add 347. Top K Frequent Elements

Author: selfboot

Heap/347_TopKFrequentElements.cpp

@@ -0,0 +1,78 @@
+/*
+ * @Author: xuezaigds@gmail.com
+ * @Last Modified time: 2016-07-09 10:20:41
+ */
+
+class Solution {
+private:
+    struct bigger{
+        bool operator()(pair<int, int> &one, pair<int, int>two){
+            return one.second > two.second;
+        }
+    };
+public:
+    vector<int> topKFrequent(vector<int>& nums, int k) {
+        /* Use unordered_map and priority_queue(minheap) solution.
+         *
+         * Use unordered_map to avoid red-black hash implement, which takes almost O(n*lgn).
+         * We build a min heap with size k, so the time complexity is O(nlgk).
+         */
+        unordered_map<int, int> num_count;
+        for(const auto &n: nums){
+            num_count[n] += 1;
+        }
+
+        priority_queue<pair<int, int>, vector<pair<int, int>>, bigger> frequent_heap;
+        // Build the min-heap with size k.
+        for(auto it = num_count.begin(); it != num_count.end(); it++){
+            if(frequent_heap.size() < k){
+                frequent_heap.push(*it);
+            }
+            else if(it->second >= frequent_heap.top().second){
+                frequent_heap.pop();
+                frequent_heap.push(*it);
+            }
+        }
+
+        vector<int> ans;
+        while(!frequent_heap.empty()){
+            auto top = frequent_heap.top();
+            frequent_heap.pop();
+            ans.push_back(top.first);
+        }
+        return ans;
+    }
+};
+
+
+class Solution_2 {
+public:
+    vector<int> topKFrequent(vector<int>& nums, int k) {
+        /* Using stl heap tool.
+         * According to:
+         * https://discuss.leetcode.com/topic/46664/36ms-neat-c-solution-using-stl-heap-tool
+         */
+        if (nums.empty()) return {};
+        unordered_map<int, int> m;
+        for (auto &n : nums) m[n]++;
+
+        vector<pair<int, int>> heap;
+        for (auto &i : m) heap.push_back({i.second, i.first});
+
+        vector<int> result;
+        make_heap(heap.begin(), heap.end());
+        while (k--) {
+            result.push_back(heap.front().second);
+            pop_heap(heap.begin(), heap.end());
+            heap.pop_back();
+        }
+        return result;
+    }
+};
+
+/*
+[1,1,1,2,2,3]
+2
+[1,1,2,3,3,3,4,4,4,4,1,1,1]
+3
+*/

Heap/347_TopKFrequentElements.py

@@ -0,0 +1,35 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Author: xuezaigds@gmail.com
+# @Last Modified time: 2016-07-09 09:41:55
+
+
+class Solution(object):
+    def topKFrequent(self, nums, k):
+        """ Given a non-empty array of integers, return the k most frequent elements.
+
+        heapq.nlargest(n, iterable[, key])
+        Return a list with the n largest elements from the dataset defined by iterable.
+        """
+        num_count = collections.Counter(nums)
+        return heapq.nlargest(k, num_count, key=lambda x: num_count[x])
+
+
+class Solution_2(object):
+    def topKFrequent(self, nums, k):
+        ''' Use Counter to extract the top k frequent elements
+
+        most_common(k) return a list of tuples,
+        where the first item of the tuple is the element,
+        and the second item of the tuple is the count
+        Thus, the built-in zip function could be used to extract
+        the first item from the tuples
+        '''
+        return zip(*collections.Counter(nums).most_common(k))[0]
+
+"""
+[1,1,1,2,2,3]
+2
+[1,1,2,3,3,3,4,4,4,4,1,1,1]
+3
+"""

README.md

@@ -108,6 +108,7 @@
 # [Heap](Heap/)
 * 023. [Merge k Sorted Lists](Heap/23_MergeKSortedLists.py)
 * 295. [Find Median from Data Stream](Heap/295_FindMedianFromDataStream.py)
+* 347. [Top K Frequent Elements](Heap/347_TopKFrequentElements.py)
 
 # [Binary Search](BinarySearch/)