哈弗曼编码（Huffman encoding）.docVIP

哈弗曼编码（Huffman encoding） { / / achieve Havermann tree and its related operation Class HaffmanTree { Public Node[] data; / / array declaration storage two binary tree node Public int leafNum; / / the number of nodes (not including node later generation) Public static int leafNUM; / / the number of nodes in a static variable to store the program to use the above. Public static string HaddCode = ; / / storing generated Havermann code Public HaffmanTree (int n) { Data=new Node[2*n+1]; LeafNum = n; LeafNUM = leafNum; } Public HaffmanTree (Node[] nd) { Data new = Node[2 * nd.Length + 1]; For (int i = 0; I 2 * nd.Length + 1; i++) { If (ind.Length) / to node already exists, directly copied to the array { Data[i]=nd[i]; } Node else// does not exist is also stored in the nodes without weight, logo, parent nodes, child nodes around { Data[i] =new (Node); } } LeafNum = nd.Length; LeafNUM = leafNum; } Public Node (CreateHuaffTree) / / Huffman tree structure { In the process of constructing / Havermann tree in need to traverse the array, find qualified small and minimum weight node array subscript Int local1; Int local2; / / in order to make the generated two tree only a morphological feature, a two node each has the original array (length leafNum), a new generation of nodes For (int i = 0; I leafNum; i++) { Local1 = Local2 = 0; If (i==0) / beginning not to generate a new node { In the absence of / / generate a new node traversal before the array, find the smallest weight and small node Local1=FindMinNode (leafNum ref data); Local2 = FindMinNode (leafNum ref data); } Else// has generated a new node will find nodes to meet the conditions in different regions, a new generation of nodes, a node from the original { Local1 = FindMinNode (leafNum ref data); Local2 = leafNum + I - 1; / / this node can be found without traversing } / / new nodes generated by the minimum weight and have found small nodes Data[local1].parent = leafNum + i; Data[local2].parent = leafNum + i; Data[leafNum +