load 'train.mat';

class_num = 40;
img_per_class = 100;

obj_start_idx = 41;
obj_end_idx = 748;
pose_start_idx = 749;
pose_end_idx = 898;

data = [];
for i = 1 : class_num
    data(i).obj = mean(features((i-1)*img_per_class+1 : i*img_per_class, ...
        obj_start_idx:obj_end_idx));
    data(i).pose = mean(features((i-1)*img_per_class+1 : i*img_per_class, ...
        pose_start_idx:pose_end_idx));
end

for i = 1 : class_num
    for j = 1 : class_num
%        value(i,j,1) = sum(abs(data(i).obj - data(j).obj)) / 44.1928;
%        value(i,j,2) = sum(abs(data(i).pose - data(j).pose)) / 3.9930;
        value(i, j) = sum(abs(data(i).obj - data(j).obj)) / 44.1928 * 2.5 ...
            + sum(abs(data(i).pose - data(j).pose)) / 3.9930 * 2.5;
    end
end

class_label = {'applauding', 'blowing_bubbles', 'brushing_teeth', 'calling', 'cleaning_the_floor', ...
    'climbing_wall', 'cooking', 'cutting_trees', 'cutting_vegetables', 'drinking', ...
    'feeding_a_horse', 'fishing', 'fixing_a_bike', 'gardening', 'holding_up_an_umbrella', ...
    'jumping', 'playing_guitar', 'playing_violin', 'pouring_liquid', 'pushing_a_cart', ...
    'reading_book', 'repairing_a_car', 'riding_a_bike', 'riding_a_horse', 'rowing_a_boat', ...
    'running', 'shooting_an_arrow', 'smoking_cigarette', 'taking_photos', 'texting_message', ...
    'throwing_a_frisby', 'using_a_computer', 'using_a_microscope', 'using_a_telescope', 'walking_a_dog', ...
    'washing_dishes', 'watching_TV', 'waving_hand', 'writing_on_a_board', 'writing_on_a_book'};
imagesc(value);
set(gca, 'ylim', [1 num_classes]);
set(gca, 'YTick', 1:num_classes);
set(gca, 'YTickLabel', display_labels(class_label));


% set(gca,'ylim',[0 num_classes+1])
% set(gca,'YTick', 1:num_classes)
% set(gca,'YTickLabel', display_labels(orig_idx(sort_idx)))
% grid;