hmemenu.m

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

% dataset      -> (1=>user data) or (2=>toy example)
% type         -> (1=> Regression model) or (2=>Classification model)
% num_glevel   -> number of hidden nodes in the net (gating levels)
% num_exp      -> number of experts in the net
% branch_fact  -> dimension of the hidden nodes in the net
% cov_dim      -> root node dimension
% res_dim      -> output node dimension
% nodes_info   -> 4 x num_glevel+2 matrix that contain all the info about the nodes:
%                 nodes_info(1,:) = nodes type: (0=>gaussian)or(1=>softmax)or(2=>mlp)
%                 nodes_info(2,:) = nodes size: [cov_dim   num_glevel x branch_fact   res_dim]
%                 nodes_info(3,:) = hidden units number (for mlp nodes)
%                                  |- optimizer iteration number (for softmax & mlp CPD)
%                 nodes_info(4,:) =|- covariance type (for gaussian CPD)-> 
%                                  | (1=>Full)or(2=>Diagonal)or(3=>Full&Tied)or(4=>Diagonal&Tied)
% fh1 -> Figure: data & decizion boundaries; fh2 -> confusion matrix; fh3 -> LL trace                                                                       
% test_data    -> test data matrix
% train_data   -> training data matrix
% ntrain       -> size(train_data,2)
% ntest        -> size(test_data,2)
% cases        -> (cell array) training data formatted for the learning engine
% bnet         -> bayesian net before learning
% bnet2        -> bayesian net after learning
% ll           -> log-likelihood before learning
% LL2          -> log-likelihood trace
% onodes       -> obs nodes in bnet & bnet2
% max_em_iter  -> maximum number of interations of the EM algorithm
% train_result -> prediction on the training set (as test_result)
% 
% IMPORTANT: CHECK the loading path (lines 64 & 364)
% ----------------------------------------------------------------------------------------------------
% -> [e-mail]   or   -> [e-mail]
% ----------------------------------------------------------------------------------------------------

error('this no longer works with the latest version of BNT')

clear all;
clc;
disp('---------------------------------------------------');
disp('  Hierarchical Mixtures of Experts models builder ');
disp('---------------------------------------------------');
disp(' ')
disp('   Using this script you can build both an HME model')
disp('as in [Wat94] and [Jor94] i.e. with ''softmax'' gating')
disp('nodes and ''gaussian'' ( for regression ) or ''softmax''') 
disp('( for classification ) expert node, and its variants')
disp('called ''gated nets'' where we use ''mlp'' models in')
disp('place of a number of ''softmax'' ones [Mor98], [Wei95].')
disp('  You can decide to train and test the model on your')
disp('datasets  or  to evaluate its  performance on  a toy')
disp('example.')
disp(' ')
disp('Reference')
disp('[Mor98] P. Moerland (1998):')
disp('        Localized mixtures of experts. (http://www.idiap.ch/~perry/)')
disp('[Jor94] M.I. Jordan, R.A. Jacobs (1994):')
disp('        HME and the EM algorithm. (http://www.cs.berkeley.edu/~jordan/)')
disp('[Wat94] S.R. Waterhouse, A.J. Robinson (1994):') 
disp('        Classification using HME. (http://www.oigeeza.com/steve/)')
disp('[Wei95] A.S. Weigend, M. Mangeas (1995):') 
disp('        Nonlinear gated experts for time series.')
disp(' ')

if 0
disp('(See the figure)')
pause(5);
%%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
im_path=which('HMEforMatlab.jpg');
fig=imread(im_path, 'jpg');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure('Units','pixels','MenuBar','none','NumberTitle','off', 'Name', 'HME model');
image(fig); 
axis image;
axis off;
clear fig;
set(gca,'Position',[0 0 1 1])
disp('(Press any key to continue)')
pause
end

