1 | |
---|
2 | |
---|
3 | #include <limits> |
---|
4 | #include "arx.h" |
---|
5 | |
---|
6 | |
---|
7 | #include <fstream> |
---|
8 | #include<iostream> |
---|
9 | #include<iomanip> |
---|
10 | |
---|
11 | namespace bdm { |
---|
12 | |
---|
13 | |
---|
14 | |
---|
15 | |
---|
16 | /* |
---|
17 | struct str_aux { |
---|
18 | vec d0; |
---|
19 | double nu0; |
---|
20 | mat L0; |
---|
21 | mat L; |
---|
22 | vec d; |
---|
23 | double nu; |
---|
24 | ivec strL; // Current structure of L and d |
---|
25 | ivec strRgr; // Structure elements currently inside regressor (after regressand) |
---|
26 | ivec strMis; // structure elements, that are currently outside regressor (before regressand) |
---|
27 | int posit1; // regressand position |
---|
28 | int nbits; // number of bits available in double |
---|
29 | bvec bitstr; |
---|
30 | double loglik; // loglikelihood |
---|
31 | }; |
---|
32 | |
---|
33 | */ |
---|
34 | |
---|
35 | |
---|
36 | /* bvec str_bitset( bvec in,ivec ns,int nbits){ |
---|
37 | //int index, bitindex,n; |
---|
38 | bvec out = in; |
---|
39 | int n; |
---|
40 | |
---|
41 | for (int i = 0; i < ns.length(); i++){ |
---|
42 | n = ns(i); |
---|
43 | out(n-2) = 1; |
---|
44 | cout << out; |
---|
45 | |
---|
46 | } |
---|
47 | |
---|
48 | return out; |
---|
49 | |
---|
50 | }*/ |
---|
51 | |
---|
52 | |
---|
53 | void str_bitset ( bvec &out, ivec ns, int nbits ) { |
---|
54 | for ( int i = 0; i < ns.length(); i++ ) { |
---|
55 | out ( ns ( i ) - 2 ) = 1; |
---|
56 | } |
---|
57 | } |
---|
58 | |
---|
59 | |
---|
60 | |
---|
61 | |
---|
62 | |
---|
63 | |
---|
64 | double seloglik1 ( str_aux in ) { |
---|
65 | // This is the loglikelihood (non-constant part) - this should be used in |
---|
66 | // frequent computation |
---|
67 | int len = length ( in.d ); |
---|
68 | int p1 = in.posit1 - 1; |
---|
69 | |
---|
70 | double i1 = -0.5 * in.nu * log ( in.d ( p1 ) ) - 0.5 * sum ( log ( in.d.right ( len - p1 - 1 ) ) ); |
---|
71 | double i0 = -0.5 * in.nu0 * log ( in.d0 ( p1 ) ) - 0.5 * sum ( log ( in.d0.right ( len - p1 - 1 ) ) ); |
---|
72 | return i1 - i0; |
---|
73 | //DEBUGGing print: |
---|
74 | //fprintf('SELOGLIK1: str=%s loglik=%g\n', strPrintstr(in), l);*/ |
---|
75 | |
---|
76 | } |
---|
77 | |
---|
78 | |
---|
79 | void sedydr ( mat &r, mat &f, double &Dr, double &Df, int R/*,int jl,int jh ,mat &rout, mat &fout, double &Drout, double &Dfoutint &kr*/ ) { |
---|
80 | /*SEDYDR dyadic reduction, performs transformation of sum of 2 dyads |
---|
81 | % |
---|
82 | % [rout, fout, Drout, Dfout, kr] = sedydr(r,f,Dr,Df,R,jl,jh); |
---|
83 | % [rout, fout, Drout, Dfout] = sedydr(r,f,Dr,Df,R); |
---|
84 | % |
---|
85 | % Description: dyadic reduction, performs transformation of sum of |
---|
86 | % 2 dyads r*Dr*r'+ f*Df*f' so that the element of r pointed by R is zeroed |
---|
87 | % |
---|
88 | % r : column vector of reduced dyad |
---|
89 | % f : column vector of reducing dyad |
---|
90 | % Dr : scalar with weight of reduced dyad |
---|
91 | % Df : scalar with weight of reducing dyad |
---|
92 | % R : scalar number giving 1 based index to the element of r, |
---|
93 | % which is to be reduced to |
---|
94 | % zero; the corresponding element of f is assumed to be 1. |
---|
95 | % jl : lower index of the range within which the dyads are |
---|
96 | % modified (can be omitted, then everything is updated) |
---|
97 | % jh : upper index of the range within which the dyads are |
---|
98 | % modified (can be omitted then everything is updated) |
---|
99 | % rout,fout,Drout,dfout : resulting two dyads |
---|
100 | % kr : coefficient used in the transformation of r |
---|
101 | % rnew = r + kr*f |
---|
102 | % |
---|
103 | % Description: dyadic reduction, performs transformation of sum of |
---|
104 | % 2 dyads r*Dr*r'+ f*Df*f' so that the element of r indexed by R is zeroed |
---|
105 | % Remark1: Constant mzero means machine zero and should be modified |
---|
106 | % according to the precision of particular machine |
---|
107 | % Remark2: jl and jh are, in fact, obsolete. It takes longer time to |
---|
108 | % compute them compared to plain version. The reason is that we |
---|
109 | % are doing vector operations in m-file. Other reason is that |
---|
110 | % we need to copy whole vector anyway. It can save half of time for |
---|
111 | % c-file, if you use it correctly. (please do tests) |
---|
112 | % |
---|
113 | % Note: naming: |
---|
114 | % se = structure estimation |
---|
115 | % dydr = dyadic reduction |
---|
116 | % |
---|
117 | % Original Fortran design: V. Peterka 17-7-89 |
---|
118 | % Modified for c-language: probably R. Kulhavy |
---|
119 | % Modified for m-language: L. Tesar 2/2003 |
---|
120 | % Updated: Feb 2003 |
---|
121 | % Project: post-ProDaCTool |
---|
122 | % Reference: none*/ |
---|
