Advent of Code 2023 - Day 15

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Advent of Code 2023 - Day 15

Problem Breakdown

Part 1: HASH Algorithm

Objective: Implement a hash function that converts a string into a single number in the range 0 to 255.

Procedure:

1. Start with current_value = 0.
2. For each character in the string:
   • Determine its ASCII code.
   • Add this ASCII code to current_value.
   • Multiply current_value by 17.
   • Set current_value to the remainder of current_value divided by 256.

Example:

• For "HASH":
  • 'H': ASCII 72; (0 + 72) * 17 = 1224; 1224 % 256 = 200.
  • 'A': ASCII 65; (200 + 65) * 17 = 4505; 4505 % 256 = 153.
  • 'S': ASCII 83; (153 + 83) * 17 = 4012; 4012 % 256 = 172.
  • 'H': ASCII 72; (172 + 72) * 17 = 4148; 4148 % 256 = 52.
• Output for "HASH" is 52.

Part 2: HASHMAP Algorithm

Objective: Organize items into 256 boxes based on their hash values and perform add/remove operations.

Procedure:

1. Initialize 256 boxes, numbered 0 to 255.
2. Each box can contain multiple labeled items (lenses).
3. For each operation in a given sequence:
   • Use the HASH algorithm to determine the box for an item based on its label.
   • Perform the specified operation:
     • "-": Remove the item with the given label from its box.
     • "=": Followed by a number (focal length), replace an existing item or add a new item to the end of the box.

Example:

• Given operations like "rn=1,cm-,qp=3", etc.
• Process each operation based on the HASH value for the label.
• After all operations, compute the total focusing power of lenses:
  • Focusing power of a lens = (1 + box number) * (position in box) * (focal length).

Goal: Write a program to execute these operations and calculate the sum of hash values (Part 1) and the total focusing power of lenses (Part 2).

index.js

const fs = require('fs');

function hashAlgorithm(s) {
    let currentValue = 0;
    for (let char of s) {
        let asciiCode = char.charCodeAt(0);
        currentValue += asciiCode;
        currentValue *= 17;
        currentValue %= 256;
    }
    return currentValue;
}

function hashmapAlgorithm(steps) {
    let boxes = Array(256).fill().map(() => []);

    for (let step of steps) {
        let [label, operation] = step.includes('=') ? step.split('=') : step.split('-');
        let boxIndex = hashAlgorithm(label);

        if (operation === '') {
            boxes[boxIndex] = boxes[boxIndex].filter(lens => lens[0] !== label);
        } else {
            let focalLength = parseInt(operation);
            let lensPresent = false;

            for (let i = 0; i < boxes[boxIndex].length; i++) {
                if (boxes[boxIndex][i][0] === label) {
                    boxes[boxIndex][i] = [label, focalLength];
                    lensPresent = true;
                    break;
                }
            }

            if (!lensPresent) {
                boxes[boxIndex].push([label, focalLength]);
            }
        }
    }

    let focusingPowerSum = 0;
    for (let boxIndex = 0; boxIndex < boxes.length; boxIndex++) {
        for (let slotIndex = 0; slotIndex < boxes[boxIndex].length; slotIndex++) {
            let [label, focalLength] = boxes[boxIndex][slotIndex];
            let focusingPower = (boxIndex + 1) * (slotIndex + 1) * focalLength;
            focusingPowerSum += focusingPower;
        }
    }

    return focusingPowerSum;
}

let exampleSequence = "rn=1,cm-,qp=3,cm=2,qp-,pc=4,ot=9,ab=5,pc-,pc=6,ot=7";
let exampleSteps = exampleSequence.split(',');
let exampleSumOfResults = exampleSteps.reduce((sum, step) => sum + hashAlgorithm(step), 0);
console.log("Example sum for HASH algorithm:", exampleSumOfResults);

let exampleFocusingPower = hashmapAlgorithm(exampleSteps);
console.log("Example focusing power for HASHMAP algorithm:", exampleFocusingPower);

