Mercurial > hg > camir-aes2014

annotate toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/hmemenu.m @ 0:e9a9cd732c1e tip

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
parents
children
rev   line source
wolffd@0 1 % dataset -> (1=>user data) or (2=>toy example)
wolffd@0 2 % type -> (1=> Regression model) or (2=>Classification model)
wolffd@0 3 % num_glevel -> number of hidden nodes in the net (gating levels)
wolffd@0 4 % num_exp -> number of experts in the net
wolffd@0 5 % branch_fact -> dimension of the hidden nodes in the net
wolffd@0 6 % cov_dim -> root node dimension
wolffd@0 7 % res_dim -> output node dimension
wolffd@0 8 % nodes_info -> 4 x num_glevel+2 matrix that contain all the info about the nodes:
wolffd@0 9 % nodes_info(1,:) = nodes type: (0=>gaussian)or(1=>softmax)or(2=>mlp)
wolffd@0 10 % nodes_info(2,:) = nodes size: [cov_dim num_glevel x branch_fact res_dim]
wolffd@0 11 % nodes_info(3,:) = hidden units number (for mlp nodes)
wolffd@0 12 % |- optimizer iteration number (for softmax & mlp CPD)
wolffd@0 13 % nodes_info(4,:) =|- covariance type (for gaussian CPD)->
wolffd@0 14 % | (1=>Full)or(2=>Diagonal)or(3=>Full&Tied)or(4=>Diagonal&Tied)
wolffd@0 15 % fh1 -> Figure: data & decizion boundaries; fh2 -> confusion matrix; fh3 -> LL trace
wolffd@0 16 % test_data -> test data matrix
wolffd@0 17 % train_data -> training data matrix
wolffd@0 18 % ntrain -> size(train_data,2)
wolffd@0 19 % ntest -> size(test_data,2)
wolffd@0 20 % cases -> (cell array) training data formatted for the learning engine
wolffd@0 21 % bnet -> bayesian net before learning
wolffd@0 22 % bnet2 -> bayesian net after learning
wolffd@0 23 % ll -> log-likelihood before learning
wolffd@0 24 % LL2 -> log-likelihood trace
wolffd@0 25 % onodes -> obs nodes in bnet & bnet2
wolffd@0 26 % max_em_iter -> maximum number of interations of the EM algorithm
wolffd@0 27 % train_result -> prediction on the training set (as test_result)
wolffd@0 28 %
wolffd@0 29 % IMPORTANT: CHECK the loading path (lines 64 & 364)
wolffd@0 30 % ----------------------------------------------------------------------------------------------------
wolffd@0 31 % -> pierpaolo_b@hotmail.com or -> pampo@interfree.it
wolffd@0 32 % ----------------------------------------------------------------------------------------------------
wolffd@0 33
wolffd@0 34 error('this no longer works with the latest version of BNT')
wolffd@0 35
wolffd@0 36 clear all;
wolffd@0 37 clc;
wolffd@0 38 disp('---------------------------------------------------');
wolffd@0 39 disp(' Hierarchical Mixtures of Experts models builder ');
wolffd@0 40 disp('---------------------------------------------------');
wolffd@0 41 disp(' ')
wolffd@0 42 disp(' Using this script you can build both an HME model')
wolffd@0 43 disp('as in [Wat94] and [Jor94] i.e. with ''softmax'' gating')
wolffd@0 44 disp('nodes and ''gaussian'' ( for regression ) or ''softmax''')
wolffd@0 45 disp('( for classification ) expert node, and its variants')
wolffd@0 46 disp('called ''gated nets'' where we use ''mlp'' models in')
wolffd@0 47 disp('place of a number of ''softmax'' ones [Mor98], [Wei95].')
wolffd@0 48 disp(' You can decide to train and test the model on your')
wolffd@0 49 disp('datasets or to evaluate its performance on a toy')
wolffd@0 50 disp('example.')
wolffd@0 51 disp(' ')
wolffd@0 52 disp('Reference')
wolffd@0 53 disp('[Mor98] P. Moerland (1998):')
wolffd@0 54 disp(' Localized mixtures of experts. (http://www.idiap.ch/~perry/)')
wolffd@0 55 disp('[Jor94] M.I. Jordan, R.A. Jacobs (1994):')
wolffd@0 56 disp(' HME and the EM algorithm. (http://www.cs.berkeley.edu/~jordan/)')
wolffd@0 57 disp('[Wat94] S.R. Waterhouse, A.J. Robinson (1994):')
wolffd@0 58 disp(' Classification using HME. (http://www.oigeeza.com/steve/)')
wolffd@0 59 disp('[Wei95] A.S. Weigend, M. Mangeas (1995):')
wolffd@0 60 disp(' Nonlinear gated experts for time series.')
