-
Notifications
You must be signed in to change notification settings - Fork 0
/
BRUG_BDMCMC_change_means.cpp
1412 lines (1233 loc) · 57 KB
/
BRUG_BDMCMC_change_means.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
/***************************************************************************************
* Copyright (C) 2020 Chan Ga Ming Angus
*
* Maintainer: Chan Ga Ming Angus <[email protected]>
* Source: https://github.com/Anguscgm/BRUG_BDMCMC
***************************************************************************************/
// Compile with:
// g++ BRUG_BDMCMC_change_means.cpp -llapack -lblas -std=c++11 -o BRUG_BDMCMC_change_means
/***************************************************************************************
* Reference
****************************************************************************************
* Title: BDgraph source code
* Author: Reza Mohammadi, Ernst C. Wit
* Date: 2012 - 2020
* Code version: 2.63
* Availability: https://github.com/cran/BDgraph
*
***************************************************************************************/
#include <iostream>
#include <fstream>
#include <omp.h>
#include <string>
#include <vector>
#include <random>
#include <cmath>
#include <cstring>
#include <getopt.h>
#include <algorithm>
using namespace std;
extern "C" void daxpy_(int* N, double* DA, double* DX, int* INCX, double* DY, int* INCY);
extern "C" void dtrmm_(char* SIDE, char* UPLO, char* TRANSA, char* DIAG, int* M, int* N, double* ALPHA, double* A, int* LDA, double* B, int* LDB);
extern "C" void dtrsm_(char* SIDE, char* UPLO, char* TRANSA, char* DIAG, int* M, int* N, double* ALPHA, double* A, int* LDA, double* B, int* LDB);
extern "C" void dgemm_(char* TRANSA, char* TRANSB, int* M, int* N, int *K, double *ALPHA, double *A, int *LDA, double *B, int *LDB, double *BETA, double* C, int* LDC);
extern "C" void dposv_(char* UPLO, int* N, int* NRHS, double* A, int* LDA, double* B, int* LDB, int* INFO);
extern "C" void dsymv_(char* UPLO, int* N, double* ALPHA, double* A, int* LDA, double* X, int* INCX, double* BETA, double* Y, int* INCY);
extern "C" void dsyr_(char* UPLO, int* N, double* ALPHA, double* X, int* INCX, double* A, int* LDA);
extern "C" double ddot_(int* N, double* DX, int* INCX, double* DY, int* INCY);
extern "C" void dpotrf_(char* UPLO, int* N, double* A, int* LDA, int* INFO);
void update_alpha(double alpha0[], double alpha1[], double K[], double X[], double Y_bar[], double mu[], int ns[], int p, int L)
{
double mu_alpha0 = 0, mu_alpha1 = 0;
double sigma2_alpha0 = 1; double sigma2_alpha1 = 1;
double sigma2_MH_alpha0 = 0.1; double sigma2_MH_alpha1 = 0.01;
default_random_engine generator;
double alpha_old, alpha_new, logr;
char transN = 'N';
int one = 1; double d_one = 1.0, d_zero = 0.0;
vector<double> S_diff(p*p);
vector<double> SK(p*p);
double trace;
for (int i=0; i<p; i++)
{
int updated = 0;
fill(&S_diff[0], &S_diff[p*p], 0);
// Update alpha0
alpha_old = alpha0[i];
normal_distribution<double> N(alpha_old,sigma2_MH_alpha0); //Can change the sd later.
