On Sun, Jun 14, 2020 at 7:49 PM Oscar Benjamin <oscar.j.benja...@gmail.com> wrote:
> > > I've had occasion to use math.isclose(), np.isclose(), and > np.allclose() > > > quite often. > > Can you elaborate a bit on the kinds of things you use them for? > > I can't elaborate on David's use but in my own experience these > functions are mostly useful for interactive checking or for something > like unit tests. They can be used extensively in the testing code for > projects with a lot of floating point functions. At times I have computations which *should be* the same mathematically, but are carried out through a different sequence of specific computations. One common example is in parallel frameworks where the order of computation is indeterminate because multiple workers/threads/processes are each calculating portions to aggregate. Another related case is when I call some library to do an operation, but I did not write the library, nor do I understand its guts well. For example, the tensor libraries used in neural networks that will calculate a loss function. Occasionally I'd like to be able to replicate (within a tolerance) a computation the library performs using something more general like NumPy. Having a few ulps difference is typical, but counts as validating the "same" answer. Another occasion I encounter it is with data measurements. Some sort of instrument collects measurements with a small jitter. Two measurements that cannot be distinguished based on the precision of the instrument might nonetheless be stored as different floating point numbers. In that case, I probably want to be able to tweak the tolerances for the specific case. -- The dead increasingly dominate and strangle both the living and the not-yet born. Vampiric capital and undead corporate persons abuse the lives and control the thoughts of homo faber. Ideas, once born, become abortifacients against new conceptions.
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