fs.readFile('input.txt', 'utf8', (err, data) => {
    if (err) throw err;
    let steps = data.trim().split(',');

    let sumOfResults = steps.reduce((sum, step) => sum + hashAlgorithm(step), 0);
    console.log("Sum for HASH algorithm:", sumOfResults);

    let focusingPower = hashmapAlgorithm(steps);
    console.log("Focusing power for HASHMAP algorithm:", focusingPower);
});

input.txt

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main.exs

defmodule Main do
  def hash_algorithm(s) do
    s
    |> String.to_charlist()
    |> Enum.reduce(0, fn char, acc ->
      acc
      |> Kernel.+(char)
      |> Kernel.*(17)
      |> rem(256)
    end)
  end

  def hashmap_algorithm(steps) do
    initial_boxes = for _ <- 0..255, do: []

    final_boxes =
      steps
      |> Enum.reduce(initial_boxes, fn step, boxes ->
        [label | operation] = String.split(step, ~r/[-=]/)
        box_index = hash_algorithm(label)

        case operation do
          [""] ->
            box = Enum.filter(Enum.at(boxes, box_index), fn {l, _} -> l != label end)
            List.replace_at(boxes, box_index, box)

          [focal_length] ->
            focal_length = String.to_integer(focal_length)
            box = Enum.at(boxes, box_index)
            lens_index = Enum.find_index(box, fn {l, _} -> l == label end)

            new_box =
              case lens_index do
                nil ->
                  # Add lens to the end
                  box ++ [{label, focal_length}]

                index ->
                  List.replace_at(box, index, {label, focal_length})
              end

            List.replace_at(boxes, box_index, new_box)
        end
      end)

    final_boxes
    |> Enum.with_index()
    |> Enum.flat_map(fn {box, box_index} ->
      Enum.with_index(box)
      |> Enum.map(fn {{_, focal_length}, slot_index} ->
        (box_index + 1) * (slot_index + 1) * focal_length
      end)
    end)
    |> Enum.sum()
  end

  def run() do
    example_sequence = "rn=1,cm-,qp=3,cm=2,qp-,pc=4,ot=9,ab=5,pc-,pc=6,ot=7"
    example_steps = String.split(example_sequence, ",")

    example_sum_of_results = Enum.sum(Enum.map(example_steps, &hash_algorithm/1))
    IO.puts("Example sum for HASH algorithm: #{example_sum_of_results}")

    example_focusing_power = hashmap_algorithm(example_steps)
    IO.puts("Example focusing power for HASHMAP algorithm: #{example_focusing_power}")

    {:ok, data} = File.read("input.txt")
    steps = String.split(String.trim(data), ",")

    sum_of_results = Enum.sum(Enum.map(steps, &hash_algorithm/1))
    IO.puts("Sum for HASH algorithm: #{sum_of_results}")

    focusing_power = hashmap_algorithm(steps)
    IO.puts("Focusing power for HASHMAP algorithm: #{focusing_power}")
  end
end

Main.run()

main.py

def hash_algorithm(s):
    """Run the HASH algorithm on a string."""
    current_value = 0
    for char in s:
        ascii_code = ord(char)
        current_value += ascii_code
        current_value *= 17
        current_value %= 256
    return current_value

def hashmap_algorithm(steps):
    """Run the HASHMAP algorithm on the initialization sequence."""
    # Initialize 256 empty boxes
    boxes = [[] for _ in range(256)]

    for step in steps:
        label, operation = step.split('=' if '=' in step else '-')
        box_index = hash_algorithm(label)

        if operation == '':  # If operation is '-', remove the lens if present
            boxes[box_index] = [lens for lens in boxes[box_index] if lens[0] != label]
        else:  # If operation is '=', add or replace the lens
            focal_length = int(operation)
            lens_present = False

            # Check if lens is already present and replace it
            for i, lens in enumerate(boxes[box_index]):
                if lens[0] == label:
                    boxes[box_index][i] = (label, focal_length)
                    lens_present = True
                    break

