KMeans群集.py

import numpy as np
import pandas as pd
k = 2 # K個群集
max_iter = 10 # 最大迭代
data = np.array([1, 2, 3, 4, 11, 12])
last = np.random.choice(data, k, replace=False) # 初始化K個中心點
func = lambda x,y: np.abs(x-y) # 計算距離方法
steps = []
for _ in range(max_iter):
    # 對每筆資料求出最近的中心點
    argmin = np.array([func(last, row).argmin() for row in data])
    # 重新分配群集
    clusters = [data[argmin==i] for i in range(k)]
    steps.append([*last, *clusters])
    # 更新中心點
    new = np.array([c.mean() for c in clusters])
    # 停止條件
    if func(new, last).sum()==0: break
    last = new
center_cols = map(lambda i:f'群心{i}', range(k))
cluster_cols = map(lambda i:f'群集{i}', range(k))
pd.DataFrame(steps,columns=[*center_cols,*cluster_cols])