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data_traintest3_multiclass_26classes_v2_revised_latin.m
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%============================================================ %
%This function generates multiclass SVM models in classifying %
%26 Latin characters %
%------------------------------------------------------------ %
%load each dataset for 26 Latin characters
% houldout $\in$ [0,1] = Randomly select and reserve p*100% of the data as
% validation data, and train the model using the rest of the data.
% s = number of runs
%k = misclassfication rate in ith iteration
%mean_k = mean of k
% label = predicted label at the last iteration
% y1 = true label at the last iteration
function [k, mean_k, label_cell, y1_cell,kFoldLoss,SVMModels,crossval_SVM]=data_traintest3_multiclass_26classes_v2_revised_latin(data_traintest1,data_traintest2,...
data_traintest3,data_traintest4,data_traintest5,data_traintest6,data_traintest7, data_traintest8,...
data_traintest9, data_traintest10, data_traintest11, data_traintest12, data_traintest13,...
data_traintest14, data_traintest15, data_traintest16, data_traintest17, data_traintest18,data_traintest19,...
data_traintest20, data_traintest21, data_traintest22, data_traintest23, data_traintest24,...
data_traintest25, data_traintest26, holdout, s)
rng('shuffle')
[row1, col1]=size(data_traintest1);
[row2, col2]=size(data_traintest2);
[row3, col3]=size(data_traintest3);
[row4, col4]=size(data_traintest4);
[row5, col5]=size(data_traintest5);
[row6, col6]=size(data_traintest6);
[row7, col7]=size(data_traintest7);
[row8, col8]=size(data_traintest8);
[row9, col9]=size(data_traintest9);
[row10,col10]=size(data_traintest10);
[row11,col11]=size(data_traintest11);
[row12,col12]=size(data_traintest12);
[row13, col13]=size(data_traintest13);
[row14, col14]=size(data_traintest14);
[row15, col15]=size(data_traintest15);
[row16, col16]=size(data_traintest16);
[row17, col17]=size(data_traintest17);
[row18, col18]=size(data_traintest18);
[row19, col19]=size(data_traintest19);
[row20, col20]=size(data_traintest20);
[row21, col21]=size(data_traintest21);
[row22, col22]=size(data_traintest22);
[row23, col23]=size(data_traintest23);
[row24, col24]=size(data_traintest24);
[row25, col25]=size(data_traintest25);
[row26, col26]=size(data_traintest26);
H01=round(row1*(1-holdout));
H02=round(row2*(1-holdout));
H03=round(row3*(1-holdout));
H04=round(row4*(1-holdout));
H05=round(row5*(1-holdout));
H06=round(row6*(1-holdout));
H07=round(row7*(1-holdout));
H08=round(row8*(1-holdout));
H09=round(row9*(1-holdout));
H10=round(row10*(1-holdout));
H11=round(row11*(1-holdout));
H12=round(row12*(1-holdout));
H13=round(row13*(1-holdout));
H14=round(row14*(1-holdout));
H15=round(row15*(1-holdout));
H16=round(row16*(1-holdout));
H17=round(row17*(1-holdout));
H18=round(row18*(1-holdout));
H19=round(row19*(1-holdout));
H20=round(row20*(1-holdout));
H21=round(row21*(1-holdout));
H22=round(row22*(1-holdout));
H23=round(row23*(1-holdout));
H24=round(row24*(1-holdout));
H25=round(row25*(1-holdout));
H26=round(row26*(1-holdout));
data1=zeros(H01,col1);
data2=zeros(H02,col2);
data3=zeros(H03,col3);
data4=zeros(H04,col4);
data5=zeros(H05,col5);
data6=zeros(H06,col6);
data7=zeros(H07,col7);
data8=zeros(H08,col8);
data9=zeros(H09,col9);
data10=zeros(H10,col10);
data11=zeros(H11,col11);
data12=zeros(H12,col12);
data13=zeros(H13,col13);
data14=zeros(H14,col14);
data15=zeros(H15,col15);
data16=zeros(H16,col16);
data17=zeros(H17,col17);
data18=zeros(H18,col18);
data19=zeros(H19,col19);
data20=zeros(H20,col20);
data21=zeros(H21,col21);
data22=zeros(H22,col22);
data23=zeros(H23,col23);
data24=zeros(H24,col24);
data25=zeros(H25,col25);
data26=zeros(H26,col26);
test1=zeros(row1-H01,col1);
test2=zeros(row2-H02,col2);
test3=zeros(row3-H03,col3);
test4=zeros(row4-H04,col4);
test5=zeros(row5-H05,col5);
test6=zeros(row6-H06,col6);
test7=zeros(row7-H07,col7);
test8=zeros(row8-H08,col8);
test9=zeros(row9-H09,col9);
test10=zeros(row10-H10,col10);
test11=zeros(row11-H11,col11);
test12=zeros(row12-H12,col12);
test13=zeros(row13-H13,col13);
test14=zeros(row14-H14,col14);
test15=zeros(row15-H15,col15);
