收集于網(wǎng)絡(luò)，如有侵權(quán)請(qǐng)聯(lián)系管理員刪除收集于網(wǎng)絡(luò)，如有侵權(quán)請(qǐng)聯(lián)系管理員刪除機(jī)器學(xué)習(xí)聚類算法實(shí)

現(xiàn)人工智能與機(jī)器學(xué)習(xí)》實(shí)驗(yàn)報(bào)告年級(jí)-XXXX班-專業(yè)_XXXXX學(xué)號(hào)6315070301XX姓名._gllh_日期—2018-5-12—實(shí)驗(yàn)五聚類算法實(shí)現(xiàn)一、實(shí)驗(yàn)?zāi)康?、了解常用聚類算法及其優(yōu)缺點(diǎn)2、掌握k-means聚類算法對(duì)數(shù)據(jù)進(jìn)行聚類分析的基本原理和劃分方法3、利用k-means聚類算法對(duì)已知數(shù)據(jù)集進(jìn)行聚類分析實(shí)驗(yàn)類型：驗(yàn)證性計(jì)劃課間：4學(xué)時(shí)二、實(shí)驗(yàn)內(nèi)容1、利用python的sklearn庫函數(shù)對(duì)給定的數(shù)據(jù)集進(jìn)行聚類分析2、分析k-means算法的實(shí)現(xiàn)流程3、根據(jù)算法描述編程實(shí)現(xiàn)，調(diào)試運(yùn)行4、對(duì)所給數(shù)據(jù)集進(jìn)行驗(yàn)證，得到分析結(jié)果三、實(shí)驗(yàn)步驟1、k-means算法原理2、k-means算法流程3、k-means算法實(shí)現(xiàn)4、對(duì)已知數(shù)據(jù)集進(jìn)行分析四、實(shí)驗(yàn)結(jié)果分析1-利用python的sklearn庫函數(shù)對(duì)給定的數(shù)據(jù)集進(jìn)行聚類分析:其中數(shù)據(jù)集選取iris鳶尾花數(shù)據(jù)集importnumpyasnpfromsklearn.datasetsimportload_irisiris=load_iris()defdist(x,y):returnsum(x*y)/(sum(x**2)*sum(y**2))**0.5defK_means(data=iris.data,k=3,ping=0,maxiter=100):n,m=data.shapecenters=data[:k,:]whileping<maxiter:dis=np.zeros([n,k+1])foriinrange(n):forjinrange(k):dis[i,j]=dist(data[i,:],centers[j,:])dis[i,k]=dis[i,:k].argmax()centers_new=np.zeros([k,m])foriinrange(k):index=dis[:,k]==icenters_new[i,:]=np.mean(data[index,:],axis=0)ifnp.all(centers==centers_new):breakcenters=centers_newping+=1returndisif__name__=='__main__':res=K_means()print(res)(1)、首先求出樣本之間的余弦相似度：sum(x*y)/(sum(x**2)*sum(y**2))**0.5⑵、設(shè)置k類別數(shù)為3,最大迭代次數(shù)為100K_means(data=iris.data,k=3,ping=0,maxiter=100):

3）、設(shè)置樣本個(gè)數(shù)、屬性個(gè)數(shù)并初始化類中心n,m=data.shapecenters=data[:k,:]（4）、求各樣本至各類中心的距離foriinrange(n):forjinrange(k):dis[i,j]=dist(data[i,:],centers[j,:])dis[i,k]=dis[i,:k].argmax()(5)、求新類中心:各類樣本均值作為新類中心foriinrange(k):index=dis[:,k]==icenters_new[i,:]=np.mean(data[index,:],axis=0)實(shí)驗(yàn)結(jié)果：聞tentl.py—Kiie—Vis4ialSludioCode劉電F}?fl(E.i話:橄5〕SZi.V)轉(zhuǎn)到⑹謂試!D〕任豆⑴脊皈申■*kMaans.p^也teocCLpyxBfemBbasketbalLtxt曽HLpyceniters_mew=np-zerosfrkj.m]}IF口廠iinrange(k):23#/渤類中心:各類樣本均值作為新類中心index=dis[:fk]==icentersnewfi^:]=np.msan(data'index:]faxis=0)'43輔出妣雄晞址攜&usef^svSta.csvIS[[0.99947519[fl.99641014[0-99935815[0.ggS?SS86&usef^svSta.csvIS[[0.99947519[fl.99641014[0-99935815[0.ggS?SS86[0-9995?368[0-99923541[0,99964661血旳g的屈[0-997S6069[0,^9681184[fi.OT9-11462[fi.99862711[0a99678141[B-99946535r^-MS39492[0-99954979[0.99951304[0.999476610.8991826^0..911550560-906951670.915S213B0..896331B50.909946290.903633640?9074870S0.915863^620.91224*450.900003IS0?912908610.909067670.889793410.87645910..887401420.888204570.901686530.0994935220B9994520.999622420.9997938920.99886211&0.99892885&0=9985108800.090862242S.9999199420.9995S48920.9995546720.9992669120.999493720.9986335500.996B4S3500.9963241&0.9974267800h999485122使用自己的數(shù)據(jù)集進(jìn)行聚類分析并進(jìn)行可視化#-*-coding:utf-8-*-importnumpyasnpimportpandasaspdfromsklearn.clusterimportBirchfromsklearn.clusterimportKMeansfrommatplotlibimportpyplotaspltfrommatplotlib.font_managerimportFontPropertiesdf=pd.read_csv("fa.csv",encoding="utf-8")X=df[['height','weight']].valuesclf=KMeans(n_clusters=2)y_pred=clf.fit_predict(X)print(clf)print(y_pred)x=[n[0]forninX]print(x)y=[n[1]forninX]print(y)plt.scatter(x,y,c=y_pred,marker='o')plt.title("K-meansBoy/Girl-height-weightData")plt.xlabel("Height/cm")精品文檔精品文檔o-io-ia3?>m@>「3ftJi.-HaSTBAun-BCfflfPytt?<plt.ylabel("Weight/kg")plt.legend(["Position"])plt.show()本數(shù)據(jù)集使用男性女性身高體重?cái)?shù)據(jù)集，k值為2,就是講數(shù)據(jù)聚合為2類，看身高體重分布結(jié)果。埠feicsv—展矣一VisualStudi-oCode立■件(F)痛曙問遠(yuǎn)掛⑸亙乏⑼垮I」(⑤c*kMeans.pyheightjweight|161^160.56169,5^1160J48161.57160.50167^53170J6012l/e,S3164.51160j47164J58166.571/4,56158,43158^0169,56mJL114?——Vhud站udnlcd.立中口c?=jsalt.査wji-Gi5>*r3ib&ti加h■*他rILpy弓爍e論1?弓爍e論1?賓elf=牌置“電^_匚1吃怔心=2號(hào)/_prfld=€If.fit_predirt(K)print(elf)primt(^pred〕13￡K-me日血Boy/Girl-helght-weight□日g*ftKkkxi120-x=l[fi|-B]for-ninX]priflt(xj100-y=M1]ninX]"int(y)100-W欄mHLV.OIWJliJQiJmGJWUJUOJi丄?