can anyone plz plz help me to solve this error : iam using a matlab online version r2020

my error is:

Unrecognized function or variable 'svmtrain'.

Error in main>multisvm (line 991)

models(k) = svmtrain(TrainingSet,G1vAll);

Error in main>pushbutton14_Callback (line 866)

result = multisvm(data_feat,data_label,test_data);

Error in gui_mainfcn (line 95)

feval(varargin{:});

Error in main (line 18)

gui_mainfcn(gui_State, varargin{:});

Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)main('pushbutton14_Callback',hObject,eventdata,guidata(hObject))

Error while evaluating UIControl Callback.

my code is:

function varargout = main(varargin)


gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @main_OpeningFcn, ...
                   'gui_OutputFcn',  @main_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT


% --- Executes just before main is made visible.
function main_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% varargin   command line arguments to main (see VARARGIN)

% Choose default command line output for main


% Update handles structure
guidata(hObject, handles);

axes(handles.axes1); axis off
% UIWAIT makes main wait for user response (see UIRESUME)
% uiwait(handles.figure1);


% --- Outputs from this function are returned to the command line.
function varargout = main_OutputFcn(hObject, eventdata, handles) 
% varargout  cell array for returning output args (see VARARGOUT);
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure
%varargout{1} = handles.output;


% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
[filename, pathname, filterindex]=uigetfile( ...
    {'*.jpg','JPEG File (*.jpg)'; ...
     '*.*','Any Image file (*.*)'}, ...
     'Pick an image file');
var=strcat(pathname,filename);
       k=imread(var);
       handles.YY = k;
       set(handles.edit1,'String',var);
       
z=k;
guidata(hObject,handles);
%guidata(hObject,handles);
axes(handles.axes1);
   imshow(k);
%   set(handles.axes1);
   title('Input Image');
      
set(handles.pushbutton1,'enable','off');    
set(handles.pushbutton16,'enable','on');


% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
k=handles.YY;
j=rgb2gray(k);
handles.XX=j;
figure,subplot(2,2,2),imshow(j);title('gray Image');
set(handles.pushbutton1,'enable','off');    
set(handles.pushbutton2,'enable','off');
set(handles.pushbutton4,'enable','on');

% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton3 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

str=get(handles.edit1,'String');
I = imread(str);
h = ones(5,5)/25;
I2 = imfilter(I,h);
figure
imshow(I2)
title('Filtered Image')
set(handles.pushbutton3,'enable','off');
set(handles.pushbutton12,'enable','on');


% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton4 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
ei=25;
st=35;
%k=10
k=ei*st;
I=handles.YY;
%I = imread('1.jpg');
%h=filter matrx
h = ones(ei,st) / k;
I1 = imfilter(I,h,'symmetric');
IG=rgb2gray(I1);
%Converting to BW
I11 = imadjust(IG,stretchlim(IG),[]);
level = graythresh(I11);
BWJ = im2bw(I11,level);
dim = size(BWJ)
IN=ones(dim(1),dim(2));
BW=xor(BWJ,IN);  %inverting
figure,subplot(2,2,2), imshow(BW), title('Black and White');
set(handles.pushbutton1,'enable','off');    
set(handles.pushbutton2,'enable','off');
set(handles.pushbutton3,'enable','on');
set(handles.pushbutton4,'enable','off');


% --- Executes on button press in pushbutton5.
function pushbutton5_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton5 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)





% --- Executes on button press in pushbutton6.
function pushbutton6_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton6 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
%I=imread('cancer.bmp');
I=handles.YY;
[y,x,z]=size(I);
myI=double(I);                 
H=zeros(y,x);
S=H;
HS_I=H;
for i=1:x
    for j=1:y
        HS_I(j,i)=((myI(j,i,1)+myI(j,i,2)+myI(j,i,3))/3);
        S(j,i)=1-3*min(myI(j,i,:))/(myI(j,i,1)+myI(j,i,2)+myI(j,i,3));
        if ((myI(j,i,1)==myI(j,i,2))&(myI(j,i,2)==myI(j,i,3)))       
            Hdegree=0;
        else    
            Hdegree=acos(0.5*(2*myI(j,i,1)-myI(j,i,2)-myI(j,i,3))/((myI(j,i,1)-myI(j,i,2))^2+(myI(j,i,1)-myI(j,i,3))*(myI(j,i,2)-myI(j,i,3)))^0.5);
        end    
        if (myI(j,i,2)>=myI(j,i,3))
            H(j,i)=Hdegree;                                    
        else
            H(j,i)=(2*pi-Hdegree);                              
        end     
    end 
end



Hth1=0.9*2*pi; Hth2=0.1*2*pi; 
Nred=0;                       
for i=1:x
    for j=1:y
        if ((H(j,i)>=Hth1)||(H(j,i)<=Hth2))
            Nred=Nred+1;       
        end
    end
end