test16=zeros(row16-H16,col16);
test17=zeros(row17-H17,col17);
test18=zeros(row18-H18,col18);
test19=zeros(row19-H19,col19);
test20=zeros(row20-H20,col20);
test21=zeros(row21-H21,col21);
test22=zeros(row22-H22,col22);
test23=zeros(row23-H23,col23);
test24=zeros(row24-H24,col24);
test25=zeros(row25-H25,col25);
test26=zeros(row26-H26,col26);
[Row1, Col1]=size(test1);
[Row2, Col2]=size(test2);
[Row3, Col3]=size(test3);
[Row4, Col4]=size(test4);
[Row5, Col5]=size(test5);
[Row6, Col6]=size(test6);
[Row7, Col7]=size(test7);
[Row8, Col8]=size(test8);
[Row9, Col9]=size(test9);
[Row10, Col10]=size(test10);
[Row11, Col11]=size(test11);
[Row12, Col12]=size(test12);
[Row13, Col13]=size(test13);
[Row14, Col14]=size(test14);
[Row15, Col15]=size(test15);
[Row16, Col16]=size(test16);
[Row17, Col17]=size(test17);
[Row18, Col18]=size(test18);
[Row19, Col19]=size(test19);
[Row20, Col20]=size(test20);
[Row21, Col21]=size(test21);
[Row22, Col22]=size(test2);
[Row23, Col23]=size(test23);
[Row24, Col24]=size(test24);
[Row25, Col25]=size(test25);
[Row26, Col26]=size(test26);
k=zeros(s,1);
kFoldLoss=zeros(s,1);
SVMModels=cell(s,1);
crossval_SVM=cell(s,1);
y1_cell=cell(s,1);
label_cell=cell(s,1);
for j=1:s
rand01=randperm(row1);
rand02=randperm(row2);
rand03=randperm(row3);
rand04=randperm(row4);
rand05=randperm(row5);
rand06=randperm(row6);
rand07=randperm(row7);
rand08=randperm(row8);
rand09=randperm(row9);
rand10=randperm(row10);
rand11=randperm(row11);
rand12=randperm(row12);
rand13=randperm(row13);
rand14=randperm(row14);
rand15=randperm(row15);
rand16=randperm(row16);
rand17=randperm(row17);
rand18=randperm(row18);
rand19=randperm(row19);
rand20=randperm(row20);
rand21=randperm(row21);
rand22=randperm(row22);
rand23=randperm(row23);
rand24=randperm(row24);
rand25=randperm(row25);
rand26=randperm(row26);
data1=data_traintest1(rand01(1:H01),:);
test1=data_traintest1(rand01(H01+1:row1),:);
data2=data_traintest2(rand02(1:H02),:);
test2=data_traintest2(rand02(H02+1:row2),:);
data3=data_traintest3(rand03(1:H03),:);
test3=data_traintest3(rand03(H03+1:row3),:);
data4=data_traintest4(rand04(1:H04),:);
test4=data_traintest4(rand04(H04+1:row4),:);
data5=data_traintest5(rand05(1:H05),:);
test5=data_traintest5(rand05(H05+1:row5),:);
data6=data_traintest6(rand06(1:H06),:);
test6=data_traintest6(rand06(H06+1:row6),:);
data7=data_traintest7(rand07(1:H07),:);
test7=data_traintest7(rand07(H07+1:row7),:);
data8=data_traintest8(rand08(1:H08),:);
test8=data_traintest8(rand08(H08+1:row8),:);
data9=data_traintest9(rand09(1:H09),:);
test9=data_traintest9(rand09(H09+1:row9),:);
data10=data_traintest10(rand10(1:H10),:);
test10=data_traintest10(rand10(H10+1:row10),:);
data11=data_traintest11(rand11(1:H11),:);
test11=data_traintest11(rand11(H11+1:row11),:);
data12=data_traintest12(rand12(1:H12),:);
test12=data_traintest12(rand12(H12+1:row12),:);
data13=data_traintest13(rand13(1:H13),:);
test13=data_traintest13(rand13(H13+1:row13),:);
data14=data_traintest14(rand14(1:H14),:);
test14=data_traintest14(rand14(H14+1:row14),:);
data15=data_traintest15(rand15(1:H15),:);
test15=data_traintest15(rand15(H15+1:row15),:);
data16=data_traintest16(rand16(1:H16),:);
test16=data_traintest16(rand16(H16+1:row16),:);
data17=data_traintest17(rand17(1:H17),:);
test17=data_traintest17(rand17(H17+1:row17),:);
data18=data_traintest18(rand18(1:H18),:);
test18=data_traintest18(rand09(H18+1:row18),:);
data19=data_traintest19(rand19(1:H19),:);
test19=data_traintest19(rand19(H19+1:row19),:);
data20=data_traintest20(rand20(1:H20),:);
test20=data_traintest20(rand20(H20+1:row20),:);
data21=data_traintest21(rand21(1:H21),:);
test21=data_traintest21(rand21(H21+1:row21),:);
data22=data_traintest22(rand22(1:H22),:);
test22=data_traintest22(rand22(H22+1:row22),:);
data23=data_traintest23(rand23(1:H23),:);
test23=data_traintest23(rand23(H23+1:row23),:);
data24=data_traintest24(rand24(1:H24),:);
test24=data_traintest24(rand24(H24+1:row24),:);
data25=data_traintest25(rand25(1:H25),:);
test25=data_traintest25(rand25(H25+1:row25),:);
data26=data_traintest26(rand26(1:H26),:);
test26=data_traintest26(rand26(H26+1:row26),:);
data0=cat(1,data1,data2,data3,data4,data5,data6,data7,data8,data9,data10,data11,data12,data13,data14,...
