One issue with adding a tolerance to np.unique for floats is say you have
[0, 0.1, 0.2, 0.3, 0.4, 0.5] with atol=0.15
Should this return a single element or multiple ones? One once side each
consecutive float is closer than the tolerance to the next one but the
first one and the last one are clearly not within atol.
Generally this is similar to what DBSCAN clustering algorithm does (e.g.
in scikit-learn) and that would probably be out of scope for np.unique.
Roman
On 16/07/2020 20:27, Amin Sadeghi wrote:
It would be handy to add "atol" and "rtol" optional arguments to the
"unique" method. I'm proposing this since uniqueness is a bit vague for
floats. This change would be clearly backwards-compatible.
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