On Mon, Mar 7, 2011 at 8:37 AM, Pauli Virtanen <p...@iki.fi> wrote: > Mon, 07 Mar 2011 08:30:11 -0500, josef.pktd wrote: > [clip] >> assert_approx_equal checks for signigicant digits in decimal system, >> which looks like it's easy to interpret. > > Ditto for tolerance=1e-7, which has the advantage that it's what > "print abs(desired-actual)" prints.
Ok, it took me a while to figure out what the issue is, atol is more intuitive then decimal, especially since decimal has the half point/rounding behavior that I'm never quite sure about. I initially thought the issue is between atol versus rtol versus nulp. Is there a reason that assert_array_almost_equal uses around(z, decimal) <= 10.0**(-decimal) while the last part of assert _almost_equal is if round(abs(desired - actual),decimal) != 0 > >> I don't have much idea what a nulp is, and whether it's machine >> dependent. > > It's the number of floating point values between the desired and the > actual results --- this depends only on the fp type. It is *the* correct > measure when you want "uniform" accuracy across the range of all floating > point values. For instance, this is quite useful for special functions, > whose range of values typically vary widely, but for which one still > wants to have accuracy which is as good as possible. Is nulp_diff exposed somewhere? It looks like a useful functions to write nulp tests, and I only saw it in the source. Josef > > -- > Pauli Virtanen > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
