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fitness_func_SVM.m
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function [avg,class_result] = fitness_func_SVM(c) %total no. of images
%training_group = ones(1,19);
% Training feature matrix of class 1
for g=1:10
str = 'AU_T_0';
file=strcat(str,num2str(g),'.bmp');
y = imread(file);
t=[];
% feature=WLD(y);
% [n,m]=size(feature);
% feature=reshape(feature,1,n*m);
% t= [t,feature];
% ed=Edge_detection(y);
% [n,m]=size(ed);
% ed=reshape(ed,1,n*m);
% t= [t,ed];
%
% spe=spatial_predict(y);
% [n,m]=size(spe);
% spe=reshape(spe,1,n*m);
% t= [t,spe];
ene = ILBP81ri(y);
t= [ t, ene];
ent = entropy(y);
t= [ t, ent];
con = Contrast_func(y);
t= [ t, con];
cs=hog_feature_vector(y);
t= [ t, cs];
idm = IDM_func(y);
t= [ t, idm];
in = inertia(y);
t= [ t,in ];
cp = clusterprominence(y);
t= [ t, cp];
asm = angularsecondmoment(y);
t= [ t, asm];
eval(['tr', num2str((g)),'= t;']);
end
for g=01:10
str = 'SP_T_0';
file=strcat(str,int2str(g),'.bmp');
y = imread(file);
%imshow(a,map) % tmp is an indexed image file using
% color map "map"
%y=double(rgb2gray(x));
q=[];
% feature=WLD(y);
% [n,m]=size(feature);
% feature=reshape(feature,1,n*m);
% t= [t,feature];
% ed=Edge_detection(y);
% [n,m]=size(ed);
% ed=reshape(ed,1,n*m);
% t= [t,ed];
%
% spe=spatial_predict(y);
% [n,m]=size(spe);
% spe=reshape(spe,1,n*m);
% t= [t,spe];
ene = ILBP81ri(y);
q= [ q, ene];
ent = entropy(y);
q= [ q, ent];
con = Contrast_func(y);
q= [ q, con];
cs=hog_feature_vector(y);
q= [ q, cs];
idm = IDM_func(y);
q= [ q, idm];
in = inertia(y);
q= [ q,in ];
cp = clusterprominence(y);
q= [ q, cp];
asm = angularsecondmoment(y);
q= [ q, asm];
eval(['tr', num2str((10+g)),'= q;']);
end
warning('off', 'Images:initSize:adjustingMag');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for g=01:10
str = 'AU_TT_O_0';
file=strcat(str,num2str(g),'.bmp');
y = imread(file);
%imshow(x,map)
% tmp is an indexed image file using
% color map "map"
%y=double(rgb2gray(x));
t=[];
% feature=WLD(y);
% [n,m]=size(feature);
% feature=reshape(feature,1,n*m);
% t= [t,feature];
% ed=Edge_detection(y);
% [n,m]=size(ed);
% ed=reshape(ed,1,n*m);
% t= [t,ed];
%
% spe=spatial_predict(y);
% [n,m]=size(spe);
% spe=reshape(spe,1,n*m);
% t= [t,spe];
ene = ILBP81ri(y);
t= [ t, ene];
ent = entropy(y);
t= [ t, ent];
con = Contrast_func(y);
t= [ t, con];
cs=hog_feature_vector(y);
t= [ t, cs];
idm = IDM_func(y);
t= [ t, idm];
in = inertia(y);
t= [ t,in ];
cp = clusterprominence(y);
t= [ t, cp];
asm = angularsecondmoment(y);
t= [ t, asm];
eval(['tr', num2str((20+g)),'= t;']);
end
for g=01:10
str = 'SP_TT_O_0';
file=strcat(str,int2str(g),'.bmp');
y = imread(file);
%imshow(a,map) % tmp is an indexed image file using
% color map "map"
%y=double(rgb2gray(x));
q=[];
% feature=WLD(y);
% [n,m]=size(feature);
% feature=reshape(feature,1,n*m);
% t= [t,feature];
% ed=Edge_detection(y);
% [n,m]=size(ed);
% ed=reshape(ed,1,n*m);
% t= [t,ed];
%
% spe=spatial_predict(y);
% [n,m]=size(spe);
% spe=reshape(spe,1,n*m);
% t= [t,spe];
ene = ILBP81ri(y);
q= [ q, ene];
ent = entropy(y);
q= [ q, ent];
con = Contrast_func(y);
q= [ q, con];
cs=hog_feature_vector(y);
q= [ q, cs];
idm = IDM_func(y);
q= [ q, idm];
in = inertia(y);
q= [ q,in ];
cp = clusterprominence(y);
q= [ q, cp];
asm = angularsecondmoment(y);
q= [ q, asm];
eval(['tr', num2str((30+g)),'= q;']);
end
warning('off', 'Images:initSize:adjustingMag');
for i=1:10
training_group(1,i)=1;
end
for i=11:20
training_group(1,i)=0;
end
training=[];
for i =1:20
training = [training;eval(['tr',num2str(i),';'])];
end
testing = [];
for i =21:40
testing = [testing;eval(['tr',num2str(i),';'])];
end
Train = training;
Test = testing;
group=training_group';
SVMStruct=svmtrain(Train,group); %'method','svm'
class_result = svmclassify(SVMStruct, Test);
display(class_result);
%display(classperf(group));
%display(classperf(class_result));
TP=0;
for i=1:10
if class_result(i)==1
TP=TP+1;
end
end
TN=0;
newcounter=10;
for i=1:10
if class_result(i+newcounter)==0
TN=TN+1;
end
end
FP=10-TP;
FN=10-TN;
Sensitivity=(TP/(TP+FN))*100;
Specificity=(TN/(TN+FP))*100;
Accuracy=((TP+TN)/(TP+TN+FP+FN))*100;
display(Accuracy);
display(Specificity);
display(Sensitivity);
%ne=FP+FN;
avg = (Sensitivity + Specificity + Accuracy)/3;
class_result = class_result';
%display(class_result);
% ans =
end