123 | |
---|
124 | /*if nargin<6; |
---|
125 | update_whole=1; |
---|
126 | else |
---|
127 | update_whole=0; |
---|
128 | end;*/ |
---|
129 | |
---|
130 | double mzero = 1e-32; |
---|
131 | |
---|
132 | if ( Dr < mzero ) { |
---|
133 | Dr = 0; |
---|
134 | } |
---|
135 | |
---|
136 | double r0 = r ( R, 0 ); |
---|
137 | double kD = Df; |
---|
138 | double kr = r0 * Dr; |
---|
139 | |
---|
140 | |
---|
141 | Df = kD + r0 * kr; |
---|
142 | |
---|
143 | if ( Df > mzero ) { |
---|
144 | kD = kD / Df; |
---|
145 | kr = kr / Df; |
---|
146 | } else { |
---|
147 | kD = 1; |
---|
148 | kr = 0; |
---|
149 | } |
---|
150 | |
---|
151 | Dr = Dr * kD; |
---|
152 | |
---|
153 | // Try to uncomment marked stuff (*) if in numerical problems, but I don't |
---|
154 | // think it can make any difference for normal healthy floating-point unit |
---|
155 | //if update_whole; |
---|
156 | r = r - r0 * f; |
---|
157 | // rout(R) = 0; // * could be needed for some nonsense cases(or numeric reasons?), normally not |
---|
158 | f = f + kr * r; |
---|
159 | // fout(R) = 1; // * could be needed for some nonsense cases(or numeric reasons?), normally not |
---|
160 | /*else; |
---|
161 | rout = r; |
---|
162 | fout = f; |
---|
163 | rout(jl:jh) = r(jl:jh) - r0 * f(jl:jh); |
---|
164 | rout(R) = 0; |
---|
165 | fout(jl:jh) = f(jl:jh) + kr * rout(jl:jh); |
---|
166 | end;*/ |
---|
167 | } |
---|
168 | |
---|
169 | |
---|
170 | |
---|
171 | /*mat*/ void seswapudl ( mat &L, vec &d , int i/*, vec &dout*/ ) { |
---|
172 | /*%SESWAPUDL swaps information matrix in decomposition V=L^T diag(d) L |
---|
173 | % |
---|
174 | % [Lout, dout] = seswapudl(L,d,i); |
---|
175 | % |
---|
176 | % L : lower triangular matrix with 1's on diagonal of the decomposistion |
---|
177 | % d : diagonal vector of diagonal matrix of the decomposition |
---|
178 | % i : index of line to be swapped with the next one |
---|
179 | % Lout : output lower triangular matrix |
---|
180 | % dout : output diagional vector of diagonal matrix D |
---|
181 | % |
---|
182 | % Description: |
---|
183 | % Lout' * diag(dout) * Lout = P(i,i+1) * L' * diag(d) * L * P(i,i+1); |
---|
184 | % |
---|
185 | % Where permutation matrix P(i,j) permutates columns if applied from the |
---|
186 | % right and line if applied from the left. |
---|
187 | % |
---|
188 | % Note: naming: |
---|
189 | % se = structure estimation |
---|
190 | % lite = light, simple |
---|
191 | % udl = U*D*L, or more precisely, L'*D*L, also called as ld |
---|
192 | % |
---|
193 | % Design : L. Tesar |
---|
194 | % Updated : Feb 2003 |
---|
195 | % Project : post-ProDaCTool |
---|
196 | % Reference: sedydr*/ |
---|
197 | |
---|
198 | int j = i + 1; |
---|
199 | |
---|
200 | double pomd = d ( i ); |
---|
201 | d ( i ) = d ( j ); |
---|
202 | d ( j ) = pomd; |
---|
203 | |
---|
204 | /*vec pomL = L.get_row(i); |
---|
205 | L.set_row(i, L.get_row(j)); |
---|
206 | L.set_row(j,pomL);*/ |
---|
207 | |
---|
208 | L.swap_rows ( i, j ); |
---|
209 | L.swap_cols ( i, j ); |
---|
210 | |
---|
211 | /*pomL = L.get_col(i); |
---|
212 | L.set_col(i, L.get_col(j)); |
---|
213 | L.set_col(j,pomL);*/ |
---|
214 | |
---|
215 | //% We must be working with LINES of matrix L ! |
---|
216 | |
---|
217 | |
---|
218 | |
---|
219 | mat r = L.get_row ( i ); |
---|
220 | r = r.transpose(); |
---|
221 | r = r.transpose(); |
---|
222 | //???????????????? |
---|
223 | mat f = L.get_row ( j ); |
---|
224 | f = f.transpose(); |
---|
225 | f = f.transpose(); |
---|
226 | |
---|
227 | |
---|
228 | |
---|
229 | |
---|
230 | double Dr = d ( i ); |
---|
231 | double Df = d ( j ); |
---|
232 | |
---|
233 | sedydr ( r, f, Dr, Df, j ); |
---|
234 | |
---|
235 | |
---|
236 | |
---|
237 | double r0 = r ( i, 0 ); |
---|
238 | Dr = Dr * r0 * r0; |
---|
239 | r = r / r0; |
---|
240 | |
---|
241 | |
---|
242 | |
---|
243 | mat pom_mat = r.transpose(); |
---|
244 | L.set_row ( i, pom_mat.get_row ( 0 ) ); |
---|
245 | pom_mat = f.transpose(); |
---|
246 | L.set_row ( j, pom_mat.get_row ( 0 ) ); |
---|
247 | |
---|
248 | d ( i ) = Dr; |
---|
249 | d ( j ) = Df; |
---|
250 | |
---|
251 | L ( i, i ) = 1; |
---|
252 | L ( j, j ) = 1; |
---|
253 | |
---|
254 | |
---|
255 | } |
---|
256 | |
---|
257 | |
---|
258 | void str_bitres ( bvec &out, ivec ns, int nbits ) { |
---|
259 | |
---|
260 | |
---|
261 | for ( int i = 0; i < ns.length(); i++ ) { |
---|
262 | out ( ns ( i ) - 2 ) = 0; |
---|
263 | } |
---|
264 | |
---|
265 | |
---|
266 | } |
---|
267 | |
---|
268 | str_aux sestrremove ( str_aux in, ivec removed_elements ) { |
---|
269 | //% Removes elements from regressor |
---|
270 | int n_strL = length ( in.strL ); |
---|
271 | str_aux out = in; |
---|
272 | for ( int i = 0; i < removed_elements.length(); i++ ) { |
---|
273 | |
---|
274 | int f = removed_elements ( i ); |