Ratio=Nred/(x*y);           

if (Ratio>=0.6)              
    Red=1
else
    Red=0
end    


HS_I=uint8(HS_I);                                                    

figure(1);
imshow(I);
figure(2);
imshow(HS_I);


% --- Executes on button press in pushbutton7.
function pushbutton7_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton7 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
createffnn


% --- Executes on button press in pushbutton8.
function pushbutton8_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton8 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% NET = trainnet(net,IMGDB);
str=get(handles.edit1,'String');
I1 = imread(str);
I = im2double(I1);
HSV = rgb2hsv(I);
H = HSV(:,:,1); H = H(:);
S = HSV(:,:,2); S = S(:);
V = HSV(:,:,3); V = V(:);
idx = kmeans([H S V], 4);
imshow(I1);
figure,imshow(ind2rgb(reshape(idx, size(I,1), size(I, 2)), [0 0 1; 0 0.8 0]))

% --- Executes on button press in pushbutton9.
function pushbutton9_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton9 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)


% --- Executes on button press in pushbutton10.
function pushbutton10_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton10 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)



function edit1_Callback(hObject, eventdata, handles)
% hObject    handle to edit1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of edit1 as text
%        str2double(get(hObject,'String')) returns contents of edit1 as a double


% --- Executes during object creation, after setting all properties.
function edit1_CreateFcn(hObject, eventdata, handles)
% hObject    handle to edit1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end

function [ Unow, center, now_obj_fcn ] = FCMforImage( img, clusterNum )


if nargin < 2
    clusterNum = 2;   % number of cluster
end

[row, col] = size(img);
expoNum = 2;      % fuzzification parameter
epsilon = 0.001;  % stopping condition
mat_iter = 100;   % number of maximun iteration


Upre = rand(row, col, clusterNum);
dep_sum = sum(Upre, 3);
dep_sum = repmat(dep_sum, [1,1, clusterNum]);
Upre = Upre./dep_sum;

center = zeros(clusterNum,1); 

for i=1:clusterNum
    center(i,1) = sum(sum(Upre(:,:,i).*img))/sum(sum(Upre(:,:,i)));
end

pre_obj_fcn = 0;
for i=1:clusterNum
    pre_obj_fcn = pre_obj_fcn + sum(sum((Upre(:,:,i) .*img - center(i)).^2));
end
%fprintf('Initial objective fcn = %f\n', pre_obj_fcn);

for iter = 1:mat_iter    

    Unow = zeros(size(Upre));
    for i=1:row
        for j=1:col

            for uII = 1:clusterNum
                tmp = 0;
                for uJJ = 1:clusterNum
                    disUp = abs(img(i,j) - center(uII));
                    disDn = abs(img(i,j) - center(uJJ));
                    tmp = tmp + (disUp/disDn).^(2/(expoNum-1));
                end
                Unow(i,j, uII) = 1/(tmp);
            end            
        end
    end   

    now_obj_fcn = 0;
    for i=1:clusterNum
        now_obj_fcn = now_obj_fcn + sum(sum((Unow(:,:,i) .*img - center(i)).^2));
    end
   % fprintf('Iter = %d, Objective = %f\n', iter, now_obj_fcn);

    if max(max(max(abs(Unow-Upre))))<epsilon || abs(now_obj_fcn - pre_obj_fcn)<epsilon 
        break;
    else
        Upre = Unow.^expoNum;
       
        for i=1:clusterNum
            center(i,1) = sum(sum(Upre(:,:,i).*img))/sum(sum(Upre(:,:,i)));
        end
        pre_obj_fcn = now_obj_fcn;
    end
end

% --- Executes on button press in pushbutton11.
function pushbutton11_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton11 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)


% --- Executes on button press in pushbutton12.
function pushbutton12_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton12 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
str=get(handles.edit1,'String');
I1 = imread(str);
str=rgb2gray(I1);
imwrite(str,'img.tif','tiff');
img = double(imread('img.tif'));
clusterNum = 2;
[ Unow, center, now_obj_fcn ] = FCMforImage( img, clusterNum );
figure;
subplot(2,2,1); imshow(img,[]);
for i=1:clusterNum
    subplot(2,2,i+1);
    imshow(Unow(:,:,i),[]);
    imwrite(Unow(:,:,i),'seg.jpg');
end
imshow('seg.jpg'); title('Segmented Image');

set(handles.pushbutton13,'enable','on');
set(handles.pushbutton12,'enable','off');

