[Numpy-discussion] Unrealistic expectations of class Polynomial or a bug?

[eat]

Hi,

Short demonstration of the issue:
In []: sys.version
Out[]: '2.7.2 (default, Jun 12 2011, 15:08:59) [MSC v.1500 32 bit (Intel)]'
In []: np.version.version
Out[]: '1.6.0'

In []: from numpy.polynomial import Polynomial as Poly
In []: def p_tst(c):
   ..:     p= Poly(c)
   ..:     r= p.roots()
   ..:     return sort(abs(p(r)))
   ..:

Now I would expect a result more like:
In []: p_tst(randn(123))[-3:]
Out[]: array([  3.41987203e-07,   2.82123675e-03,   2.82123675e-03])

be the case, but actually most result seems to be more like:
In []: p_tst(randn(123))[-3:]
Out[]: array([  9.09325898e+13,   9.09325898e+13,   1.29387029e+72])
In []: p_tst(randn(123))[-3:]
Out[]: array([  8.60862087e-11,   8.60862087e-11,   6.58784520e+32])
In []: p_tst(randn(123))[-3:]
Out[]: array([  2.00545673e-09,   3.25537709e+32,   3.25537709e+32])
In []: p_tst(randn(123))[-3:]
Out[]: array([  3.22753481e-04,   1.87056454e+00,   1.87056454e+00])
In []: p_tst(randn(123))[-3:]
Out[]: array([  2.98556327e+08,   2.98556327e+08,   8.23588003e+12])

So, does this phenomena imply that
- I'm testing with too high order polynomials (if so, does there exists a
definite upper limit of polynomial order I'll not face this issue)
or
- it's just the 'nature' of computations with float values (if so, probably
I should be able to tackle this regardless of the polynomial order)
or
- it's a nasty bug in class Polynomial


Regards,
eat
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