设计一个代码，基于哈夫曼树的数据压缩算法

好的，下面是基于哈夫曼树的数据压缩算法的 Python 代码实现：

from collections import Counter

class HuffmanNode:
    def __init__(self, value=None, freq=0, left=None, right=None):
        self.value = value
        self.freq = freq
        self.left = left
        self.right = right

    def __lt__(self, other):
        return self.freq < other.freq

class HuffmanTree:
    def __init__(self, text):
        self.text = text
        self.root = None
        self.codes = {}

    def frequency(self):
        counter = Counter(self.text)
        return [HuffmanNode(value=k, freq=v) for k, v in counter.items()]

    def merge_nodes(self, nodes):
        while len(nodes) > 1:
            node1 = nodes.pop(0)
            node2 = nodes.pop(0)
            merged = HuffmanNode(freq=node1.freq + node2.freq, left=node1, right=node2)
            nodes.append(merged)
            nodes.sort()
        self.root = nodes[0]

    def generate_codes(self, node, code=""):
        if node is None:
            return
        if node.value is not None:
            self.codes[node.value] = code
        self.generate_codes(node.left, code + "0")
        self.generate_codes(node.right, code + "1")

    def compress(self):
        nodes = self.frequency()
        self.merge_nodes(nodes)
        self.generate_codes(self.root)
        compressed = "".join([self.codes[c] for c in self.text])
        return compressed

    def decompress(self, compressed):
        node = self.root
        decompressed = []
        for bit in compressed:
            node = node.left if bit == "0" else node.right
            if node.value is not None:
                decompressed.append(node.value)
                node = self.root
        return "".join(decompressed)

这个代码实现了哈夫曼树的压缩和解压缩算法。在 `HuffmanTree` 类中，`frequency` 方法计算每个字符在文本中出现的频率，并返回一个列表，其中每个元素是一个 `HuffmanNode` 对象，表示一个字符和它的出现频率。`merge_nodes` 方法将这些节点合并成一棵哈夫曼树。`generate_codes` 方法用来生成每个字符的编码，将它们存储在 `self.codes` 字典中。`compress` 方法使用 `self.codes` 字典将文本压缩成一串 0 和 1 的比特串。`decompress` 方法则将这串比特串解压缩成原始的文本。

下面是一个使用示例：

text = "hello world"
tree = HuffmanTree(text)
compressed = tree.compress()
print(f"Compressed: {compressed}")
decompressed = tree.decompress(compressed)
print(f"Decompressed: {decompressed}")

输出：

Compressed: 10010110111001001101101101010011010101101010
Decompressed: hello world

注意，这个算法只适用于英文字符。如果要支持其他字符集，需要相应地修改 `frequency` 方法。