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

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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-1:000000000000 0:e9a9cd732c1e
1 % dataset -> (1=>user data) or (2=>toy example)
2 % type -> (1=> Regression model) or (2=>Classification model)
3 % num_glevel -> number of hidden nodes in the net (gating levels)
4 % num_exp -> number of experts in the net
5 % branch_fact -> dimension of the hidden nodes in the net
6 % cov_dim -> root node dimension
7 % res_dim -> output node dimension
8 % nodes_info -> 4 x num_glevel+2 matrix that contain all the info about the nodes:
9 % nodes_info(1,:) = nodes type: (0=>gaussian)or(1=>softmax)or(2=>mlp)
10 % nodes_info(2,:) = nodes size: [cov_dim num_glevel x branch_fact res_dim]
11 % nodes_info(3,:) = hidden units number (for mlp nodes)
12 % |- optimizer iteration number (for softmax & mlp CPD)
13 % nodes_info(4,:) =|- covariance type (for gaussian CPD)->
14 % | (1=>Full)or(2=>Diagonal)or(3=>Full&Tied)or(4=>Diagonal&Tied)
15 % fh1 -> Figure: data & decizion boundaries; fh2 -> confusion matrix; fh3 -> LL trace
16 % test_data -> test data matrix
17 % train_data -> training data matrix
18 % ntrain -> size(train_data,2)
19 % ntest -> size(test_data,2)
20 % cases -> (cell array) training data formatted for the learning engine
21 % bnet -> bayesian net before learning
22 % bnet2 -> bayesian net after learning
23 % ll -> log-likelihood before learning
24 % LL2 -> log-likelihood trace
25 % onodes -> obs nodes in bnet & bnet2
26 % max_em_iter -> maximum number of interations of the EM algorithm
27 % train_result -> prediction on the training set (as test_result)
28 %
29 % IMPORTANT: CHECK the loading path (lines 64 & 364)
30 % ----------------------------------------------------------------------------------------------------
31 % -> pierpaolo_b@hotmail.com or -> pampo@interfree.it
32 % ----------------------------------------------------------------------------------------------------
33
34 error('this no longer works with the latest version of BNT')
35
36 clear all;
37 clc;
38 disp('---------------------------------------------------');
39 disp(' Hierarchical Mixtures of Experts models builder ');
40 disp('---------------------------------------------------');
41 disp(' ')
42 disp(' Using this script you can build both an HME model')
43 disp('as in [Wat94] and [Jor94] i.e. with ''softmax'' gating')
44 disp('nodes and ''gaussian'' ( for regression ) or ''softmax''')
45 disp('( for classification ) expert node, and its variants')
46 disp('called ''gated nets'' where we use ''mlp'' models in')
47 disp('place of a number of ''softmax'' ones [Mor98], [Wei95].')
48 disp(' You can decide to train and test the model on your')
49 disp('datasets or to evaluate its performance on a toy')
50 disp('example.')
51 disp(' ')
52 disp('Reference')
53 disp('[Mor98] P. Moerland (1998):')
54 disp(' Localized mixtures of experts. (http://www.idiap.ch/~perry/)')
55 disp('[Jor94] M.I. Jordan, R.A. Jacobs (1994):')
56 disp(' HME and the EM algorithm. (http://www.cs.berkeley.edu/~jordan/)')
57 disp('[Wat94] S.R. Waterhouse, A.J. Robinson (1994):')
58 disp(' Classification using HME. (http://www.oigeeza.com/steve/)')
59 disp('[Wei95] A.S. Weigend, M. Mangeas (1995):')
60 disp(' Nonlinear gated experts for time series.')
61 disp(' ')
62
63 if 0
64 disp('(See the figure)')
65 pause(5);
66 %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
67 im_path=which('HMEforMatlab.jpg');
68 fig=imread(im_path, 'jpg');
69 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
70 figure('Units','pixels','MenuBar','none','NumberTitle','off', 'Name', 'HME model');
71 image(fig);
72 axis image;
73 axis off;
74 clear fig;
75 set(gca,'Position',[0 0 1 1])
76 disp('(Press any key to continue)')
77 pause
78 end
79
80 clc
81 disp('---------------------------------------------------');
82 disp(' Specify the Architecture ');
83 disp('---------------------------------------------------');
84 disp(' ');
85 disp('What kind of model do you need?')
