necessarily mean division of a scalar.
>>
>> >>> np.poly1d([1])/np.poly1d([-0.8, 1])
>> (poly1d([ 0.]), poly1d([ 1.]))
>>
>> I didn't find any polynomial division that does the expansion of the
>> remainder. The same problem, I think is inherited, by the
>> scipy.signal.lti, and it took me a while to find the usefulness of
>> lfilter in this case.
>>
>> If it were possible to extend the methods for the polynomial class to
>> do a longer expansions, it would make them more useful for arma and
>> lti.
>>
>> (in some areas, I'm still trying to figure out whether some
>> functionality is just hidden to me, or actually a limitation of the
>> implementation or a missing feature.)
>>
>
> Could you describe the sort of problems you want to solve? There are lots of
> curious things out there we could maybe work with. Covariances, for
> instance, are closely related to Chebyshev series.
I am working on a discrete time arma process of the form
a(L) x_t = b(L) u_t, where L is the lag operator L^k x_t = x_(t-k)
what I just programmed using lfilter is
x_t = b(L)/a(L) u_t where b(L)/a(L) is the impulse response function
or moving average representation
a(L)/b(L) is the autoregressive representation
the extension
a(L)(1-L)^d x_t = b(L) u_t, where d = 0,1,2,... (standard) or also
continuous d <1 (fractional integration)
a(L)/b(L), b(L)/a(L) (1-L)^(-d) or (1-L)^d (0<d<1) are infinite
dimensional lag polynomials in the general case.
Initially I was looking for an easy way to do these calculation as polynomials.
(The fractional case (1-L)^d (0<d<1) might be a pretty special case,
and I just looked it up today, but is a popular model class in
econometrics, fractionally integrated arma processes)
multiplication works well np.poly1d([-1, 1])*np.poly1d([-0.8, 1])
(with reversed poly coefficient scipy.signal I think)
the functions in scipy.signal for lti are only for continuous time
processes and use poly1d under the hood, which means for example
>>> from scipy import signal
>>> signal.impulse(([1, -0.8],[1]), N=10)
raise ValueError, "Improper transfer function."
ValueError: Improper transfer function.
while this works
>>> signal.impulse(([1],[1, -0.8]), N=10)
(It's been a while since I looked inside scipy.signal.lti)
A separate issue would be the multivariate version VARMA, or MIMO in
system modeling. a(L), b(L) are matrix polynomials and x_t, u_t are
1d arrays evolving in time.
But that is a different discussion.
I'm not very familiar with Chebychev polynomials, the last time I
wanted to use them I didn't see anything about their use as a base for
functions in several variables and gave up. I've seen papers that use
them as base for functions in one variable, but I'm not doing anything
like this right now.
Thanks,
Josef
>
> Chuck
>
>
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