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| ^{:nextjournal.clerk/visibility {:code :hide :result :hide}} | ||
| (ns day01 | ||
| {:nextjournal.clerk/auto-expand-results? true} | ||
| {:nextjournal.clerk/auto-expand-results? true | ||
| :nextjournal.clerk/toc :collapsed} | ||
| (:require aoc)) | ||
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| ;; # Day 01: ..... | ||
| ;; # Day 1: Historian Hysteria | ||
| ;; | ||
| ;; Finally! AoC 2024 is here! | ||
| ;; | ||
| ;; Let's see what's the theme this year... | ||
| ;; | ||
| ;; It seems this year we'll have to answer one question:\ | ||
| ;; **_Where is ~~Jessica Hyde~~ Chief Historian?_** | ||
| ;; | ||
| ;; Ok, let's help Senior Historians with today's task. | ||
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| ;; ## Input parsing | ||
| ;; | ||
| (def input (aoc/read-input 1)) | ||
| ;; We are holding two lists side by side: | ||
| ;; | ||
| (def example | ||
| "3 4 | ||
| 4 3 | ||
| 2 5 | ||
| 1 3 | ||
| 3 9 | ||
| 3 3") | ||
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| ;; Each line consists of two numbers, separated by multiple spaces. | ||
| ;; | ||
| ;; Fortunately, I have a helper function `aoc/integers` (if you haven't already, | ||
| ;; take a look at my [aoc module](./aoc), lots of goodies there which will get | ||
| ;; used throughout these tasks), which extracts | ||
| ;; all integers from a line, and I can invoke it when parsing the input | ||
| ;; with `:ints`.\ | ||
| ;; Next, we have to transform a list of lines into a list of columns. | ||
| ;; This is what `aoc/transpose` does. | ||
| ;; Finally, for the first part, we need to `sort` both columns, so we'll | ||
| ;; do that here too. | ||
| ;; | ||
| (defn parse-data [input] | ||
| (->> (aoc/parse-lines input :ints) | ||
| aoc/transpose | ||
| (mapv sort))) | ||
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| (def example-columns (parse-data example)) | ||
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| ;; The example looks correct, so we can do the same for the real input: | ||
| ;; | ||
| ^{:nextjournal.clerk/auto-expand-results? false} | ||
| (def columns (parse-data (aoc/read-input 1))) | ||
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| (def data (aoc/parse-lines input :int)) | ||
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| ;; ## Part 1 | ||
| ;; | ||
| (def pt1 (aoc/count-if odd? data)) | ||
| ;; Our task for the first part is: | ||
| ;; > pair up the numbers and measure how far apart they are. | ||
| ;; | ||
| ;; We have sorted columns already, so for each pair of numbers we need to find | ||
| ;; the difference between the numbers.\ | ||
| ;; What the example doesn't show is that a number on the left can | ||
| ;; be larger than a number on the right, so we also need to take `abs` of | ||
| ;; their difference. | ||
| ;; We can do that by `comp`-osing those two functions. | ||
| ;; | ||
| ;; The `map` function also works with multiple collections, taking the first | ||
| ;; element of each collection, then the second element of each, etc. — exactly | ||
| ;; what we need.\ | ||
| ;; After we calculate all the differences, we need to take their sum. | ||
| ;; | ||
| (defn part-1 [[left right]] | ||
| (->> (map (comp abs -) right left) | ||
| (reduce +))) | ||
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| (part-1 example-columns) | ||
| (part-1 columns) | ||
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| ;; ## Part 2 | ||
| ;; | ||
| (def pt2 (aoc/find-first #(> % 40) data)) | ||
| ;; For part 2 we need to: | ||
| ;; > figure out exactly how often each number from the left list appears in the right list. | ||
| ;; | ||
| ;; and then: | ||
| ;; > Calculate a total similarity score by adding up each number in the left list | ||
| ;; > after multiplying it by the number of times that number appears in the right list | ||
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| ;; ### Version 1: The naive way | ||
| ;; | ||
| ;; We'll go through every number in the left column and calculate its occurrences | ||
| ;; in the right column. | ||
| ;; | ||
