> If you have some spare cycles, maybe you can open a pull request to add
> np.isclose to the "See Also" section?
That would be great.
Remember that equality for flits is bit-for but equality ( baring NaN
and inf...).
But you hardly ever actually want to do that with floats.
But probably np.all
On Do, 2015-12-17 at 13:43 +, Nico Schlömer wrote:
> Hi everyone,
>
>
> I noticed a funny behavior in numpy's array_equal. The two arrays
> ```
> a1 = numpy.array(
> [3.14159265358979320],
> dtype=numpy.float64
> )
> a2 = numpy.array(
> [3.14159265358979329],
> dtype=numpy
On 17 December 2015 at 14:43, Nico Schlömer
wrote:
> I'm not sure where I'm going wrong here. Any hints?
You are dancing around the boundary between close floating point numbers,
and when you are dealing with ULPs, number of decimal places is a bad
measure. Working with plain numbers, instead o
Hi everyone,
I noticed a funny behavior in numpy's array_equal. The two arrays
```
a1 = numpy.array(
[3.14159265358979320],
dtype=numpy.float64
)
a2 = numpy.array(
[3.14159265358979329],
dtype=numpy.float64
)
```
(differing the in the 18th overall digit) are reported equal