---|
275 | int posit1 = ( find ( out.strL == 1 ) ) ( 0 ); |
---|
276 | int positf = ( find ( out.strL == f ) ) ( 0 ); |
---|
277 | int pom_strL; |
---|
278 | for ( int g = positf - 1; g > posit1 - 1; g-- ) { |
---|
279 | //% BEGIN: We are swapping g and g+1 NOW!!!! |
---|
280 | seswapudl ( out.L, out.d, g ); |
---|
281 | seswapudl ( out.L0, out.d0, g ); |
---|
282 | |
---|
283 | pom_strL = out.strL ( g ); |
---|
284 | out.strL ( g ) = out.strL ( g + 1 ); |
---|
285 | out.strL ( g + 1 ) = pom_strL; |
---|
286 | |
---|
287 | //% END |
---|
288 | } |
---|
289 | } |
---|
290 | out.posit1 = ( find ( out.strL == 1 ) ) ( 0 ) + 1; |
---|
291 | out.strRgr = out.strL.right ( n_strL - out.posit1 ); |
---|
292 | out.strMis = out.strL.left ( out.posit1 - 1 ); |
---|
293 | str_bitres ( out.bitstr, removed_elements, out.nbits ); |
---|
294 | out.loglik = seloglik1 ( out ); |
---|
295 | |
---|
296 | return out; |
---|
297 | } |
---|
298 | |
---|
299 | |
---|
300 | ivec setdiff ( ivec a, ivec b ) { |
---|
301 | ivec pos; |
---|
302 | |
---|
303 | for ( int i = 0; i < b.length(); i++ ) { |
---|
304 | pos = find ( a == b ( i ) ); |
---|
305 | for ( int j = 0; j < pos.length(); j++ ) { |
---|
306 | a.del ( pos ( j ) - j ); |
---|
307 | } |
---|
308 | } |
---|
309 | return a; |
---|
310 | } |
---|
311 | |
---|
312 | |
---|
313 | |
---|
314 | /* |
---|
315 | |
---|
316 | Array<str_aux> add_new(Array<str_aux> global_best,str_aux newone,int nbest){ |
---|
317 | // Eventually add to global best, but do not go over nbest values |
---|
318 | // Also avoids repeating things, which makes this function awfully slow |
---|
319 | |
---|
320 | Array<str_aux> global_best_out; |
---|
321 | if (global_best.length() >= nbest){ |
---|
322 | //logliks = [global_best.loglik]; |
---|
323 | |
---|
324 | vec logliks(1); |
---|
325 | logliks(0) = global_best(0).loglik; |
---|
326 | for (int j = 1; j < global_best.length(); j++) |
---|
327 | logliks = concat(logliks, global_best(j).loglik); |
---|
328 | |
---|
329 | int i, addit; |
---|
330 | double loglik = min(logliks, i); |
---|
331 | global_best_out = global_best; |
---|
332 | if (loglik < newone.loglik){ |
---|
333 | // if ~any(logliks == new.loglik); |
---|
334 | addit=1; |
---|
335 | |
---|
336 | |
---|
337 | |
---|
338 | if (newone.bitstr.length() == 1) { |
---|
339 | for (int j = 0; j < global_best.length(); j++){ |
---|
340 | for(int i = 0; i < global_best(j).bitstr.length(); i++){ |
---|
341 | |
---|
342 | if (newone.bitstr(0) == global_best(j).bitstr(i)){ |
---|
343 | addit = 0; |
---|
344 | break; |
---|
345 | } |
---|
346 | } |
---|
347 | } |
---|
348 | } |
---|
349 | if (addit){ |
---|
350 | global_best_out(i) = newone; |
---|
351 | // DEBUGging print: |
---|
352 | // fprintf('ADDED structure, add_new: %s, loglik=%g\n', strPrintstr(new), new.loglik); |
---|
353 | } |
---|
354 | } |
---|
355 | } |
---|
356 | else |
---|
357 | global_best_out = concat(global_best, newone); |
---|
358 | |
---|
359 | return global_best_out; |
---|
360 | |
---|
361 | } |
---|
362 | |
---|
363 | */ |
---|
364 | |
---|
365 | void add_new ( Array<str_aux> &global_best, str_aux newone, int nbest ) { |
---|
366 | // Eventually add to global best, but do not go over nbest values |
---|
367 | // Also avoids repeating things, which makes this function awfully slow |
---|
368 | |
---|
369 | int addit, i = 0; |
---|
370 | if ( global_best.length() >= nbest ) { |
---|
371 | //logliks = [global_best.loglik]; |
---|
372 | |
---|
373 | |
---|
374 | for ( int j = 1; j < global_best.length(); j++ ) { |
---|
375 | if ( global_best ( j ).loglik < global_best ( i ).loglik ) { |
---|
376 | i = j; |
---|
377 | } |
---|
378 | } |
---|
379 | |
---|
380 | if ( global_best ( i ).loglik < newone.loglik ) { |
---|
381 | // if ~any(logliks == new.loglik); |
---|
382 | addit = 1; |
---|
383 | |
---|
384 | |
---|
385 | //???????????????????????????????????????????? |
---|
386 | if ( newone.bitstr.length() == 1 ) { |
---|
387 | for ( int j = 0; j < global_best.length(); j++ ) { |
---|
388 | for ( int i = 0; i < global_best ( j ).bitstr.length(); i++ ) { |
---|
389 | |
---|
390 | if ( newone.bitstr ( 0 ) == global_best ( j ).bitstr ( i ) ) { |
---|
391 | addit = 0; |
---|
392 | break; |
---|
393 | } |
---|
394 | } |
---|
395 | } |
---|
396 | } |
---|
397 | |
---|
398 | |
---|
399 | //????????????????????????????????????????????????? |
---|
400 | |
---|
401 | if ( addit ) { |
---|
402 | global_best ( i ) = newone; |
---|
403 | // DEBUGging print: |
---|
404 | // fprintf('ADDED structure, add_new: %s, loglik=%g\n', strPrintstr(new), new.loglik); |
---|
405 | } |
---|
406 | } |
---|
407 | } else |
---|
408 | global_best = concat ( global_best, newone ); |
---|
409 | |
---|
410 | } |
---|
411 | |
---|
412 | |
---|
413 | |
---|