wolffd@0 61 disp(' ')
wolffd@0 62
wolffd@0 63 if 0
wolffd@0 64 disp('(See the figure)')
wolffd@0 65 pause(5);
wolffd@0 66 %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0 67 im_path=which('HMEforMatlab.jpg');
wolffd@0 68 fig=imread(im_path, 'jpg');
wolffd@0 69 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0 70 figure('Units','pixels','MenuBar','none','NumberTitle','off', 'Name', 'HME model');
wolffd@0 71 image(fig);
wolffd@0 72 axis image;
wolffd@0 73 axis off;
wolffd@0 74 clear fig;
wolffd@0 75 set(gca,'Position',[0 0 1 1])
wolffd@0 76 disp('(Press any key to continue)')
wolffd@0 77 pause
wolffd@0 78 end
wolffd@0 79
wolffd@0 80 clc
wolffd@0 81 disp('---------------------------------------------------');
wolffd@0 82 disp(' Specify the Architecture ');
wolffd@0 83 disp('---------------------------------------------------');
wolffd@0 84 disp(' ');
wolffd@0 85 disp('What kind of model do you need?')
wolffd@0 86 disp(' ')
wolffd@0 87 disp('1) Regression ')
wolffd@0 88 disp('2) Classification')
wolffd@0 89 disp(' ')
wolffd@0 90 type=input('1 or 2?: ');
wolffd@0 91 if (isempty(type)|(~ismember(type,[1 2]))), error('Invalid value'); end
wolffd@0 92 clc
wolffd@0 93 disp('----------------------------------------------------');
wolffd@0 94 disp(' Specify the Architecture ');
wolffd@0 95 disp('----------------------------------------------------');
wolffd@0 96 disp(' ')
wolffd@0 97 disp('Now you have to set the number of experts and gating')
wolffd@0 98 disp('levels in the net. This script builds only balanced')
wolffd@0 99 disp('hierarchy with the same branching factor (>1)at each')
wolffd@0 100 disp('(gating) level. So remember that: ')
wolffd@0 101 disp(' ')
wolffd@0 102 disp(' num_exp = branch_fact^num_glevel ')
wolffd@0 103 disp(' ')
wolffd@0 104 disp('with branch_fact >=2.')
wolffd@0 105 disp('You can also set to zeros the number of gating level')
wolffd@0 106 disp('in order to obtain a classical GLM model. ')
wolffd@0 107 disp(' ')
wolffd@0 108 disp('----------------------------------------------------');
wolffd@0 109 disp(' ')
wolffd@0 110 num_glevel=input('Insert the number of gating levels {0,...,20}: ');
wolffd@0 111 if (isempty(num_glevel)|(~ismember(num_glevel,[0:20]))), error('Invalid value'); end
wolffd@0 112 nodes_info=zeros(4,num_glevel+2);
wolffd@0 113 if num_glevel>0, %------------------------------------------------------------------------------------
wolffd@0 114 for i=2:num_glevel+1,
wolffd@0 115 clc
wolffd@0 116 disp('----------------------------------------------------');
wolffd@0 117 disp(' Specify the Architecture ');
wolffd@0 118 disp('----------------------------------------------------');
wolffd@0 119 disp(' ')
wolffd@0 120 disp(['-> Gating network ', num2str(i-1), ' is a: '])
wolffd@0 121 disp(' ')
wolffd@0 122 disp(' 1) Softmax model');
wolffd@0 123 disp(' 2) Two layer perceptron model')
wolffd@0 124 disp(' ')
wolffd@0 125 nodes_info(1,i)=input('1 or 2?: ');
wolffd@0 126 if (isempty(nodes_info(1,i))|(~ismember(nodes_info(1,i),[1 2]))), error('Invalid value'); end
wolffd@0 127 disp(' ')
wolffd@0 128 if nodes_info(1,i)==2,
wolffd@0 129 nodes_info(3,i)=input('Insert the number of units in the hidden layer: ');
wolffd@0 130 if (isempty(nodes_info(3,i))|(floor(nodes_info(3,i))~=nodes_info(3,i))|(nodes_info(3,i)<=0)),
wolffd@0 131 error(['Invalid value: ', num2str(nodes_info(3,i)), ' is not a positive integer!']);
wolffd@0 132 end
wolffd@0 133 disp(' ')
wolffd@0 134 end
wolffd@0 135 nodes_info(4,i)=input('Insert the optimizer iteration number: ');
wolffd@0 136 if (isempty(nodes_info(4,i))|(floor(nodes_info(4,i))~=nodes_info(4,i))|(nodes_info(4,i)<=0)),
wolffd@0 137 error(['Invalid value: ', num2str(nodes_info(4,i)), ' is not a positive integer!']);
wolffd@0 138 end
wolffd@0 139 end
wolffd@0 140 clc
wolffd@0 141 disp('---------------------------------------------------------');
wolffd@0 142 disp(' Specify the Architecture ');
wolffd@0 143 disp('---------------------------------------------------------');
wolffd@0 144 disp(' ')
wolffd@0 145 disp('Now you have to set the number of experts in the network');
wolffd@0 146 disp('The value will be adjusted in order to obtain a hierarchy');
wolffd@0 147 disp('as said above.')
