K-means聚类的Python实现

生物信息学原理作业第五弹：K-means聚类的实现。

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K-means聚类的Python实现

原理参考：K-means聚类（上）

数据是老师给的，二维，2 * 3800的数据。plot一下可以看到有7类。

怎么确定分类个数我正在学习，这个脚本就直接给了初始分类了，等我学会了再发。

下面贴上Python代码，版本为Python3.6。

 # -*- coding: utf-8 -*-
 """
 Created on Wed Dec  6 16:01:17 2017

 @author: zxzhu
 """
 import numpy as np
 import matplotlib.pyplot as plt
 from numpy import random

 def Distance(x):
     def Dis(y):
         return np.sqrt(sum((x-y)**2))                      #欧式距离
     return Dis

 def init_k_means(k):
     k_means = {}
     for i in range(k):
         k_means[i] = []
     return k_means

 def cal_seed(k_mean):                                      #重新计算种子点
     k_mean = np.array(k_mean)
     new_seed = np.mean(k_mean,axis=0)                      #各维度均值
     return new_seed

 def K_means(data,seed_k,k_means):
     for i in data:
         f = Distance(i)
         dis = list(map(f,seed_k))                        #某一点距所有种子点的距离
         index = dis.index(min(dis))
         k_means[index].append(i)

     new_seed = []                                           #存储新种子
     for i in range(len(seed_k)):
         new_seed.append(cal_seed(k_means[i]))
     new_seed = np.array(new_seed)
     return k_means,new_seed

 def run_K_means(data,k):
     seed_k = data[random.randint(len(data),size=k)]       #随机产生种子点
     k_means = init_k_means(k)                                #初始化每一类
     result = K_means(data,seed_k,k_means)
     count = 0
     while not (result[1] == seed_k).all():                     #种子点改变，继续聚类
         count+=1
         seed_k = result[1]
         k_means = init_k_means(k=7)
         result = K_means(data,seed_k,k_means)
     print('Done')
     #print(result[1])
     print(count)
     plt.figure(figsize=(8,8))
     Color = 'rbgyckm'
     for i in range(k):
         mydata = np.array(result[0][i])
         plt.scatter(mydata[:,0],mydata[:,1],color = Color[i])
     return result[0]

 data = np.loadtxt('K-means_data')
 run_K_means(data,k=7)   

附上结果图：

这个算法太依赖于初始种子点的选取了，随机选点很有可能会得到局部最优的结果，所以下一步学习一下怎么设置初始种子点以及分类数目。