% --- Executes on button press in pushbutton13.
function pushbutton13_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton13 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
s=get(handles.edit1,'String');
I = imread(s);
I=rgb2gray(I);
glcms = graycomatrix(I);
getData()
% Derive Statistics from GLCM
stats = graycoprops(glcms,'Contrast Correlation Energy Homogeneity');
Contrast = stats.Contrast;
Correlation = stats.Correlation;
Energy = stats.Energy;
Homogeneity = stats.Homogeneity;
seg_img=imread('seg.jpg');
Mean = mean2(seg_img);
Standard_Deviation = std2(seg_img);
Entropy = entropy(seg_img);
RMS = mean2(rms(seg_img));
%Skewness = skewness(img)
Variance = mean2(var(double(seg_img)));
a = sum(double(seg_img(:)));
Smoothness = 1-(1/(1+a));
Kurtosis = kurtosis(double(seg_img(:)));
Skewness = skewness(double(seg_img(:)));
% Inverse Difference Movement
m = size(seg_img,1);
n = size(seg_img,2);
in_diff = 0;
for i = 1:m
    for j = 1:n
        temp = seg_img(i,j)./(1+(i-j).^2);
        in_diff = in_diff+temp;
    end
end
IDM = double(in_diff);
    
feat = [Contrast,Correlation,Energy,Homogeneity, Mean, Standard_Deviation, Entropy, RMS, Variance, Smoothness, Kurtosis, Skewness, IDM];
img = imread(s);
 gaborArray = gaborFilterBank(5,8,39,39);  % Generates the Gabor filter bank
 featureVector = gaborFeatures(img,gaborArray,4,4);
 disp('Gabor Feature');
 featureVector
 save('Gaborfeature.mat','featureVector');
% GLCM2 = graycomatrix(I,'Offset',[2 0;0 2]);
% features = GLCM_Features1(GLCM2,0)
set(handles.pushbutton14,'enable','on');
set(handles.pushbutton13,'enable','off');
msgbox('Feature Extracted Done');
clear all;
function [out] = GLCM_Features1(glcmin,pairs)
if ((nargin > 2) || (nargin == 0))
   error('Too many or too few input arguments. Enter GLCM and pairs.');
elseif ( (nargin == 2) ) 
    if ((size(glcmin,1) <= 1) || (size(glcmin,2) <= 1))
       error('The GLCM should be a 2-D or 3-D matrix.');
    elseif ( size(glcmin,1) ~= size(glcmin,2) )
        error('Each GLCM should be square with NumLevels rows and NumLevels cols');
    end    
elseif (nargin == 1) 
    pairs = 0; 
    if ((size(glcmin,1) <= 1) || (size(glcmin,2) <= 1))
       error('The GLCM should be a 2-D or 3-D matrix.');
    elseif ( size(glcmin,1) ~= size(glcmin,2) )
       error('Each GLCM should be square with NumLevels rows and NumLevels cols');
    end    
end


format long e
if (pairs == 1)
    newn = 1;
    for nglcm = 1:2:size(glcmin,3)
        glcm(:,:,newn)  = glcmin(:,:,nglcm) + glcmin(:,:,nglcm+1);
        newn = newn + 1;
    end
elseif (pairs == 0)
    glcm = glcmin;
end

size_glcm_1 = size(glcm,1);
size_glcm_2 = size(glcm,2);
size_glcm_3 = size(glcm,3);


out.autoc = zeros(1,size_glcm_3); 
out.contr = zeros(1,size_glcm_3); 
out.corrm = zeros(1,size_glcm_3);
out.corrp = zeros(1,size_glcm_3); 
out.cprom = zeros(1,size_glcm_3); 
out.cshad = zeros(1,size_glcm_3); 
out.dissi = zeros(1,size_glcm_3);
out.energ = zeros(1,size_glcm_3); 
out.entro = zeros(1,size_glcm_3); 
out.homom = zeros(1,size_glcm_3); 
out.homop = zeros(1,size_glcm_3); 
out.maxpr = zeros(1,size_glcm_3);

out.sosvh = zeros(1,size_glcm_3); 
out.savgh = zeros(1,size_glcm_3);
out.svarh = zeros(1,size_glcm_3); 
out.senth = zeros(1,size_glcm_3); 
out.dvarh = zeros(1,size_glcm_3);

out.denth = zeros(1,size_glcm_3);
out.inf1h = zeros(1,size_glcm_3); 
out.inf2h = zeros(1,size_glcm_3); 

out.indnc = zeros(1,size_glcm_3);
out.idmnc = zeros(1,size_glcm_3); 



glcm_sum  = zeros(size_glcm_3,1);
glcm_mean = zeros(size_glcm_3,1);
glcm_var  = zeros(size_glcm_3,1);


u_x = zeros(size_glcm_3,1);
u_y = zeros(size_glcm_3,1);
s_x = zeros(size_glcm_3,1);
s_y = zeros(size_glcm_3,1);




