python ML 笔记：Kmeans

kmeans算法的python实现：

参考与样本来源《Machine Learning in Action》

 #-*-coding:UTF-8-*-
 '''
 Created on 2015年8月19日
 @author: Ayumi Phoenix
 '''
 import numpy as np

 def distL2(a,b):
     """ 计算两个向量之间的L2距离 """
     return np.sqrt(np.sum((a-b)**2))

 class Kmeans():
     def __init__(self, dataset,k):
         self.dataset = dataset
         self.k = k
         self.m, self.n = dataset.shape

     def randcent(self):
         """ 根据输入数据集获得随机生成一组簇质心 """
         maxn = np.max(self.dataset, 0)  # 获取每一维的最大值
         minn = np.min(self.dataset, 0)  # 获取每一维的最小值
         centoroid = np.random.rand(self.k,self.n) * (maxn - minn) + minn    # k x n
         return centoroid

     def train(self, dist, iter = 1):
         """
         # 1. 计算每个样本与所有簇心的最近匹配距离数组 m x 1：
             # 计算某样本与所有簇心的距离,
             # 找到最小距离所属的下标序号 0...k-1
         # 2. 根据当前类标的分配，重新计算平均聚类中心
             # 按照当前分配索引样本数据
             # 迭代次数减一
         # 3. 返回最终的质心与分配的序号
         """
         centoroid = self.randcent()
         while iter:
             labels = np.zeros((self.m,), int)
             for i in range(self.m):
                 d = [dist(self.dataset[i,:],centoroid[j])
                      for j in range(self.k)]
                 labels[i] = np.argmin(d)
             for i in range(self.k):
                 x = self.dataset[labels==i]
                 centoroid[i] = np.mean(x, 0)
             iter -= 1
         return centoroid, labels    

读取数据与测试函数：

 ef loadDataSet(filename):
     dataMat = []
     with open(filename) as f:
         for line in f.readlines():
             curline = line.strip().split('\t')
             fltline = map(np.float, curline)
             dataMat.append(fltline)
     return dataMat

 if __name__=="__main__":
     pass
     datMat = np.array(loadDataSet('testSet.txt'))
     km = Kmeans(datMat,4)
     centoroid, labels = km.train(distL2, iter=20)

     # 根据当前质心显示样本分布
     import matplotlib.pylab as pl
     pl.figure()
     c = ['ro','go','bo','yo','co','ko','wo','mo']
     for i in range(datMat.shape[0]):
         pl.plot(datMat[i][0],datMat[i][1],c[labels[i]])
     for cen in centoroid:
         pl.plot(cen[0],cen[1],'mo')
     pl.show()

结果：