From my background in imperative programming, I'm accustomed to doing
for (i = 0; i < 1000000; i++) {
for (j = i + 1; j < 1000000; j++) {
doSomething(array[i], array[j])
}
}
to examine all unique pairs in a million element array. doSomething is some operation that yields trivial results on diagonal and symmetric or antisymmetric results off diagonal--- that's why I only want to work on the upper triangle. (There's a minor variant of this where the i == j case is interesting; that's easy to fix.)
I find myself oddly stuck trying to do this in Scala. I have a large List and want to do something to all the pairwise combinations, but
list.flatMap(x => list.map(y => doSomething(x, y))
includes all the redundant or trivial cases (a factor of two too much work) and
(0 until 1000000).flatMap({i =>
(0 until 1000000).map({j =>
doSomething(list(i), list(j))
})
})
would be very wrong because Lists are not random access (a factor of N^2 too much work). I could convert my Lists to Arrays, but that feels like it misses the point. Lists are linked lists, so the j + 1 element from my imperative example is only a step away from the i I'm currently examining. I'm sure I could write an efficient upper-triangular loop over linked lists in C/Python/whatever.
I suppose I can just swallow the factor of two for now, but this is such a common situation to run into that it feels like there ought to be a nice solution to it.
Also, does this "upper-triangular loop" have a common name? I couldn't find a good search string for it.
Edit: Here's an example of a bad solution:
list.zipWithIndex.flatMap({case (x, i) =>
list.zipWithIndex.map({case (y, j) =>
if (j > i)
doSomething(x, y)
else
Nil
})
})
because it still visits the unwanted nodes.