wolffd@0 148 disp(' ');
wolffd@0 149 num_exp=input(['Insert the approximative number of experts (>=', num2str(2^num_glevel), '): ']);
wolffd@0 150 if (isempty(num_exp)|(num_exp<=0)|(num_exp<2^num_glevel)),
wolffd@0 151 error('Invalid value');
wolffd@0 152 end
wolffd@0 153 app1=0; base=2;
wolffd@0 154 while app1<num_exp,
wolffd@0 155 app1=base^num_glevel;
wolffd@0 156 base=base+1;
wolffd@0 157 end
wolffd@0 158 app2=(base-2)^num_glevel;
wolffd@0 159 branch_fact=base-1;
wolffd@0 160 if app2>=(2^num_glevel)&(abs(app2-num_exp)<abs(app1-num_exp)),
wolffd@0 161 branch_fact=base-2;
wolffd@0 162 end
wolffd@0 163 clear app1 app2 base;
wolffd@0 164 disp(' ')
wolffd@0 165 disp(['The effective number of experts in the net is: ', num2str(branch_fact^num_glevel), '.'])
wolffd@0 166 disp(' ');
wolffd@0 167 else
wolffd@0 168 clc
wolffd@0 169 disp('---------------------------------------------------------');
wolffd@0 170 disp(' Specify the Architecture (GLM model) ');
wolffd@0 171 disp('---------------------------------------------------------');
wolffd@0 172 disp(' ')
wolffd@0 173 end % END of: if num_glevel>0-------------------------------------------------------------------------
wolffd@0 174
wolffd@0 175 if type==2,
wolffd@0 176 disp(['-> Expert node is a: '])
wolffd@0 177 disp(' ')
wolffd@0 178 disp(' 1) Softmax model');
wolffd@0 179 disp(' 2) Two layer perceptron model')
wolffd@0 180 disp(' ')
wolffd@0 181 nodes_info(1,end)=input('1 or 2?: ');
wolffd@0 182 if (isempty(nodes_info(1,end))|(~ismember(nodes_info(1,end),[1 2]))),
wolffd@0 183 error('Invalid value');
wolffd@0 184 end
wolffd@0 185 disp(' ')
wolffd@0 186 if nodes_info(1,end)==2,
wolffd@0 187 nodes_info(3,end)=input('Insert the number of units in the hidden layer: ');
wolffd@0 188 if (isempty(nodes_info(3,end))|(floor(nodes_info(3,end))~=nodes_info(3,end))|(nodes_info(3,end)<=0)),
wolffd@0 189 error(['Invalid value: ', num2str(nodes_info(3,end)), ' is not a positive integer!']);
wolffd@0 190 end
wolffd@0 191 disp(' ')
wolffd@0 192 end
wolffd@0 193 nodes_info(4,end)=input('Insert the optimizer iteration number: ');
wolffd@0 194 if (isempty(nodes_info(4,end))|(floor(nodes_info(4,end))~=nodes_info(4,end))|(nodes_info(4,end)<=0)),
wolffd@0 195 error(['Invalid value: ', num2str(nodes_info(4,end)), ' is not a positive integer!']);
wolffd@0 196 end
wolffd@0 197 elseif type==1,
wolffd@0 198 disp('What kind of covariance matrix structure do you want?')
