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  4 
@author: zxzhu  6 """
import numpy as np  8 import matplotlib.pyplot as plt  9 from numpy import random 10 
def Distance(x): 12     def Dis(y): 13         return np.sqrt(sum((x-y)**2))                      #欧式距离
    return Dis 15 
def init_k_means(k): 17     k_means = {} 18     for i in range(k): 19         k_means[i] = [] 20     return k_means 21 
def cal_seed(k_mean):                                      #重新计算种子点
    k_mean = np.array(k_mean) 24     new_seed = np.mean(k_mean,axis=0)                      #各维度均值
    return new_seed 26     
def K_means(data,seed_k,k_means): 28     for i in data: 29         f = Distance(i) 30         dis = list(map(f,seed_k))                        #某一点距所有种子点的距离
        index = dis.index(min(dis)) 32  k_means[index].append(i) 33     
    new_seed = []                                           #存储新种子
    for i in range(len(seed_k)): 36  new_seed.append(cal_seed(k_means[i])) 37     new_seed = np.array(new_seed) 38     return k_means,new_seed 39     
def run_K_means(data,k): 41     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) 44     count = 0 45     while not (result[1] == seed_k).all():                     #种子点改变，继续聚类
        count+=1
        seed_k = result[1] 48         k_means = init_k_means(k=7) 49         result = K_means(data,seed_k,k_means) 50     print('Done') 51     #print(result[1])
    print(count) 53     plt.figure(figsize=(8,8)) 54     Color = 'rbgyckm'
    for i in range(k): 56         mydata = np.array(result[0][i]) 57         plt.scatter(mydata[:,0],mydata[:,1],color = Color[i]) 58     return result[0] 59 
data = np.loadtxt('K-means_data') 61 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。