p_x = zeros(size_glcm_1,size_glcm_3); 
p_y = zeros(size_glcm_2,size_glcm_3); 
p_xplusy = zeros((size_glcm_1*2 - 1),size_glcm_3); 
p_xminusy = zeros((size_glcm_1),size_glcm_3);

hxy  = zeros(size_glcm_3,1);
hxy1 = zeros(size_glcm_3,1);
hx   = zeros(size_glcm_3,1);
hy   = zeros(size_glcm_3,1);
hxy2 = zeros(size_glcm_3,1);



for k = 1:size_glcm_3 

    glcm_sum(k) = sum(sum(glcm(:,:,k)));
    glcm(:,:,k) = glcm(:,:,k)./glcm_sum(k); 
    glcm_mean(k) = mean2(glcm(:,:,k)); 
    glcm_var(k)  = (std2(glcm(:,:,k)))^2;
    
    for i = 1:size_glcm_1

        for j = 1:size_glcm_2

            out.contr(k) = out.contr(k) + (abs(i - j))^2.*glcm(i,j,k);
            out.dissi(k) = out.dissi(k) + (abs(i - j)*glcm(i,j,k));
            out.energ(k) = out.energ(k) + (glcm(i,j,k).^2);
            out.entro(k) = out.entro(k) - (glcm(i,j,k)*log(glcm(i,j,k) + eps));
            out.homom(k) = out.homom(k) + (glcm(i,j,k)/( 1 + abs(i-j) ));
            out.homop(k) = out.homop(k) + (glcm(i,j,k)/( 1 + (i - j)^2));
           
            out.sosvh(k) = out.sosvh(k) + glcm(i,j,k)*((i - glcm_mean(k))^2);
            
            
            out.indnc(k) = out.indnc(k) + (glcm(i,j,k)/( 1 + (abs(i-j)/size_glcm_1) ));
            out.idmnc(k) = out.idmnc(k) + (glcm(i,j,k)/( 1 + ((i - j)/size_glcm_1)^2));
            u_x(k)          = u_x(k) + (i)*glcm(i,j,k); 
            u_y(k)          = u_y(k) + (j)*glcm(i,j,k); 

        end
        
    end
    out.maxpr(k) = max(max(glcm(:,:,k)));
end


for k = 1:size_glcm_3
    
    for i = 1:size_glcm_1
        
        for j = 1:size_glcm_2
            p_x(i,k) = p_x(i,k) + glcm(i,j,k); 
            p_y(i,k) = p_y(i,k) + glcm(j,i,k); % taking i for j and j for i
            if (ismember((i + j),[2:2*size_glcm_1])) 
                p_xplusy((i+j)-1,k) = p_xplusy((i+j)-1,k) + glcm(i,j,k);
            end
            if (ismember(abs(i-j),[0:(size_glcm_1-1)])) 
                p_xminusy((abs(i-j))+1,k) = p_xminusy((abs(i-j))+1,k) +...
                    glcm(i,j,k);
            end
        end
    end
    

    
end


for k = 1:(size_glcm_3)
    
    for i = 1:(2*(size_glcm_1)-1)
        out.savgh(k) = out.savgh(k) + (i+1)*p_xplusy(i,k);

        out.senth(k) = out.senth(k) - (p_xplusy(i,k)*log(p_xplusy(i,k) + eps));
    end

end

for k = 1:(size_glcm_3)
    
    for i = 1:(2*(size_glcm_1)-1)
        out.svarh(k) = out.svarh(k) + (((i+1) - out.senth(k))^2)*p_xplusy(i,k);

    end

end

for k = 1:size_glcm_3

    for i = 0:(size_glcm_1-1)
        out.denth(k) = out.denth(k) - (p_xminusy(i+1,k)*log(p_xminusy(i+1,k) + eps));
        out.dvarh(k) = out.dvarh(k) + (i^2)*p_xminusy(i+1,k);
    end
end


for k = 1:size_glcm_3
    hxy(k) = out.entro(k);
    for i = 1:size_glcm_1
        
        for j = 1:size_glcm_2
            hxy1(k) = hxy1(k) - (glcm(i,j,k)*log(p_x(i,k)*p_y(j,k) + eps));
            hxy2(k) = hxy2(k) - (p_x(i,k)*p_y(j,k)*log(p_x(i,k)*p_y(j,k) + eps));

        end
        hx(k) = hx(k) - (p_x(i,k)*log(p_x(i,k) + eps));
        hy(k) = hy(k) - (p_y(i,k)*log(p_y(i,k) + eps));
    end
    out.inf1h(k) = ( hxy(k) - hxy1(k) ) / ( max([hx(k),hy(k)]) );
    out.inf2h(k) = ( 1 - exp( -2*( hxy2(k) - hxy(k) ) ) )^0.5;

