On Thu, Dec 4, 2014 at 4:25 PM, Ryan Nelson wrote:
>
> I guess I'm a little confused about how the spacing values are calculated.
np.spacing(x) is basically the same as np.nextafter(x, np.inf) - x,
i.e., it returns the minimum positive number that can be added to x to
get a number that's differen
It looks to me like spacing is calculating the 1ulp precision for each
of your numbers, while x*eps is suffering from a tidge of rounding
error and giving you 1-or-2 ulp precisions. Notice that the x*eps
values are either equal to or twice the values returned by spacing.
-n
On Fri, Dec 5, 2014 at
Hello everyone,
I was working through the example usage for the test function
`assert_array_almost_equal_nulp`, and it brought up a question regarding
the function `spacing`. Here's some example code:
import numpy as np
from numpy.testing import assert_array_almost_equal_nulp
np.set_printopt
On 2014-12-04 03:41:35, Jaime Fernández del Río wrote:
> nx = np.arange(A.shape[0])[:, np.newaxis]
> ny = np.arange(A.shape[1])
> C = A[nx, ny, B]
That's the correct answer--in my answer I essentially wrote
C = A[B] (== A[B, :, :])
which broadcasts the shape of B against the second and third di