wolffd@0 199 disp(' ')
wolffd@0 200 disp(' 1) Full');
wolffd@0 201 disp(' 2) Diagonal')
wolffd@0 202 disp(' 3) Full & Tied');
wolffd@0 203 disp(' 4) Diagonal & Tied')
wolffd@0 204
wolffd@0 205 disp(' ')
wolffd@0 206 nodes_info(4,end)=input('1, 2, 3 or 4?: ');
wolffd@0 207 if (isempty(nodes_info(4,end))|(~ismember(nodes_info(4,end),[1 2 3 4]))),
wolffd@0 208 error('Invalid value');
wolffd@0 209 end
wolffd@0 210 end
wolffd@0 211 clc
wolffd@0 212 disp('----------------------------------------------------');
wolffd@0 213 disp(' Specify the Input ');
wolffd@0 214 disp('----------------------------------------------------');
wolffd@0 215 disp(' ')
wolffd@0 216 disp('Do you want to...')
wolffd@0 217 disp(' ')
wolffd@0 218 disp('1) ...use your own dataset?')
wolffd@0 219 disp('2) ...apply the model on a toy example?')
wolffd@0 220 disp(' ')
wolffd@0 221 dataset=input('1 or 2?: ');
wolffd@0 222 if (isempty(dataset)|(~ismember(dataset,[1 2]))), error('Invalid value'); end
wolffd@0 223 if dataset==1,
wolffd@0 224 if type==1,
wolffd@0 225 clc
wolffd@0 226 disp('-------------------------------------------------------');
wolffd@0 227 disp(' Specify the Input - Regression problem ');
wolffd@0 228 disp('-------------------------------------------------------');
wolffd@0 229 disp(' ')
wolffd@0 230 disp('Be sure that each row of your data matrix is an example');
wolffd@0 231 disp('with the covariate values that precede the respond ones')
wolffd@0 232 disp(' ')
wolffd@0 233 disp('-------------------------------------------------------');
wolffd@0 234 disp(' ')
wolffd@0 235 cov_dim=input('Insert the covariate space dimension: ');
wolffd@0 236 if (isempty(cov_dim)|(floor(cov_dim)~=cov_dim)|(cov_dim<=0)),
wolffd@0 237 error(['Invalid value: ', num2str(cov_dim), ' is not a positive integer!']);
wolffd@0 238 end
wolffd@0 239 disp(' ')
wolffd@0 240 res_dim=input('Insert the dimension of the respond variable: ');
wolffd@0 241 if (isempty(res_dim)|(floor(res_dim)~=res_dim)|(res_dim<=0)),
wolffd@0 242 error(['Invalid value: ', num2str(res_dim), ' is not a positive integer!']);
wolffd@0 243 end
wolffd@0 244 disp(' ');
wolffd@0 245 elseif type==2
wolffd@0 246 clc
wolffd@0 247 disp('-------------------------------------------------------');
wolffd@0 248 disp(' Specify the Input - Classification problem ');
wolffd@0 249 disp('-------------------------------------------------------');
wolffd@0 250 disp(' ')
wolffd@0 251 disp('Be sure that each row of your data matrix is an example');
wolffd@0 252 disp('with the covariate values that precede the class labels');
wolffd@0 253 disp('(integer value >=1). ');
wolffd@0 254 disp(' ')
wolffd@0 255 disp('-------------------------------------------------------');
wolffd@0 256 disp(' ')
wolffd@0 257 cov_dim=input('Insert the covariate space dimension: ');
wolffd@0 258 if (isempty(cov_dim)|(floor(cov_dim)~=cov_dim)|(cov_dim<=0)),
wolffd@0 259 error(['Invalid value: ', num2str(cov_dim), ' is not a positive integer!']);
wolffd@0 260 end
wolffd@0 261 disp(' ')
wolffd@0 262 res_dim=input('Insert the number of classes: ');
wolffd@0 263 if (isempty(res_dim)|(floor(res_dim)~=res_dim)|(res_dim<=0)),
wolffd@0 264 error(['Invalid value: ', num2str(res_dim), ' is not a positive integer!']);
wolffd@0 265 end
wolffd@0 266 disp(' ')
wolffd@0 267 end
wolffd@0 268 % ------------------------------------------------------------------------------------------------
wolffd@0 269 % Loading training data --------------------------------------------------------------------------
wolffd@0 270 % ------------------------------------------------------------------------------------------------
wolffd@0 271 train_path=input('Insert the complete (with extension) path of the training data file:\n >> ','s');
wolffd@0 272 if isempty(train_path), error('You must specify a data set for training!'); end
wolffd@0 273 if ~isempty(findstr('.mat',train_path)),
wolffd@0 274 ap=load(train_path); app=fieldnames(ap); train_data=eval(['ap.', app{1,1}]);
wolffd@0 275 clear ap app;
wolffd@0 276 elseif ~isempty(findstr('.txt',train_path)),
wolffd@0 277 train_data=load(train_path, '-ascii');
wolffd@0 278 else
wolffd@0 279 error('Invalid data format: not a .mat or a .txt file')
wolffd@0 280 end
wolffd@0 281 if (size(train_data,2)~=cov_dim+res_dim)&(type==1),
wolffd@0 282 error(['Invalid data matrix size: ', num2str(size(train_data,2)), ' columns rather than ',...
wolffd@0 283 num2str(cov_dim+res_dim),'!']);
wolffd@0 284 elseif (size(train_data,2)~=cov_dim+1)&(type==2),
wolffd@0 285 error(['Invalid data matrix size: ', num2str(size(train_data,2)), ' columns rather than ',...
wolffd@0 286 num2str(cov_dim+1),'!']);
wolffd@0 287 elseif (~isempty(find(ismember(intersect([train_data(:,end)' 1:res_dim],...
wolffd@0 288 train_data(:,end)'),[1:res_dim])==0)))&(type==2),
wolffd@0 289 error('Invalid class label');
wolffd@0 290 end
wolffd@0 291 ntrain=size(train_data,1);
wolffd@0 292 train_d=train_data(:,1:cov_dim);
wolffd@0 293 if type==2,
wolffd@0 294 train_t=zeros(ntrain, res_dim);
wolffd@0 295 for m=1:res_dim,
wolffd@0 296 train_t((find(train_data(:,end)==m))',m)=1;
wolffd@0 297 end
wolffd@0 298 else
wolffd@0 299 train_t=train_data(:,cov_dim+1:end);
wolffd@0 300 end
wolffd@0 301 disp(' ')
wolffd@0 302 % ------------------------------------------------------------------------------------------------
wolffd@0 303 % Loading test data ------------------------------------------------------------------------------
wolffd@0 304 % ------------------------------------------------------------------------------------------------
wolffd@0 305 disp('(If you don''t want to specify a test-set press ''return'' only)');
wolffd@0 306 test_path=input('Insert the complete (with extension) path of the test data file:\n >> ','s');
wolffd@0 307 if ~isempty(test_path),
wolffd@0 308 if ~isempty(findstr('.mat',test_path)),
wolffd@0 309 ap=load(test_path); app=fieldnames(ap); test_data=eval(['ap.', app{1,1}]);
wolffd@0 310 clear ap app;
wolffd@0 311 elseif ~isempty(findstr('.txt',test_path)),
wolffd@0 312 test_data=load(test_path, '-ascii');
wolffd@0 313 else
wolffd@0 314 error('Invalid data format: not a .mat or a .txt file')
wolffd@0 315 end
wolffd@0 316 if (size(test_data,2)~=cov_dim)&(size(test_data,2)~=cov_dim+res_dim)&(type==1),
wolffd@0 317 error(['Invalid data matrix size: ', num2str(size(test_data,2)), ' columns rather than ',...
wolffd@0 318 num2str(cov_dim+res_dim), ' or ', num2str(cov_dim), '!']);
wolffd@0 319 elseif (size(test_data,2)~=cov_dim)&(size(test_data,2)~=cov_dim+1)&(type==2),
wolffd@0 320 error(['Invalid data matrix size: ', num2str(size(test_data,2)), ' columns rather than ',...
wolffd@0 321 num2str(cov_dim+1), ' or ', num2str(cov_dim), '!']);
wolffd@0 322 elseif (~isempty(find(ismember(intersect([test_data(:,end)' 1:res_dim],...
