人工智能导论实验四 聚类算法

广州大学学生实验报告开课学院及实验室：计算机科学与工程实验室 2020年12月3日学院计算机科学与网络工程学院年级传业/班姓名学号实验课程名称人工智能导论实验成绩实验项目名称聚类算法指导老师范立牛（***报告只能为文字和图片,老师评语将添加到此处,学生请勿作答***）一、实验内容1.针对教师指定的两类公用数据集（纯数值型例如UCIIris,混杂型数据例如UCIBankMarketing），学生至少实现两种聚类算法，并比较分析结果原因。本次实验主要内容包括数据处理、算法实现和评价方法。鼓励与其他方法尤其是业界领先算法进行比较分析，鼓励创新设计求解方法。二、实验设备实验设备：计算机；平台：Windows操作系统，VisualC++6.0/PythonAnaconda三、实验步骤读取数据，并做预处理。至少实现两种聚类算法，选择评价方法比较结果并分析原因选择适当可视化方法显示结果。四、分析说明（包括核心代码及解释）Kmeans算法:importmathfromcollectionsimportdefaultdictimportnumpyasnpdataname="iris.txt"#数据预处理defloadlRISdata(filename):data=[]withopen(filename,mode="r",encoding="utf-8")asrf:forlineinrf:ifline=='\n':continuedata.append(list(map(float,line.split(""))))returndatadefgenerateCenters(data):'''求解初始聚类中心’’centers=[]'''已知维度为4'''''分三类，取第0,50,100的三个向量作为分界'''centers.append(data[0])centers.append(data50])centers.append(data100])returncentersdefdistance(a,b):'''欧式距离'''sum=0foriinrange(4):sq=(a[i]-b[i])*(a[i]-b[i])sum+=sqreturnmath.sqrt(sum)defpoint_avg(points):'''对维度求平均值？’’new_center=[]foriinrange(4):sum=0forpinpoints:sum+=p[i]new_center.appendfloat("%.8F"%(sum/float(len(points)))))returnnew_centerdefupdataCenters(data,assigments):new_means=defaultdict(List)centers=[]forassigment,pointinzip(assigments,data):new_means[assigment].append(point)'''将同一类的数据进行整合'''foriinrange(3):points=new_means[i]centers.append(point_avg(points))returncentersdefassignment(data,centers):assignments=[]对应位置显示对应类群'''forpointindata:'''遍历所有数据'''shortest=float('inF')shortestindex=0foriinrange(3):'''遍历三个中心向量，与哪个类中心欧氏距离最短就将其归为哪类'''value=distance(point,centers[i])ifvalue<shortest:shortest=valueshortestindex=iassignments.append(shortestindex)returnassignmentsdefkmeans(data):k_data=generateCenters(data)assigments=assignment(data,k_data)old_assigments=Nonewhileassigments!=old_assigments:new_centers=updataCenters(data,assigments)old_assigments=assigmentsassigments=assignment(data,new_centers)result=list(zip(assigments,data))returnresultdefacc(result):sum=0all=0foriinrange(50):ifresult[i][0]==0:sum+1all+=1foriinrange(50):ifresult[i+50][0]==1:sum+1all+=1foriinrange(50):ifresult[i+100][0]==2:sum+1all+=1print('sum:',sum,'all:',all)returnsum,allif name ==" ain ":data=loadIRISdata(dataname)result=kmeans(data)foriinrange(3):tag=0print('\n')print("第％d类数据有："%(i+1))fortupleinrange(len(result)):if(result[tuple][0]==i):print(tuple,end=' ')tag1=iftag>20:print('\n')tag0=#print(result)print('\n')sum,all=acc(result)print('c-means准确度为：%2f%%'%((sum/all)*100))第]■夹S?据有=弓9123 5 7 101112151415.16171S02B~ 212223弟25海272&餌抽S132弗酣翦話173AM烧41■爼百3城45自占47A目昨弟2土钢抵码=BG 51 53 尉 55 碍盟56&960 6162殆64 柘6$仍6$ 2797172 73 “ 75 7& 7-6 79&061&2 B3目百辟関展7盟卿9fl 叫9293如般则 97 侗酬 1SL136L1S 口411*121 12512*127 1331瑚142 1461的第H卖豉据甬-5277190102103]_目耳L9&10710B19?119111L1Z1151U11711?12S12212*12512B1291301311321酹L35I隔137L3?lAil^L1.翻1和1砖14714flsum.