end

corm = zeros(size_glcm_3,1);
corp = zeros(size_glcm_3,1);

for k = 1:size_glcm_3
    for i = 1:size_glcm_1
        for j = 1:size_glcm_2
            s_x(k)  = s_x(k)  + (((i) - u_x(k))^2)*glcm(i,j,k);
            s_y(k)  = s_y(k)  + (((j) - u_y(k))^2)*glcm(i,j,k);
            corp(k) = corp(k) + ((i)*(j)*glcm(i,j,k));
            corm(k) = corm(k) + (((i) - u_x(k))*((j) - u_y(k))*glcm(i,j,k));
            out.cprom(k) = out.cprom(k) + (((i + j - u_x(k) - u_y(k))^4)*...
                glcm(i,j,k));
            out.cshad(k) = out.cshad(k) + (((i + j - u_x(k) - u_y(k))^3)*...
                glcm(i,j,k));
        end
    end
    
    s_x(k) = s_x(k) ^ 0.5;
    s_y(k) = s_y(k) ^ 0.5;
    out.autoc(k) = corp(k);
    out.corrp(k) = (corp(k) - u_x(k)*u_y(k))/(s_x(k)*s_y(k));
    out.corrm(k) = corm(k) / (s_x(k)*s_y(k));

end
function gaborArray = gaborFilterBank(u,v,m,n)





if (nargin ~= 4)    % Check correct number of arguments
    error('There must be four input arguments (Number of scales and orientations and the 2-D size of the filter)!')
end


%% Create Gabor filters
% Create u*v gabor filters each being an m by n matrix

gaborArray = cell(u,v);
fmax = 0.25;
gama = sqrt(2);
eta = sqrt(2);

for i = 1:u
    
    fu = fmax/((sqrt(2))^(i-1));
    alpha = fu/gama;
    beta = fu/eta;
    
    for j = 1:v
        tetav = ((j-1)/v)*pi;
        gFilter = zeros(m,n);
        
        for x = 1:m
            for y = 1:n
                xprime = (x-((m+1)/2))*cos(tetav)+(y-((n+1)/2))*sin(tetav);
                yprime = -(x-((m+1)/2))*sin(tetav)+(y-((n+1)/2))*cos(tetav);
                gFilter(x,y) = (fu^2/(pi*gama*eta))*exp(-((alpha^2)*(xprime^2)+(beta^2)*(yprime^2)))*exp(1i*2*pi*fu*xprime);
            end
        end
        gaborArray{i,j} = gFilter;
        
    end
end


%% Show Gabor filters (Please comment this section if not needed!)

% Show magnitudes of Gabor filters:
figure('NumberTitle','Off','Name','Magnitudes of Gabor filters');
for i = 1:u
    for j = 1:v        
        subplot(u,v,(i-1)*v+j);        
        imshow(abs(gaborArray{i,j}),[]);
    end
end

% Show real parts of Gabor filters:
figure('NumberTitle','Off','Name','Real parts of Gabor filters');
for i = 1:u
    for j = 1:v        
        subplot(u,v,(i-1)*v+j);        
        imshow(real(gaborArray{i,j}),[]);
    end
end

function featureVector = gaborFeatures(img,gaborArray,d1,d2)



if (nargin ~= 4)        
    error('Please use the correct number of input arguments!')
end

if size(img,3) == 3     
    warning('The input RGB image is converted to grayscale!')
    img = rgb2gray(img);
end

img = double(img);



[u,v] = size(gaborArray);
gaborResult = cell(u,v);
for i = 1:u
    for j = 1:v
        gaborResult{i,j} = imfilter(img, gaborArray{i,j});
    end
end



featureVector = [];
for i = 1:u
    for j = 1:v
        
        gaborAbs = abs(gaborResult{i,j});
        gaborAbs = downsample(gaborAbs,d1);
        gaborAbs = downsample(gaborAbs.',d2);
        gaborAbs = gaborAbs(:);
        
        
        gaborAbs = (gaborAbs-mean(gaborAbs))/std(gaborAbs,1);
        
        featureVector =  [featureVector; gaborAbs];
        
    end
end

function getData()
%adds an extraction angle per pixel
offsets = [0 1; -1 1;-1 0;-1 -1;2 2];
jpgImagesDir = fullfile('Dataset/Train', '*.jpg');
total = numel( dir(jpgImagesDir) );
jpg_files = dir(jpgImagesDir);
jpg_counter = 0;
%total=length(filename);
gambar={total};
data_feat={total};
stats={total};
data_label={total};
label=1; 
limit=5; 