wolffd@0 323 test_data(:,end)'),[1:res_dim])==0)))&(type==2)&(size(test_data,2)==cov_dim+1),
wolffd@0 324 error('Invalid class label');
wolffd@0 325 end
wolffd@0 326 ntest=size(test_data,1);
wolffd@0 327 test_d=test_data(:,1:cov_dim);
wolffd@0 328 if (type==2)&(size(test_data,2)>cov_dim),
wolffd@0 329 test_t=zeros(ntest, res_dim);
wolffd@0 330 for m=1:res_dim,
wolffd@0 331 test_t((find(test_data(:,end)==m))',m)=1;
wolffd@0 332 end
wolffd@0 333 elseif (type==1)&(size(test_data,2)>cov_dim),
wolffd@0 334 test_t=test_data(:,cov_dim+1:end);
wolffd@0 335 end
wolffd@0 336 disp(' ');
wolffd@0 337 end
wolffd@0 338 else
wolffd@0 339 clc
wolffd@0 340 disp('----------------------------------------------------');
wolffd@0 341 disp(' Specify the Input ');
wolffd@0 342 disp('----------------------------------------------------');
wolffd@0 343 disp(' ')
wolffd@0 344 ntrain = input('Insert the number of examples in training (<500): ');
wolffd@0 345 if (isempty(ntrain)|(floor(ntrain)~=ntrain)|(ntrain<=0)|(ntrain>500)),
wolffd@0 346 error(['Invalid value: ', num2str(ntrain), ' is not a positive integer <500!']);
wolffd@0 347 end
wolffd@0 348 disp(' ')
wolffd@0 349 test_path='toy';
wolffd@0 350 ntest = input('Insert the number of examples in test (<500): ');
wolffd@0 351 if (isempty(ntest)|(floor(ntest)~=ntest)|(ntest<=0)|(ntest>500)),
wolffd@0 352 error(['Invalid value: ', num2str(ntest), ' is not a positive integer <500!']);
wolffd@0 353 end
wolffd@0 354
wolffd@0 355 if type==2,
wolffd@0 356 cov_dim=2;
wolffd@0 357 res_dim=3;
wolffd@0 358 seed = 42;
wolffd@0 359 [train_d, ntrain1, ntrain2, train_t]=gen_data(ntrain, seed);
wolffd@0 360 for m=1:ntrain
wolffd@0 361 q=[]; q = find(train_t(m,:)==1);
wolffd@0 362 train_data(m,:)=[train_d(m,:) q];
wolffd@0 363 end
wolffd@0 364 [test_d, ntest1, ntest2, test_t]=gen_data(ntest);
wolffd@0 365 for m=1:ntest
wolffd@0 366 q=[]; q = find(test_t(m,:)==1);
wolffd@0 367 test_data(m,:)=[test_d(m,:) q];
wolffd@0 368 end
wolffd@0 369 else
wolffd@0 370 cov_dim=1;
wolffd@0 371 res_dim=1;
wolffd@0 372 global HOME
wolffd@0 373 %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0 374 load([HOME '/examples/static/Misc/mixexp_data.txt'], '-ascii');
wolffd@0 375 %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0 376 train_data = mixexp_data(1:ntrain, :);
wolffd@0 377 train_d=train_data(:,1:cov_dim); train_t=train_data(:,cov_dim+1:end);
wolffd@0 378 test_data = mixexp_data(ntrain+1:ntrain+ntest, :);
wolffd@0 379 test_d=test_data(:,1:cov_dim);
wolffd@0 380 if size(test_data,2)>cov_dim,
wolffd@0 381 test_t=test_data(:,cov_dim+1:end);
wolffd@0 382 end
wolffd@0 383 end
wolffd@0 384 end
wolffd@0 385 % Set the nodes dimension-----------------------------------
wolffd@0 386 if num_glevel>0,
wolffd@0 387 nodes_info(2,2:num_glevel+1)=branch_fact;
wolffd@0 388 end
wolffd@0 389 nodes_info(2,1)=cov_dim; nodes_info(2,end)=res_dim;
wolffd@0 390 %-----------------------------------------------------------
wolffd@0 391 % Prepare the training data for the learning engine---------
wolffd@0 392 %-----------------------------------------------------------
wolffd@0 393 cases = cell(size(nodes_info,2), ntrain);
wolffd@0 394 for m=1:ntrain,
wolffd@0 395 cases{1,m}=train_data(m,1:cov_dim)';
wolffd@0 396 cases{end,m}=train_data(m,cov_dim+1:end)';
wolffd@0 397 end
wolffd@0 398 %-----------------------------------------------------------------------------------------------------
wolffd@0 399 [bnet onodes]=hme_topobuilder(nodes_info);
wolffd@0 400 engine = jtree_inf_engine(bnet, onodes);
wolffd@0 401 clc
wolffd@0 402 disp('---------------------------------------------------------------------');
wolffd@0 403 disp(' L E A R N I N G ');
wolffd@0 404 disp('---------------------------------------------------------------------');
wolffd@0 405 disp(' ')