[/沁2瀚20P&?9^&67774S.8W6561S1.pit?scatter-fKjpc-yjpred^narkeipit.t:it1b("K-?aii&Bay/Girl-heigh'pit.mIbMh!C"Hei￡>it/cm*}pitW欄mHLV.OIWJliJQiJmGJWUJUOJi丄?[/沁2瀚20P&?9^&67774S.8W6561S1.1551E41S517fl1751B4IBS19□-[iDone]ejdtedtritheodt-ain1-[iDone]ejdtedtritheodt-ain1-949secoii[RdFini<x￡]|python'^c:\Us?r5\Acl?ini5.trat-Dr\Desktop\!I^MlL.購購FWL=>可尬收集于網(wǎng)絡(luò)，如有侵權(quán)請(qǐng)聯(lián)系管理員刪除收集于網(wǎng)絡(luò)，如有侵權(quán)請(qǐng)聯(lián)系管理員刪除K-meftngBcyKjlrl-Height-weight。負(fù)V1■RsAionLM-155ME1?170LT5］155ME1?170LT5］加W1WHeightitm血圈塑圉亙I從此次實(shí)驗(yàn)看出，聚類數(shù)據(jù)集必須可以分類才能使用此算法否則就會(huì)產(chǎn)生如下結(jié)果此數(shù)據(jù)集為UCI香水?dāng)?shù)據(jù)，無法進(jìn)行聚類分析>d丘用C此數(shù)據(jù)集為UCI香水?dāng)?shù)據(jù)，無法進(jìn)行聚類分析>d丘用C1祀MrEUKr^iioM和盃停：M>T■細(xì)EEL棊朝閩MachineLoamingRepository弊區(qū)穎氏：iSffi■BaWJ>卿翻：ewMtt55S：SSM1E￥S：2莊■日星jflTJ斗了耳HE無M：■b?.E*NlA準(zhǔn)*W：B8WCFl#:?5?■血鴉附秤EM惶iI?Fifi?:Ot*rKARLIKES雷srt厲rtutr”土S?衍眄彷閔前11尢乎1-JEflZ3■frs.三#.壬亞巴如溥件］|購車艮訕“刃疑rjmm疑rjmm年莖坊凹*丈孑3哄旦広工舊7古昭酒h■”精品文檔精品文檔收集于網(wǎng)絡(luò)，如有侵權(quán)請(qǐng)聯(lián)系管理員刪除收集于網(wǎng)絡(luò)，如有侵權(quán)請(qǐng)聯(lián)系管理員刪除D?3信總：庖邑曲5是應(yīng)匚便用手押巫耳ttI：OI^-0FWT:Hm〕H莎沖不同申屁馮旳.衛(wèi)登首序的舍孚是■耶■曲.即flR..卿r陰?4G?d?舫射匚砂，buMW「tUrtfiT盯「關(guān)阿MUHKi.jj他-舊08那?ro&H.卸他血味,TfeaT^eOi.Z±s-t-.[口.HQ.<on9i!?1ed2.聊皿咤」電吩畔.3力-胞0妙刪.conBireciedi.：^Jfi.齊且弐55應(yīng)i：flrtV—應(yīng)）恫黃論丈；KARLIK氏應(yīng)BAST^KI伽胡「■￡舸UM耳乜口8?訓(xùn)科啲螳鳳般3已曲7與呂砧更W5EAS荘EsudgonFIwIjw配!iai5?t2,-g!1.m33陰花比胡年i月TEWEL2a"3LlK日北r.?冊(cè)峻嚴(yán)停利51系焼?廈-1胖習(xí)蕓曼量琳呼的4.別廿疔?櫛阿電￥貝，?敘■?右時(shí)rirE豈屏隹釣居轉(zhuǎn)耿s神淫網(wǎng)活n詢它尊mg世缶吉.i?[4）,ssi誨-2和頂e釦口年j-karlik圉柏和代轉(zhuǎn)注展補(bǔ)-^.在化字口丄聞？年卩口立整心陽！>國.網(wǎng)池掏丄al.r&叮城I.、&拙.丘于九工田遠(yuǎn)昭豁的左誌蘭劃乓宦池班.計(jì)H機(jī)魂孚:ag.篁一亀占.沿也li.ppe?i-ea?2（hs.五、實(shí)驗(yàn)心得通過這次實(shí)驗(yàn)，使用K-means算法對(duì)數(shù)據(jù)集進(jìn)行聚類分析，了解了算法的過程，首先選擇K個(gè)點(diǎn)作為初始質(zhì)心，之后進(jìn)行重復(fù)并將每個(gè)點(diǎn)指派到最近的質(zhì)心，形成K個(gè)簇后重新計(jì)算每個(gè)簇的質(zhì)心直到簇不發(fā)生變化或達(dá)到最大迭代次數(shù)為止，首先我們要直到有多少種分類，選擇K個(gè)初始質(zhì)心，其中K