j=1;
for i=1:total
    
    %msgbox(jpg_files(j).name)
    s=strcat(num2str(i),'.jpg');
    file=fullfile('Dataset/Train',s);
    %file=fullfile(pathname,filename{i});
    gambar{i}=imread(file);
    gambar{i}=imresize(gambar{i},[600 600]);
    gambar{i}=rgb2gray(gambar{i});
%     gambar{i}=imadjust(gambar{i},stretchlim(gambar{i} ));
%     gambar{i}=imsharpen(gambar{i},'Radius',1,'Amount',0.5);
    glcm=graycomatrix(gambar{i}, 'Offset', offsets, 'Symmetric', true);
    stats{i}=graycoprops(glcm);
    


    iglcm=1;
    for x=1:5
      data_feat{i,x}=stats{i}.Contrast(iglcm);
      iglcm=iglcm+1;
    end
    iglcm=1;
    for x=6:10
        data_feat{i,x}=stats{i}.Correlation(iglcm);
        iglcm=iglcm+1;
    end
    iglcm=1;
    for x=12:16
        data_feat{i,x}=stats{i}.Energy(iglcm);
        iglcm=iglcm+1;
    end
        iglcm=1;
    for x=18:22
        data_feat{i,x}=stats{i}.Homogeneity(iglcm);
        iglcm=iglcm+1;
    end
        data_feat{i,24}=mean2(gambar{i});
        data_feat{i,25}=std2(gambar{i});
        data_feat{i,26}=entropy(gambar{i});
        data_feat{i,27}= mean2(var(double(gambar{i}))); %average image variance
        data_feat{i,28}=kurtosis(double(gambar{i}(:)));
        data_feat{i,29}=skewness(double(gambar{i}(:)));
        
        %labeling
        if i>limit
            label=label+1;
            data_label{i}=label;
            limit=limit+5;
        else
            data_label{1,i}=label;
        end         
end
% data is converted to the appropriate data type so that svm is not confused
data_feat=cell2mat(data_feat);
disp(data_feat);
data_label=cell2mat(data_label);
save('data_1.mat','data_feat','data_label');
% --- Executes on button press in pushbutton14.
function pushbutton14_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton14 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
s=get(handles.edit1,'String');
test=imread(s);

test=imresize(test,[600 600]);
test=rgb2gray(test);
offsets = [0 1; -1 1;-1 0;-1 -1;2 2];

  glcm=graycomatrix(test, 'Offset', offsets, 'Symmetric', true);

stats=graycoprops(glcm);
data_glcm=struct2array(stats);
iglcm=1;
glcm_contrast={5};
glcm_correlation={5};
glcm_energy={5};
glcm_homogeneity={5};
    for x=1:5
      glcm_contrast{x}=data_glcm(iglcm);
      iglcm=iglcm+1;
    end
    for x=1:5
        glcm_correlation{x}=data_glcm(iglcm);
        iglcm=iglcm+1;
    end
    for x=1:5
        glcm_energy{x}=data_glcm(iglcm);
        iglcm=iglcm+1;
    end
    for x=1:5
        glcm_homogeneity{x}=data_glcm(iglcm);
        iglcm=iglcm+1;
    end
rata2=mean2(test);
std_deviation=std2(test);
glcm_entropy=entropy(test);
rata2_variance= mean2(var(double(test)));
glcm_kurtosis=kurtosis(double(test(:)));
glcm_skewness=skewness(double(test(:)));
buat_train=[glcm_contrast(1:5),glcm_correlation(1:5),glcm_energy(1:5),glcm_homogeneity(1:5),rata2,std_deviation,glcm_entropy,rata2_variance,glcm_kurtosis,glcm_skewness];

test_data=cell2mat(buat_train);

disp('GLCM Feature')
disp('Contrast(1) Correlation(2) Energy(3)  Homogeneity(4) Mean(5)  Standard_Deviation(6) Entropy(7) RMS(8) Variance(9) smoothness(10) Kurtosis(11) Skewness(12) IDM(13)')

input_Feature=test_data 

load('data_1.mat');
%load('Test_data.mat');
%disp('Gabor Feature');
load('Gaborfeature.mat');
%Input_Feat=test_data

result = multisvm(data_feat,data_label,test_data);

