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In a group of N people (labelled 0, 1, 2, ..., N-1), each person has different amounts of money, and different levels of quietness.
For convenience, we'll call the person with label x, simply "person x".
We'll say that richer[i] = [x, y] if person x definitely has more money than person y. Note that richer may only be a subset of valid observations.
Also, we'll say quiet[x] = q if person x has quietness q.
Now, return answer, where answer[x] = y if y is the least quiet person (that is, the person y with the smallest value of quiet[y]), among all people who definitely have equal to or more money than person x.
Example 1:
Input: richer = [[1,0],[2,1],[3,1],[3,7],[4,3],[5,3],[6,3]], quiet = [3,2,5,4,6,1,7,0]
Output: [5,5,2,5,4,5,6,7]
Explanation:
answer[0] = 5.
Person 5 has more money than 3, which has more money than 1, which has more money than 0.
The only person who is quieter (has lower quiet[x]) is person 7, but
it isn't clear if they have more money than person 0.
answer[7] = 7.
Among all people that definitely have equal to or more money than person 7
(which could be persons 3, 4, 5, 6, or 7), the person who is the quietest (has lower quiet[x])
is person 7.
The other answers can be filled out with similar reasoning.
Note:
1 <= quiet.length = N <= 500
0 <= quiet[i] < N, all quiet[i] are different.
0 <= richer.length <= N * (N-1) / 2
0 <= richer[i][j] < N
richer[i][0] != richer[i][1]
richer[i]'s are all different.
The observations in richer are all logically consistent.
In a group of N people (labelled
0, 1, 2, ..., N-1
), each person has different amounts of money, and different levels of quietness.For convenience, we'll call the person with label
x
, simply "personx
".We'll say that
richer[i] = [x, y]
if personx
definitely has more money than persony
. Note thatricher
may only be a subset of valid observations.Also, we'll say
quiet[x] = q
if person x has quietnessq
.Now, return
answer
, whereanswer[x] = y
ify
is the least quiet person (that is, the persony
with the smallest value ofquiet[y]
), among all people who definitely have equal to or more money than personx
.Example 1:
Note:
1 <= quiet.length = N <= 500
0 <= quiet[i] < N
, allquiet[i]
are different.0 <= richer.length <= N * (N-1) / 2
0 <= richer[i][j] < N
richer[i][0] != richer[i][1]
richer[i]
's are all different.richer
are all logically consistent.这道题说是有N个人,给了我们一个二维数组 richer,告诉了一些人之间的贫富关系,还有一个 quiet 数组,表示每个人的安静程度,对于每一个人,让找出最安静,且不比其贫穷的人,注意这里可以包括自己。说实话,博主光开始没有看懂这道题的例子,因为博主以为返回的应该安静值,其实返回的应该是人的编号才对,这样题目中的例子才能讲得通,就比如说对于编号为2的那人,richer 数组中只说明了其比编号为1的人富有,但没有显示任何人比其富有,所有返回的人应该是其本身,所以 answer[2] = 2,还有就是编号为7的人,这里编号为3,4,5,6的人都比起富有,但是其本身的安静值最低,为0,所以还是返回其本身的编号,所以answer[7] = 7。
理解了题意之后就可以开始做题了,这道题的本质其实就是有向图的遍历,LeetCode 中还是有一些类似的题目的,比如 Course Schedule,Course Schedule II,Minimum Height Trees,和 Reconstruct Itinerary 等。对于这类的题,其实解法都差不多,首先都是需要建立图的结构,一般都是用邻接链表来表示,在博主之前的那篇博客 Possible Bipartition,分别使用了二维数组和 HashMap 来建立邻接链表,一般来说,使用 HashMap 能节省一些空间,且更加灵活一些,所以这里还是用 HashMap 来建立。由于要找的人必须等于或者富于自己,所以我们可以建立每个人和其所有富于自己的人的集合,因为这里除了自己,不可能有人和你一样富的人。建立好图的结构后,我们可以对每个人进行分别查找了,首先每个人的目标安静值可以初始化为自身,因为就算没有比你富有的人,你自己也可以算满足条件的人,从 HashMap 中可以直接查找到比你富有的人,他们的安静值是可以用来更新的,但是还可能有人比他们还富有,那些更富有的人的安静值也得查一遍,所以就需要用递归来做,遍历到每个比你富有的人,对他再调用递归,这样返回的就是比他富有或相等,且安静值最小的人,用这个安静值来更新当前人的安静值即可,注意我们在递归的开始首先要查一下,若某人的安静值已经更新了,直接返回即可,不用再重复计算了,参见代码如下:
解法一:
我们也可以使用迭代的写法,这里还是要用邻接链表来建立图的结构,但是有所不同的是,这里需要建立的映射是每个人跟所有比他穷的人的集合。然后还有建立每个人的入度,将所有入度为0的人将入队列 queue,先开始遍历,入度为0,表示是已经条件中没有人比他更富,那么就可以通过 HashMap 来遍历所有比他穷的人,若当前的人的安静值小于比他穷的人的安静值,那么更新比他穷的人的安静值,可以看到这里每次更新的都是别人的安静值,而上面递归的解法更新的都是当前人的安静值。然后将比他穷的人的入度减1,当减到0时,加入到队列 queue 中继续遍历,参见代码如下:
解法二:
Github 同步地址:
#851
类似题目:
Course Schedule
Course Schedule II
Minimum Height Trees
Reconstruct Itinerary
参考资料:
https://leetcode.com/problems/loud-and-rich/
https://leetcode.com/problems/loud-and-rich/discuss/137918/C%2B%2BJavaPython-Concise-DFS
https://leetcode.com/problems/loud-and-rich/discuss/138088/C%2B%2B-with-topological-sorting
LeetCode All in One 题目讲解汇总(持续更新中...)
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