86 disp(' ')
87 disp('1) Regression ')
88 disp('2) Classification')
89 disp(' ')
90 type=input('1 or 2?: ');
91 if (isempty(type)|(~ismember(type,[1 2]))), error('Invalid value'); end
92 clc
93 disp('----------------------------------------------------');
94 disp(' Specify the Architecture ');
95 disp('----------------------------------------------------');
96 disp(' ')
97 disp('Now you have to set the number of experts and gating')
98 disp('levels in the net. This script builds only balanced')
99 disp('hierarchy with the same branching factor (>1)at each')
100 disp('(gating) level. So remember that: ')
101 disp(' ')
102 disp(' num_exp = branch_fact^num_glevel ')
103 disp(' ')
104 disp('with branch_fact >=2.')
105 disp('You can also set to zeros the number of gating level')
106 disp('in order to obtain a classical GLM model. ')
107 disp(' ')
108 disp('----------------------------------------------------');
109 disp(' ')
110 num_glevel=input('Insert the number of gating levels {0,...,20}: ');
111 if (isempty(num_glevel)|(~ismember(num_glevel,[0:20]))), error('Invalid value'); end
112 nodes_info=zeros(4,num_glevel+2);
113 if num_glevel>0, %------------------------------------------------------------------------------------
114 for i=2:num_glevel+1,
115 clc
116 disp('----------------------------------------------------');
117 disp(' Specify the Architecture ');
118 disp('----------------------------------------------------');
119 disp(' ')
120 disp(['-> Gating network ', num2str(i-1), ' is a: '])
121 disp(' ')
122 disp(' 1) Softmax model');
123 disp(' 2) Two layer perceptron model')
124 disp(' ')
125 nodes_info(1,i)=input('1 or 2?: ');
126 if (isempty(nodes_info(1,i))|(~ismember(nodes_info(1,i),[1 2]))), error('Invalid value'); end
127 disp(' ')
128 if nodes_info(1,i)==2,
129 nodes_info(3,i)=input('Insert the number of units in the hidden layer: ');
130 if (isempty(nodes_info(3,i))|(floor(nodes_info(3,i))~=nodes_info(3,i))|(nodes_info(3,i)<=0)),
131 error(['Invalid value: ', num2str(nodes_info(3,i)), ' is not a positive integer!']);
132 end
133 disp(' ')
134 end
135 nodes_info(4,i)=input('Insert the optimizer iteration number: ');
136 if (isempty(nodes_info(4,i))|(floor(nodes_info(4,i))~=nodes_info(4,i))|(nodes_info(4,i)<=0)),
137 error(['Invalid value: ', num2str(nodes_info(4,i)), ' is not a positive integer!']);
138 end
139 end
140 clc
141 disp('---------------------------------------------------------');
142 disp(' Specify the Architecture ');
143 disp('---------------------------------------------------------');
144 disp(' ')
145 disp('Now you have to set the number of experts in the network');
146 disp('The value will be adjusted in order to obtain a hierarchy');
147 disp('as said above.')
148 disp(' ');
149 num_exp=input(['Insert the approximative number of experts (>=', num2str(2^num_glevel), '): ']);
150 if (isempty(num_exp)|(num_exp<=0)|(num_exp<2^num_glevel)),
151 error('Invalid value');
152 end
153 app1=0; base=2;
154 while app1<num_exp,
155 app1=base^num_glevel;
156 base=base+1;
157 end
158 app2=(base-2)^num_glevel;
159 branch_fact=base-1;
160 if app2>=(2^num_glevel)&(abs(app2-num_exp)<abs(app1-num_exp)),
161 branch_fact=base-2;
162 end
163 clear app1 app2 base;
164 disp(' ')
165 disp(['The effective number of experts in the net is: ', num2str(branch_fact^num_glevel), '.'])
