aoc/year2017/
day04.rs

1//! # High-Entropy Passphrases
2//!
3//! ## Part One
4//!
5//! We use a [`FastSet`] to detect duplicates. Sorting the words in each line
6//! then checking for duplicates in adjacent values also works but is slower.
7//!
8//! ## Part Two
9//!
10//! To detect anagrams we first convert each word into a histogram of its letter frequency values.
11//! As the cardinality is at most 26 we can use a fixed size array to represent the set.
12//!
13//! Then a [`FastSet`] is used to detect duplicates. Sorting the letters in each word so that
14//! anagrams become the same also works but is slower.
15use crate::util::hash::*;
16
17type Input<'a> = Vec<&'a str>;
18
19pub fn parse(input: &str) -> Input<'_> {
20    input.lines().collect()
21}
22
23pub fn part1(input: &Input<'_>) -> usize {
24    let mut seen = FastSet::new();
25    input
26        .iter()
27        .filter(|line| {
28            seen.clear();
29            line.split_ascii_whitespace().all(|token| seen.insert(token.as_bytes()))
30        })
31        .count()
32}
33
34pub fn part2(input: &Input<'_>) -> usize {
35    // Calculate the frequency of each letter as anagrams will have the same values.
36    fn convert(token: &str) -> [u8; 26] {
37        let mut freq = [0; 26];
38        for b in token.bytes() {
39            freq[(b - b'a') as usize] += 1;
40        }
41        freq
42    }
43
44    let mut seen = FastSet::new();
45    input
46        .iter()
47        .filter(|line| {
48            seen.clear();
49            line.split_ascii_whitespace().all(|token| seen.insert(convert(token)))
50        })
51        .count()
52}
aoc/year2017/day04.rs

aoc/year2017/
day04.rs
