半监督K均值聚类python代码 - 好文

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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