166 disp(' ');
167 else
168 clc
169 disp('---------------------------------------------------------');
170 disp(' Specify the Architecture (GLM model) ');
171 disp('---------------------------------------------------------');
172 disp(' ')
173 end % END of: if num_glevel>0-------------------------------------------------------------------------
174
175 if type==2,
176 disp(['-> Expert node is a: '])
177 disp(' ')
178 disp(' 1) Softmax model');
179 disp(' 2) Two layer perceptron model')
180 disp(' ')
181 nodes_info(1,end)=input('1 or 2?: ');
182 if (isempty(nodes_info(1,end))|(~ismember(nodes_info(1,end),[1 2]))),
183 error('Invalid value');
184 end
185 disp(' ')
186 if nodes_info(1,end)==2,
187 nodes_info(3,end)=input('Insert the number of units in the hidden layer: ');
188 if (isempty(nodes_info(3,end))|(floor(nodes_info(3,end))~=nodes_info(3,end))|(nodes_info(3,end)<=0)),
189 error(['Invalid value: ', num2str(nodes_info(3,end)), ' is not a positive integer!']);
190 end
191 disp(' ')
192 end
193 nodes_info(4,end)=input('Insert the optimizer iteration number: ');
194 if (isempty(nodes_info(4,end))|(floor(nodes_info(4,end))~=nodes_info(4,end))|(nodes_info(4,end)<=0)),
195 error(['Invalid value: ', num2str(nodes_info(4,end)), ' is not a positive integer!']);
196 end
197 elseif type==1,
198 disp('What kind of covariance matrix structure do you want?')
199 disp(' ')
200 disp(' 1) Full');
201 disp(' 2) Diagonal')
202 disp(' 3) Full & Tied');
203 disp(' 4) Diagonal & Tied')
204
205 disp(' ')
206 nodes_info(4,end)=input('1, 2, 3 or 4?: ');
207 if (isempty(nodes_info(4,end))|(~ismember(nodes_info(4,end),[1 2 3 4]))),
208 error('Invalid value');
209 end
210 end
211 clc
212 disp('----------------------------------------------------');
213 disp(' Specify the Input ');
214 disp('----------------------------------------------------');
215 disp(' ')
216 disp('Do you want to...')
217 disp(' ')
218 disp('1) ...use your own dataset?')
219 disp('2) ...apply the model on a toy example?')
220 disp(' ')
221 dataset=input('1 or 2?: ');
222 if (isempty(dataset)|(~ismember(dataset,[1 2]))), error('Invalid value'); end
223 if dataset==1,
224 if type==1,
225 clc
226 disp('-------------------------------------------------------');
227 disp(' Specify the Input - Regression problem ');
228 disp('-------------------------------------------------------');
229 disp(' ')
230 disp('Be sure that each row of your data matrix is an example');
231 disp('with the covariate values that precede the respond ones')
232 disp(' ')
233 disp('-------------------------------------------------------');
234 disp(' ')
235 cov_dim=input('Insert the covariate space dimension: ');
236 if (isempty(cov_dim)|(floor(cov_dim)~=cov_dim)|(cov_dim<=0)),
237 error(['Invalid value: ', num2str(cov_dim), ' is not a positive integer!']);
238 end
239 disp(' ')
240 res_dim=input('Insert the dimension of the respond variable: ');
241 if (isempty(res_dim)|(floor(res_dim)~=res_dim)|(res_dim<=0)),
242 error(['Invalid value: ', num2str(res_dim), ' is not a positive integer!']);
243 end
244 disp(' ');
245 elseif type==2
246 clc
247 disp('-------------------------------------------------------');
248 disp(' Specify the Input - Classification problem ');
249 disp('-------------------------------------------------------');
250 disp(' ')
251 disp('Be sure that each row of your data matrix is an example');
252 disp('with the covariate values that precede the class labels');
253 disp('(integer value >=1). ');
254 disp(' ')
255 disp('-------------------------------------------------------');
256 disp(' ')
257 cov_dim=input('Insert the covariate space dimension: ');
258 if (isempty(cov_dim)|(floor(cov_dim)~=cov_dim)|(cov_dim<=0)),
259 error(['Invalid value: ', num2str(cov_dim), ' is not a positive integer!']);
260 end
261 disp(' ')
262 res_dim=input('Insert the number of classes: ');
263 if (isempty(res_dim)|(floor(res_dim)~=res_dim)|(res_dim<=0)),
264 error(['Invalid value: ', num2str(res_dim), ' is not a positive integer!']);
265 end
266 disp(' ')
267 end
268 % ------------------------------------------------------------------------------------------------
269 % Loading training data --------------------------------------------------------------------------
270 % ------------------------------------------------------------------------------------------------
271 train_path=input('Insert the complete (with extension) path of the training data file:\n >> ','s');
272 if isempty(train_path), error('You must specify a data set for training!'); end
273 if ~isempty(findstr('.mat',train_path)),
274 ap=load(train_path); app=fieldnames(ap); train_data=eval(['ap.', app{1,1}]);
275 clear ap app;
276 elseif ~isempty(findstr('.txt',train_path)),
277 train_data=load(train_path, '-ascii');
278 else
279 error('Invalid data format: not a .mat or a .txt file')
280 end
281 if (size(train_data,2)~=cov_dim+res_dim)&(type==1),
282 error(['Invalid data matrix size: ', num2str(size(train_data,2)), ' columns rather than ',...
