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@ -17,9 +17,9 @@
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This is a lot like @secref{Day_9}, where we had to compute the optimal path between cities. In that puzzle, the distance between city A and city B was a single number. In this case, the ``happiness score'' between person A and person B is the sum of two numbers — A's happiness being next to B, and B's happiness being next to A. (Unlike distances, happiness scores can be negative.)
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This is a lot like @secref{Day_9}, where we had to compute the optimal path between cities. In that puzzle, the distance between city A and city B was a single number. In this case, the ``happiness score'' between person A and person B is the sum of two numbers — A's happiness being next to B, and B's happiness being next to A. (Unlike distances, happiness scores can be negative.)
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Also, whereas a path between cities had a start and end, a seating arrangement is circular. So if we model a seating arrangement as a list of people, we have to compute the happiness between each pair of people, but also between the last and first, to capture the circularity of the arrangement.
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Also, whereas a path between cities had a start and end, a seating arrangement is circular. So if we model a seating arrangement as a list of people, we have to compute the happiness between each duo of people, but also between the last and first, to capture the circularity of the arrangement.
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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 — the keys will be of the form @racket[(list name1 name2)] and the values will be the happiness scores for that duo, in that order. Then we'll loop through all possible seating arrangements with @racket[in-permutations] and see what the best score is.
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@ -39,14 +39,14 @@ Those wrinkles noted, we'll proceed as we did in @secref{Day_9}. We'll parse the
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(define (calculate-happiness table-arrangement)
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(define (calculate-happiness table-arrangement)
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(define table-arrangement-rotated-one-place
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(define table-arrangement-rotated-one-place
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(append (cdr table-arrangement) (list (car table-arrangement))))
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(append (drop table-arrangement 1) (take table-arrangement 1)))
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(define clockwise-pairs
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(define clockwise-duos
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(map list table-arrangement table-arrangement-rotated-one-place))
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(map list table-arrangement table-arrangement-rotated-one-place))
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(define counterclockwise-pairs (map reverse clockwise-pairs))
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(define counterclockwise-duos (map reverse clockwise-duos))
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(define all-pairs (append clockwise-pairs counterclockwise-pairs))
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(define all-duos (append clockwise-duos counterclockwise-duos))
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(for/sum ([pair (in-list all-pairs)])
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(for/sum ([duo (in-list all-duos)])
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(hash-ref happiness-scores pair)))
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(hash-ref happiness-scores duo)))
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]
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]
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@ -66,7 +66,8 @@ How? By only looking at arrangements starting with a particular name. Doesn't ma
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(remove-duplicates (flatten (hash-keys happiness-scores))))
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(remove-duplicates (flatten (hash-keys happiness-scores))))
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(define table-arrangement-scores
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(define table-arrangement-scores
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(for/list ([partial-table-arrangement (in-permutations (cdr names))])
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(for/list ([partial-table-arrangement (in-permutations (cdr names))])
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(calculate-happiness (cons (car names) partial-table-arrangement))))
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(define table-arrangement (cons (car names) partial-table-arrangement))
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(calculate-happiness table-arrangement)))
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(apply max table-arrangement-scores))]
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(apply max table-arrangement-scores))]
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@ -82,15 +83,17 @@ We can reuse our hash table of @racket[happiness-scores], but we have to update
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(remove-duplicates (flatten (hash-keys happiness-scores))))
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(remove-duplicates (flatten (hash-keys happiness-scores))))
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(for ([name (in-list names)])
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(for ([name (in-list names)])
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(define me-duo (list "me" name))
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(hash-set*! happiness-scores
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(hash-set*! happiness-scores
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(list "me" name) 0
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me-duo 0
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(list name "me") 0))
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(reverse me-duo) 0))
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(define table-arrangement-scores
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(define table-arrangement-scores
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(for/list ([partial-table-arrangement (in-permutations names)])
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(for/list ([partial-table-arrangement (in-permutations names)])
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(calculate-happiness (cons "me" partial-table-arrangement))))
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(define table-arrangement (cons "me" partial-table-arrangement))
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(calculate-happiness table-arrangement)))
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(apply max table-arrangement-scores))
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(apply max table-arrangement-scores))
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]
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]
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