[Numpy-discussion] comparing floating point numbers
Wed Jul 21 03:20:13 CDT 2010
On Mon, Jul 19, 2010 at 6:40 PM, Keith Goodman <firstname.lastname@example.org> wrote:
> On Mon, Jul 19, 2010 at 6:31 PM, Ondrej Certik <email@example.com> wrote:
>> I was always using something like
>> abs(x-y) < eps
>> (abs(x-y) < eps).all()
>> but today I needed to also make sure this works for larger numbers,
>> where I need to compare relative errors, so I found this:
>> and wrote this:
>> def feq(a, b, max_relative_error=1e-12, max_absolute_error=1e-12):
>> a = float(a)
>> b = float(b)
>> # if the numbers are close enough (absolutely), then they are equal
>> if abs(a-b) < max_absolute_error:
>> return True
>> # if not, they can still be equal if their relative error is small
>> if abs(b) > abs(a):
>> relative_error = abs((a-b)/b)
>> relative_error = abs((a-b)/a)
>> return relative_error <= max_relative_error
>> Is there any function in numpy, that implements this? Or maybe even
>> the better, integer based version, as referenced in the link above?
>> I need this in tests, where I calculate something on some mesh, then
>> compare to the correct solution projected on some other mesh, so I
>> have to deal with accuracy issues.
> Is allclose close enough?
> np.allclose(a, b, rtol=1.0000000000000001e-05, atol=1e-08)
> Returns True if two arrays are element-wise equal within a tolerance.
> The tolerance values are positive, typically very small numbers. The
> relative difference (`rtol` * abs(`b`)) and the absolute difference
> `atol` are added together to compare against the absolute difference
> between `a` and `b`.
thanks for this. This should do the job. I'll give it a shot and report back.
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