Python机器学习(1)：KMeans聚类
Python进行KMeans聚类是比较简单的，首先需要import numpy，从sklearn.cluster中import KMeans模块：
import numpy as np
from sklearn.cluster import KMeans
然后读取txt文件，获取相应的数据并转换成numpy array：
X = []
f = open('rktj4.txt')
for v in f:
regex = re.compile('\s+')
X.append([float(regex.split(v)[3]), float(regex.split(v)[6])])
X = np.array(X)
设置类的数量，并聚类：
n_clusters = 5
cls = KMeans(n_clusters).fit(X)完整代码：
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import re
X = []
f = open('rktj4.txt')
for v in f:
regex = re.compile('\s+')
X.append([float(regex.split(v)[3]), float(regex.split(v)[6])])
X = np.array(X)
n_clusters = 5
cls = KMeans(n_clusters).fit(X)
cls.labels_
markers = ['^','x','o','*','+']
for i in range(n_clusters):
members = cls.labels_ == i
plt.scatter(X[members, 0], X[members, 1], s=60, marker=markers[i], c='b', alpha=0.5)
plt.title('')
plt.show()
运行结果：
注意！
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