414 | |
---|
415 | |
---|
416 | |
---|
417 | str_aux sestrinsert ( str_aux in, ivec inserted_elements ) { |
---|
418 | // Moves elements into regressor |
---|
419 | int n_strL = in.strL.length(); |
---|
420 | str_aux out = in; |
---|
421 | for ( int j = 0; j < inserted_elements.length(); j++ ) { |
---|
422 | int f = inserted_elements ( j ); |
---|
423 | int posit1 = ( find ( out.strL == 1 ) ) ( 0 ); |
---|
424 | int positf = ( find ( out.strL == f ) ) ( 0 ); |
---|
425 | for ( int g = positf; g <= posit1 - 1; g++ ) { |
---|
426 | |
---|
427 | // BEGIN: We are swapping g and g+1 NOW!!!! |
---|
428 | seswapudl ( out.L, out.d, g ); |
---|
429 | seswapudl ( out.L0, out.d0, g ); |
---|
430 | |
---|
431 | |
---|
432 | int pom_strL = out.strL ( g ); |
---|
433 | out.strL ( g ) = out.strL ( g + 1 ); |
---|
434 | out.strL ( g + 1 ) = pom_strL; |
---|
435 | |
---|
436 | // END |
---|
437 | } |
---|
438 | } |
---|
439 | |
---|
440 | out.posit1 = ( find ( out.strL == 1 ) ) ( 0 ) + 1; |
---|
441 | out.strRgr = out.strL.right ( n_strL - out.posit1 ); |
---|
442 | out.strMis = out.strL.left ( out.posit1 - 1 ); |
---|
443 | str_bitset ( out.bitstr, inserted_elements, out.nbits ); |
---|
444 | |
---|
445 | out.loglik = seloglik1 ( out ); |
---|
446 | |
---|
447 | |
---|
448 | |
---|
449 | return out; |
---|
450 | |
---|
451 | } |
---|
452 | |
---|
453 | double seloglik2 ( str_aux in ) { |
---|
454 | // This is the loglikelihood (constant part) - this should be added to |
---|
455 | // everything at the end. It needs some computation, so it is useless to |
---|
456 | // make it for all the stuff |
---|
457 | double logpi = log ( pi ); |
---|
458 | |
---|
459 | double i1 = lgamma ( in.nu / 2 ) - 0.5 * in.nu * logpi; |
---|
460 | double i0 = lgamma ( in.nu0 / 2 ) - 0.5 * in.nu0 * logpi; |
---|
461 | return i1 - i0; |
---|
462 | } |
---|
463 | |
---|
464 | |
---|
465 | |
---|
466 | |
---|
467 | ivec straux1 ( ldmat Ld, double nu, ldmat Ld0, double nu0, ivec belief, int nbest, int max_nrep, double lambda, int order_k, Array<str_aux> &rgrsout/*, stat &statistics*/ ) { |
---|
468 | // see utia_legacy/ticket_12/ implementation and str_test.m |
---|
469 | |
---|
470 | const mat &L = Ld._L(); |
---|
471 | const vec &d = Ld._D(); |
---|
472 | |
---|
473 | const mat &L0 = Ld0._L(); |
---|
474 | const vec &d0 = Ld0._D(); |
---|
475 | |
---|
476 | int n_data = d.length(); |
---|
477 | |
---|
478 | ivec belief_out = find ( belief == 4 ) + 2; // we are avoiding to put this into regressor |
---|
479 | ivec belief_in = find ( belief == 1 ) + 2; // we are instantly keeping this in regressor |
---|
480 | |
---|
481 | |
---|
482 | str_aux full; |
---|
483 | |
---|
484 | full.d0 = d0; |
---|
485 | full.nu0 = nu0; |
---|
486 | full.L0 = L0; |
---|
487 | full.L = L; |
---|
488 | full.d = d; |
---|
489 | |
---|
490 | |
---|
491 | |
---|
492 | /*full.d0 = "0.012360650875200 0.975779169502626 1.209840558439000"; |
---|
493 | |
---|
494 | full.L0 = "1.0000 0 0;" |
---|
495 | "0.999427690134298 1.0000 0;" |
---|
496 | "0.546994424043659 0.534335486953833 1.0000"; |
---|
497 | |
---|
498 | |
---|
499 | full.L = "1.0000 0 0;" |
---|
500 | "0.364376353850780 1.0000 0;" |
---|
501 | "1.222141096674815 1.286534510946323 1.0000"; |
---|
502 | |
---|
503 | full.d = "0.001610356837691 3.497566869589465 3.236640487818002"; |
---|
504 | */ |
---|
505 | |
---|
506 | fstream F; |
---|
507 | |
---|
508 | |
---|
509 | |
---|
510 | |
---|
511 | F.open ( "soubor3.txt", ios::in ); |
---|
512 | F << setiosflags ( ios::scientific ); |
---|
513 | F << setprecision ( 16 ); |
---|
514 | // F.flags ( 0x1 ); |
---|
515 | |
---|
516 | |
---|
517 | for ( int i = 0; i < n_data ; i++ ) { |
---|
518 | F >> full.d0 ( i ); |
---|
519 | } |
---|
520 | |
---|
521 | |
---|
522 | |
---|
523 | for ( int i = 0; i < n_data; i++ ) { |
---|
524 | for ( int j = 0 ; j < n_data ; j++ ) { |
---|
525 | F >> full.L0 ( j, i ); |
---|
526 | } |
---|
527 | } |
---|
528 | |
---|
529 | for ( int i = 0; i < n_data ; i++ ) { |
---|
530 | F >> full.d ( i ); |
---|
531 | } |
---|
532 | |
---|
533 | |
---|
534 | for ( int i = 0; i < n_data; i++ ) { |
---|
535 | for ( int j = 0 ; j < n_data ; j++ ) { |
---|
536 | F >> full.L ( j, i ); |
---|
537 | } |
---|
538 | } |
---|
539 | |
---|
540 | |
---|
541 | full.nu0 = nu0; |
---|
542 | full.nu = nu; |
---|
543 | full.strL = linspace ( 1, n_data ); |
---|
544 | full.strRgr = linspace ( 2, n_data ); |
---|
545 | full.strMis = ivec ( 0 ); |
---|
546 | full.posit1 = 1; |
---|
547 | full.bitstr.set_size ( n_data - 1 ); |
---|
548 | full.bitstr.clear(); |
---|
549 | str_bitset ( full.bitstr, full.strRgr, full.nbits ); |
---|
550 | //full.nbits = std::numeric_lim its<double>::digits-1; // number of bits available in double |
---|
551 | /*bvec in(n_data-1); |
---|