alpha_new = N(generator);
logr = (pow(alpha_old-mu_alpha0,2.0)-pow(alpha_new-mu_alpha0,2.0))/2/sigma2_alpha0;
for (int l=0; l<L; l++)
{
S_diff[i*(p+1)] = pow(alpha_new,2.0)-pow(alpha_old,2.0) - 2*(alpha_new-alpha_old)*(Y_bar[l*p+i]-alpha1[i]*X[l]);
for (int j=0; j<p && j!=i; j++)
{
S_diff[i*p+j] = (alpha_old-alpha_new)*(Y_bar[l*p+j]-mu[l*p+j]);
S_diff[j*p+i] = S_diff[i*p+j];
}
dgemm_( &transN, &transN, &p, &p, &p, &d_one, &S_diff[0], &p, &K[l*p*p], &p, &d_zero, &SK[0], &p );
trace = 0; //reset
for (int j=0; j<p; j++) trace += SK[j*(p+1)];
logr -= ns[l]*trace/2.0;
}
if (logr > log((double)rand()/RAND_MAX))
{
updated = 1;
alpha0[i] = alpha_new;
}
// alpha1
alpha_old = alpha1[i];
normal_distribution<double> N_2(alpha_old,sigma2_MH_alpha1); //Can change the sd later.
logr = (pow(alpha_old-mu_alpha1,2.0)-pow(alpha_new-mu_alpha1,2.0))/2/sigma2_alpha1;
for (int l=0; l<L; l++)
{
S_diff[i*(p+1)] = (pow(alpha_new,2.0)-pow(alpha_old,2.0))*pow(X[l],2.0) -2*(alpha_new-alpha_old)*(Y_bar[l*p+i]-alpha0[i]);
for (int j=0; j<p && j!=i; j++)
{
S_diff[i*p+j] = (alpha_old-alpha_new)*X[l]*(Y_bar[l*p+j]-mu[l*p+j]);
S_diff[j*p+i] = S_diff[i*p+j];
}
dgemm_( &transN, &transN, &p, &p, &p, &d_one, &S_diff[0], &p, &K[l*p*p], &p, &d_zero, &SK[0], &p );
trace = 0; //reset
for (int j=0; j<p; j++) trace += SK[j*(p+1)];
logr -= ns[l]*trace/2.0;
}
if (logr > log((double)rand()/RAND_MAX))
{
updated = 1;
alpha1[i] = alpha_new;
}
if (updated)
{
for (int l = 0; l<L; l++)
mu[l*p + i] = alpha0[i] + alpha1[i]*X[l];
}
}
}
// Write a function to update \beta_lm
void update_beta(double beta0[], double beta1[], int G[], double X[], int p, int L, int ns[])
{
int lm = 0;
default_random_engine generator;
for (int i=1; i<p; i++) // all potential edges.
for (int j = 0; j<i; j++)
{
int ij = i*p + j;
// Update beta0
//prior N(-1000, 1.0)
//MH N(beta^{t-1}, 0.1)
double beta_old = beta0[lm];
normal_distribution<double> N(beta_old,0.01); //Can change the sd later.
double beta_new = N(generator);
double logr = (pow(beta_old + 1000, 2.0) - pow(beta_new + 1000, 2.0))/2; //assuming prior of -1000 for beta0
for (int l=0; l<L; l++) //groups
{
double x = X[l];
logr += ns[l]*(G[l*p*p + ij]*(beta_new - beta_old) - log(1 + exp(beta_new + beta1[lm]*x)) + log(1 + exp(beta_old + beta1[lm]*x)));
}
if (logr > log((double)rand()/RAND_MAX)) beta0[lm] = beta_new;
// Update beta1
//prior N(0, 1.0)
//MH N(beta^{t-1}, 0.1)
beta_old = beta1[lm];
normal_distribution<double> N_2(beta_old,0.01); //Can change the sd later.
beta_new = N_2(generator);
//prior
logr = (pow(beta_old, 2.0) - pow(beta_new, 2.0))/2; // assume \sigma = 1.0?
for (int l=0; l<L; l++) //groups
{
double x = X[l];
logr += ns[l]*(G[l*p*p + ij]*(beta_new - beta_old)*x - log(1 + exp(beta0[lm] + beta_new*x)) + log(1 + exp(beta0[lm] + beta_new*x)));
}
if (logr > log((double)rand()/RAND_MAX)) beta1[lm] = beta_new;
lm++;
}
}
int factorial(int n)
{
int result = 1;
for( int i=2; i<=n; i++) result *= i;
return(result);
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// Determinant of a symmetric possitive-definite matrix ( A )
// > > > > > > > > > WARNING: Matrix you pass is overwritten < < < < < < < < <
// For any symmetric PD Matrix A, we have: |A| = |T| ^ 2, where T is cholesky decomposition of A.
