Hamming_Distance(漢明距離).py

import numpy as np
A = np.array([['男', '市場部', '24000', '無'],
       ['女', '研發部', '45000', '有'],
       ['男', '會計部', '45000', '無'],
       ['男', '研發部', '40000', '無'],
       ['女', '市場部', '24000', '有'],
       ['女', '研發部', '40000', '無'],
       ['男', '市場部', '24000', '有']])

def Hamming_distance(A):
    # 維度n列p行
    n, p = A.shape
    M = np.zeros((n,n))
    for i in range(p):
        # 取出第i行，並向右複製n次
        B = np.tile(A[:,i],(n,1))
        # 與轉置矩陣比對後加總
        M += B==B.T
    # 取上三角矩陣，並擷取上方一格
    M = np.triu(M, k=1)
    # 取log2(n)時跳過0
    log2 = lambda M: np.log2(M,where=M!=0)
    # 轉換成機率
    p = M / p
    # 計算熵
    H = -(p*log2(p)+(1-p)*log2(1-p))
    # 加總
    return H.sum()

Hamming_distance(A)
for i,col in enumerate(['性別','部門','薪資','配偶']):
    H = Hamming_distance(np.delete(A, i, axis=1))
    print(col, H) # H 降低越多代表該特徵越重要