hmemenu.m

贝叶斯算法(matlab编写) 安装,添加目录 /home/ai2/murphyk/matlab/FullBNT

    else
        error('Invalid data format: not a .mat or a .txt file')
    end
    if (size(train_data,2)~=cov_dim+res_dim)&(type==1),
        error(['Invalid data matrix size: ', num2str(size(train_data,2)), ' columns rather than ',...
            num2str(cov_dim+res_dim),'!']);
    elseif (size(train_data,2)~=cov_dim+1)&(type==2),
        error(['Invalid data matrix size: ', num2str(size(train_data,2)), ' columns rather than ',...
            num2str(cov_dim+1),'!']);    
    elseif (~isempty(find(ismember(intersect([train_data(:,end)' 1:res_dim],...
            train_data(:,end)'),[1:res_dim])==0)))&(type==2),
        error('Invalid class label');
    end    
    ntrain=size(train_data,1);
    train_d=train_data(:,1:cov_dim);
    if type==2,
        train_t=zeros(ntrain, res_dim);
        for m=1:res_dim,
            train_t((find(train_data(:,end)==m))',m)=1;
        end
    else
        train_t=train_data(:,cov_dim+1:end);
    end        
    disp(' ')
    % ------------------------------------------------------------------------------------------------
    % Loading test data ------------------------------------------------------------------------------
    % ------------------------------------------------------------------------------------------------
    disp('(If you don''t want to specify a test-set press ''return'' only)');
    test_path=input('Insert the complete (with extension) path of the test data file:\n >> ','s');  
    if ~isempty(test_path),
        if ~isempty(findstr('.mat',test_path)),        
            ap=load(test_path); app=fieldnames(ap); test_data=eval(['ap.', app{1,1}]);
            clear ap app;
        elseif ~isempty(findstr('.txt',test_path)),
            test_data=load(test_path, '-ascii');
        else
            error('Invalid data format: not a .mat or a .txt file')
        end
        if (size(test_data,2)~=cov_dim)&(size(test_data,2)~=cov_dim+res_dim)&(type==1),
            error(['Invalid data matrix size: ', num2str(size(test_data,2)), ' columns rather than ',...
                num2str(cov_dim+res_dim), ' or ', num2str(cov_dim), '!']);
        elseif (size(test_data,2)~=cov_dim)&(size(test_data,2)~=cov_dim+1)&(type==2),
            error(['Invalid data matrix size: ', num2str(size(test_data,2)), ' columns rather than ',...
                num2str(cov_dim+1), ' or ', num2str(cov_dim), '!']);
        elseif (~isempty(find(ismember(intersect([test_data(:,end)' 1:res_dim],...
                test_data(:,end)'),[1:res_dim])==0)))&(type==2)&(size(test_data,2)==cov_dim+1),
            error('Invalid class label');
        end
        ntest=size(test_data,1);        
        test_d=test_data(:,1:cov_dim);
        if (type==2)&(size(test_data,2)>cov_dim),
            test_t=zeros(ntest, res_dim);
            for m=1:res_dim,
                test_t((find(test_data(:,end)==m))',m)=1;
            end
        elseif (type==1)&(size(test_data,2)>cov_dim),
            test_t=test_data(:,cov_dim+1:end);
        end
        disp(' ');
    end
else    
    clc
    disp('----------------------------------------------------');
    disp('                  Specify the Input                 ');
    disp('----------------------------------------------------');
    disp(' ')
    ntrain = input('Insert the number of examples in training (<500): ');
    if (isempty(ntrain)|(floor(ntrain)~=ntrain)|(ntrain<=0)|(ntrain>500)), 
          error(['Invalid value: ', num2str(ntrain), ' is not a positive integer <500!']);
    end        
    disp(' ')
    test_path='toy';
    ntest = input('Insert the number of examples in test (<500): ');
    if (isempty(ntest)|(floor(ntest)~=ntest)|(ntest<=0)|(ntest>500)), 
          error(['Invalid value: ', num2str(ntest), ' is not a positive integer <500!']);
    end        

    if type==2,
        cov_dim=2;
        res_dim=3;
        seed = 42;
        [train_d, ntrain1, ntrain2, train_t]=gen_data(ntrain, seed);