clc
disp('---------------------------------------------------');
disp('              Specify the Architecture             ');
disp('---------------------------------------------------');
disp(' ');
disp('What kind of model do you need?')
disp(' ')
disp('1) Regression ')
disp('2) Classification')
disp(' ')
type=input('1 or 2?: ');
if (isempty(type)|(~ismember(type,[1 2]))), error('Invalid value'); end
clc
disp('----------------------------------------------------');
disp('               Specify the Architecture             ');
disp('----------------------------------------------------');
disp(' ')
disp('Now you have to set the number of experts and gating')
disp('levels in the net.  This script builds only balanced')
disp('hierarchy with the same branching factor (>1)at each')
disp('(gating) level. So remember that: ')
disp(' ')
disp('         num_exp = branch_fact^num_glevel           ')
disp(' ')
disp('with branch_fact >=2.')
disp('You can also set to zeros the number of gating level')
disp('in order to obtain a classical GLM model.           ')
disp(' ')
disp('----------------------------------------------------');
disp(' ')
num_glevel=input('Insert the number of gating levels {0,...,20}: ');
if (isempty(num_glevel)|(~ismember(num_glevel,[0:20]))), error('Invalid value'); end
nodes_info=zeros(4,num_glevel+2);
if num_glevel>0, %------------------------------------------------------------------------------------
    for i=2:num_glevel+1,
        clc
        disp('----------------------------------------------------');
        disp('               Specify the Architecture             ');
        disp('----------------------------------------------------');
        disp(' ')   
        disp(['-> Gating network ', num2str(i-1), ' is a: '])
        disp(' ')
        disp('   1) Softmax model');
        disp('   2) Two layer perceptron model')
        disp(' ')
        nodes_info(1,i)=input('1 or 2?: ');
        if (isempty(nodes_info(1,i))|(~ismember(nodes_info(1,i),[1 2]))), error('Invalid value'); end
        disp(' ')
        if nodes_info(1,i)==2,
           nodes_info(3,i)=input('Insert the number of units in the hidden layer: ');
           if (isempty(nodes_info(3,i))|(floor(nodes_info(3,i))~=nodes_info(3,i))|(nodes_info(3,i)<=0)), 
              error(['Invalid value: ', num2str(nodes_info(3,i)), ' is not a positive integer!']);
           end
           disp(' ')
        end
        nodes_info(4,i)=input('Insert the optimizer iteration number: ');
        if (isempty(nodes_info(4,i))|(floor(nodes_info(4,i))~=nodes_info(4,i))|(nodes_info(4,i)<=0)), 
           error(['Invalid value: ', num2str(nodes_info(4,i)), ' is not a positive integer!']);
        end    
    end
    clc
    disp('---------------------------------------------------------');
    disp('                 Specify the Architecture                ');
    disp('---------------------------------------------------------');
    disp(' ')
    disp('Now you have to set the number  of experts in the network');
    disp('The value will be adjusted in order to obtain a hierarchy');
    disp('as said above.')
    disp(' ');    
    num_exp=input(['Insert the approximative number of experts (>=', num2str(2^num_glevel), '): ']);
    if (isempty(num_exp)|(num_exp<=0)|(num_exp<2^num_glevel)), 
        error('Invalid value');
    end
    app1=0; base=2;
    while app1<num_exp,
        app1=base^num_glevel;
        base=base+1;
    end
    app2=(base-2)^num_glevel;
    branch_fact=base-1;
    if app2>=(2^num_glevel)&(abs(app2-num_exp)<abs(app1-num_exp)),
        branch_fact=base-2;
    end
    clear app1 app2 base;
    disp(' ')
    disp(['The effective number of experts in the net is: ', num2str(branch_fact^num_glevel), '.'])
    disp(' ');
else
    clc
    disp('---------------------------------------------------------');
    disp('        Specify the Architecture (GLM model)             ');
    disp('---------------------------------------------------------');
    disp(' ')
end % END of: if num_glevel>0-------------------------------------------------------------------------

