image_dims = [112, 92];
num_images = 40;
test_images = cell(40, 3);
train_images = zeros(prod(image_dims), num_images);

input_dir = 'att_faces';

for i=1:40
    sub_dir = strcat('s', num2str(i));
    images = cell(10);
    for j=1:10
        filename = fullfile(input_dir, sub_dir, strcat(num2str(j), '.pgm'));
        images{j} = imread(filename); 
    end
   
    images = images(randperm(10));
    
    img = zeros(image_dims);
    
    for j=1:7
        img = img + double(images{j});
    end
    
    img = img / 7;
    train_images(:, i) = img(:);
    
    for j=8:10
        test_images{i,j-7}=images{j};
    end
end

mean_face = mean(train_images, 2);
shifted_images = train_images - repmat(mean_face, 1, num_images);

[full_evectors, score, evalues] = pca(train_images');

figure;
for i = 1:num_eigenfaces
    subplot(5, ceil(num_eigenfaces/5), i);
    evector = MinMaxTransform(reshape(evectors(:,i), image_dims));
    imshow(evector);
end

function [res] = MinMaxTransform(image)
[m,n] = size(image);
Min = min(min(image));
Max = max(max(image));
range = Max - Min; 
res = zeros(m,n);
for i=1:m
    for j=1:n
        res(i,j)=(image(i,j)-Min)*255/range;
    end
end
res = uint8(res);
end

evectors = full_evectors(:, 1:num_eigenfaces);
features = evectors' * shifted_images;

cnt = 0;
for i=1:40
    for j=1:3
        input_image = double(test_images{i,j});
        feature_vec = evectors' * (input_image(:) - mean_face);
        
        similarity_score = arrayfun(@(n) 1 / (1 + norm(features(:,n) - feature_vec)), 1:num_images);

        % find the image with the highest similarity
        [match_score, match_ix] = max(similarity_score);

        if match_ix == i
            cnt = cnt + 1;
    end
end