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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