552 | in.clear(); |
---|
553 | full.bitstr = str_bitset(in,full.strRgr,full.nbits);*/ |
---|
554 | |
---|
555 | |
---|
556 | |
---|
557 | |
---|
558 | full.loglik = seloglik1 ( full ); // % loglikelihood |
---|
559 | |
---|
560 | |
---|
561 | |
---|
562 | |
---|
563 | |
---|
564 | //% construct full and empty structure |
---|
565 | full = sestrremove ( full, belief_out ); |
---|
566 | str_aux empty = sestrremove ( full, setdiff ( full.strRgr, belief_in ) ); |
---|
567 | |
---|
568 | //% stopping rule calculation: |
---|
569 | |
---|
570 | |
---|
571 | |
---|
572 | |
---|
573 | bmat local_max ( 0, 0 ); |
---|
574 | int to, muto = 0; |
---|
575 | |
---|
576 | //% statistics: |
---|
577 | //double cputime0 = cputime; |
---|
578 | //if nargout>=3; |
---|
579 | |
---|
580 | CPU_Timer timer; |
---|
581 | timer.start(); |
---|
582 | |
---|
583 | ivec mutos ( max_nrep + 2 ); |
---|
584 | vec maxmutos ( max_nrep + 2 ); |
---|
585 | mutos.zeros(); |
---|
586 | maxmutos.zeros(); |
---|
587 | |
---|
588 | |
---|
589 | //end; |
---|
590 | //% ---------------------- |
---|
591 | |
---|
592 | //% For stopping-rule calculation |
---|
593 | //%so = 2^(n_data -1-length(belief_in)- length(belief_out)); % do we use this ? |
---|
594 | //% ---------------------- |
---|
595 | |
---|
596 | ivec all_str = linspace ( 1, n_data ); |
---|
597 | |
---|
598 | Array<str_aux> global_best ( 1 ); |
---|
599 | global_best ( 0 ) = full; |
---|
600 | |
---|
601 | |
---|
602 | //% MAIN LOOP is here. |
---|
603 | |
---|
604 | str_aux best; |
---|
605 | for ( int n_start = -1; n_start <= max_nrep; n_start++ ) { |
---|
606 | str_aux last, best; |
---|
607 | |
---|
608 | to = n_start + 2; |
---|
609 | |
---|
610 | if ( n_start == -1 ) { |
---|
611 | //% start from the full structure |
---|
612 | last = full; |
---|
613 | } else { |
---|
614 | if ( n_start == 0 ) |
---|
615 | //% start from the empty structure |
---|
616 | last = empty; |
---|
617 | |
---|
618 | else { |
---|
619 | //% start from random structure |
---|
620 | |
---|
621 | ivec last_str = find ( to_bvec<int> ( ::concat<int> ( 0, floor_i ( 2 * randu ( n_data - 1 ) ) ) ) );// this creates random vector consisting of indexes, and sorted |
---|
622 | last = sestrremove ( full, setdiff ( all_str, ::concat<int> ( ::concat<int> ( 1 , last_str ), empty.strRgr ) ) ); |
---|
623 | |
---|
624 | } |
---|
625 | } |
---|
626 | //% DEBUGging print: |
---|
627 | //%fprintf('STRUCTURE generated in loop %2i was %s\n', n_start, strPrintstr(last)); |
---|
628 | |
---|
629 | //% The loop is repeated until likelihood stops growing (break condition |
---|
630 | //% used at the end; |
---|
631 | |
---|
632 | |
---|
633 | while ( 1 ) { |
---|
634 | //% This structure is going to hold the best elements |
---|
635 | best = last; |
---|
636 | //% Nesting by removing elements (enpoorment) |
---|
637 | ivec removed_items = setdiff ( last.strRgr, belief_in ); |
---|
638 | |
---|
639 | ivec removed_item; |
---|
640 | str_aux newone; |
---|
641 | |
---|
642 | for ( int i = 0; i < removed_items.length(); i++ ) { |
---|
643 | removed_item = vec_1 ( removed_items ( i ) ); |
---|
644 | newone = sestrremove ( last, removed_item ); |
---|
645 | if ( nbest > 1 ) { |
---|
646 | add_new ( global_best, newone, nbest ); |
---|
647 | } |
---|
648 | if ( newone.loglik > best.loglik ) { |
---|
649 | best = newone; |
---|
650 | } |
---|
651 | } |
---|
652 | //% Nesting by adding elements (enrichment) |
---|
653 | ivec added_items = setdiff ( last.strMis, belief_out ); |
---|
654 | ivec added_item; |
---|
655 | |
---|
656 | for ( int j = 0; j < added_items.length(); j++ ) { |
---|
657 | added_item = vec_1 ( added_items ( j ) ); |
---|
658 | newone = sestrinsert ( last, added_item ); |
---|
659 | if ( nbest > 1 ) { |
---|
660 | add_new ( global_best, newone, nbest ); |
---|
661 | } |
---|
662 | if ( newone.loglik > best.loglik ) { |
---|
663 | best = newone; |
---|
664 | } |
---|
665 | } |
---|
666 | |
---|
667 | |
---|
668 | |
---|
669 | |
---|
670 | |
---|
671 | //% Break condition if likelihood does not change. |
---|
672 | if ( best.loglik <= last.loglik ) |
---|
673 | break; |
---|
674 | else |
---|
675 | //% Making best structure last structure. |
---|
676 | last = best; |
---|
677 | |
---|
678 | |
---|
679 | } |
---|
680 | |
---|
681 | |
---|
682 | |
---|
683 | |
---|
684 | |
---|
685 | // % DEBUGging print: |
---|
686 | //%fprintf('STRUCTURE found (local maxima) in loop %2i was %s randun_seed=%11lu randun_counter=%4lu\n', n_start, strPrintstr(best), randn('seed'), RANDUN_COUNTER); |
---|
687 | |
---|
688 | //% Collecting of the best structure in case we don't need the second parameter |
---|
689 | if ( nbest <= 1 ) { |
---|
690 | if ( best.loglik > global_best ( 0 ).loglik ) { |
---|
691 | global_best = best; |
---|