// Thus, |T| = \prod_{i = 1}^p T_{ii}.
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
double determinant( double A[], int dim )
{
char uplo = 'U';
int info, dim1 = dim + 1;
dpotrf_( &uplo, &dim, &A[0], &dim, &info );
double result = 1;
for( int i = 0; i < dim; i++ ) result *= A[ i * dim1 ];
return ( result * result );
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// Part of function "gnorm"
// which is for calculating Normalizing constant of G-Wishart distribution
// based on Monto Carlo algorithm
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void log_exp_mc( int G[], int nu[], int b_c, double H[], int check_H, int mc_iter, int dim, double f_T[] )
{
int iter, i, j, ij, h, r, pxp = dim * dim;
double sumPsi, sumPsiH, sumPsiHi, sumPsiHj;
double max_numeric_limits_ld = numeric_limits<double>::max() / 1000;
double min_numeric_limits_ld = numeric_limits<double>::min() * 1000;
vector<double> psi( pxp, 0.0 );
//GetRNGstate();
default_random_engine generator;
normal_distribution<double> rnorm( 0.0, 1.0 );
if( check_H == 1 )
{
for( iter = 0; iter < mc_iter; iter++ )
{
for( i = 0; i < dim; i++ )
{
gamma_distribution<double> rgamma( ( b_c + nu[ i ] ) / 2.0, 2.0 );
psi[ i * dim + i ] = sqrt( rgamma(generator));
//psi[i * dim + i] = sqrt( rchisq( b_c + nu[i] ) );
}
for( i = 0; i < dim - 1; i++ )
for( j = i + 1; j < dim; j++ )
{
ij = j * dim + i;
//if( G[ij] == 1 ) psi[ij] = rnorm( 0, 1 ); else psi[ij] = 0.0;
psi[ ij ] = ( G[ ij ] == 1 ) ? rnorm(generator) : 0.0;
}
for( i = 0; i < dim - 1; i++ )
for( j = i + 1; j < dim; j++ )
{
ij = j * dim + i;
if( G[ ij ] == 0 )
{
psi[ ij ] = 0.0; // it's not necessary
if( i > 0 )
{
sumPsi = 0.0;
//sum( psi[ 1 : ( i - 1 ), i ] * psi[ 1 : ( i - 1 ), j ] )
// for( h = 0; h < ( i - 1 ); h++ )
for( h = 0; h < i; h++ )
{
if( sumPsi > max_numeric_limits_ld ) sumPsi = max_numeric_limits_ld;
if( sumPsi > min_numeric_limits_ld ) sumPsi = min_numeric_limits_ld;
sumPsi += ( psi[ i * dim + h ] * psi[ j * dim + h ] );
}
//psi[i, j] <- - sum( psi[ 1 : ( i - 1 ), i ] * psi[ 1 : ( i - 1 ), j ] ) / psi[i, i]
psi[ ij ] = - sumPsi / psi[ i * dim + i ];
}
if( psi[ ij ] > max_numeric_limits_ld ) psi[ ij ] = max_numeric_limits_ld;
if( psi[ ij ] > min_numeric_limits_ld ) psi[ ij ] = min_numeric_limits_ld;
//f_T[k] <- f_T[k] + psi[i, j] ^ 2
f_T[ iter ] += ( psi[ ij ] * psi[ ij ] );
}
}
// checking Inf values
if( f_T[ iter ] > max_numeric_limits_ld ) f_T[ iter ] = max_numeric_limits_ld;
}
}else{
for( iter = 0; iter < mc_iter; iter++ )
{
