Python機器學習(1)：KMeans聚類

Python進行KMeans聚類是比較簡單的，首先需要import numpy，從sklearn.cluster中import KMeans模塊：

import numpy as np
from sklearn.cluster import KMeans

然後讀取txt文件，獲取相應的數據並轉換成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)

設置類的數量，並聚類：

n_clusters = 5
cls = KMeans(n_clusters).fit(X)

完整代碼：

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)
    print 
    
plt.title('')
plt.show()

運行結果：