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> The set of possible data type systems to choose from was very large, and to make the process more manageable I had written a program to select the competitive

> The set of possible data type systems to choose from was very large, and to make the process more manageable I had written a program to select the competitive (Pareto-optimal) candidate systems. Interestingly, but quite incidentally, that selection program itself was written in SETL. Wow, now that I'd like to see. Awesome and informative comments, cheers!

kragen 2y ago
i'm glad they were helpful!
fuzztester 2y ago
I can't even make a guess as to how such a program could be written. anyone has an idea?
- kragen 2y ago
  it sounds a little bogus to me, but i can outline some ways to approach it if you have a set of features, generating its powerset is fairly simple >>> powerset = lambda xs: [x0 + others for others in powerset(xs[1:]) for x0 in [[], [xs[0]]]] if xs else [[]] >>> powerset(['int', 'float', 'set', 'array']) [[], ['int'], ['float'], ['int', 'float'], ['set'], ['int', 'set'], ['float', 'set'], ['int', 'float', 'set'], ['array'], ['int', 'array'], ['float', 'array'], ['int', 'float', 'array'], ['set', 'array'], ['int', 'set', 'array'], ['float', 'set', 'array'], ['int', 'float', 'set', 'array']] then you just need some way to calculate various merits of the different designs. one merit is simplicity, which you could maybe try to operationalize as something like this: >>> [(d, {'simplicity': 10 - len(d)}) for d in powerset(['int', 'set', 'array'])] [([], {'simplicity': 10}), (['int'], {'simplicity': 9}), (['set'], {'simplicity': 9}), (['int', 'set'], {'simplicity': 8}), (['array'], {'simplicity': 9}), (['int', 'array'], {'simplicity': 8}), (['set', 'array'], {'simplicity': 8}), (['int', 'set', 'array'], {'simplicity': 7})] comparing two merit-maps such as {'simplicity': 8} and {'simplicity': 6} for pareto optimality is simple enough with some thought, especially if we assume the keys are the same: >>> some_way_better = lambda a, b: any(a[k] > b[k] for k in a) >>> defeats = lambda a, b: some_way_better(a, b) and not some_way_better(b, a) >>> defeats({'simplicity': 9}, {'simplicity': 8}) True >>> defeats({'simplicity': 8}, {'simplicity': 9}) False >>> defeats({'simplicity': 9, 'turing-complete': 0}, {'simplicity': 8, 'turing-complete': 1}) False >>> defeats({'simplicity': 9, 'turing-complete': 1}, {'simplicity': 8, 'turing-complete': 1}) True then it's easy enough to calculate which of a set of candidate designs can be eliminated because some other design defeats them the bogus part is how you automatically calculate the various merits of a hypothetical collection of language features
  - carapace 2y ago
    Yeah, what was the representation of these "data type systems" and what were the metrics I wonder.

kragen 2y ago

i'm glad they were helpful!

fuzztester 2y ago

I can't even make a guess as to how such a program could be written. anyone has an idea?

kragen 2y ago
it sounds a little bogus to me, but i can outline some ways to approach it if you have a set of features, generating its powerset is fairly simple >>> powerset = lambda xs: [x0 + others for others in powerset(xs[1:]) for x0 in [[], [xs[0]]]] if xs else [[]] >>> powerset(['int', 'float', 'set', 'array']) [[], ['int'], ['float'], ['int', 'float'], ['set'], ['int', 'set'], ['float', 'set'], ['int', 'float', 'set'], ['array'], ['int', 'array'], ['float', 'array'], ['int', 'float', 'array'], ['set', 'array'], ['int', 'set', 'array'], ['float', 'set', 'array'], ['int', 'float', 'set', 'array']] then you just need some way to calculate various merits of the different designs. one merit is simplicity, which you could maybe try to operationalize as something like this: >>> [(d, {'simplicity': 10 - len(d)}) for d in powerset(['int', 'set', 'array'])] [([], {'simplicity': 10}), (['int'], {'simplicity': 9}), (['set'], {'simplicity': 9}), (['int', 'set'], {'simplicity': 8}), (['array'], {'simplicity': 9}), (['int', 'array'], {'simplicity': 8}), (['set', 'array'], {'simplicity': 8}), (['int', 'set', 'array'], {'simplicity': 7})] comparing two merit-maps such as {'simplicity': 8} and {'simplicity': 6} for pareto optimality is simple enough with some thought, especially if we assume the keys are the same: >>> some_way_better = lambda a, b: any(a[k] > b[k] for k in a) >>> defeats = lambda a, b: some_way_better(a, b) and not some_way_better(b, a) >>> defeats({'simplicity': 9}, {'simplicity': 8}) True >>> defeats({'simplicity': 8}, {'simplicity': 9}) False >>> defeats({'simplicity': 9, 'turing-complete': 0}, {'simplicity': 8, 'turing-complete': 1}) False >>> defeats({'simplicity': 9, 'turing-complete': 1}, {'simplicity': 8, 'turing-complete': 1}) True then it's easy enough to calculate which of a set of candidate designs can be eliminated because some other design defeats them the bogus part is how you automatically calculate the various merits of a hypothetical collection of language features
- carapace 2y ago
  Yeah, what was the representation of these "data type systems" and what were the metrics I wonder.