On May 8, 2015, at 19:58, Stephen J. Turnbull email@example.com wrote:
Koos Zevenhoven writes:
As a random example, (root @ mean @ square)(x) would produce the right order for rms when using .
Hardly interesting. :-) The result is an exception, as root and square are conceptually scalar-to-scalar, while mean is sequence-to-scalar.
Unless you're using an elementwise square and an array-to-scalar mean, like the ones in NumPy, in which case it works perfectly well...
I suppose you could write (root @ mean @ (map square)) (xs),
Actually, you can't. You could write (root @ mean @ partial(map, square))(xs), but that's pretty clearly less readable than root(mean(map(square, xs))) or root(mean(x*x for x in xs). And that's been my main argument: Without a full suite of higher-level operators and related syntax, compose alone doesn't do you any good except for toy examples.
But Koos's example, even if it was possibly inadvertent, shows that I may be wrong about that. Maybe compose together with element-wise operators actually _is_ sufficient for something beyond toy examples.
Of course the fact that we have two groups of people each arguing that obviously the only possible reading of @ is compose/rcompose respectively points out a whole other problem with the idea. If people just were going to have to look up which way it went and learn it through experience, that would be one thing; if everyone already knows intuitively and half of them are wrong, that's a different story...
which seems to support your argument. But will all such issues and solutions give the same support? This kind of thing is a conceptual problem that has to be discussed pretty thoroughly (presumably based on experience with implementations) before discussion of order can be conclusive.
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