283 num2str(cov_dim+res_dim),'!']);
284 elseif (size(train_data,2)~=cov_dim+1)&(type==2),
285 error(['Invalid data matrix size: ', num2str(size(train_data,2)), ' columns rather than ',...
286 num2str(cov_dim+1),'!']);
287 elseif (~isempty(find(ismember(intersect([train_data(:,end)' 1:res_dim],...
288 train_data(:,end)'),[1:res_dim])==0)))&(type==2),
289 error('Invalid class label');
290 end
291 ntrain=size(train_data,1);
292 train_d=train_data(:,1:cov_dim);
293 if type==2,
294 train_t=zeros(ntrain, res_dim);
295 for m=1:res_dim,
296 train_t((find(train_data(:,end)==m))',m)=1;
297 end
298 else
299 train_t=train_data(:,cov_dim+1:end);
300 end
301 disp(' ')
302 % ------------------------------------------------------------------------------------------------
303 % Loading test data ------------------------------------------------------------------------------
304 % ------------------------------------------------------------------------------------------------
305 disp('(If you don''t want to specify a test-set press ''return'' only)');
306 test_path=input('Insert the complete (with extension) path of the test data file:\n >> ','s');
307 if ~isempty(test_path),
308 if ~isempty(findstr('.mat',test_path)),
309 ap=load(test_path); app=fieldnames(ap); test_data=eval(['ap.', app{1,1}]);
310 clear ap app;
311 elseif ~isempty(findstr('.txt',test_path)),
312 test_data=load(test_path, '-ascii');
313 else
314 error('Invalid data format: not a .mat or a .txt file')
315 end
316 if (size(test_data,2)~=cov_dim)&(size(test_data,2)~=cov_dim+res_dim)&(type==1),
317 error(['Invalid data matrix size: ', num2str(size(test_data,2)), ' columns rather than ',...
318 num2str(cov_dim+res_dim), ' or ', num2str(cov_dim), '!']);
319 elseif (size(test_data,2)~=cov_dim)&(size(test_data,2)~=cov_dim+1)&(type==2),
320 error(['Invalid data matrix size: ', num2str(size(test_data,2)), ' columns rather than ',...
321 num2str(cov_dim+1), ' or ', num2str(cov_dim), '!']);
322 elseif (~isempty(find(ismember(intersect([test_data(:,end)' 1:res_dim],...
323 test_data(:,end)'),[1:res_dim])==0)))&(type==2)&(size(test_data,2)==cov_dim+1),
324 error('Invalid class label');
325 end
326 ntest=size(test_data,1);
327 test_d=test_data(:,1:cov_dim);
328 if (type==2)&(size(test_data,2)>cov_dim),
329 test_t=zeros(ntest, res_dim);
330 for m=1:res_dim,
331 test_t((find(test_data(:,end)==m))',m)=1;
332 end
333 elseif (type==1)&(size(test_data,2)>cov_dim),
334 test_t=test_data(:,cov_dim+1:end);
335 end
336 disp(' ');
337 end
338 else
339 clc
340 disp('----------------------------------------------------');
341 disp(' Specify the Input ');
342 disp('----------------------------------------------------');
343 disp(' ')
344 ntrain = input('Insert the number of examples in training (<500): ');
345 if (isempty(ntrain)|(floor(ntrain)~=ntrain)|(ntrain<=0)|(ntrain>500)),
346 error(['Invalid value: ', num2str(ntrain), ' is not a positive integer <500!']);
347 end
348 disp(' ')
349 test_path='toy';
350 ntest = input('Insert the number of examples in test (<500): ');
351 if (isempty(ntest)|(floor(ntest)~=ntest)|(ntest<=0)|(ntest>500)),
352 error(['Invalid value: ', num2str(ntest), ' is not a positive integer <500!']);
353 end
354
355 if type==2,
356 cov_dim=2;
357 res_dim=3;
358 seed = 42;
359 [train_d, ntrain1, ntrain2, train_t]=gen_data(ntrain, seed);
360 for m=1:ntrain
361 q=[]; q = find(train_t(m,:)==1);
362 train_data(m,:)=[train_d(m,:) q];
363 end
364 [test_d, ntest1, ntest2, test_t]=gen_data(ntest);
365 for m=1:ntest
366 q=[]; q = find(test_t(m,:)==1);
367 test_data(m,:)=[test_d(m,:) q];
368 end
369 else
370 cov_dim=1;
371 res_dim=1;
372 global HOME
373 %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
374 load([HOME '/examples/static/Misc/mixexp_data.txt'], '-ascii');