；134NIL；L5Bc-meane准融为：辭门歸盟孰ProcessfinishedwithbxltcodeBFCM算法:frompylabimport*fromnumpyimport*importpandasaspdimportnumpyasnpimportoperatorimportmathimportmatplotlib.pyplotaspltimportrandom#数据预处理df_full=pd.read_csv("iris.csv")columns=list(df_full.columns)features=columns[:len(columns)一1]df=df_full[features]#维度num_attr=len(df.columns)一1#分类数k=3#最大迭代数MAX_ITER=100#样本数n=len(df)#thenumberofrow#模糊参数m=2.00#初始化模糊矩阵udefinitializeMembershipMatrix():membership_mat=list()foriinrange(n):random_num_list=[random.random()foriinrange(k)]summation=sum(random_num_list)temp_list=[x/summationforxinrandom_num_list]#首先归一化membership_mat.append(temp_list)returnmembership_mat#计算类中心点defcalculateClusterCenter(membership_mat):cluster_mem_val=zip(*membership_mat)cluster_centers=list()cluster_mem_val_list=list(cluster_mem_val)forjinrange(k):x=cluster_mem_val_list[j]xraised=[e**mforeinx]denominator=sum(xraised)temp_num=list()foriinrange(n):data_point=ist(df.iloc[i])prod=[xraised[i]*vaforvalindata_point]temp_num.append(prod)numerator=map(sum,zip(*temp_num))center=[z/denominatorforzinnumerator]#每一维都要计算。cluster_centers.append(center)returncluster_centers#更新隶属度defupdateMembershipValue(membership_mat,cluster_centers):#p=float(2/(m—1))data=[]foriinrange(n):x=list(df.iloc[i])#取出文件中的每一彳丁数据data.append(x)distances=[np.linalg.normlist(map(operator.sub,x,cluster_centers[j])))forjinrange(k)]forjinrange(k):densum([math.pow(float(distances[j]/distances[c]),2)forcinrange(k)])membership_mat[i][j]=loat(1/den)returnmembership_mat,data#得到聚类结果defgetClusters(membership_mat):cluster_labels=list()foriinrange(n):max_val,idx=max((val,idx)for(idx,val)inenumerate(membership_mat[i]))cluster_labels.append(idx)returncluster_labelsdeffuzzyCMeansClustering():#主程序membership_mat=initializeMembershipMatrix()curr=0whilecurr<=MAX_ITER:#最大迭代次数cluster_centers=calculateClusterCenter(membership_mat)membership_mat,data=updateMembershipValue(membership_mat,cluster_centers)cluster_labels=getClusters(membership_mat)curr+=1print(membership_mat)returncluster_labels,cluster_centers,data,membership_matdefxie_beni(membership_mat,center,data):sum_cluster_distance=0min_cluster_center_distance=infforiinrange(k):forjinrange(n):sum_cluster_distance=sum_cluster_distance+membership_mat[j][i] **2*sum(power(data[j,:]-center[i,:2)) #计算类致性foriinrange(k一1):forjinrange(i+1,k):cluster_center_distance=sum(power(center[i,:]一center[j,:],2)) #计算类间距离ifcluster_center_distance<min_cluster_center_distance:min_cluster_center_distance=cluster_center_distancereturnsumclusterdistance/(n*minclustercenterdistance)labels,centers,data,membership=fuzzyCMeansClustering()print(labels)print(centers)center_array=array(centers)label=array(labels)datas=array(data)#Xie-Beni聚类有效性print("聚类有效性："，xie_beni(membership,center_array,datas))xlim(0, 10)ylim(0, 10)#做散点图fig=plt.gcf()fig.set_size_inches(16.5,12.5)