%result1 = multisvm(data_feat,data_label,data_feat1);
%[Cmat,Accuracy]= confusion_matrix(result1,data_label,{'Desert','Forest','Mountain','Residential','River'});
%[c_matrixp,Result]= confusion.getMatrix(data_label,result1);
%C = confusionmat(data_label,result1)
%disp(result);
 
if result == 1
    A1 = 'actinic keratosis';
    set(handles.edit2,'string',A1);
    helpdlg('actinic keratosis');
    disp('actinic keratosis');
elseif result == 2
    A2 = 'Basel cell carcinoma';
    set(handles.edit2,'string',A2);
    helpdlg('Basel cell carcinoma');
    disp('Basel cell carcinoma');
elseif result == 3
    A3 = 'cherry nevus';
    set(handles.edit2,'string',A3);
    helpdlg('cherry nevus');
    disp('cherry nevus');
elseif result == 4
    A4 = 'dermatofibroma';
    set(handles.edit2,'string',A4);
    helpdlg('dermatofibroma');
    disp('dermatofibroma');
elseif result == 5
    A5 = 'Melanocytic nevus';
    set(handles.edit2,'string',A5);
    helpdlg('Melanocytic nevus');
    disp('Melanocytic nevus');
    elseif result == 6
    A5 = 'Melanoma';
    set(handles.edit2,'string',A5);
    helpdlg('Melanoma');
    disp('Melanoma');
end

% function [itrfin] = multisvm( T,C,test )
% 
% 
% itrind=size(test,1);
% itrfin=[];
% Cb=C;
% Tb=T;
% itr=1;
% 
% for tempind=1:itrind
%     tst=test(tempind,:);
%     C=Cb;
%     T=Tb;
%     u=unique(C);
%     N=length(u);
%     c4=[];
%     c3=[];
%     j=1;
%     k=1;
%     if(N>2)
%         itr=1;
%         classes=0;
%         cond=max(C)-min(C);
%         while((classes~=1)&&(itr<=length(u))&& size(C,2)>1 && cond>0)
%         %This while loop is the multiclass SVM Trick
%             c1=(C==u(itr));
%             newClass=c1;
%             svmStruct = svmtrain(T,newClass,'kernel_function','rbf');   % I am using rbf kernel function, you must change it also
%             classes = svmclassify(svmStruct,tst);
%         
%             % This is the loop for Reduction of Training Set
%             for i=1:size(newClass,2)
%                 if newClass(1,i)==0;
%                     c3(k,:)=T(i,:);
%                     k=k+1;
%                 end
%             end
%         T=c3;
%         c3=[];
%         k=1;
%         
%             % This is the loop for reduction of group
%             for i=1:size(newClass,2)
%                 if newClass(1,i)==0;
%                     c4(1,j)=C(1,i);
%                     j=j+1;
%                 end
%             end
%         C=c4;
%         c4=[];
%         j=1;
%         
%         cond=max(C)-min(C); % Condition for avoiding group 
%                             %to contain similar type of values 
%                             %and the reduce them to process
%         
%             % This condition can select the particular value of iteration
%             % base on classes
%             if classes~=1
%                 itr=itr+1;
%             end    
%         end
%     end
% 
% valt=Cb==u(itr);		% This logic is used to allow classification
% val=Cb(valt==1);		% of multiple rows testing matrix
% val=unique(val);
% itrfin(tempind,:)=val;  
% end



% Give more suggestions for improving the program.
function [result] = multisvm(TrainingSet,GroupTrain,TestSet)


u=unique(GroupTrain);
numClasses=length(u);
result = zeros(length(TestSet(:,1)),1);

%build models
for k=1:numClasses
    %Vectorized statement that binarizes Group
    %where 1 is the current class and 0 is all other classes
    G1vAll=(GroupTrain==u(k));
    models(k) = svmtrain(TrainingSet,G1vAll);
end

%classify test cases
for j=1:size(TestSet,1)
    for k=1:numClasses
     
        if(svmclassify(models(k),TestSet(j,:))) 
            break;
        end
    end
    result(j) = k;
    %[Cmat,Accuracy]= confusion_matrix(TestSet(j,:),models(k),{'A','B','C','D','E','F'});
    %disp('Result')
    %disp(result(j));
    
end
function exCode1()
folder = 'Dataset'; 
dirImage = dir( folder ); 

numData = size(dirImage,1); 

M ={} ; 




for i=1:numData
    nama = dirImage(i).name;  
    if regexp(nama, '(D|F)-[0-9]{1,2}.jpg')
   
       B = cell(1,2); 
        if regexp(nama, 'D-[0-9]{1,2}.jpg')
      
            B{1,1} = double(imread([folder, '/', nama]));
            B{1,2} = 1; 
        elseif regexp(nama, 'F-[0-9]{1,2}.jpg')
         
            B{1,1} = double(imread([folder, '/', nama]));
            B{1,2} = -1; 
      end
        M = cat(1,M,B); 
    end
end


numDataTrain = size(M,1); 
class = zeros(numDataTrain,1);
arrayImage = zeros(numDataTrain, 300 * 300);

for i=1:numDataTrain
    im = M{i,1} ;
    im = rgb2gray(im); 
    im = imresize(im, [300 300]); 
    im = reshape(im', 1, 300*300); 
    arrayImage(i,:) = im; 
    class(i) = M{i,2}; 
end