            # If lens is not present, add it to the end of the box
            if not lens_present:
                boxes[box_index].append((label, focal_length))

    # Calculate the focusing power of each lens and sum them up
    focusing_power_sum = 0
    for box_index, lenses in enumerate(boxes):
        for slot_index, (label, focal_length) in enumerate(lenses, start=1):
            focusing_power = (box_index + 1) * slot_index * focal_length
            focusing_power_sum += focusing_power

    return focusing_power_sum

# Example usage for the HASH algorithm
example_sequence = "rn=1,cm-,qp=3,cm=2,qp-,pc=4,ot=9,ab=5,pc-,pc=6,ot=7"
example_steps = example_sequence.split(',')
example_sum_of_results = sum(hash_algorithm(step) for step in example_steps)
print("Example sum for HASH algorithm:", example_sum_of_results)

# Example usage for the HASHMAP algorithm
example_focusing_power = hashmap_algorithm(example_steps)
print("Example focusing power for HASHMAP algorithm:", example_focusing_power)

with open('input.txt', 'r') as file:
    # Read the contents and split into steps
    steps = file.read().strip().split(',')

sum_of_results = sum(hash_algorithm(step) for step in steps)
print("Sum for HASH algorithm:", sum_of_results)

focusing_power = hashmap_algorithm(steps)
print("Focusing power for HASHMAP algorithm:", focusing_power)

main.rs

use std::fs;

fn hash_algorithm(s: &str) -> u32 {
    let mut current_value = 0;
    for char in s.chars() {
        let ascii_code = char as u32;
        current_value += ascii_code;
        current_value *= 17;
        current_value %= 256;
    }
    current_value
}

fn hashmap_algorithm(steps: Vec<&str>) -> u32 {
    let mut boxes: Vec<Vec<(String, u32)>> = vec![vec![]; 256];

    for step in steps {
        let parts: Vec<&str> = if step.contains("=") { step.split('=').collect() } else { step.split('-').collect() };
        let (label, operation) = (parts[0], parts.get(1).unwrap_or(&""));
        let box_index = hash_algorithm(label) as usize;

        if operation.is_empty() {
            boxes[box_index] = boxes[box_index].clone().into_iter().filter(|(lens, _)| lens != label).collect();
        } else {
            let focal_length = operation.parse::<u32>().unwrap();
            let mut lens_present = false;

            for i in 0..boxes[box_index].len() {
                if boxes[box_index][i].0 == label {
                    boxes[box_index][i] = (label.to_string(), focal_length);
                    lens_present = true;
                    break;
                }
            }

            if !lens_present {
                boxes[box_index].push((label.to_string(), focal_length));
            }
        }
    }

    let mut focusing_power_sum = 0;
    for (box_index, box_) in boxes.iter().enumerate() {
        for (slot_index, (_label, focal_length)) in box_.iter().enumerate() {
            let focusing_power = (box_index + 1) * (slot_index + 1) * (*focal_length as usize);
            focusing_power_sum += focusing_power;
        }
    }

    focusing_power_sum as u32
}

fn main() {
    let example_sequence = "rn=1,cm-,qp=3,cm=2,qp-,pc=4,ot=9,ab=5,pc-,pc=6,ot=7";
    let example_steps: Vec<&str> = example_sequence.split(',').collect();
    let example_sum_of_results: u32 = example_steps.iter().map(|step| hash_algorithm(step)).sum();
    println!("Example sum for HASH algorithm: {}", example_sum_of_results);

    let example_focusing_power = hashmap_algorithm(example_steps);
    println!("Example focusing power for HASHMAP algorithm: {}", example_focusing_power);

    let data = fs::read_to_string("input.txt").expect("Unable to read file");
    let steps: Vec<&str> = data.trim().split(',').collect();

    let sum_of_results: u32 = steps.iter().map(|step| hash_algorithm(step)).sum();
    println!("Sum for HASH algorithm: {}", sum_of_results);

    let focusing_power = hashmap_algorithm(steps);
    println!("Focusing power for HASHMAP algorithm: {}", focusing_power);
}