wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  
wolffd@0: % Main script of KRBM-Knowledge-based Restricted Boltzmann Machine   %
wolffd@0: % -*-sontran2012-*-                                                  %
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: %% loading background knowledge 
wolffd@0: % bk_file  = 'XOR.bk';
wolffd@0: % w = 5;
wolffd@0: W  = rand(0,0);
wolffd@0: mW = rand(0,0);
wolffd@0: % [W mW nNames] = encode_bk(bk_file,w)
wolffd@0: %%% visualize_system(W,nNames,w,100,100);
wolffd@0: %% Loading data
wolffd@0: PRO_DIR = 'C://Pros/';
wolffd@0: % dat_file = 'XOR.dat';
wolffd@0: % dat_file = 'DNA.dat';
wolffd@0: % train_file  = strcat(PRO_DIR,'/Data/ICDAR/digit_train_data.mat';
wolffd@0: % train_label = strcat(PRO_DIR,'/Data/ICDAR/digit_train_label.mat';
wolffd@0: % test_file   = strcat(PRO_DIR,'/Data/ICDAR/digit_test_data.mat';
wolffd@0: % test_label  = strcat(PRO_DIR,'/Data/ICDAR/digit_test_label.mat';
wolffd@0: %-----------------------------------------------------
wolffd@0: % train_file  = strcat(PRO_DIR,'/Data/MNIST/mnist_train_data.mat';
wolffd@0: % train_label = strcat(PRO_DIR,'/Data/MNIST/mnist_train_label.mat';
wolffd@0: % test_file   = strcat(PRO_DIR,'/Data/MNIST/mnist_test_data.mat';
wolffd@0: % test_label  = strcat(PRO_DIR,'/Data/MNIST/mnist_test_label.mat';
wolffd@0: %-----------------------------------------------------
wolffd@0: % train_file  = strcat(PRO_DIR,'/RBMLIB/train_dat.mat');
wolffd@0: % train_label = strcat(PRO_DIR,'/RBMLIB/train_lab.mat');
wolffd@0: % test_file   = strcat(PRO_DIR,'/RBMLIB/test_dat.mat');
wolffd@0: % test_label  = strcat(PRO_DIR,'/RBMLIB/test_lab.mat');
wolffd@0: %-----------------------------------------------------
wolffd@0: % train_file  = strcat(PRO_DIR,'/RBMLIB/train_dat.mat');
wolffd@0: data_file = strcat(PRO_DIR,'/DATA/XOR/xorall.mat');
wolffd@0: mod_file = strcat(PRO_DIR,'/DATA/XOR/model.mat');
wolffd@0: %% Move weights encoded from rule to right position of input if needed
wolffd@0: 
wolffd@0: %% Setting parameters
wolffd@0: conf.hidNum = 4;
wolffd@0: conf.eNum   = 50;                                                           % number of epoch
wolffd@0: conf.bNum   = 1;                                                           % number of batches devides the data set
wolffd@0: conf.sNum   = 4;                                                          % number of samples per batch
wolffd@0: conf.gNum   = 1;                                                            % number of iteration for gibb sampling
wolffd@0: conf.params = [0.5 0.5 0.1 0.00002];                                        % [lr1 lr2 momentum cost] 
wolffd@0:                                                                             % lr1: learning rate in first 10 epoches
wolffd@0:                                                                             % lr2: learning rate in the other epoches
wolffd@0: %% Training system
wolffd@0: % training_kbrm(conf,W,mW,dat_file);                                        % unsupevised learning
wolffd@0: % [W visB hidB] = gen_training_kbrbm(conf,W,mW,train_file,train_label);     % unsupervised learning for generative classification
wolffd@0: % dis_training_kbrm(conf,W,mW,dat_file,dat_label);                          % unsupervised learning for discriminative classification
wolffd@0: [W visB hidB] = training_rbm(conf,W,mW,data_file);
wolffd@0: save(mod_file,'W','mW','visB','hidB');
wolffd@0: nNames = {'X' 'Y' 'Z'};
wolffd@0: visualize_system(W,nNames,100,100);
wolffd@0: %% Classify (if wanted)
wolffd@0: % gen_classify(W, visB, hidB,mW,test_file,test_label);
wolffd@0: %% Generate features (if wanted)
wolffd@0: % in_f_file = '../Data/ICDAR/images.bin';
wolffd@0: % out_f_file = '../Data/ICDAR/feature.mat';
wolffd@0: % input_features = mnist2mat(in_f_file);
wolffd@0: % output_features = input_features*W;
wolffd@0: % save(out_f_file,'output_features');
wolffd@0: %% Extract knowledge (if wanted)
wolffd@0: % name_file = 'names.txt';                                                    % File contains name of input
wolffd@0: % rule_file = 'rules.txt';
wolffd@0: % extract_knowledge(rule_file,W);
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% END%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: clear;