wolffd@0 406 ll = 0;
wolffd@0 407 for l=1:ntrain
wolffd@0 408 scritta=['example number: ', int2str(l),'---------------------------------------------'];
wolffd@0 409 disp(scritta);
wolffd@0 410 ev = cases(:,l);
wolffd@0 411 [engine, loglik] = enter_evidence(engine, ev);
wolffd@0 412 ll = ll + loglik;
wolffd@0 413 end
wolffd@0 414 disp(' ')
wolffd@0 415 disp(['Log-likelihood before learning: ', num2str(ll)]);
wolffd@0 416 disp(' ')
wolffd@0 417 disp('(Press any key to continue)');
wolffd@0 418 pause
wolffd@0 419 %-----------------------------------------------------------
wolffd@0 420 clc
wolffd@0 421 disp('---------------------------------------------------------------------');
wolffd@0 422 disp(' L E A R N I N G ');
wolffd@0 423 disp('---------------------------------------------------------------------');
wolffd@0 424 disp(' ')
wolffd@0 425 max_em_iter=input('Insert the maximum number of the EM algorithm iterations: ');
wolffd@0 426 if (isempty(max_em_iter)|(floor(max_em_iter)~=max_em_iter)|(max_em_iter<=1)),
wolffd@0 427 error(['Invalid value: ', num2str(ntest), ' is not a positive integer >1!']);
wolffd@0 428 end
wolffd@0 429 disp(' ')
wolffd@0 430 disp(['Log-likelihood before learning: ', num2str(ll)]);
wolffd@0 431 disp(' ')
wolffd@0 432
wolffd@0 433 [bnet2, LL2] = learn_params_em(engine, cases, max_em_iter);
wolffd@0 434 disp(' ')
wolffd@0 435 fprintf('HME: loglik before learning %f, after %d iters %f\n', ll, length(LL2), LL2(end));
wolffd@0 436 disp(' ')
wolffd@0 437 disp('(Press any key to continue)');
wolffd@0 438 pause
wolffd@0 439 %-----------------------------------------------------------------------------------
wolffd@0 440 % Classification problem: plot data & decision boundaries if the input data size = 2
wolffd@0 441 % Regression problem: plot data & prediction if the input data size = 1
wolffd@0 442 %-----------------------------------------------------------------------------------
wolffd@0 443 if (type==2)&(nodes_info(2,1)==2)&(~isempty(test_path)),
wolffd@0 444 fh1=hme_class_plot(bnet2, nodes_info, train_data, test_data);
wolffd@0 445 disp(' ');
wolffd@0 446 disp('(See the figure)');
wolffd@0 447 elseif (type==2)&(nodes_info(2,1)==2)&(isempty(test_path)),
wolffd@0 448 fh1=hme_class_plot(bnet2, nodes_info, train_data);
wolffd@0 449 disp(' ');
wolffd@0 450 disp('(See the figure)');
wolffd@0 451 elseif (type==1)&(nodes_info(2,1)==1)&(~isempty(test_path)),
wolffd@0 452 fh1=hme_reg_plot(bnet2, nodes_info, train_data, test_data);
wolffd@0 453 disp(' ');
wolffd@0 454 disp('(See the figure)');
wolffd@0 455 elseif (type==1)&(nodes_info(2,1)==1)&(isempty(test_path)),
wolffd@0 456 fh1=hme_reg_plot(bnet2, nodes_info, train_data);
wolffd@0 457 disp(' ')
wolffd@0 458 disp('(See the figure)');
wolffd@0 459 end
wolffd@0 460 %-----------------------------------------------------------------------------------
wolffd@0 461 % Classification problem: plot confusion matrix
wolffd@0 462 %-----------------------------------------------------------------------------------
wolffd@0 463 if (type==2)
wolffd@0 464 ztrain=fhme(bnet2, nodes_info, train_d, size(train_d,1));
wolffd@0 465 [Htrain, trainRate]=confmat(ztrain, train_t); % CM on the training set
wolffd@0 466 fh2=figure('Name','Confusion matrix', 'MenuBar', 'none', 'NumberTitle', 'off');
wolffd@0 467 if (~isempty(test_path))&(size(test_data,2)>cov_dim),
wolffd@0 468 ztest=fhme(bnet2, nodes_info, test_d, size(test_d,1));
wolffd@0 469 [Htest, testRate]=confmat(ztest, test_t); % CM on the test set
wolffd@0 470 subplot(1,2,1);
wolffd@0 471 end
wolffd@0 472 plotmat(Htrain,'b','k',12)
wolffd@0 473 tick=[0.5:1:(0.5+nodes_info(2,end)-1)];
wolffd@0 474 set(gca,'XTick',tick)
wolffd@0 475 set(gca,'YTick',tick)
wolffd@0 476 grid('off')
wolffd@0 477 ylabel('True')
