feat: Add the section of Top-K problem (#551)

* Add the section of Top-K problem

* Update my_heap.py

* Update build_heap.md

* Update my_heap.py

codes/cpp/chapter_heap/heap.cpp

@@ -61,5 +61,6 @@ int main() {
     priority_queue<int, vector<int>, greater<int>> minHeap(input.begin(), input.end());
     cout << "输入列表并建立小顶堆后" << endl;
     printHeap(minHeap);
+
     return 0;
-}
+}

codes/cpp/chapter_heap/my_heap.cpp

@@ -151,4 +151,6 @@ int main() {
     /* 判断堆是否为空 */
     bool isEmpty = maxHeap.empty();
     cout << "\n堆是否为空 " << isEmpty << endl;
+
+    return 0;
 }

codes/cpp/chapter_heap/top_k.cpp

@@ -0,0 +1,37 @@
+/**
+ * File: top_k.cpp
+ * Created Time: 2023-06-12
+ * Author: Krahets (krahets@163.com)
+ */
+
+#include "../utils/common.hpp"
+
+/* 基于堆查找数组中最大的 k 个元素 */
+priority_queue<int, vector<int>, greater<int>> topKHeap(vector<int> &nums, int k) {
+    priority_queue<int, vector<int>, greater<int>> heap;
+    // 将数组的前 k 个元素入堆
+    for (int i = 0; i < k; i++) {
+        heap.push(nums[i]);
+    }
+    // 从第 k+1 个元素开始，保持堆的长度为 k
+    for (int i = k; i < nums.size(); i++) {
+        // 若当前元素大于堆顶元素，则将堆顶元素出堆、当前元素入堆
+        if (nums[i] > heap.top()) {
+            heap.pop();
+            heap.push(nums[i]);
+        }
+    }
+    return heap;
+}
+
+// Driver Code
+int main() {
+    vector<int> nums = {1, 7, 6, 3, 2};
+    int k = 3;
+
+    priority_queue<int, vector<int>, greater<int>> res = topKHeap(nums, k);
+    cout << "最大的 " << k << " 个元素为: ";
+    printHeap(res);
+
+    return 0;
+}

codes/cpp/utils/print_utils.hpp

@@ -13,16 +13,6 @@
 #include <sstream>
 #include <string>
 
-/* Expose the underlying storage of the priority_queue container */
-template <typename T, typename S, typename C> S &Container(priority_queue<T, S, C> &pq) {
-    struct HackedQueue : private priority_queue<T, S, C> {
-        static S &Container(priority_queue<T, S, C> &pq) {
-            return pq.*&HackedQueue::c;
-        }
-    };
-    return HackedQueue::Container(pq);
-}
-
 /* Find an element in a vector */
 template <typename T> int vecFind(const vector<T> &vec, T ele) {
     int j = INT_MAX;
@@ -217,6 +207,16 @@ template <typename TKey, typename TValue> void printHashMap(unordered_map<TKey,
     }
 }
 
+/* Expose the underlying storage of the priority_queue container */
+template <typename T, typename S, typename C> S &Container(priority_queue<T, S, C> &pq) {
+    struct HackedQueue : private priority_queue<T, S, C> {
+        static S &Container(priority_queue<T, S, C> &pq) {
+            return pq.*&HackedQueue::c;
+        }
+    };
+    return HackedQueue::Container(pq);
+}
+
 /* Print a Heap (PriorityQueue) */
 template <typename T, typename S, typename C> void printHeap(priority_queue<T, S, C> &heap) {
     vector<T> vec = Container(heap);

codes/java/chapter_heap/top_k.java

@@ -0,0 +1,39 @@
+/**
+ * File: top_k.java
+ * Created Time: 2023-06-12
+ * Author: Krahets (krahets@163.com)
+ */
+
+package chapter_heap;
+
+import utils.*;
+import java.util.*;
+
+public class top_k {
+    /* 基于堆查找数组中最大的 k 个元素 */
+    static Queue<Integer> topKHeap(int[] nums, int k) {
+        Queue<Integer> heap = new PriorityQueue<Integer>();
+        // 将数组的前 k 个元素入堆
+        for (int i = 0; i < k; i++) {
+            heap.add(nums[i]);
+        }
+        // 从第 k+1 个元素开始，保持堆的长度为 k
+        for (int i = k; i < nums.length; i++) {
+            // 若当前元素大于堆顶元素，则将堆顶元素出堆、当前元素入堆
+            if (nums[i] > heap.peek()) {
+                heap.poll();
+                heap.add(nums[i]);
+            }
+        }
+        return heap;
+    }
+
+    public static void main(String[] args) {
+        int[] nums = { 1, 7, 6, 3, 2 };
+        int k = 3;
+
+        Queue<Integer> res = topKHeap(nums, k);
+        System.out.println("最大的 " + k + " 个元素为");
+        PrintUtil.printHeap(res);
+    }
+}

codes/python/chapter_heap/my_heap.py

@@ -14,7 +14,7 @@ class MaxHeap:
     """大顶堆"""
 
     def __init__(self, nums: list[int]):
-        """构造方法"""
+        """构造方法，根据输入列表建堆"""
         # 将列表元素原封不动添加进堆
         self.max_heap = nums
         # 堆化除叶节点以外的其他所有节点

codes/python/chapter_heap/top_k.py

@@ -0,0 +1,37 @@
+"""
+File: top_k.py
+Created Time: 2023-06-10
+Author: Krahets (krahets@163.com)
+"""
+
+import sys, os.path as osp
+
+sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
+from modules import *
+
+import heapq
+
+
+def top_k_heap(nums: list[int], k: int) -> list[int]:
+    """基于堆查找数组中最大的 k 个元素"""
+    heap = []
+    # 将数组的前 k 个元素入堆
+    for i in range(k):
+        heapq.heappush(heap, nums[i])
+    # 从第 k+1 个元素开始，保持堆的长度为 k
+    for i in range(k, len(nums)):
+        # 若当前元素大于堆顶元素，则将堆顶元素出堆、当前元素入堆
+        if nums[i] > heap[0]:
+            heapq.heappop(heap)
+            heapq.heappush(heap, nums[i])
+    return heap
+
+
+"""Driver Code"""
+if __name__ == "__main__":
+    nums = [1, 7, 6, 3, 2]
+    k = 3
+
+    res = top_k_heap(nums, k)
+    print(f"最大的 {k} 个元素为")   
+    print_heap(res)