SVMStruct = svmtrain(arrayImage, class);



imTest = double(imread(s)); 
imTest = rgb2gray(imTest); 
imTest = imresize(imTest, [300 300]); 
imTest = reshape(imTest',1, 300*300); 
result = svmclassify(SVMStruct, imTest);
if(result==1)
    msgbox('Desert');
else
    msgbox('Forest');
end
function exCode2()
    folder = 'Dataset'; 
dirImage = dir( folder ); 

numData = size(dirImage,1); 

M ={} ; 




for i=1:numData
    nama = dirImage(i).name;  
    if regexp(nama, '(M|R)-[0-9]{1,2}.jpg')
   
       B = cell(1,2); 
        if regexp(nama, 'M-[0-9]{1,2}.jpg')
      
            B{1,1} = double(imread([folder, '/', nama]));
            B{1,2} = 1; 
        elseif regexp(nama, 'R-[0-9]{1,2}.jpg')
         
            B{1,1} = double(imread([folder, '/', nama]));
            B{1,2} = -1; 
      end
        M = cat(1,M,B); 
    end
end


numDataTrain = size(M,1); 
class = zeros(numDataTrain,1);
arrayImage = zeros(numDataTrain, 300 * 300);

for i=1:numDataTrain
    im = M{i,1} ;
    im = rgb2gray(im); 
    im = imresize(im, [300 300]); 
    im = reshape(im', 1, 300*300); 
    arrayImage(i,:) = im; 
    class(i) = M{i,2}; 
end

SVMStruct = svmtrain(arrayImage, class);



imTest = double(imread(s)); 
imTest = rgb2gray(imTest); 
imTest = imresize(imTest, [300 300]); 
imTest = reshape(imTest',1, 300*300); 
result = svmclassify(SVMStruct, imTest);
if(result==1)
    msgbox('Mountain');
else
    msgbox('Residential');
end
function exCode3()
    folder = 'Dataset'; 
dirImage = dir( folder ); 

numData = size(dirImage,1); 

M ={} ; 




for i=1:numData
    nama = dirImage(i).name;  
    if regexp(nama, '(RI|D)-[0-9]{1,2}.jpg')
   
       B = cell(1,2); 
        if regexp(nama, 'RI-[0-9]{1,2}.jpg')
      
            B{1,1} = double(imread([folder, '/', nama]));
            B{1,2} = 1; 
        elseif regexp(nama, 'D-[0-9]{1,2}.jpg')
         
            B{1,1} = double(imread([folder, '/', nama]));
            B{1,2} = -1; 
      end
        M = cat(1,M,B); 
    end
end


numDataTrain = size(M,1); 
class = zeros(numDataTrain,1);
arrayImage = zeros(numDataTrain, 300 * 300);

for i=1:numDataTrain
    im = M{i,1} ;
    im = rgb2gray(im); 
    im = imresize(im, [300 300]); 
    im = reshape(im', 1, 300*300); 
    arrayImage(i,:) = im; 
    class(i) = M{i,2}; 
end

SVMStruct = svmtrain(arrayImage, class);



imTest = double(imread(s)); 
imTest = rgb2gray(imTest); 
imTest = imresize(imTest, [300 300]); 
imTest = reshape(imTest',1, 300*300); 
result = svmclassify(SVMStruct, imTest);
if(result==1)
    msgbox('River');
else
    msgbox('Desert');
end
function edit2_Callback(hObject, eventdata, handles)
% hObject    handle to edit2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of edit2 as text
%        str2double(get(hObject,'String')) returns contents of edit2 as a double


% --- Executes during object creation, after setting all properties.
function edit2_CreateFcn(hObject, eventdata, handles)
% hObject    handle to edit2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end


% --- Executes on button press in pushbutton15.
function pushbutton15_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton15 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)


% --- Executes on button press in pushbutton16.
function pushbutton16_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton16 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
str=get(handles.edit1,'String');
I1 = imread(str);
I4 = imadjust(I1,stretchlim(I1));
I5 = imresize(I4,[300,400]);
figure
imshow(I5);title(' Processed Image ');
set(handles.pushbutton1,'enable','off');    
set(handles.pushbutton16,'enable','off');
set(handles.pushbutton12,'enable','on');


% --- Executes on button press in pushbutton17.
function pushbutton17_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton17 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

Answer 1

[Cris LaPierre]

That function was removed from MATLAB in R2019a. MATLAB Online is always the most recent release.

Try using fitcsvm() instead. For other alternatives, see the warning banner at the top of the R2018b documentation page for svmtrain.

[SHALINI SATHYA]

how do i change code using fitcsvm() ?

plz help

[Cris LaPierre]

I'm not familiar with either function. Try just replacing svmtrain with fitcsvm and see what happens. Use the documentation examples to help you out.