Iam getting error for svm predection section please help me

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Savita Hanchinal
Savita Hanchinal 2021-7-8
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

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