updateHN.m

function [pop, meta_HN] = updateHN(pop, meta_HN, time, episode_name)
    global content_name;
    global HE_CLUSTER_K;
    global CLUSTER_THRESHOLD;
    global initial_Meand;
    
    run 'configure';
    load([dic_path '/' 'dic.mat']);
    
    dir_path = ['Updated_HN/Sampling_policy' num2str(SAMPLING_POLICY) '/'];
    mkdir(dir_path);
    HN_dir_path = [dir_path 'HN/'];
    mkdir(HN_dir_path);
    meta_HN_dir_path = [dir_path 'meta_HN/'];
    mkdir(meta_HN_dir_path);
    HE_Center_dir_path = [dir_path 'clustering_info/Learning_rate' num2str(LEARNING_RATE) '/HE_Center/'];
    mkdir(HE_Center_dir_path);
    HE_assgnm_dir_path = [dir_path 'clustering_info/Learning_rate' num2str(LEARNING_RATE) '/HE_assgnm/'];
    mkdir(HE_assgnm_dir_path);
    HN_save_path = [HN_dir_path 'Upated_HN_' num2str(time) '.mat'];
    meta_HN_save_path = [meta_HN_dir_path 'meta_HN_' num2str(time) '.mat'];
    HE_Center_save_path = [HE_Center_dir_path 'HE_Center_' num2str(time) '.mat'];
    HE_assgnm_save_path = [HE_assgnm_dir_path 'HE_assgnm_' num2str(time) '.mat'];
    %% load newly observed HN
    dir_path = ['HN/Sampling_policy' num2str(SAMPLING_POLICY) '/'];
    load_path = [dir_path 'HN_' episode_name '.mat'];
    load(load_path);
    
    %% modify meta HN 
    if ~isempty(meta_HN)
        tmp_cell1 = cell(1,1); tmp_cell1{1,1} = content_name;
        tmp_cell2 = cell(1,1); tmp_cell2{1,1} = episode_name;
        tmp_cell3 = cell(1,1); tmp_cell3{1,1} = meta_HN{time-1, 3}+size(HE, 1);
        meta_HN = [meta_HN; tmp_cell1 tmp_cell2 tmp_cell3];
    else
        tmp_cell1 = cell(1,1); tmp_cell1{1,1} = content_name;
        tmp_cell2 = cell(1,1); tmp_cell2{1,1} = episode_name;
        tmp_cell3 = cell(1,1); tmp_cell3{1,1} = size(HE, 1);
        meta_HN = [meta_HN; tmp_cell1 tmp_cell2 tmp_cell3];
    end
    %% UpdateHN
    pop = [pop; HE];

    load([pair_path '/' content_name '/pair_' episode_name '.mat']);
    load([bundle_path '/' content_name '/bundle_' episode_name '.mat']);
    load([cp_path '/' content_name '/cp_' episode_name '.mat']);
    
    train_pair = pair;
    train_bundle = bundle;
    train_presence = cp;

    % TF matrix ���
    disp('TF matrix ...');
    tf_matrix = zeros(size(train_pair, 1), size(dic, 1));
    tf_matrix(:) = 0.001;
    for i=1:size(train_pair, 1)    
        tf_matrix(i, ismember(dic, train_pair{i, 2}))=1;
    end
    fprintf('done\n');
%     mm = sum(tf_matrix, 1);
    idf_vector = log(size(train_pair, 1)./(sum(tf_matrix, 1)+1));
    idf_vector = repmat(idf_vector, size(train_pair, 1), 1);
    tf_idf = log(tf_matrix+1);
    tf_idf = tf_idf .* idf_vector;
    sum_tf_idf = sum(tf_idf, 2);
    n_tf_idf = tf_idf ./ repmat(sum_tf_idf, 1, size(tf_idf, 2));

