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by pdexter 2319 days ago
Interesting. Can you give an example of generic algorithms plus custom types, in practice? Off the top of my head I thought that any dynamic language or static language with good genetics would have this property, but maybe there's something that Julia does differently.
2 comments
ddragon 2319 days ago
As an additional example, I really like the combination of unitful and diffeq [1]. But you're right that the core feature that allows this stuff is duck typing, but by itself it's not enough. Your notduck had to not only quack, but other animals have to look at it and act like it's a duck sometimes and like a notduck when you want it to do more than a duck. Multiple Dispatch (plus parametric subtyping) allows you to trivially define both notduck + A (notduck.+(A) in OOP languages) and A + notduck (extending A whatever A is) and it's really fast. That allows for the core Julia concept of specialization, easily customizing the particular behavior of any agent at any point to get both the common behavior right and the extended behavior.

For static languages you can implement part of it with, for example, interfaces (you'll face the same restrictions if the language is single dispatch), but even if you can extend the interface freely for already existing objects, there must be an agreement between the multiple packages to comply to the same interfaces (and you might either end with tons of interfaces since there are tons of possible behaviors for each entity and purpose or giant interfaces to fit all). In Julia you can use specialization to surgically smooth the integration between two packages that had no knowledge of each other and didn't even decide to comply to any (informal) interface (which do exist in Julia, like the Julia Array and Tables.jl interfaces).

[1] https://tutorials.juliadiffeq.org/html/type_handling/03-unit...

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oxinabox 2319 days ago
DiffEq on ForwardDiff Dual numbers to calculate sensitivity via forward mode AD. DiffEq on Tracker's TrackedArray to calculate sensitivity via reverse mode AD.

Measurements.jl's numbers that track measurement error input any algorithm to compute the transformed measurement error after the algorithm is applied.

NamedDims.jl + Flux.jl to give everything that PyTorch's awesome Named Tensors feature gives.

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