Python计算样本熵

Python计算样本熵

样本熵算法

来自 算法原理（2）：样本熵（SampEn） - 程序员大本营 (pianshen.com)

代码

import pandas as pd
import numpy as np

def sampEn(L:np.array, std : float ,m: int= 2, r: float = 0.15):
    """ 
    计算时间序列的样本熵
    
    Input: 
        L: 时间序列
        std: 原始序列的标准差
        m: 1或2
        r: 阈值
        
    Output: 
        SampEn
    """
    N = len(L)
    B = 0.0
    A = 0.0

    # Split time series and save all templates of length m
    xmi = np.array([L[i:i+m] for i in range(N-m)])
    xmj = np.array([L[i:i+m] for i in range(N-m+1)])
    
    # Save all matches minus the self-match, compute B
    B = np.sum([np.sum(np.abs(xmii-xmj).max(axis=1) <= r * std)-1 for xmii in xmi])
    # Similar for computing A
    m += 1
    xm = np.array([L[i:i+m] for i in range(N-m+1)])
    
    A = np.sum([np.sum(np.abs(xmi-xm).max(axis=1) <= r * std)-1 for xmi in xm])
    # Return SampEn
    return -np.log(A/B)