        for m=1:ntrain
            q=[]; q = find(train_t(m,:)==1);
            train_data(m,:)=[train_d(m,:) q];
        end
        [test_d, ntest1, ntest2, test_t]=gen_data(ntest);
        for m=1:ntest
            q=[]; q = find(test_t(m,:)==1);
            test_data(m,:)=[test_d(m,:) q];
        end
    else
        cov_dim=1;
        res_dim=1;
        global HOME
        %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        load([HOME '/examples/static/Misc/mixexp_data.txt'], '-ascii');
        %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        train_data = mixexp_data(1:ntrain, :);
        train_d=train_data(:,1:cov_dim); train_t=train_data(:,cov_dim+1:end);
        test_data = mixexp_data(ntrain+1:ntrain+ntest, :);
        test_d=test_data(:,1:cov_dim); 
        if size(test_data,2)>cov_dim,
            test_t=test_data(:,cov_dim+1:end);
        end
    end    
end
% Set the nodes dimension-----------------------------------
if num_glevel>0,
    nodes_info(2,2:num_glevel+1)=branch_fact;
end
nodes_info(2,1)=cov_dim; nodes_info(2,end)=res_dim;
%-----------------------------------------------------------
% Prepare the training data for the learning engine---------
%-----------------------------------------------------------
cases = cell(size(nodes_info,2), ntrain);
for m=1:ntrain,
    cases{1,m}=train_data(m,1:cov_dim)';
    cases{end,m}=train_data(m,cov_dim+1:end)';
end
%-----------------------------------------------------------------------------------------------------
[bnet onodes]=hme_topobuilder(nodes_info);
engine = jtree_inf_engine(bnet, onodes);
clc
disp('---------------------------------------------------------------------');
disp('                         L  E  A  R  N  I  N  G                      ');
disp('---------------------------------------------------------------------');
disp(' ')
ll = 0;
for l=1:ntrain
  scritta=['example number: ', int2str(l),'---------------------------------------------'];
  disp(scritta);
  ev = cases(:,l);
  [engine, loglik] = enter_evidence(engine, ev);
  ll = ll + loglik;
end
disp(' ')
disp(['Log-likelihood before learning: ', num2str(ll)]);
disp(' ')
disp('(Press any key to continue)');
pause
%-----------------------------------------------------------
clc
disp('---------------------------------------------------------------------');
disp('                         L  E  A  R  N  I  N  G                      ');
disp('---------------------------------------------------------------------');
disp(' ')
max_em_iter=input('Insert the maximum number of the EM algorithm iterations: ');
if (isempty(max_em_iter)|(floor(max_em_iter)~=max_em_iter)|(max_em_iter<=1)), 
          error(['Invalid value: ', num2str(ntest), ' is not a positive integer >1!']);
end 
disp(' ')
disp(['Log-likelihood before learning: ', num2str(ll)]);
disp(' ')

[bnet2, LL2] = learn_params_em(engine, cases, max_em_iter);
disp(' ')
fprintf('HME: loglik before learning %f, after %d iters %f\n', ll, length(LL2),  LL2(end));
disp(' ')
disp('(Press any key to continue)');
pause
%-----------------------------------------------------------------------------------
% Classification problem: plot data & decision boundaries if the input data size = 2
% Regression problem: plot data & prediction if the input data size = 1
%-----------------------------------------------------------------------------------
if (type==2)&(nodes_info(2,1)==2)&(~isempty(test_path)),
    fh1=hme_class_plot(bnet2, nodes_info, train_data, test_data);
    disp(' ');
    disp('(See the figure)');
elseif (type==2)&(nodes_info(2,1)==2)&(isempty(test_path)),
    fh1=hme_class_plot(bnet2, nodes_info, train_data);
    disp(' ');
    disp('(See the figure)');
elseif (type==1)&(nodes_info(2,1)==1)&(~isempty(test_path)),
    fh1=hme_reg_plot(bnet2, nodes_info, train_data, test_data);
    disp(' ');
    disp('(See the figure)');
elseif (type==1)&(nodes_info(2,1)==1)&(isempty(test_path)),
    fh1=hme_reg_plot(bnet2, nodes_info, train_data);
    disp(' ')