692 | } |
---|
693 | } |
---|
694 | |
---|
695 | //% uniqueness of the structure found |
---|
696 | int append = 1; |
---|
697 | |
---|
698 | |
---|
699 | for ( int j = 0; j < local_max.rows() ; j++ ) { |
---|
700 | if ( best.bitstr == local_max.get_row ( j ) ) { |
---|
701 | append = 0; |
---|
702 | break; |
---|
703 | } |
---|
704 | } |
---|
705 | |
---|
706 | |
---|
707 | if ( append ) { |
---|
708 | local_max.append_row ( best.bitstr ); |
---|
709 | muto = muto + 1; |
---|
710 | } |
---|
711 | |
---|
712 | //% stopping rule: |
---|
713 | double maxmuto = ( to - order_k - 1 ) / lambda - to + 1; |
---|
714 | if ( to > 2 ) { |
---|
715 | if ( maxmuto >= muto ) { |
---|
716 | //% fprintf('*'); |
---|
717 | break; |
---|
718 | } |
---|
719 | } |
---|
720 | |
---|
721 | // do statistics if necessary: |
---|
722 | //if (nargout>=3){ |
---|
723 | mutos ( to - 1 ) = muto; |
---|
724 | maxmutos ( to - 1 ) = maxmuto; |
---|
725 | //} |
---|
726 | } |
---|
727 | |
---|
728 | //% Aftermath: The best structure was in: global_best |
---|
729 | |
---|
730 | //% Updating loglikelihoods: we have to add the constant stuff |
---|
731 | |
---|
732 | |
---|
733 | |
---|
734 | for ( int f = 0 ; f < global_best.length(); f++ ) { |
---|
735 | global_best ( f ).loglik = global_best ( f ).loglik + seloglik2 ( global_best ( f ) ); |
---|
736 | } |
---|
737 | |
---|
738 | /*for f=1:length(global_best); |
---|
739 | global_best(f).loglik = global_best(f).loglik + seloglik2(global_best(f)); |
---|
740 | end;*/ |
---|
741 | |
---|
742 | |
---|
743 | //% Making first output parameter: |
---|
744 | |
---|
745 | int max_i = 0; |
---|
746 | for ( int j = 1; j < global_best.length(); j++ ) |
---|
747 | if ( global_best ( max_i ).loglik < ( global_best ( j ).loglik ) ) max_i = j; |
---|
748 | |
---|
749 | best = global_best ( max_i ); |
---|
750 | |
---|
751 | //% Making the second output parameter |
---|
752 | |
---|
753 | vec logliks ( global_best.length() ); |
---|
754 | for ( int j = 0; j < logliks.length(); j++ ) |
---|
755 | logliks ( j ) = global_best ( j ).loglik; |
---|
756 | |
---|
757 | ivec i = sort_index ( logliks ); |
---|
758 | rgrsout.set_length ( global_best.length() ); |
---|
759 | |
---|
760 | for ( int j = global_best.length() - 1; j >= 0; j-- ) |
---|
761 | rgrsout ( j ) = global_best ( i ( j ) ); |
---|
762 | |
---|
763 | //if (nargout>=3); |
---|
764 | |
---|
765 | |
---|
766 | str_statistics statistics; |
---|
767 | |
---|
768 | statistics.allstrs = 2 ^ ( n_data - 1 - length ( belief_in ) - length ( belief_out ) ); |
---|
769 | statistics.nrand = to - 2; |
---|
770 | statistics.unique = muto; |
---|
771 | statistics.to = to; |
---|
772 | statistics.cputime_seconds = timer.get_time(); |
---|
773 | statistics.itemspeed = statistics.to / statistics.cputime_seconds; |
---|
774 | statistics.muto = muto; |
---|
775 | statistics.mutos = mutos; |
---|
776 | statistics.maxmutos = maxmutos; |
---|
777 | //end; |
---|
778 | |
---|
779 | return best.strRgr; |
---|
780 | |
---|
781 | } |
---|
782 | |
---|
783 | #ifdef LADIM |
---|
784 | //% randun seed stuff: |
---|
785 | //%randn('seed',SEED); |
---|
786 | |
---|
787 | //% --------------------- END of MAIN program -------------------- |
---|
788 | |
---|
789 | % This is needed for bitstr manipulations |
---|
790 | /*function out = str_bitset(in,ns,nbits) |
---|
791 | out = in; |
---|
792 | for n = ns; |
---|
793 | index = 1+floor((n-2)/nbits); |
---|
794 | bitindex = 1+rem(n-2,nbits); |
---|
795 | out(index) = bitset(out(index),bitindex); |
---|
796 | end; |
---|
797 | function out = str_bitres(in,ns,nbits) |
---|
798 | out = in; |
---|
799 | for n = ns; |
---|
800 | index = 1+floor((n-2)/nbits); |
---|
801 | bitindex = 1+rem(n-2,nbits); |
---|
802 | mask = bitset(0,bitindex); |
---|
803 | out(index) = bitxor(bitor(out(index),mask),mask); |
---|
804 | end;*/ |
---|
805 | |
---|
806 | function out = strPrintstr ( in ) |
---|
807 | out = '0'; |
---|
808 | nbits = in.nbits; |
---|
809 | for f = 2: |
---|
810 | length ( in.d0 ); |
---|
811 | index = 1 + floor ( ( f - 2 ) / nbits ); |
---|
812 | bitindex = 1 + rem ( f - 2, nbits ); |
---|
813 | if bitget ( in.bitstr ( index ), bitindex ); |
---|
814 | out ( f ) = '1'; |
---|
815 | else; |
---|
816 | out ( f ) = '0'; |
---|
817 | end; |
---|
818 | end; |
---|
819 | |
---|
820 | /*function global_best_out = add_new(global_best,new,nbest) |
---|
821 | % Eventually add to global best, but do not go over nbest values |
---|
822 | % Also avoids repeating things, which makes this function awfully slow |
---|
823 | if length(global_best)>=nbest; |
---|
824 | logliks = [global_best.loglik]; |
---|
825 | [loglik i] = min(logliks); |
---|
826 | global_best_out = global_best; |
---|
827 | if loglik<new.loglik; |
---|
828 | % if ~any(logliks == new.loglik); |
