Given a non-empty list of words, return the k most frequent elements.
Your answer should be sorted by frequency from highest to lowest. If two words have the same frequency, then the word with the lower alphabetical order comes first.
Example 1:
Input: ["i", "love", "leetcode", "i", "love", "coding"], k = 2
Output: ["i", "love"]
Explanation: "i" and "love" are the two most frequent words.
Note that "i" comes before "love" due to a lower alphabetical order.
Example 2:
Input: ["the", "day", "is", "sunny", "the", "the", "the", "sunny", "is", "is"], k = 4
Output: ["the", "is", "sunny", "day"]
Explanation: "the", "is", "sunny" and "day" are the four most frequent words,
with the number of occurrence being 4, 3, 2 and 1 respectively.
代码
Approach #1: Sorting
Intuition and Algorithm
Count the frequency of each word, and sort the words with a custom ordering relation that uses these frequencies. Then take the best k of them.
Complexity Analysis
Time Complexity: O(_NlogN), where N is the length of words. We count the frequency of each word in O(_N) time, then we sort the given words in O_(_NlogN) time.
Space Complexity: O_(_N), the space used to store our candidates.
class Solution {
public List<String> topKFrequent(String[] words, int k) {
Map<String, Integer> count = new HashMap();
for (String word: words) {
count.put(word, count.getOrDefault(word, 0) + 1);
}
List<String> candidates = new ArrayList(count.keySet());
Collections.sort(candidates, (w1, w2) -> count.get(w1).equals(count.get(w2))) ? w1.compareTo(w2) : count.get(w2) - count.get(w1));
return candidates.subList(0, k);
}
}
Approach #2 Heap
Intuition and Algorithm
Count the frequency of each word, then add it to heap that stores the best k candidates. Here, "best" is defined with our custom ordering relation, which puts the worst candidates at the top of the heap. At the end, we pop off the heap up to k times and reverse the result so that the best candidates are first.
class Solution {
public List<String> topKFrequent(String[] words, int k) {
Map<String, Integer> count = new HashMap();
for (String word : words) {
count.put(word, count.getOrDefault(word, 0) + 1);
}
PriorityQueue<String> heap = new PriorityQueue<String>(
(w1, w2) -> count.get(w1).equals(count.get(w2)) ? w2.compareTo(w1) : count.get(w1) - count.get(w2));
for (String word : count.keySet()) {
heap.offer(word);
if (heap.size() > k) heap.poll();
}
List<String> ans = new ArrayList();
while (!heap.isEmpty()) {
ans.add(heap.poll());
}
Collections.reverse(ans);
return ans;
}
}