Using curried OR tuple arg lists requires remembering the name of an argument by its position. This saves room on the screen but is mental overhead.
The fact is that arguments do always have names anyway and you always have to know what they are.
Using curried OR tuple arg lists requires remembering the name of an argument by its position. This saves room on the screen but is mental overhead.
The fact is that arguments do always have names anyway and you always have to know what they are.
Maybe there could be a rule that parameters have to be named only if their type doesn’t already disambiguate them and if there isn’t some concordance between the naming in the argument expression and the parameter, or something along those lines. But the ergonomics of that might be annoying as well.
let warn_user ~message = ... (* the ~ makes this a named parameter *)
let error = "fatal error!!" in
warn_user ~message:error; (* different names, have to specify both *)
let message = "fatal error!!" in
warn_user ~message; (* same names, elided *)
The elision doesn't always kick in, because sometimes you want the variable to have a different name, but in practice it kicks in a lot, and makes a real difference. In a way, cases when it doesn't kick in are also telling you something, because you're crossing some sort of context boundary where some value is called different things on either side. fn compute_thing(cost: whatever, num_widgets: whatever) -> Whatever;
let cost = …;
let num_widgets = …;
let result = compute_thing(num_widgets, cost);
(This can by most any language including Haskell or Lean, with slightly different syntax.)One can prevent this very verbosely with the Builder pattern. Or one can use named parameters in languages that support them.
An interesting analogue is tensor math. In Einstein’s work, there were generally four dimensions and you probably wouldn’t lose track of which letter was which. In linear algebra, at least at the high school or early undergrad level, there are usually vectors and tensors and, well, that’s it. But in data crunching or modern ML, tensors have all kinds of cool axes, and for some reason we usually just identify them by which slot they are in the order that they happen to be in in the input tensor. Some people try to creatively make this “type safe” by specializing on the length of the dimension, which is an incomplete solution at best. I would love to see adoption of some solution that gives these things explicit names and does not ever guess which axis is being referenced.
(I find 95% of ML code and a respectable fraction of papers and descriptions to be locally incomprehensible because you need to look somewhere else to figure out what on Earth A • B' actually means.