---|
829 | addit=1; |
---|
830 | for f = [global_best.bitstr]; |
---|
831 | if f == new.bitstr; |
---|
832 | addit = 0; |
---|
833 | break; |
---|
834 | end; |
---|
835 | end; |
---|
836 | if addit; |
---|
837 | global_best_out(i) = new; |
---|
838 | % DEBUGging print: |
---|
839 | % fprintf('ADDED structure, add_new: %s, loglik=%g\n', strPrintstr(new), new.loglik); |
---|
840 | end; |
---|
841 | end; |
---|
842 | else; |
---|
843 | global_best_out = [global_best new]; |
---|
844 | end;*/ |
---|
845 | |
---|
846 | /*function out = sestrremove(in,removed_elements); |
---|
847 | % Removes elements from regressor |
---|
848 | n_strL = length(in.strL); |
---|
849 | out = in; |
---|
850 | for f=removed_elements; |
---|
851 | posit1 = find(out.strL==1); |
---|
852 | positf = find(out.strL==f); |
---|
853 | for g=(positf-1):-1:posit1; |
---|
854 | % BEGIN: We are swapping g and g+1 NOW!!!! |
---|
855 | [out.L, out.d] = seswapudl(out.L, out.d, g); |
---|
856 | [out.L0, out.d0] = seswapudl(out.L0, out.d0, g); |
---|
857 | out.strL([g g+1]) = [out.strL(g+1) out.strL(g)]; |
---|
858 | % END |
---|
859 | end; |
---|
860 | end; |
---|
861 | out.posit1 = find(out.strL==1); |
---|
862 | out.strRgr = out.strL((out.posit1+1):n_strL); |
---|
863 | out.strMis = out.strL(1:(out.posit1-1)); |
---|
864 | out.bitstr = str_bitres(out.bitstr,removed_elements,out.nbits); |
---|
865 | out.loglik = seloglik1(out);*/ |
---|
866 | |
---|
867 | /*function out = sestrinsert(in,inserted_elements); |
---|
868 | % Moves elements into regressor |
---|
869 | n_strL = length(in.strL); |
---|
870 | out = in; |
---|
871 | for f=inserted_elements; |
---|
872 | posit1 = find(out.strL==1); |
---|
873 | positf = find(out.strL==f); |
---|
874 | for g=positf:(posit1-1); |
---|
875 | % BEGIN: We are swapping g and g+1 NOW!!!! |
---|
876 | [out.L, out.d] = seswapudl(out.L, out.d, g); |
---|
877 | [out.L0, out.d0] = seswapudl(out.L0, out.d0, g); |
---|
878 | out.strL([g g+1]) = [out.strL(g+1) out.strL(g)]; |
---|
879 | % END |
---|
880 | end; |
---|
881 | end; |
---|
882 | out.posit1 = find(out.strL==1); |
---|
883 | out.strRgr = out.strL((out.posit1+1):n_strL); |
---|
884 | out.strMis = out.strL(1:(out.posit1-1)); |
---|
885 | out.bitstr = str_bitset(out.bitstr,inserted_elements,out.nbits); |
---|
886 | out.loglik = seloglik1(out);*/ |
---|
887 | |
---|
888 | % |
---|
889 | % seloglik_real = seloglik1 + seloglik2 |
---|
890 | % |
---|
891 | |
---|
892 | /*function l = seloglik1(in) |
---|
893 | % This is the loglikelihood (non-constant part) - this should be used in |
---|
894 | % frequent computation |
---|
895 | len = length(in.d); |
---|
896 | p1 = in.posit1; |
---|
897 | |
---|
898 | i1 = -0.5*in.nu *log(in.d (p1)) -0.5*sum(log(in.d ((p1+1):len))); |
---|
899 | i0 = -0.5*in.nu0*log(in.d0(p1)) -0.5*sum(log(in.d0((p1+1):len))); |
---|
900 | l = i1-i0; |
---|
901 | |
---|
902 | % DEBUGGing print: |
---|
903 | % fprintf('SELOGLIK1: str=%s loglik=%g\n', strPrintstr(in), l);*/ |
---|
904 | |
---|
905 | |
---|
906 | function l = seloglik2 ( in ) |
---|
907 | % This is the loglikelihood ( constant part ) - this should be added to |
---|
908 | % everything at the end. It needs some computation, so it is useless to |
---|
909 | % make it for all the stuff |
---|
910 | logpi = log ( pi ); |
---|
911 | |
---|
912 | i1 = gammaln ( in.nu / 2 ) - 0.5*in.nu *logpi; |
---|
913 | i0 = gammaln ( in.nu0 / 2 ) - 0.5*in.nu0*logpi; |
---|
914 | l = i1 - i0; |
---|
915 | |
---|
916 | |
---|
917 | /*function [Lout, dout] = seswapudl(L,d,i); |
---|
918 | %SESWAPUDL swaps information matrix in decomposition V=L^T diag(d) L |
---|
919 | % |
---|
920 | % [Lout, dout] = seswapudl(L,d,i); |
---|
921 | % |
---|
922 | % L : lower triangular matrix with 1's on diagonal of the decomposistion |
---|
923 | % d : diagonal vector of diagonal matrix of the decomposition |
---|
924 | % i : index of line to be swapped with the next one |
---|
925 | % Lout : output lower triangular matrix |
---|
926 | % dout : output diagional vector of diagonal matrix D |
---|
927 | % |
---|
928 | % Description: |
---|
929 | % Lout' * diag(dout) * Lout = P(i,i+1) * L' * diag(d) * L * P(i,i+1); |
---|
930 | % |
---|
931 | % Where permutation matrix P(i,j) permutates columns if applied from the |
---|
932 | % right and line if applied from the left. |
---|
933 | % |
---|
934 | % Note: naming: |
---|
935 | % se = structure estimation |
---|
936 | % lite = light, simple |
---|
937 | % udl = U*D*L, or more precisely, L'*D*L, also called as ld |
---|
938 | % |
---|
939 | % Design : L. Tesar |
---|
940 | % Updated : Feb 2003 |
---|
941 | % Project : post-ProDaCTool |
---|
942 | % Reference: sedydr |
---|
943 | |
---|
944 | j = i+1; |
---|
945 | |
---|
946 | pomd = d(i); |
---|
947 | d(i) = d(j); |
