Iam getting error for svm predection section please help me

clc

close all;

close all;

cd Database

DF=[]

for i=1:45

i

str1=int2str(i);

str2=strcat(str1,'.jpg');

nor=imread(str2);

%color

cmap=rgb2hsv(nor);

H=cmap(:,:,1);

S=cmap(:,:,2);

V=cmap(:,:,3);

Hmean=mean(mean(H));

Hst=std2(H);

Smean=mean(mean(S));

Sst=std2(S);

Vmean=mean(mean(V));

Vst=std2(V);

Hsk=sum(skewness(H));

Ssk=sum(skewness(S));

Vsk=sum(skewness(V));

Hmin=min(imhist(H));

Hmax=max(imhist(H));

Smin=min(imhist(S));

Smax=max(imhist(S));

Vmin=min(imhist(V));

Vmax=max(imhist(V));

%texture

I=rgb2gray(nor);

glcm=graycomatrix(I,'offset',[2 0;0 2]);

stats1=graycoprops(glcm,{'contrast','homogeneity'});

stats2=graycoprops(glcm,{'correlation','energy'});

t1=stats1.Contrast;

t2=stats1.Homogeneity;

t3=stats2.Correlation;

t4=stats2.Energy;

%Shape

FEAT=horzcat(1,[Hmean Hst Smean Sst Vmean Vst Hsk Vsk Ssk Hmin Hmax Smin Smax Vmin Vmax t1 t2 t3 t4]);

DF=[DF;FEAT];

end

cd ..

[fname,path]=uigetfile('.jpg','Provide currency for testing');

filename=strcat(path,fname);

nor=imread(filename);

%color

cmap=rgb2hsv(nor);

H=cmap(:,:,1);

S=cmap(:,:,2);

V=cmap(:,:,3);

Hmean=mean(mean(H));

Hst=std2(H);

Smean=mean(mean(S));

Sst=std2(S);

Vmean=mean(mean(V));

Vst=std2(V);

Hsk=sum(skewness(H));

Ssk=sum(skewness(S));

Vsk=sum(skewness(V));

Hmin=min(imhist(H));

Hmax=max(imhist(H));

Smin=min(imhist(S));

Smax=max(imhist(S));

Vmin=min(imhist(V));

Vmax=max(imhist(V));

%texture

I=rgb2gray(nor);

glcm=graycomatrix(I,'offset',[2 0;0 2]);

stats1=graycoprops(glcm,{'contrast','homogeneity'});

stats2=graycoprops(glcm,{'correlation','energy'});

t1=stats1.Contrast;

t2=stats1.Homogeneity;

t3=stats2.Correlation;

t4=stats2.Energy;

%Shape

QF=horzcat(1,[Hmean Hst Smean Sst Vmean Vst Hsk Vsk Ssk Hmin Hmax Smin Smax Vmin Vmax t1 t2 t3 t4]);

%Multi svm

Trainigset=[DF(1,:);DF(2,:);DF(3,:);DF(4,:);DF(5,:);DF(6,:);DF(7,:);DF(8,:);DF(9,:);DF(10,:);DF(11,:);DF(12,:);DF(13,:);DF(14,:);DF(15,:);DF(16,:);DF(17,:);DF(18,:);DF(19,:);DF(20,:);DF(21,:);DF(22,:);DF(23,:);DF(24,:);DF(25,:);DF(26,:);DF(27,:);DF(28,:);DF(29,:);DF(30,:);DF(31,:);DF(32,:);DF(33,:);DF(34,:);DF(35,:);DF(36,:);DF(37,:);DF(38,:);DF(39,:);DF(40,:);DF(41,:);DF(42,:);DF(43,:);DF(44,:);DF(45,:)];

GroupTrain={'1' '1' '1' '1' '1' '1' '1' '1' '1' '2' '2' '2' '2' '2' '2' '2' '2' '2' '3' '3' '3' '3' '3' '3' '3' '3' '3' '4' '4' '4' '4' '4' '4' '4' '4' '4' '5' '5' '5' '5' '5' '5' '5' '5' '5' };

TestSet=QF;

SVMModels=cell(5,1);

y=GroupTrain

classes=unique(y);

rng(1);

for j=1:numel(classes)

indx=strcmp(y',classes(j));

SVMMOdels{j}=fitcsvm(DF,indx,'ClassNames',[false true],'Standardize',true,'KernelFunction','rbf','BoxConstraint',1);

end

xGrid=QF;

for j=1:numel(classes)

[~,score]=predict(SVMModels{j},xGrid)

Scores(:,j)=score(:,2);

end

[~,maxScore]=max(score,[],2);

result=maxScore;

figure.imshow(nor)

title('input')

if result==1

msgbox('10')

elseif result==2

msgbox('20')

elseif result==3

msgbox('50')

elseif result==4

msgbox('100')

elseif result==5

msgbox('500')

end