    disp('(See the figure)');
end
%-----------------------------------------------------------------------------------
% Classification problem: plot confusion matrix
%-----------------------------------------------------------------------------------
if (type==2)
    ztrain=fhme(bnet2, nodes_info, train_d, size(train_d,1));  
    [Htrain, trainRate]=confmat(ztrain, train_t); % CM on the training set
    fh2=figure('Name','Confusion matrix', 'MenuBar', 'none', 'NumberTitle', 'off');
    if (~isempty(test_path))&(size(test_data,2)>cov_dim),
        ztest=fhme(bnet2, nodes_info, test_d, size(test_d,1));
        [Htest, testRate]=confmat(ztest, test_t);   % CM on the test set
        subplot(1,2,1);
    end
    plotmat(Htrain,'b','k',12)
    tick=[0.5:1:(0.5+nodes_info(2,end)-1)];
    set(gca,'XTick',tick)
    set(gca,'YTick',tick)
    grid('off')
    ylabel('True')
    xlabel('Prediction')
    title(['Confusion Matrix: training set (' num2str(trainRate(1)) '%)'])
    if (~isempty(test_path))&(size(test_data,2)>cov_dim),
        subplot(1,2,2)
        plotmat(Htest,'b','k',12)
        set(gca,'XTick',tick)
        set(gca,'YTick',tick)
        grid('off')
        ylabel('True')
        xlabel('Prediction')
        title(['Confusion Matrix: test set (' num2str(testRate(1)) '%)'])
    end
    disp(' ')
    disp('(Press any key to continue)');
    pause
end
%-----------------------------------------------------------------------------------
% Regression & Classification problem: calculate the predictions & plot the LL trace
%-----------------------------------------------------------------------------------
train_result=fhme(bnet2,nodes_info,train_d,size(train_d,1));
if ~isempty(test_path),
    test_result=fhme(bnet2,nodes_info,test_d,size(test_d,1));
end
fh3=figure('Name','Log-likelihood trace', 'MenuBar', 'none', 'NumberTitle', 'off')
plot(LL2,'-ro',...
                'MarkerEdgeColor','k',...
                'MarkerFaceColor',[1 1 0],...
                'MarkerSize',4)
title('Log-likelihood trace')
%-----------------------------------------------------------------------------------
% Regression & Classification problem: save the predictions
%-----------------------------------------------------------------------------------
clc
disp('------------------------------------------------------------------');
disp('                           Save the results                       ');
disp('------------------------------------------------------------------');
disp(' ')
%-----------------------------------------------------------------------------------
save_quest_m=input('Do you want to save the HME model (Y/N)? [Y default]: ', 's');
if isempty(save_quest_m),
    save_quest_m='Y';
end
if ~findstr(save_quest_m, ['Y', 'N']), error('Invalid input'); end
if save_quest_m=='Y',
    disp(' ');
    m_save=input('Insert the complete path for save the HME model (.mat):\n >> ', 's');
    if isempty(m_save), error('You must specify a path!'); end
    save(m_save, 'bnet2');
end
%-----------------------------------------------------------------------------------    
disp(' ')
save_quest=input('Do you want to save the HME predictions (Y/N)? [Y default]: ', 's');
disp(' ')
if isempty(save_quest),
    save_quest='Y';
end
if ~findstr(save_quest, ['Y', 'N']), error('Invalid input'); end
if save_quest=='Y',
    tr_save=input('Insert the complete path for save the training data prediction (.mat):\n >> ', 's');    
    if isempty(tr_save), error('You must specify a path!'); end
    save(tr_save, 'train_result');  
    if ~isempty(test_path),
        disp(' ')
        te_save=input('Insert the complete path for save the test data prediction (.mat):\n >> ', 's');
        if isempty(te_save), error('You must specify a path!'); end
        save(te_save, 'test_result');
    end
end
clc
disp('----------------------------------------------------');
disp('                      B  Y  E !                     ');
disp('----------------------------------------------------');
pause(2)
%clear 
clc