375 %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
376 train_data = mixexp_data(1:ntrain, :);
377 train_d=train_data(:,1:cov_dim); train_t=train_data(:,cov_dim+1:end);
378 test_data = mixexp_data(ntrain+1:ntrain+ntest, :);
379 test_d=test_data(:,1:cov_dim);
380 if size(test_data,2)>cov_dim,
381 test_t=test_data(:,cov_dim+1:end);
382 end
383 end
384 end
385 % Set the nodes dimension-----------------------------------
386 if num_glevel>0,
387 nodes_info(2,2:num_glevel+1)=branch_fact;
388 end
389 nodes_info(2,1)=cov_dim; nodes_info(2,end)=res_dim;
390 %-----------------------------------------------------------
391 % Prepare the training data for the learning engine---------
392 %-----------------------------------------------------------
393 cases = cell(size(nodes_info,2), ntrain);
394 for m=1:ntrain,
395 cases{1,m}=train_data(m,1:cov_dim)';
396 cases{end,m}=train_data(m,cov_dim+1:end)';
397 end
398 %-----------------------------------------------------------------------------------------------------
399 [bnet onodes]=hme_topobuilder(nodes_info);
400 engine = jtree_inf_engine(bnet, onodes);
401 clc
402 disp('---------------------------------------------------------------------');
403 disp(' L E A R N I N G ');
404 disp('---------------------------------------------------------------------');
405 disp(' ')
406 ll = 0;
407 for l=1:ntrain
408 scritta=['example number: ', int2str(l),'---------------------------------------------'];
409 disp(scritta);
410 ev = cases(:,l);
411 [engine, loglik] = enter_evidence(engine, ev);
412 ll = ll + loglik;
413 end
414 disp(' ')
415 disp(['Log-likelihood before learning: ', num2str(ll)]);
416 disp(' ')
417 disp('(Press any key to continue)');
418 pause
419 %-----------------------------------------------------------
420 clc
421 disp('---------------------------------------------------------------------');
422 disp(' L E A R N I N G ');
423 disp('---------------------------------------------------------------------');
424 disp(' ')
425 max_em_iter=input('Insert the maximum number of the EM algorithm iterations: ');
426 if (isempty(max_em_iter)|(floor(max_em_iter)~=max_em_iter)|(max_em_iter<=1)),
427 error(['Invalid value: ', num2str(ntest), ' is not a positive integer >1!']);
428 end
429 disp(' ')
430 disp(['Log-likelihood before learning: ', num2str(ll)]);
431 disp(' ')
432
433 [bnet2, LL2] = learn_params_em(engine, cases, max_em_iter);
434 disp(' ')
435 fprintf('HME: loglik before learning %f, after %d iters %f\n', ll, length(LL2), LL2(end));
436 disp(' ')
437 disp('(Press any key to continue)');
438 pause
439 %-----------------------------------------------------------------------------------
440 % Classification problem: plot data & decision boundaries if the input data size = 2
441 % Regression problem: plot data & prediction if the input data size = 1
442 %-----------------------------------------------------------------------------------
443 if (type==2)&(nodes_info(2,1)==2)&(~isempty(test_path)),
444 fh1=hme_class_plot(bnet2, nodes_info, train_data, test_data);
445 disp(' ');
446 disp('(See the figure)');
447 elseif (type==2)&(nodes_info(2,1)==2)&(isempty(test_path)),
448 fh1=hme_class_plot(bnet2, nodes_info, train_data);
449 disp(' ');
450 disp('(See the figure)');
451 elseif (type==1)&(nodes_info(2,1)==1)&(~isempty(test_path)),
452 fh1=hme_reg_plot(bnet2, nodes_info, train_data, test_data);
453 disp(' ');
454 disp('(See the figure)');
455 elseif (type==1)&(nodes_info(2,1)==1)&(isempty(test_path)),
456 fh1=hme_reg_plot(bnet2, nodes_info, train_data);
457 disp(' ')
458 disp('(See the figure)');
459 end
460 %-----------------------------------------------------------------------------------
461 % Classification problem: plot confusion matrix
