说明：下面的半监督K-means的初始聚类中心的选择是根据有标签数据而定的，聚类个数=类别个数，初始聚类中心=各个类样本的均值。
# -*- coding: utf-8 -*-
import numpy as np
def distEclud(vecA, vecB):
'''
输入：向量A和B
输出：A和B间的欧式距离
'''
return np.sqrt(sum(np.power(vecA - vecB, 2)))
def newCent(L):
'''
输入：有标签数据集L
输出：根据L确定初始聚类中心
'''
centroids = []
label_list = np.unique(L[:,-1])
for i in label_list:
L_i = L[(L[:,-1])==i]
cent_i = np.mean(L_i,0)
centroids.append(cent_i[:-1])
return np.array(centroids)
def semi_kMeans(L, U, distMeas=distEclud, initial_centriod=newCent):
'''
输入：有标签数据集L（最后一列为类别标签）、无标签数据集U（无类别标签）
输出：聚类结果
'''
dataSet = np.vstack((L[:,:-1],U))#合并L和U
label_list = np.unique(L[:,-1])
k = len(label_list) #L中类别个数
m = np.shape(dataSet)[0]
clusterAssment = np.zeros(m)#初始化样本的分配
centroids = initial_centriod(L)#确定初始聚类中心
clusterChanged = True
while clusterChanged:
clusterChanged = False
for i in range(m):#将每个样本分配给最近的聚类中心
minDist = np.inf; minIndex = -1
for j in range(k):
distJI = distMeas(centroids[j,:],dataSet[i,:])
if distJI < minDist:
minDist = distJI; minIndex = j
if clusterAssment[i] != minIndex: clusterChanged = True
clusterAssment[i] = minIndex
return clusterAssment
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作者：tyh70537
原文：https://blog.csdn.net/tyh70537/article/details/80483654