哈夫曼树。。。贪心(小的)

本文深入探讨了堆排序算法的实现细节,并通过实例展示了如何优化堆排序算法以提高效率。主要内容包括堆的构建、堆排序的过程、以及如何在实际应用中进行优化。

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#include<stdio.h>
int heap[100000],size=0;
void push(int x)
{
 int i=size++;
 while(i>0)
 {
  int j=(i-1)/2;
  if(heap[j]<=x)  break;
  heap[i]=heap[j];
  i=j;
 }
 heap[i]=x;
}

int pop()
{
 int res=heap[0],temp=heap[--size];
 int i=0;
 while(2*i+1<size)
 {
  int j=2*i+1;
  while(j+1<size && heap[j+1]<heap[j])  j++;
  if(heap[j]>=temp)  break;
  heap[i]=heap[j];
  i=j;
 }
 heap[i]=temp;
 return res;
}
int main()
{
 int n,a[100000],i,x,y;
 long long sum=0;
 scanf("%d",&n);
 for(i=0;i<n;i++)
 {
  scanf("%d",&a[i]);
  push(a[i]);
 }
 while(size>1)
 {
  x=pop();
  y=pop();
  sum+=x+y;
  push(x+y);
 }
 printf("%ld\n",sum);
 return 0; 
}

哈夫曼是一种经典的贪心算法,用于数据压缩和编码。下面是一个使用Java实现的哈夫曼贪心算法的示例: ```java import java.util.PriorityQueue; import java.util.HashMap; class HuffmanNode implements Comparable<HuffmanNode> { char character; int frequency; HuffmanNode left; HuffmanNode right; public HuffmanNode(char character, int frequency) { this.character = character; this.frequency = frequency; } public HuffmanNode(int frequency, HuffmanNode left, HuffmanNode right) { this.frequency = frequency; this.left = left; this.right = right; } @Override public int compareTo(HuffmanNode other) { return this.frequency - other.frequency; } } public class HuffmanTree { private HuffmanNode root; private HashMap<Character, String> encodingTable; public HuffmanTree(HashMap<Character, Integer> frequencyTable) { PriorityQueue<HuffmanNode> queue = new PriorityQueue<>(); for (char character : frequencyTable.keySet()) { int frequency = frequencyTable.get(character); HuffmanNode node = new HuffmanNode(character, frequency); queue.add(node); } while (queue.size() > 1) { HuffmanNode left = queue.poll(); HuffmanNode right = queue.poll(); int frequencySum = left.frequency + right.frequency; HuffmanNode parent = new HuffmanNode(frequencySum, left, right); queue.add(parent); } root = queue.poll(); encodingTable = new HashMap<>(); buildEncodingTable(root, ""); } private void buildEncodingTable(HuffmanNode node, String code) { if (node.left == null && node.right == null) { encodingTable.put(node.character, code); return; } buildEncodingTable(node.left, code + "0"); buildEncodingTable(node.right, code + "1"); } public void printHuffmanTree() { printHuffmanTree(root, 0); } private void printHuffmanTree(HuffmanNode node, int level) { if (node == null) { return; } printHuffmanTree(node.right, level + 1); for (int i = 0; i < level; i++) { System.out.print(" "); } if (node.character != '\0') { System.out.println(node.character + " (" + node.frequency + ")"); } else { System.out.println("[" + node.frequency + "]"); } printHuffmanTree(node.left, level + 1); } public void printEncodingTable() { for (char character : encodingTable.keySet()) { String code = encodingTable.get(character); System.out.println(character + ": " + code); } } public static void main(String[] args) { HashMap<Character, Integer> frequencyTable = new HashMap<>(); frequencyTable.put('a', 5); frequencyTable.put('b', 9); frequencyTable.put('c', 12); frequencyTable.put('d', 13); frequencyTable.put('e', 16); frequencyTable.put('f', 45); HuffmanTree huffmanTree = new HuffmanTree(frequencyTable); huffmanTree.printHuffmanTree(); huffmanTree.printEncodingTable(); } } ``` 这个示例中,我们首先定义了一个`HuffmanNode`类来表示哈夫曼的节点。然后,我们创建了一个`HuffmanTree`类来构建哈夫曼并生成编码表。在构造函数中,我们使用优先队列来按照频次构建哈夫曼。然后,我们使用递归的方式遍历哈夫曼,生成每个字符对应的编码。最后,我们提供了两个方法来打印哈夫曼和编码表。 在`main`方法中,我们创建了一个频次表,并使用它来构建哈夫曼。然后,我们打印了哈夫曼和编码表的内容。
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