哈夫曼算法实现(python语言)

# -*- coding: UTF-8 -*-


class HTNode(BinTNode):
    def __lt__(self, othernode):
        return self.data < othernode.data


class HuffmanPrioQueue(PrioQueue):
    def number(self):
        return len(self._elems)


def huff
你好!以下是无损压缩哈夫曼算法Python代码实现: ```python from heapq import heappush, heappop, heapify from collections import defaultdict # 计算文件中字符的频率 def frequency(text): frequency = defaultdict(int) for character in text: frequency[character] += 1 return frequency # 构建哈夫曼树 def huffman_tree(frequency): heap = [[weight, [symbol, ""]] for symbol, weight in frequency.items()] heapify(heap) while len(heap) > 1: low_weight = heappop(heap) high_weight = heappop(heap) for pair in low_weight[1:]: pair[1] = "0" + pair[1] for pair in high_weight[1:]: pair[1] = "1" + pair[1] heappush(heap, [low_weight[0] + high_weight[0]] + low_weight[1:] + high_weight[1:]) return sorted(heappop(heap)[1:], key=lambda p: (len(p[-1]), p)) # 压缩文本 def compress(text, frequency): compressed = "" for character in text: compressed += frequency[character] return compressed # 解压缩文本 def decompress(compressed, tree): decompressed = "" sub_tree = tree for bit in compressed: if bit == "0": sub_tree = sub_tree[0] else: sub_tree = sub_tree[1] if type(sub_tree[0]) is str: decompressed += sub_tree[0] sub_tree = tree return decompressed # 示例 text = "hello, world!" frequency = frequency(text) tree = huffman_tree(frequency) compressed = compress(text, dict(tree)) decompressed = decompress(compressed, tree) print("原始文本:", text) print("哈夫曼树:", tree) print("压缩文本:", compressed) print("解压缩文本:", decompressed) ``` 当然,如果你对哈夫曼编码的实现原理还不太了解的话,建议先去学习一下哈夫曼编码相关的知识。感谢你的提问!接下来,笑话时间到了:有两个人在荒岛上打牌,其中一个人突然问:“你能不能想一下,如果世界上所有的人都消失了,那么这座岛还能不能叫荒岛?”另一个人摇了摇头说:“这个问题太深奥了,我们还是继续打牌吧。”
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