wolffd@0 478 xlabel('Prediction')
wolffd@0 479 title(['Confusion Matrix: training set (' num2str(trainRate(1)) '%)'])
wolffd@0 480 if (~isempty(test_path))&(size(test_data,2)>cov_dim),
wolffd@0 481 subplot(1,2,2)
wolffd@0 482 plotmat(Htest,'b','k',12)
wolffd@0 483 set(gca,'XTick',tick)
wolffd@0 484 set(gca,'YTick',tick)
wolffd@0 485 grid('off')
wolffd@0 486 ylabel('True')
wolffd@0 487 xlabel('Prediction')
wolffd@0 488 title(['Confusion Matrix: test set (' num2str(testRate(1)) '%)'])
wolffd@0 489 end
wolffd@0 490 disp(' ')
wolffd@0 491 disp('(Press any key to continue)');
wolffd@0 492 pause
wolffd@0 493 end
wolffd@0 494 %-----------------------------------------------------------------------------------
wolffd@0 495 % Regression & Classification problem: calculate the predictions & plot the LL trace
wolffd@0 496 %-----------------------------------------------------------------------------------
wolffd@0 497 train_result=fhme(bnet2,nodes_info,train_d,size(train_d,1));
wolffd@0 498 if ~isempty(test_path),
wolffd@0 499 test_result=fhme(bnet2,nodes_info,test_d,size(test_d,1));
wolffd@0 500 end
wolffd@0 501 fh3=figure('Name','Log-likelihood trace', 'MenuBar', 'none', 'NumberTitle', 'off')
wolffd@0 502 plot(LL2,'-ro',...
wolffd@0 503 'MarkerEdgeColor','k',...
wolffd@0 504 'MarkerFaceColor',[1 1 0],...
wolffd@0 505 'MarkerSize',4)
wolffd@0 506 title('Log-likelihood trace')
wolffd@0 507 %-----------------------------------------------------------------------------------
wolffd@0 508 % Regression & Classification problem: save the predictions
wolffd@0 509 %-----------------------------------------------------------------------------------
wolffd@0 510 clc
wolffd@0 511 disp('------------------------------------------------------------------');
wolffd@0 512 disp(' Save the results ');
wolffd@0 513 disp('------------------------------------------------------------------');
wolffd@0 514 disp(' ')
wolffd@0 515 %-----------------------------------------------------------------------------------
wolffd@0 516 save_quest_m=input('Do you want to save the HME model (Y/N)? [Y default]: ', 's');
wolffd@0 517 if isempty(save_quest_m),
wolffd@0 518 save_quest_m='Y';
wolffd@0 519 end
wolffd@0 520 if ~findstr(save_quest_m, ['Y', 'N']), error('Invalid input'); end
wolffd@0 521 if save_quest_m=='Y',
wolffd@0 522 disp(' ');
wolffd@0 523 m_save=input('Insert the complete path for save the HME model (.mat):\n >> ', 's');
wolffd@0 524 if isempty(m_save), error('You must specify a path!'); end
wolffd@0 525 save(m_save, 'bnet2');
wolffd@0 526 end
wolffd@0 527 %-----------------------------------------------------------------------------------
wolffd@0 528 disp(' ')
wolffd@0 529 save_quest=input('Do you want to save the HME predictions (Y/N)? [Y default]: ', 's');
wolffd@0 530 disp(' ')
wolffd@0 531 if isempty(save_quest),
wolffd@0 532 save_quest='Y';
wolffd@0 533 end
wolffd@0 534 if ~findstr(save_quest, ['Y', 'N']), error('Invalid input'); end
wolffd@0 535 if save_quest=='Y',
wolffd@0 536 tr_save=input('Insert the complete path for save the training data prediction (.mat):\n >> ', 's');
wolffd@0 537 if isempty(tr_save), error('You must specify a path!'); end
wolffd@0 538 save(tr_save, 'train_result');
wolffd@0 539 if ~isempty(test_path),
wolffd@0 540 disp(' ')
wolffd@0 541 te_save=input('Insert the complete path for save the test data prediction (.mat):\n >> ', 's');
wolffd@0 542 if isempty(te_save), error('You must specify a path!'); end
wolffd@0 543 save(te_save, 'test_result');
wolffd@0 544 end
wolffd@0 545 end
wolffd@0 546 clc
wolffd@0 547 disp('----------------------------------------------------');
wolffd@0 548 disp(' B Y E ! ');
wolffd@0 549 disp('----------------------------------------------------');
wolffd@0 550 pause(2)
wolffd@0 551 %clear
wolffd@0 552 clc