It’s late and I’m probably missing something
The issue is not one of range as you showed there, but of precision. Here’s
the test case you’re missing:
def get_err(u64):
""" return the absolute error incurred by storing a uint64 in a float64 ""
u64 = np.uint64(u64)
return u64 - u64.ast
On Wed, Apr 25, 2018 at 11:00 PM, Eric Wieser
wrote:
> For precision loss of the order of float64 eps, I disagree.
>
> I was thinking more about precision loss on the order of 1, for large
> 64-bit integers that can’t fit in a float64
>
It's late and I'm probably missing something, but:
>>> np.i
>
> I seem to recall that there was a discussion on this and it was a lot
>> trickier then expected.
>>
>
> But given that numpy has the weights already for cov, then I don't see
> any additional issues
> whith adding it also to corrcoef.
>
>
corrcoef is just rescaling the cov, so there is nothing
