python自适应算术编码

Python自适应算术编码是一种用于数据压缩的无损压缩算法。它将一段数据中的符号（通常是字符或字节）映射到一个区间，并将该区间编码为一个二进制数。通过不断更新区间的上下限来逐步缩小该区间，最终得到一个紧凑的编码。与传统的霍夫曼编码相比，自适应算术编码可以自适应地调整编码表，从而更好地适应不同的数据分布。Python中可以使用第三方库bitarray来实现自适应算术编码，也可以自己实现。

相关问题

使用python，实现自适应算术编码

以下是一个简单的Python实现自适应算术编码的例子：

import bisect
from collections import defaultdict

class ArithmeticEncoder:
    def __init__(self, bitout):
        self.low = 0
        self.high = 0xFFFFFFFF
        self.bitout = bitout
        self.pending_bits = 0
        self.pending_data = []

    def get_scaled_value(self, value_range, offset, total_range):
        return ((value_range * (self.high - self.low)) // total_range) + offset

    def encode(self, symbol, freqs):
        total_freq = sum(freqs.values())
        if total_freq >= 0xFFFFFFFF:
            raise ValueError("Cannot code symbol with frequency >= 2^32")
        value_range = (self.high - self.low) + 1
        symbol_low = 0
        for k, v in freqs.items():
            if k < symbol:
                symbol_low += v
        symbol_high = symbol_low + freqs[symbol] - 1
        self.low = self.get_scaled_value(symbol_low, self.low, total_freq)
        self.high = self.get_scaled_value(symbol_high, self.low, total_freq)
        while ((self.low ^ self.high) & 0xFF000000) == 0:
            self.bitout.write(self.high >> 24)
            for b in self.pending_data:
                self.bitout.write(b ^ 0xFF)
            self.pending_data.clear()
            self.low <<= 8
            self.high <<= 8
            self.high |= 0xFF
            self.pending_bits += 8
        while self.pending_bits > 0:
            self.bitout.write(self.high >> 24)
            self.pending_data.append((self.high >> 24) ^ 0xFF)
            self.high <<= 8
            self.pending_bits -= 8

    def finish(self):
        self.bitout.write((self.high >> 24) - 1)
        for b in self.pending_data:
            self.bitout.write(self.high >> 24)
        self.bitout.write(self.high >> 24)

class ArithmeticDecoder:
    def __init__(self, bitin):
        self.low = 0
        self.high = 0xFFFFFFFF
        self.bitin = bitin
        self.code = 0
        for i in range(4):
            self.code = (self.code << 8) | self.bitin.read()

    def get_value(self, freqs):
        total_freq = sum(freqs.values())
        if total_freq >= 0xFFFFFFFF:
            raise ValueError("Cannot decode symbol with frequency >= 2^32")
        value_range = (self.high - self.low) + 1
        offset = self.code - self.low
        offset = ((offset * total_freq) // value_range)
        symbol = 0
        symbol_low = 0
        for k, v in freqs.items():
            symbol_high = symbol_low + v - 1
            if symbol_low <= offset <= symbol_high:
                symbol = k
                break
            symbol_low = symbol_high + 1
        symbol_high = symbol_low + freqs[symbol] - 1
        self.low = self.get_scaled_value(symbol_low, self.low, total_freq)
        self.high = self.get_scaled_value(symbol_high, self.low, total_freq)
        while ((self.low ^ self.high) & 0xFF000000) == 0:
            self.code = (self.code << 8) | self.bitin.read()
            self.low <<= 8
            self.high <<= 8
            self.high |= 0xFF
        return symbol

    def get_scaled_value(self, value_range, offset, total_range):
        return ((value_range * (self.high - self.low)) // total_range) + offset

使用方法如下：

from bitarray import bitarray

def encode_string(s):
    freqs = defaultdict(int)
    for c in s:
        freqs[c] += 1
    bitout = bitarray()
    enc = ArithmeticEncoder(bitout)
    for c in s:
        enc.encode(c, freqs)
        freqs[c] += 1
    enc.finish()
    return bitout

def decode_string(bitstring):
    freqs = defaultdict(int)
    for b in bitstring:
        freqs[b] += 1
    bitin = bitstring.itersearch(bitarray('1'))
    dec = ArithmeticDecoder(bitin)
    s = ''
    while True:
        try:
            symbol = dec.get_value(freqs)
            s += symbol
            freqs[symbol] += 1
        except IndexError:
            break
    return s

这里我们使用了bitarray库来处理比特流。encode_string函数将一个字符串进行编码，并返回一个比特流。decode_string函数接受一个比特流，并对其进行解码，返回原始字符串。

用python实现算术编码

好的，算术编码是一种无损数据压缩算法，它可以将一个符号串编码为一个实数，实现数据的压缩。下面是用 Python 实现算术编码的代码：

# 算术编码实现
def arithmetic_encode(symbols, probabilities):
    # 计算符号的累积概率
    cumulative_probabilities = [0.0] * len(probabilities)
    cumulative_probabilities[0] = probabilities[0]
    for i in range(1, len(probabilities)):
        cumulative_probabilities[i] = cumulative_probabilities[i - 1] + probabilities[i]
    # 初始化区间
    low = 0.0
    high = 1.0
    # 编码
    for symbol in symbols:
        symbol_index = symbol - 1
        symbol_range = high - low
        high = low + symbol_range * cumulative_probabilities[symbol_index + 1]
        low = low + symbol_range * cumulative_probabilities[symbol_index]
    # 返回编码结果
    return low

# 测试代码
symbols = [1, 2, 3, 4, 5]
probabilities = [0.1, 0.2, 0.3, 0.15, 0.25]
encoded_result = arithmetic_encode(symbols, probabilities)
print(encoded_result)

在代码中，我们先计算了每个符号的累积概率，然后使用区间编码的方法对符号串进行编码，最后返回编码结果。需要注意的是，这里的符号需要是整数，概率列表中的索引对应符号减一的值。