Appendix I
ARENA training algorithm
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implementation of Arena algorithm.
%
%----------------------------------------------------%
% code written by : Gregory Tambasis
%
% (c)2000
%
%----------------------------------------------------%
% The algorithm is calculating the "distance"
%
% between the images of the training set. The result %
% is used as an input to the neural network,
%
% in order to train it.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Aimages, Bimages = database images
%RAimages, RBimages = reduced resolution database images
%data = number of images used from the training dataset
%power = p value
%Turn on echoing of commands inside the script-file.
echo on
%Clear the workspace (all variables).
clear all
%Clear command window.
clc
%K = MENU('Choose a dataset','1: orl','2:feret');
%User defined parameters
power = input('insert a value for p: ');
data = input('insert number of images in training and testing set: ');
rez = input('insert resolution: ');
%read input image
for j = 1:data
string = ['train\'
Train_Aimages{j} =
end;
for i = 1:data
string = ['train\'
Train_Bimages{i} =
end;
num2str(j) '.tif'];
imread(string);
num2str(i) '.tif'];
imread(string);
%Reduce resolution (resize) input and database images to 16x16.
for j = 1:data
Train_RAimages{j}= imresize(Train_Aimages{j},[rez,rez],'nearest');
end;
for i = 1:data
Train_RBimages{i}= imresize(Train_Bimages{i},[rez,rez],'nearest');
end;
I
Appendix I
%measure the distance L
for i = 1:data;
for j = 1:data;
if power==0;
Train_FSum{j,i}= 1;
else
Train_X{j,i} = double(Train_RAimages{j}) double(Train_RBimages{i});
Train_Y{j,i} = abs(Train_X{j,i});
Train_Matrix_Sum{j,i} = sum((Train_Y{j,i})^power);
Train_Vector_Sum{j,i} = sum(Train_Matrix_Sum{j,i});
Train_FSum{j,i} =min(Train_Vector_Sum{j,i});
end;
end;
end;
clc;
%display output
Train_Fsum
II
Appendix I
ARENA testing algorithm
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implementation of Arena algorithm.
%
%----------------------------------------------------%
% code written by : Gregory Tambasis
%
% (c)2000
%
%----------------------------------------------------%
% The algorithm is calculating the "distance"
%
% between one of the images in the Test set and
%
% the images in the training set.
%
% The result is used as an input to the
%
% neural network, in order to simulate and test.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Aimages, Bimages = database images
%RAimages, RBimages = reduced resolution database images
%data = number of images used from the training dataset
%power = p value
%Turn on echoing of commands inside the script-file.
echo on
%Clear the workspace (all variables).
Clear all
%Clear command window.
clc
%User defined value of p
power = input('insert a value for p: ');
data = input('insert number of images in training and Test set: ');
rez = input('insert resolution: ');
for n = 1:data
string = ['test\' num2str(n) '.tif'];
Test_Aimages{n} = imread(string);
% imshow(Test_Aimages{n})
% pause
end;
for i = 1:data
string = ['train\' num2str(i) '.tif'];
Test_Bimages{i} = imread(string);
end;
%Reduce resolution (resize) input and databases images to 16x16.
tj = input('image: ');
Test_RA = imresize(Test_Aimages{tj},[rez,rez],'nearest');
for i = 1:data
Test_RBimages{i}= imresize(Test_Bimages{i},[rez,rez],'nearest');
end;
III
Appendix I
%measure the distance L
for i = 1:data;
if power==0;
Test_FSum{i}= 1;
else
Test_X{i} = double(Test_RA) - double(Test_RBimages{i});
Test_Y{i} = abs(Test_X{i});
Test_Matrix_Sum{i} = sum((Test_Y{i})^power);
Test_Vector_Sum{i} = sum(Test_Matrix_Sum{i});
Test_FSum{i} =min(Test_Vector_Sum{i});
end;
end;
%output
Test_FSum
IV
Appendix I
Training the neural network
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implementation of Arena algorithm.
%
%----------------------------------------------------%
% code written by : Gregory Tambasis
%
% (c)2000
%
%----------------------------------------------------%
% The program is training the neural network
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc;
classes = input('insert number of classes: ');
if data==5
net = newff([-1 1;-1 1;-1 1;-1 1;-1
1;],[10,classes],{'tansig','purelin'}, 'traingd');
elseif data==10
net = newff([-1 1;-1 1;-1 1;-1 1;-1 1;-1 1;-1 1;-1 1;-1 1;-1
1;],[10,classes],{'tansig','purelin'}, 'traingd');
end;
net.layers{1}.initFcn = 'initwb';
net.inputWeights{1,1}.initFcn = 'rands';
net.biases{1,1}.initFcn = 'rands';
net.biases{2,1}.initFcn = 'rands';
net=init(net);
net.biases{1,1}.learnFcn = 'learngd';
net.biases{2,1}.learnFcn = 'learngd';
net.layerWeights{2,1}.learnFcn = 'learngd';
net.layerWeights{1,1}.learnFcn = 'learngd';
net.adaptParam.passes = 500;
%-------------------------------for q=1:data
for w=1:data
p(q,w)=Train_FSum{q,w};
end;
end;
%generate target matrix
for y=1:classes
for u=1:classes
if y==u
t(y,u)=0;
else
t(y,u)=2000000;
end;
end;
end;
if classes<data
for y=1:classes
for u=classes+1:data
V
Appendix I
if u==y+5
t(y,u)=0;
else
t(y,u)=2000000;
end;
end;
end;
end;
%normilised from 0 to 1
%for k = 1:10
%
for l = 1:10
%
if p(k,l)==0;
%
pn(k,l)=0;
%
else
%
pn(k,l)=1./p(k,l);
%
end;
%
end;
%end;
%[net,a,e] = adapt(net,pn,t);
%b = sim(net,pn)
%normilised from -1 to 1
[pn,minp,maxp,tn,mint,maxt] = premnmx(p,t);
[net,a,e] = adapt(net,pn,tn);
b= sim(net,pn)
%not normilised
%p = num2cell(p,1);
%t = num2cell(t,1);
%[net,a,e] = adapt(net,p,t);
%celldisp(a)
%celldisp(e)
%b= sim(net,p)
%celldisp(b)
VI
Appendix I
Testing the neural network
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implementation of Arena algorithm.
%
%----------------------------------------------------%
% code written by : Gregory Tambasis
%
% (c)2000
%
%----------------------------------------------------%
% The program is testing the neural network
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for q=1:data
for w=1:data
tp(q,w)=Test_FSum{w};
end;
end;
%normilised from -1 to 1
[tpn,minp,maxp,tn,mint,maxt] = premnmx(tp,t);
%[net,a,e] = adapt(net,pn,tn);
g= sim(net,tpn)
%not normilised
%p = num2cell(p,1);
%t = num2cell(t,1);
%[net,a,e] = adapt(net,p,t);
%celldisp(a)
%celldisp(e)
%b= sim(net,p)
%celldisp(b)
VII