if type==2,
    disp(['-> Expert node is a: '])
    disp(' ')
    disp('   1) Softmax model');
    disp('   2) Two layer perceptron model')
    disp(' ')
    nodes_info(1,end)=input('1 or 2?: ');
    if (isempty(nodes_info(1,end))|(~ismember(nodes_info(1,end),[1 2]))), 
        error('Invalid value'); 
    end
    disp(' ')
    if nodes_info(1,end)==2,
       nodes_info(3,end)=input('Insert the number of units in the hidden layer: ');
       if (isempty(nodes_info(3,end))|(floor(nodes_info(3,end))~=nodes_info(3,end))|(nodes_info(3,end)<=0)), 
           error(['Invalid value: ', num2str(nodes_info(3,end)), ' is not a positive integer!']);
       end
       disp(' ')
    end
    nodes_info(4,end)=input('Insert the optimizer iteration number: ');
    if (isempty(nodes_info(4,end))|(floor(nodes_info(4,end))~=nodes_info(4,end))|(nodes_info(4,end)<=0)), 
        error(['Invalid value: ', num2str(nodes_info(4,end)), ' is not a positive integer!']);
    end
elseif type==1,
    disp('What kind of covariance matrix structure do you want?')
    disp(' ')
    disp('   1) Full');
    disp('   2) Diagonal')
    disp('   3) Full & Tied');
    disp('   4) Diagonal & Tied')

    disp(' ')
    nodes_info(4,end)=input('1, 2, 3 or 4?: ');
    if (isempty(nodes_info(4,end))|(~ismember(nodes_info(4,end),[1 2 3 4]))), 
        error('Invalid value'); 
    end  
end
clc
disp('----------------------------------------------------');
disp('                    Specify the Input               ');
disp('----------------------------------------------------');
disp(' ')
disp('Do you want to...')
disp(' ')
disp('1) ...use your own dataset?')
disp('2) ...apply the model on a toy example?')
disp(' ')
dataset=input('1 or 2?: ');
if (isempty(dataset)|(~ismember(dataset,[1 2]))), error('Invalid value'); end
if dataset==1,
    if type==1,
        clc
        disp('-------------------------------------------------------');
        disp('        Specify the Input - Regression problem         ');
        disp('-------------------------------------------------------');
        disp(' ')
        disp('Be sure that each row of your data matrix is an example');
        disp('with the covariate values that precede the respond ones')
        disp(' ')
        disp('-------------------------------------------------------');
        disp(' ')
        cov_dim=input('Insert the covariate space dimension: ');
        if (isempty(cov_dim)|(floor(cov_dim)~=cov_dim)|(cov_dim<=0)), 
          error(['Invalid value: ', num2str(cov_dim), ' is not a positive integer!']);
        end
        disp(' ')
        res_dim=input('Insert the dimension of the respond variable: ');
        if (isempty(res_dim)|(floor(res_dim)~=res_dim)|(res_dim<=0)), 
            error(['Invalid value: ', num2str(res_dim), ' is not a positive integer!']);
        end 
        disp(' ');
    elseif type==2
        clc
        disp('-------------------------------------------------------');
        disp('      Specify the Input - Classification problem       ');
        disp('-------------------------------------------------------');
        disp(' ')
        disp('Be sure that each row of your data matrix is an example');
        disp('with the covariate values that precede the class labels');
        disp('(integer value >=1).                                   ');
        disp(' ')
        disp('-------------------------------------------------------');
        disp(' ')
        cov_dim=input('Insert the covariate space dimension: ');
        if (isempty(cov_dim)|(floor(cov_dim)~=cov_dim)|(cov_dim<=0)), 
          error(['Invalid value: ', num2str(cov_dim), ' is not a positive integer!']);
        end
        disp(' ')
        res_dim=input('Insert the number of classes: ');
        if (isempty(res_dim)|(floor(res_dim)~=res_dim)|(res_dim<=0)), 
          error(['Invalid value: ', num2str(res_dim), ' is not a positive integer!']);
        end        
        disp(' ')               
    end    
    % ------------------------------------------------------------------------------------------------
    % Loading training data --------------------------------------------------------------------------
    % ------------------------------------------------------------------------------------------------
    train_path=input('Insert the complete (with extension) path of the training data file:\n >> ','s');    
    if isempty(train_path), error('You must specify a data set for training!'); end
    if ~isempty(findstr('.mat',train_path)),
        ap=load(train_path); app=fieldnames(ap); train_data=eval(['ap.', app{1,1}]);
        clear ap app;
    elseif ~isempty(findstr('.txt',train_path)),
        train_data=load(train_path, '-ascii');