---|
948 | d(j) = pomd; |
---|
949 | |
---|
950 | pomL = L(i,:); |
---|
951 | L(i,:) = L(j,:); |
---|
952 | L(j,:) = pomL; |
---|
953 | |
---|
954 | pomL = L(:,i); |
---|
955 | L(:,i) = L(:,j); |
---|
956 | L(:,j) = pomL; |
---|
957 | |
---|
958 | % We must be working with LINES of matrix L ! |
---|
959 | |
---|
960 | r = L(i,:)'; |
---|
961 | f = L(j,:)'; |
---|
962 | Dr = d(i); |
---|
963 | Df = d(j); |
---|
964 | |
---|
965 | [r, f, Dr, Df] = sedydr(r, f, Dr, Df, j); |
---|
966 | |
---|
967 | r0 = r(i); |
---|
968 | Dr = Dr*r0*r0; |
---|
969 | r = r/r0; |
---|
970 | |
---|
971 | L(i,:) = r'; |
---|
972 | L(j,:) = f'; |
---|
973 | d(i) = Dr; |
---|
974 | d(j) = Df; |
---|
975 | |
---|
976 | L(i,i) = 1; |
---|
977 | L(j,j) = 1; |
---|
978 | |
---|
979 | Lout = L; |
---|
980 | dout = d;*/ |
---|
981 | |
---|
982 | /*function [rout, fout, Drout, Dfout, kr] = sedydr(r,f,Dr,Df,R,jl,jh); |
---|
983 | %SEDYDR dyadic reduction, performs transformation of sum of 2 dyads |
---|
984 | % |
---|
985 | % [rout, fout, Drout, Dfout, kr] = sedydr(r,f,Dr,Df,R,jl,jh); |
---|
986 | % [rout, fout, Drout, Dfout] = sedydr(r,f,Dr,Df,R); |
---|
987 | % |
---|
988 | % Description: dyadic reduction, performs transformation of sum of |
---|
989 | % 2 dyads r*Dr*r'+ f*Df*f' so that the element of r pointed by R is zeroed |
---|
990 | % |
---|
991 | % r : column vector of reduced dyad |
---|
992 | % f : column vector of reducing dyad |
---|
993 | % Dr : scalar with weight of reduced dyad |
---|
994 | % Df : scalar with weight of reducing dyad |
---|
995 | % R : scalar number giving 1 based index to the element of r, |
---|
996 | % which is to be reduced to |
---|
997 | % zero; the corresponding element of f is assumed to be 1. |
---|
998 | % jl : lower index of the range within which the dyads are |
---|
999 | % modified (can be omitted, then everything is updated) |
---|
1000 | % jh : upper index of the range within which the dyads are |
---|
1001 | % modified (can be omitted then everything is updated) |
---|
1002 | % rout,fout,Drout,dfout : resulting two dyads |
---|
1003 | % kr : coefficient used in the transformation of r |
---|
1004 | % rnew = r + kr*f |
---|
1005 | % |
---|
1006 | % Description: dyadic reduction, performs transformation of sum of |
---|
1007 | % 2 dyads r*Dr*r'+ f*Df*f' so that the element of r indexed by R is zeroed |
---|
1008 | % Remark1: Constant mzero means machine zero and should be modified |
---|
1009 | % according to the precision of particular machine |
---|
1010 | % Remark2: jl and jh are, in fact, obsolete. It takes longer time to |
---|
1011 | % compute them compared to plain version. The reason is that we |
---|
1012 | % are doing vector operations in m-file. Other reason is that |
---|
1013 | % we need to copy whole vector anyway. It can save half of time for |
---|
1014 | % c-file, if you use it correctly. (please do tests) |
---|
1015 | % |
---|
1016 | % Note: naming: |
---|
1017 | % se = structure estimation |
---|
1018 | % dydr = dyadic reduction |
---|
1019 | % |
---|
1020 | % Original Fortran design: V. Peterka 17-7-89 |
---|
1021 | % Modified for c-language: probably R. Kulhavy |
---|
1022 | % Modified for m-language: L. Tesar 2/2003 |
---|
1023 | % Updated: Feb 2003 |
---|
1024 | % Project: post-ProDaCTool |
---|
1025 | % Reference: none |
---|
1026 | |
---|
1027 | if nargin<6; |
---|
1028 | update_whole=1; |
---|
1029 | else |
---|
1030 | update_whole=0; |
---|
1031 | end; |
---|
1032 | |
---|
1033 | mzero = 1e-32; |
---|
1034 | |
---|
1035 | if Dr<mzero; |
---|
1036 | Dr=0; |
---|
1037 | end; |
---|
1038 | |
---|
1039 | r0 = r(R); |
---|
1040 | kD = Df; |
---|
1041 | kr = r0 * Dr; |
---|
1042 | Dfout = kD + r0 * kr; |
---|
1043 | |
---|
1044 | if Dfout > mzero; |
---|
1045 | kD = kD / Dfout; |
---|
1046 | kr = kr / Dfout; |
---|
1047 | else; |
---|
1048 | kD = 1; |
---|
1049 | kr = 0; |
---|
1050 | end; |
---|
1051 | |
---|
1052 | Drout = Dr * kD; |
---|
1053 | |
---|
1054 | % Try to uncomment marked stuff (*) if in numerical problems, but I don't |
---|
1055 | % think it can make any difference for normal healthy floating-point unit |
---|
1056 | if update_whole; |
---|
1057 | rout = r - r0*f; |
---|
1058 | % rout(R) = 0; % * could be needed for some nonsense cases(or numeric reasons?), normally not |
---|
1059 | fout = f + kr*rout; |
---|
1060 | % fout(R) = 1; % * could be needed for some nonsense cases(or numeric reasons?), normally not |
---|
1061 | else; |
---|
1062 | rout = r; |
---|
1063 | fout = f; |
---|
1064 | rout(jl:jh) = r(jl:jh) - r0 * f(jl:jh); |
---|
1065 | rout(R) = 0; |
---|
1066 | fout(jl:jh) = f(jl:jh) + kr * rout(jl:jh); |
---|
1067 | end;*/ |
---|
1068 | |
---|
1069 | |
---|
1070 | |
---|
1071 | #endif |
---|
1072 | |
---|
1073 | |
---|
1074 | } |
---|