解决方案:

use reunion::{UnionFind, UnionFindTrait};
use std::collections::HashSet;

fn main() {
    // Create a UnionFind data structure of arbitrary size that contains subsets of usizes.
    let mut uf1 = UnionFind::<usize>::new();

	println!("Initial state: {}", &uf);
	println!("All elements form their own group (singletons).");
	println!(format!("{:?}", uf.subsets());
	uf.union(2, 1);
	println!("After combining the groups that contains 2 and 1: {}", &uf);
	uf.union(4, 3);
	println!("After combining the groups that contains 4 and 3: {}", &uf);
	uf.union(6, 5);
	println!("After combining the groups that contains 6 and 5: {}", &uf);

	let mut hs1 = HashSet::new();
	hs1.insert(1);
	hs1.insert(2);
	let mut hs2 = HashSet::new();
	hs2.insert(3);
	hs2.insert(4);
	let mut hs3 = HashSet::new();
	hs3.insert(5);
	hs3.insert(6);

	let mut subsets = uf.subsets();
	assert_eq!(subsets.len(), 3);

	assert!(&subsets.contains(&hs1));
	assert!(&subsets.contains(&hs2));
	assert!(&subsets.contains(&hs3));

	uf.union(1, 5);

	println!("After combining the groups that contains 1 and 5: {}", &uf);

	subsets = uf.subsets();
	assert_eq!(subsets.len(), 2);

	hs3.extend(&hs1);

	assert!(&subsets.contains(&hs3));
	assert!(&subsets.contains(&hs2));

	let mut uf_clone = uf.clone();
	uf_clone.find(2);

	assert_eq!(&uf, &uf_clone);
	println!("{}", &uf);

    // It is possible to iterate over the subsets.

    for partition in uf1 {
        println!("{:?}", partition);
    }
}

示例 2

任务：创建一个大小至少为 10 的 UnionFind 数据结构，其中包含 u16 元素。

// Create a UnionFind data structure of a fixed size that contains subsets of u16.
let mut uf2 = UnionFind::<u16>::with_capacity(10);

println!("{}", uf2);

#Find: 2497150, #Union: 1048575, #Total: 3545725, Time: 1.013497126s, Time per operation: 285ns
#Find: 2497150, #Union: 1048575, #Total: 3545725, Time: 1.013323348s, Time per operation: 285ns
#Find: 2497150, #Union: 1048575, #Total: 3545725, Time: 1.012333206s, Time per operation: 285ns
...

Big Merge (20, 10000)   time:   [1.0175 s 1.0190 s 1.0205 s]                                     
                        change: [-0.4773% -0.2721% -0.0647%] (p = 0.01 < 0.05)
                        Change within noise threshold.
Found 13 outliers among 100 measurements (13.00%)
  10 (10.00%) high mild
  3 (3.00%) high severe

...