k-means聚类

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

#从磁盘读取城市经纬度数据

X = []
with open('data.txt') as f:
    for v in f:
        X.append([float(v.split('，')[2]), float(v.split('，')[3])])

#转换成numpy array

X = np.array(X)

#类簇的数量

n_clusters = 5

#现在把数据和对应的分类数放入聚类函数中进行聚类

cls = KMeans(n_clusters, random_state=0).fit(X)

#X中每项所属分类的一个列表

cls.labels_
pred = cls.predict([[36.2, 189.5], [26.05, 119.18]])   # 返回预测类别的index
print(pred, cls.cluster_centers_)   # cluster_centers_  各聚类中心

#画图

markers = ['^', 'x', 'o', '*', '+']
for i in range(n_clusters):
    members = cls.labels_ == i
    plt.scatter(X[members, 0], X[members, 1], s=60, mark