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Those wrinkles noted, we'll proceed as we did in @secref{Day_9}. We'll parse the input data and put the happiness scores into a hash table. Then we'll loop through all possible seating arrangements with @racket[in-permutations] and see what the best score is.
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Those wrinkles noted, we'll proceed as we did in @secref{Day_9}. We'll parse the input data and put the happiness scores into a hash table. Then we'll loop through all possible seating arrangements with @racket[in-permutations] and see what the best score is.
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@margin-note{Math jocks might note that because our seating arrangement is circular, our permutations will include a lot of ``rotationally equivalent'' arrangements — e.g., @racket['(A B C D)] is the same as @racket['(B C D A)] and @racket['(C D A B)]. If we had more people, or the happiness function were more expensive, we might want to prune out these equivalent arrangements as a performance optimization. But in this case, it's unnecessary.}
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Math jocks might note that because our seating arrangement is circular, our permutations will include a lot of ``rotationally equivalent'' arrangements — e.g., @racket['(A B C D)] is the same as @racket['(B C D A)] and @racket['(C D A B)]. If we had more people, or the happiness function were more expensive, we might want to prune out these equivalent arrangements as a performance optimization. But in this case, it's unnecessary.
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