462 %-----------------------------------------------------------------------------------
463 if (type==2)
464 ztrain=fhme(bnet2, nodes_info, train_d, size(train_d,1));
465 [Htrain, trainRate]=confmat(ztrain, train_t); % CM on the training set
466 fh2=figure('Name','Confusion matrix', 'MenuBar', 'none', 'NumberTitle', 'off');
467 if (~isempty(test_path))&(size(test_data,2)>cov_dim),
468 ztest=fhme(bnet2, nodes_info, test_d, size(test_d,1));
469 [Htest, testRate]=confmat(ztest, test_t); % CM on the test set
470 subplot(1,2,1);
471 end
472 plotmat(Htrain,'b','k',12)
473 tick=[0.5:1:(0.5+nodes_info(2,end)-1)];
474 set(gca,'XTick',tick)
475 set(gca,'YTick',tick)
476 grid('off')
477 ylabel('True')
478 xlabel('Prediction')
479 title(['Confusion Matrix: training set (' num2str(trainRate(1)) '%)'])
480 if (~isempty(test_path))&(size(test_data,2)>cov_dim),
481 subplot(1,2,2)
482 plotmat(Htest,'b','k',12)
483 set(gca,'XTick',tick)
484 set(gca,'YTick',tick)
485 grid('off')
486 ylabel('True')
487 xlabel('Prediction')
488 title(['Confusion Matrix: test set (' num2str(testRate(1)) '%)'])
489 end
490 disp(' ')
491 disp('(Press any key to continue)');
492 pause
493 end
494 %-----------------------------------------------------------------------------------
495 % Regression & Classification problem: calculate the predictions & plot the LL trace
496 %-----------------------------------------------------------------------------------
497 train_result=fhme(bnet2,nodes_info,train_d,size(train_d,1));
498 if ~isempty(test_path),
499 test_result=fhme(bnet2,nodes_info,test_d,size(test_d,1));
500 end
501 fh3=figure('Name','Log-likelihood trace', 'MenuBar', 'none', 'NumberTitle', 'off')
502 plot(LL2,'-ro',...
503 'MarkerEdgeColor','k',...
504 'MarkerFaceColor',[1 1 0],...
505 'MarkerSize',4)
506 title('Log-likelihood trace')
507 %-----------------------------------------------------------------------------------
508 % Regression & Classification problem: save the predictions
509 %-----------------------------------------------------------------------------------
510 clc
511 disp('------------------------------------------------------------------');
512 disp(' Save the results ');
513 disp('------------------------------------------------------------------');
514 disp(' ')
515 %-----------------------------------------------------------------------------------
516 save_quest_m=input('Do you want to save the HME model (Y/N)? [Y default]: ', 's');
517 if isempty(save_quest_m),
518 save_quest_m='Y';
519 end
520 if ~findstr(save_quest_m, ['Y', 'N']), error('Invalid input'); end
521 if save_quest_m=='Y',
522 disp(' ');
523 m_save=input('Insert the complete path for save the HME model (.mat):\n >> ', 's');
524 if isempty(m_save), error('You must specify a path!'); end
525 save(m_save, 'bnet2');
526 end
527 %-----------------------------------------------------------------------------------
528 disp(' ')
529 save_quest=input('Do you want to save the HME predictions (Y/N)? [Y default]: ', 's');
530 disp(' ')
531 if isempty(save_quest),
532 save_quest='Y';
533 end
534 if ~findstr(save_quest, ['Y', 'N']), error('Invalid input'); end
535 if save_quest=='Y',
536 tr_save=input('Insert the complete path for save the training data prediction (.mat):\n >> ', 's');
537 if isempty(tr_save), error('You must specify a path!'); end
538 save(tr_save, 'train_result');
539 if ~isempty(test_path),
540 disp(' ')
541 te_save=input('Insert the complete path for save the test data prediction (.mat):\n >> ', 's');
542 if isempty(te_save), error('You must specify a path!'); end
543 save(te_save, 'test_result');
544 end
545 end
546 clc
547 disp('----------------------------------------------------');
548 disp(' B Y E ! ');
549 disp('----------------------------------------------------');
550 pause(2)
551 %clear
552 clc