matlab神經(jīng)網(wǎng)絡(luò)30個(gè)案例分析源代碼demochapter

該案例作者申明 byliyang[faruto]@faruto'sStudio~ .cn/faruto 子函數(shù)functionfunctioncloseall;format載入測試數(shù)據(jù)wine,其中包含的數(shù)據(jù)為classnumber3,wine:178*13的矩陣,wine_labes:178*1的列向量loadchapter13_wine.mat;畫出測試數(shù)據(jù)的可視化圖holdforrun=1:178forrun=2:14holdon;str=['attrib',num2str(run-1)];fori=1:178%選定訓(xùn)練集和測試%將第一類的1-30,第二類的60-95,第三類的131-153做為訓(xùn)練train_wine=%相應(yīng)的訓(xùn)練集 train_wine_labels= testwine=%相應(yīng)的測試集 test_wine_labels=%數(shù)據(jù)預(yù)處理,將訓(xùn)練集和測試集歸一化到[0,1]區(qū)%mapminmax [train_wine,pstrain]=將映射函數(shù)的范圍參數(shù)分別置為0和1pstrain.ymin=0;pstrain.ymax=%對訓(xùn)練集進(jìn)行[0,1]歸一[train_wine,pstrain]=%mapminmax [test_wine,pstest]=將映射函數(shù)的范圍參數(shù)分別置為0和1pstest.ymin=0;pstest.ymax=%對測試集進(jìn)行[0,1]歸一[test_wine,pstest]=%對訓(xùn)練集和測試集進(jìn)行轉(zhuǎn)置,以符合libsvm工具箱的數(shù)據(jù)train_wine=train_wine';test_wine=test_wine';選擇最佳的SVM參數(shù)%%首先進(jìn)行粗略選擇:c&g的變化范圍是2^(-10),2^(-[bestacc,bestc,bestg]=SVMcgForClass(train_wine_labels,train_wine,-10,10,-%打印粗略選擇結(jié)str=sprintf('BestCrossValidationAccuracy=%g%%Bestc=%gBestg%%根據(jù)粗略選擇的結(jié)果圖再進(jìn)行精細(xì)選擇:c的變化范圍是2^(-2),2^(-1.5),...,2^(4),g的變化范圍是[bestacc,bestc,bestg]=SVMcgForClass(train_wine_labels,train_wine,-2,4,-%str=sprintf('BestCrossValidationAccuracy=%g%%Bestc=%gBestg打印粗略選擇結(jié)BestCrossValidationAccuracy=98.8764%Bestc=0.5Bestg=打印精細(xì)選擇結(jié)BestCrossValidationAccuracy=98.8764%Bestc=0.353553Bestg=利用最佳的參數(shù)進(jìn)行SVMcmdcmd=['-c',num2str(bestc),'-g',num2str(bestg)];model=SVM[predict_label,accuracy][predict_label,accuracy]=%打印測試total=rightsum(predict_labeltest_wine_labels);str=sprintf('Accuracy=%g%%(%d/%d)',accuracy(1),right,total);Accuracy=96.6292%(86/89)打印測試集分類Accuracy=96.6292%%%測試集的實(shí)際分類和預(yù)測分%通過圖可以看出只有三個(gè)測試樣本是holdon;%web web /forum-31-1.html-子函數(shù)functionfunction[bestacc,bestc,bestg]=%%輸入%%%%%%%%%cmin:懲罰參數(shù)c的變化范圍的最小值(取以2為底的對數(shù)后),即c_min=2^(cmin).默認(rèn)為cmax:懲罰參數(shù)c的變化范圍的最大值(取以2為底的對數(shù)后),即c_max=2^(cmax).默認(rèn)為gmin:參數(shù)g的變化范圍的最小值(取以2為底的對數(shù)后),即g_min=2^(gmin).默認(rèn)為--5gmax:參數(shù)g的變化范圍的最小值(取以2為底的對數(shù)后),即g_minv:crossvalidation的參數(shù),即給測試集分為幾部分進(jìn)行crosscstep:參數(shù)c步進(jìn)的大小.默認(rèn)為1gstep:參數(shù)g步進(jìn)的大小.默認(rèn)為=2^(gmax).默認(rèn)為3%accstep:最后顯示準(zhǔn)確率圖時(shí)的步進(jìn)大小.默認(rèn)為%輸出%bestacc:CrossValidation過程中的最高分類準(zhǔn)確%bestc:最佳的參數(shù)%bestg:最佳的參數(shù)%abouttheparametersofSVMcgForClassifnargin<10accstep=ifnargin<accstep=cstep=gstep=ifnargin<accstep=v=cstep=gstep=ifnargin<accstep=v=cstep=gstep=gmax=ifnargin<accstep=v=cstep=gstep=gmax=gmin=-ifnargin<accstep=v=cstep=gstep=gmax=gmin=-cmax=ifnargin<accstep=v=cstep=gstep=gmax=gmin=-cmax=cmin=-%X:cY:g[X,Y]=meshgrid(cmin:cstep:cmax,gmin:gstep:gmax);[m,n]=size(X);cg=%recordaccuracywithdifferentc&g,andfindthebestaccuracywiththesmallestcbestc=0;bestg=bestacc=basenum=2;fori=1:mforj=cmd=['-v',num2str(v),'-c',num2str(basenum^X(i,j)),'-g',num2str(basenum^Y(i,j))];cg(i,j)=svmtrain(train_label,train,ifcg(i,j)>bestaccbestacc=cg(i,j);bestc=basenum^X(i,j);bestg=if(cg(i,j)==bestacc&&bestc>basenum^X(i,j))bestacc=cg(i,j);bestc=base