from copy import deepcopy
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] = (16,9)
plt.style.use('ggplot')
data = pd.read_csv('xclara.csv')
f1 = data['V1'].values
f2 = data['V2'].values
X = np.array(list(zip(f1, f2)))
# 距离计算函数
def dist(a, b, ax=1):
    return np.linalg.norm(a - b, axis=ax)

# 设置聚类数
k = 3

# 随机初始化质心（修正：使用数据范围）
C_x = np.random.randint(0,np.max(X)-20, size=k)
C_y = np.random.randint(0,np.max(X)-20, size=k)
C = np.array(list(zip(C_x, C_y)), dtype=np.float32)

C_old = np.zeros(C.shape)
print(C)
clusters = np.zeros(len(X))
iteration_flag = dist(C,C_old,1)
tmp = 1
while iteration_flag.any() != 0 and tmp<20:
    for i in range(len(X)):
        distances = dist(X[i],C,1)
        clusters[i] = clusters
    C_old = deepcopy(C)
    for i in range(C):
        points = [X[j] for j in range(len(X)) if clusters[j] == i]
        C[i] = np.mean(points,axis=0)

        
    print('%d'%tmp)
    tmp = tmp + 1
    iteraction_flag = dist(C,C_old,1)
    print('distance:',iteraction_flag)
colors = ['r','g','b','y','c','m']
fig,ax = plt.subplots()
for i in range(k):
    points = np.array([X[j] for j in range(len(X) if clusters[j] == i)])
    ax.scatter(points[:,0],points[:,1],s=7,c=colors[i])
ax.scatter(C[:,0],C[:,1],marker="*",s=200,c='black')
plt.show()