    % ��ġ�鰣�� ���缺 ��Ʈ���� ���
    disp('make similarity matrix');
    patch_cnt = 0;
    all_patches = [];
    for i=1:size(train_bundle, 1)
        fprintf('%d\n', i);
        patch_cnt = patch_cnt + size(train_bundle{i, 1}, 2);
        for j=1:size(train_bundle{i, 1}, 2)
            img_vec = train_bundle{i,1}(1, j).img_vector';
            all_patches = [all_patches; img_vec]; 
        end
    end
    
    patch_matrix = zeros(patch_cnt);
    for i=1:patch_cnt
        tmp_patch = repmat(all_patches(i, :), patch_cnt, 1);
        val = all_patches - tmp_patch;
        val = val .* val;
        dist = sqrt(sum(val, 2));
        patch_matrix(i, :) = dist';
    end

    sum_patches = sum(patch_matrix, 2);
    n_patch_matrix = patch_matrix ./ repmat(sum_patches, 1, size(patch_matrix, 2));
    patch_mean = mean(mean(patch_matrix));
    same_patch_matrix = zeros(size(patch_matrix));
    same_patch_matrix(find(patch_matrix < patch_mean*0.5)) = 1;
    sum_same_patch_matrix = sum(same_patch_matrix, 2);

    disp('update HN..');
    ep_idx = 1;
    ep_content = meta_HN{ep_idx,1};
    ep_name = meta_HN{ep_idx,2};
    ep_length = meta_HN{ep_idx,3};
    load([bundle_path '/' ep_content '/bundle_' ep_name '.mat']);
    for i=1:size(pop, 1)
        if i <= ep_length
            org_bundle = bundle;
        else
            ep_idx = ep_idx + 1;
            ep_content = meta_HN{ep_idx,1};
            ep_name = meta_HN{ep_idx,2};
            ep_length = meta_HN{ep_idx,3};
            load([bundle_path '/' ep_content '/bundle_' ep_name '.mat']);
            org_bundle = bundle;
        end
        pop(i,1).t_cover = [];
        pop(i,1).i_cover = [];
        for j=1:size(train_pair, 1)
            if size(train_bundle{j, 1}, 2) < 2
                continue;
            end
            sumval = sum(ismember(train_pair{j, 2}, pop(i,1).t_words))/size(pop(i,1).t_words, 1);
            if sumval > 0.8
                pop(i,1).t_cover = [pop(i,1).t_cover j];
            end    
            cnt = 0;
            img_vectors = [];
            for k=1:size(train_bundle{j, 1}, 2)
                img_vec = train_bundle{j,1}(1, k).img_vector';
                img_vectors = [img_vectors; img_vec];
            end
            if size(org_bundle{pop(i,1).file{1,3},1}, 2) < 2
                continue;
            end
            for k=1:i_order
                img_vec = org_bundle{pop(i,1).file{1,3},1}(1, pop(i,1).file{1,4}(1, k)).img_vector';
                dist = img_vectors - repmat(img_vec, size(img_vectors, 1), 1);
                dist = sqrt(sum(dist .* dist, 2));
                if min(dist) < patch_mean*0.5
                    cnt = cnt + 1;
                end
            end
            if cnt > 0
                pop(i,1).i_cover = [pop(i,1).i_cover j];
            end
        end
        aa = sum(power(prod(n_tf_idf(pop(i, 1).t_cover, ismember(dic, pop(i, 1).t_words)), 2), 1/3));    
        pop(i,1).t_weight = TIME_RATIO*pop(i,1).t_weight + (1-TIME_RATIO)*aa;   
        pop(i,1).i_weight = TIME_RATIO*pop(i,1).i_weight + (1-TIME_RATIO)*1/5*size(pop(i,1).i_cover, 2);  
        disp(sprintf('%s-%d iter', episode_name, i));
    end
    disp('save HN..');
    save(HN_save_path, 'pop', '-v7.3');
    save(meta_HN_save_path, 'meta_HN', '-v7.3');
end