data15,data16,data17,data18,data19,data20,data21,data22,data23,data24,data25,data26);
data0=single(data0);
test0=cat(1,test1,test2,test3,test4,test5,test6,test7,test8,test9,test10,test11,test12,test13,test14,test15,test16,test17,...
test18, test19, test20, test21, test22, test23, test24, test25, test26);
test0=single(test0);
[row, col]=size(data0);
P=randperm(row);
DATA_Train=zeros(row,col);
DATA_Train=data0(P(:),:);
y=zeros(row,1);
for i=1:row
if P(i)<= H01
y(i)=1;
elseif P(i)>H01 && P(i)<=H01+H02
y(i)=2;
elseif P(i)>H01+H02 && P(i)<=H01+H02+H03
y(i)=3;
elseif P(i)>H01+H02+H03 && P(i)<=sum([H01 H02 H03 H04])
y(i)=4;
elseif P(i)>sum([H01 H02 H03 H04]) && P(i)<=sum([H01 H02 H03 H04 H05])
y(i)=5;
elseif P(i)>sum([H01 H02 H03 H04 H05]) && P(i)<=sum([H01 H02 H03 H04 H05 H06])
y(i)=6;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07])
y(i)=7;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08])
y(i)=8;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09])
y(i)=9;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10])
y(i)=10;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11])
y(i)=11;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12])
y(i)=12;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13])
y(i)=13;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14])
y(i)=14;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15])
y(i)=15;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16])
y(i)=16;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17])
y(i)=17;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18])
y(i)=18;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19])
y(i)=19;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20])
y(i)=20;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21])
y(i)=21;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22])
y(i)=22;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23])
y(i)=23;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24])
y(i)=24;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24]) && P(i)<=sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 H25])
y(i)=25;
elseif P(i)>sum([H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 H25])
y(i)=26;
end
end
tic;
t = templateSVM('Standardize',true,'KernelFunction','rbf','Kernelscale','auto','Boxconstraint',Inf);
cl2= fitcecoc(DATA_Train,y,'Learners',t,'FitPosterior',true');
SVMModels{j}=cl2;
%see-ell
cl=crossval(cl2,'Holdout',0.01);
toc;
Trained_cl=cl.Trained{1};
crossval_SVM{j}=Trained_cl;
kFoldLoss(j)=kfoldLoss(cl);
[row0, col0]=size(test0);
PP=randperm(row0);
Xtest=zeros(row0,col0);
Xtest=test0(PP(:),:);
y1=zeros(row0,1);
for ii=1:row0
if PP(ii)<= Row1
y1(ii)=1;
elseif PP(ii)>Row1 && PP(ii)<=Row1+Row2
y1(ii)=2;
elseif PP(ii)>Row1+Row2 && PP(ii)<=Row1+Row2+Row3
y1(ii)=3;
elseif PP(ii)>Row1+Row2+Row3 && PP(ii)<=Row1+Row2+Row3+Row4
y1(ii)=4;
elseif PP(ii)>Row1+Row2+Row3+Row4 && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5])
y1(ii)=5;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6])
y1(ii)=6;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7])
y1(ii)=7;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8])
y1(ii)=8;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9])
y1(ii)=9;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10])
y1(ii)=10;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11])
y1(ii)=11;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12])
y1(ii)=12;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13])
y1(ii)=13;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14])
y1(ii)=14;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15])
y1(ii)=15;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16])
y1(ii)=16;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17])
y1(ii)=17;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18])
y1(ii)=18;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19])
y1(ii)=19;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20])
y1(ii)=20;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20 Row21])
y1(ii)=21;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20 Row21]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20 Row21 Row22])
y1(ii)=22;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20 Row21 Row22]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20 Row21 Row22 Row23])
y1(ii)=23;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20 Row21 Row22 Row23]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20 Row21 Row22 Row23 Row24])
y1(ii)=24;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20 Row21 Row22 Row23 Row24]) && PP(ii)<=sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20 Row21 Row22 Row23 Row24 Row25])
y1(ii)=25;
elseif PP(ii)>sum([Row1 Row2 Row3 Row4 Row5 Row6 Row7 Row8 Row9 Row10 Row11 Row12 Row13 Row14 Row15 Row16 Row17 Row18 Row19 Row20 Row21 Row22 Row23 Row24 Row25])
y1(ii)=26;
end
end
y1_cell{j}=y1;
[label,~,~,Posterior]=predict(Trained_cl,Xtest);
label_cell{j}=label;
mis =label~=y1;
mis_rate=sum(mis)/length(y1);
k(j)=mis_rate;
end
mean_k=mean(k);
end