机器学习之--kmeans聚类简单算法实例

import numpy as np

import sklearn.datasets             #加载原数据

import matplotlib.pyplot as plt

import random

#点到各点距离

def PointToData(point,dataset):

    a = np.multiply(dataset - point,dataset - point)

    # print('a',a)

    distence = np.sqrt(a[:,0]+a[:,1])

    return distence

#选择初始的k个中心簇

def startpoint(k,dataset):

    m, n = np.shape(dataset)

    index1 = random.randint(0,len(dataset) - 1)

    A = []  # 初始的k个中心簇

    A_dit = []  # 初始所有点到中心簇的距离

    A.append(dataset[index1])

    sum_dis = np.zeros((m, 1))

    flag_mat = np.ones((m,1))

    flag_mat[index1] = 0

    for i in range(0, k - 1):

        A_dit.append((PointToData(A[i], dataset)).reshape(-1,1) )

        # print('A_dit[{}]:{}'.format(i,A_dit[i]))

        sum_dis =(sum_dis  + A_dit[i]) * flag_mat

        # print('sum_dis[{}]:{}'.format(i,sum_dis))

        Index = np.argmax(sum_dis)

        flag_mat[Index] = 0

        # print('选的Index：',Index)

        A.append(dataset[Index])

    return A

#加载数据

Data = sklearn.datasets.load_iris()

dataset = Data.data[:,0:2]

# #小数据测试编码

# test = dataset[0:15,:]

# testm,testn = np.shape(test)

# print(test)

#测试k

# k = 4

#初始点测试函数

# Apoint = startpoint(k,test)

# print('Apoint',Apoint)

#距离函数测试

# d = PointToData(test[0,:],test)

# print('d,d+d:',d,d+d)

def classfy(dataset,Apoint):

    m,n = np.shape(dataset)

    dis_li = []

    num = 0

    for point in Apoint:

        distence = PointToData(point,dataset)

        dis_li.append(distence)

        if num == 0:

            dis_li_mat = dis_li[num]

        else:

            dis_li_mat = np.column_stack((dis_li_mat,dis_li[num]))

        num += 1

    result = np.argmin(dis_li_mat,axis=1)

    # print('dis_li:',dis_li)

    # print('dis_li_mat:\n', dis_li_mat)

    # print('classfy:',result)

    return result

# label2 = classfy(test,Apoint)

# print('label2:',label2)

#求分类的新中心

def Center(dataset,label,k):

    i = 0

    newpoint = []

    for index in range(k):

        flag = (label==index)

        # print('flag,i:',flag,i)

        num = sum(flag)

        # print('num:',num,index)

        a = flag.reshape(-1,1) * dataset

        newpoint.append(np.sum(a,axis = 0)/num)

        i += 1

    # print(newpoint)

    return newpoint

# testcenter = center(test,label2,k)

# print('testcenter:',testcenter)

#K-means主体函数

def myK(k,dataset):

    Startpoint = startpoint(k,dataset)

    m,n = np.shape(Startpoint)

    centerpoint = Startpoint

    labelset = classfy(dataset,Startpoint)

    newcenter = Center(dataset,labelset,k)

    # print('外:cecnterpoint', centerpoint)

    # print('外:newcenter', newcenter)

    flag = 0

    for i in range(k):

        for j in range(n):

            if centerpoint[i][j] != newcenter[i][j]:

                flag = 1

    while flag:

        print('循环')

        # print('里:cecnterpoint', centerpoint)

        # print('里:newcenter', newcenter)

        flag = 0

        for i in range(k):

            for j in range(n):

                if centerpoint[i][j] != newcenter[i][j]:

                    flag = 1

        # print('flag:',flag)

        centerpoint = newcenter[:]

        labelset = classfy(dataset,centerpoint)

        newcenter = Center(dataset, labelset, k)

    # print('final_resultlabel:',labelset)

    # print('cenerpoint:', centerpoint)

    return labelset,centerpoint

#测试

k=5

final_label,centerpoint = myK(k,dataset)

print('centerpoint:',centerpoint)

mat_center = np.mat(centerpoint)

#画图

# plt.scatter(test[:,0],test[:,1],40,10*(labelset+1))

plt.scatter(dataset[:, 0], dataset[:, 1],40,10*(final_label+1))

plt.show()