排列熵算法python

排列熵（Permutation Entropy），也称序熵，是一种衡量时间序列复杂度的统计量，常用于信号分析、生物信息学等领域。在Python中，你可以使用`scikit-learn`库中的`entropy`函数结合自定义计算方法来实现排列熵。

以下是一个简单的步骤来计算排列熵：

1. 导入必要的库：

import numpy as np
from sklearn.feature_extraction import entropy

2. 准备时间序列数据：

data = ...  # 你需要处理的时间序列数组

3. 确定窗口大小和延迟（滑动步长）：

window_size = 5
delay = 1

4. 计算排列熵：

def permutation_entropy(seq, window_size=window_size, delay=delay):
    seqs = [seq[i:i+window_size] for i in range(len(seq) - window_size + 1)]
    seqs = [np.roll(x, -delay) for x in seqs]
    seqs = [x[:-delay] for x in seqs]

    seq_lengths = [len(s) for s in seqs]
    max_length = max(seq_lengths)

    unique_orders = []
    for s in seqs:
        if len(s) == max_length:
            unique_orders.append(tuple(s))

    unique_orders_counts = np.bincount([ord(o) for o in unique_orders])
    
    prob_dist = unique_orders_counts / len(seqs)
    return entropy(prob_dist, base=np.log(window_size))

permutation_entropy_value = permutation_entropy(data)