2D K-means algorithem python code

K-means算法的尝试，以随机生成的两簇点集作为例子。初学的原因，代码比较粗糙，以后有待优化。

# -*- coding: utf-8 -*-
"""
Created on Fri Mar  9 09:16:45 2018

@author: user
"""
import numpy as np
import matplotlib.pyplot as plt
import math
#generate random points in two clusters
x1=2+1.3*np.random.randn(55)
y1=3+2*np.random.randn(55)
x2=7+1.5*np.random.randn(55)
y2=7+1.3*np.random.randn(55)
x1=np.concatenate((x1,y1));x2=np.concatenate((x2,y2));
x1=x1.reshape(int(0.5*len(x1)),2);x2=x2.reshape(int(0.5*len(x2)),2)
x=np.concatenate((x1,x2))
x=list(x)
#define distance between two point:
def distance(x1,x2):
    return math.sqrt(pow((x1[0]-x2[0]),2)+pow(x1[1]-x2[1],2))
#initialization ,show original points
u=[[10,2],[2,8]];r=[];x1=[];x2=[];a=b=c=d=0;
fig = plt.figure();
x=np.array(x)
plt.scatter(x[:,0],x[:,1],marker='x')
plt.xlim(-1,12);plt.ylim(-1,12)
plt.show()
#M-step
for z in range(1,6):
    for i in range(len(x)):
        if distance(x[i],u[0]) < distance(x[i],u[1]):
            r.append(1);
        else:
            r.append(0);
        if r[i]==1:
            x1.append(x[i]);
        else:
            x2.append(x[i]);
   
    for j in range(len(x1)):
        a = a + x1[j][0];b = b + x1[j][1]
    u[0][0] = a / len(x1);u[0][1] = b / len(x1)
    for k in range(len(x2)):
       c = c + x2[k][0];d = d + x2[k][1]
    #E-step47 　　u[1][0] = c / len(x2);u[1][1] = d / len(x2)
    x1=np.array(x1);x2=np.array(x2);
    plt.scatter(x1[:,0],x1[:,1],c='b',marker='x')
    plt.scatter(x2[:,0],x2[:,1],c='r',marker='x')

    plt.xlim(-1,12);plt.ylim(-1,12)
    r=[];x1=[];x2=[];a=b=c=d=0;

    if z==5:
            plt.scatter(u[0][0],u[0][1],c='g',marker='s')
            plt.scatter(u[1][0],u[1][1],c='y',marker='s')
            plt.show()
    else: pass