| ;; To count all values which satisfy a predicate, I use the `aoc/count-if` | ||
| ;; function. It is, in essence, `(count (filter ...))`.\ | ||
| ;; Also, I have the `aoc/sum-map` helper function which will sum up all the values | ||
| ;; after applying a `map` to a collection. Similar to `(reduce + (map ...))`. | ||
| ;; | ||
| (defn part-2-v1 [[left right]] | ||
| (aoc/sum-map | ||
| (fn [nr] (* nr (aoc/count-if #{nr} right))) | ||
| left)) | ||
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| (part-2-v1 example-columns) | ||
| (part-2-v1 columns) | ||
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| ;; ### Version 2: Frequencies | ||
| ;; | ||
| ;; You'll notice that in the version 1 of the code, for each number in the left | ||
| ;; column we iterate through the whole right column each time. | ||
| ;; Since both columns are of the same size (`n` elements), this means we're | ||
| ;; doing `n*n` iterations. | ||
| ;; | ||
| ;; While this year my aim is not to chase every bit of performance, and | ||
| ;; inefficiency won't cause us any trouble in this task, let's pick this | ||
| ;; low-hanging fruit and improve our solution. | ||
| ;; | ||
| ;; The built-in [`frequencies function`](https://clojuredocs.org/clojure.core/frequencies) | ||
| ;; will be of great help. | ||
| ;; We'll use it on the right column to calculate how many times each | ||
| ;; number appears.\ | ||
| ;; There might be numbers from the left column which don't appear at all in | ||
| ;; the right column, so we pass the `0` as the default value for such cases. | ||
| ;; | ||
| (defn part-2-v2 [[left right]] | ||
| (let [right-freqs (frequencies right)] | ||
| (aoc/sum-map | ||
| (fn [nr] (* nr (right-freqs nr 0))) | ||
| left))) | ||
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| (part-2-v2 example-columns) | ||
| (part-2-v2 columns) | ||
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| ;; ### Performance comparison | ||
| ;; | ||
| ;; Let's see how both versions compare. | ||
| ;; We'll use the [`criterium` library](https://github.com/hugoduncan/criterium) | ||
| ;; for benchmarking. | ||
| ;; | ||
| ;; ``` | ||
| ;; (require '[criterium.core :as c]) | ||
| ;; | ||
| ;; | ||
| ;; (c/quick-bench (part-2-v1 columns)) | ||
| ;; | ||
| ;; Evaluation count : 30 in 6 samples of 5 calls. | ||
| ;; Execution time mean : 22.253470 ms | ||
| ;; Execution time std-deviation : 50.561843 µs | ||
| ;; Execution time lower quantile : 22.206475 ms ( 2.5%) | ||
| ;; Execution time upper quantile : 22.327963 ms (97.5%) | ||
| ;; Overhead used : 1.835833 ns) | ||
| ;; | ||
| ;; | ||
| ;; | ||
| ;; (c/quick-bench (part-2-v2 columns)) | ||
| ;; | ||
| ;; Evaluation count : 4848 in 6 samples of 808 calls. | ||
| ;; Execution time mean : 132.993876 µs | ||
| ;; Execution time std-deviation : 12.297005 µs | ||
| ;; Execution time lower quantile : 124.482119 µs ( 2.5%) | ||
| ;; Execution time upper quantile : 151.412166 µs (97.5%) | ||
| ;; Overhead used : 1.835833 ns) | ||
| ;; ``` | ||
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| ;; More than 150 times faster!!\ | ||
| ;; Not bad, not bad at all for such a small and simple change. | ||
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| ;; ## Conclusion | ||
| ;; | ||
| ;; Much easier Day 1 than we had last year. | ||
| ;; And I like that. | ||
| ;; These first days should be about having fun, not about chasing some edge | ||
| ;; cases (_hello `oneight` my old friend..._). | ||
| ;; | ||
| ;; Faster tasks mean more time for writing this "blog" and exploring | ||
| ;; different solutions. | ||
| ;; I hope this will continue in the next days too. | ||
| ;; | ||
| ;; Today's highlights: | ||
| ;; | ||
| ;; - `aoc/transpose` - get a list of columns from a list of rows | ||
| ;; - `comp` - compose two functions | ||
| ;; - `frequencies` - count appearances of items | ||
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| ^{:nextjournal.clerk/visibility {:code :hide :result :hide}} | ||
| (defn -main [_] | ||
| [pt1 pt2]) | ||
| (defn -main [input] | ||
| (let [data (parse-data input)] | ||
| [(part-1 data) | ||
| (part-2-v2 data)])) |
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