{"id":77,"date":"2024-12-03T22:03:57","date_gmt":"2024-12-03T22:03:57","guid":{"rendered":"https:\/\/blogs.oregonstate.edu\/codedcat\/?p=77"},"modified":"2024-12-03T22:03:57","modified_gmt":"2024-12-03T22:03:57","slug":"advent-of-code-2024-day-1-historian-hysteria","status":"publish","type":"post","link":"https:\/\/blogs.oregonstate.edu\/codedcat\/2024\/12\/03\/advent-of-code-2024-day-1-historian-hysteria\/","title":{"rendered":"Advent of Code 2024: Day 1 \u2014 Historian Hysteria"},"content":{"rendered":"\n

This year, I’m diving into Advent of Code for the first time, and I couldn’t be more excited! As a newcomer to this coding challenge, I wasn’t sure what to expect, but Day 1 proved to be the perfect introduction. The puzzles were engaging yet approachable, providing a fantastic opportunity to flex my problem-solving muscles while building confidence in my skills.<\/p>\n\n\n\n

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Day 1 Puzzle: Reconciling Historians’ Notes<\/h2>\n\n\n\n

The scenario for Day 1 was intriguing: two mismatched lists of location IDs needed to be reconciled to help a group of Elvish Senior Historians track down their missing Chief Historian. The task was split into two parts:<\/p>\n\n\n\n

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  1. Calculate the total distance between the two lists by pairing the numbers from smallest to largest and summing their absolute differences.<\/li>\n\n\n\n
  2. Determine a similarity score by calculating how many times each number from the first list appears in the second and summing weighted contributions.<\/li>\n<\/ol>\n\n\n\n
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    Pseudocode for My Solutions<\/h2>\n\n\n\n

    Here’s how I approached each part of the puzzle:<\/p>\n\n\n\n

    Part 1: Total Distance<\/strong><\/p>\n\n\n\n

    1. Parse Input<\/p>\n\n\n\n

    Split the raw input into two separate lists of integers.<\/p>\n\n\n\n

    Function: parse_input(input_string)\n    Initialize list1 and list2\n    For each line in input_string:\n        If line is not empty:\n            Split the line into two numbers\n            Append the first number to list1\n            Append the second number to list2\n    Return list1 and list2\n<\/code><\/pre>\n\n\n\n
    2. Sort the Lists:<\/p>\n\n\n\n
    Sorting ensures that the smallest numbers from each list are paired together.<\/p>\n\n\n\n
    Function: sort_list(list)\n    Return list sorted in ascending order\n<\/code><\/pre>\n\n\n\n
    3. Compute Total Distance:<\/p>\n\n\n\n
    For each index, compute the absolute difference between paired numbers and sum them.<\/p>\n\n\n\n
    Function: calculate_total_distance(list1, list2)\n    Initizialize total_distance to 0\n    For i from 0 to length of list1:\n        Calculate absolute difference between list1[i] and list2[i]\n        Add difference to total_distance\n    Return total_distance\n<\/code><\/pre>\n\n\n\n
    Part 2: Similarity Score<\/strong><\/p>\n\n\n\n
    Calculate Occurrence Similarity:<\/p>\n\n\n\n
    1. Count how many times each number in the first list appears in the second list and create a list.<\/p>\n\n\n\n
    Function: similarity_list(list1, list2)\n    Initialize similarity_list as empty\n    For each num1 in list1:\n        Count occurrences of num1 in list 2\n        Append count to similarity_list\n    Return similarity_list\n<\/code><\/pre>\n\n\n\n
    2. Use these counts to compute the weighted similarity score.<\/p>\n\n\n\n
    Function: similarity_score(list1, similarity_list)\n    Initialize similarity_score to 0\n    For i from 0 to length of list1:\n        similarity_score += list1[i] * similarity_list{i}\n    Return similarity_score\n<\/code><\/pre>\n\n\n\n
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    Implementing with TDD<\/h2>\n\n\n\n
    For both parts, I used Test-Driven Development (TDD) to structure my work:<\/p>\n\n\n\n
    1. Write Tests First:<\/p>\n\n\n\n
    Before writing any functional code, I created tests for each function. For example:<\/p>\n\n\n\n