for( i = 0; i < dim; i++ )
{
gamma_distribution<double> rgamma( ( b_c + nu[ i ] ) / 2.0, 2.0 );
psi[ i * dim + i ] = sqrt(rgamma( generator ));
//psi[i * dim + i] = sqrt( rchisq( b_c + nu[i] ) );
}
for( i = 0; i < dim - 1; i++ )
for( j = i + 1; j < dim; j++ )
{
ij = j * dim + i;
//if( G[ij] == 1 ) psi[ij] = rnorm( 0, 1 ); elsepsi[ij] = 0.0;
psi[ ij ] = ( G[ ij ] == 1 ) ? rnorm(generator) : 0.0;
}
for( i = 0; i < dim - 1; i++ )
for( j = i + 1; j < dim; j++ )
{
ij = j * dim + i;
if( G[ ij ] == 0 )
{
//psi[i, j] = - sum( psi[ i, i : ( j - 1 ) ] * H[ i : ( j - 1 ), j ] )
sumPsiH = 0.0;
for( h = i; h < j; h++ )
{
if( sumPsiH > max_numeric_limits_ld ) sumPsiH = max_numeric_limits_ld;
if( sumPsiH > min_numeric_limits_ld ) sumPsiH = min_numeric_limits_ld;
sumPsiH += ( psi[ h * dim + i ] * H[ j * dim + h ] );
}
psi[ ij ] = - sumPsiH;
if( i > 0 ) //if( i > 1 )
for( r = 0; r < i; r++ ) //for( r in 1 : ( i - 1 ) )
{
//sum( psi[ r, r : i ] * H[ r : i, i ] )
sumPsiHi = 0.0;
for( h = r; h < i + 1; h++ )
{
if( sumPsiHi > max_numeric_limits_ld ) sumPsiHi = max_numeric_limits_ld;
if( sumPsiHi > min_numeric_limits_ld ) sumPsiHi = min_numeric_limits_ld;
sumPsiHi += ( psi[ h * dim + r ] * H[ i * dim + h ] );
}
//sum( psi[ r, r : j ] * H[ r : j, j ] ) )
sumPsiHj = 0.0;
for( h = r; h < j + 1; h++ )
sumPsiHj += ( psi[ h * dim + r ] * H[ j * dim + h ] );
//psi[i, j] <- psi[i, j] - ( ( sum( psi[ r, r : i ] * H[ r : i, i ] ) ) * ( sum( psi[ r, r : j ] * H[ r : j, j ] ) ) ) / ( psi[i, i] )
psi[ ij ] -= ( sumPsiHi * sumPsiHj ) / psi[ i * dim + i ];
}
if( psi[ ij ] > max_numeric_limits_ld ) psi[ ij ] = max_numeric_limits_ld;
if( psi[ ij ] > min_numeric_limits_ld ) psi[ ij ] = min_numeric_limits_ld;
//f_T[k] <- f_T[k] + psi[i, j] ^ 2
f_T[ iter ] += ( psi[ ij ] * psi[ ij ] );
}
}
// checking Inf values
if( f_T[ iter ] > max_numeric_limits_ld ) f_T[ iter ] = max_numeric_limits_ld;
}
}
//PutRNGstate();
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// inverse function for symmetric positive-definite matrices (p x p)
// WARNING: Matrix you pass is overwritten with the result
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void inverse( double A[], double A_inv[], int dim )
{
int info;
char uplo = 'U';
// creating an identity matrix
#pragma omp parallel for
for( int i = 0; i < dim; i++ )
for( int j = 0; j < dim; j++ )
A_inv[ j * dim + i ] = ( i == j );
// LAPACK function: computes solution to A * X = B, where A is symmetric positive definite matrix
//F77_NAME(dposv)( &uplo, &dim, &dim, A, &dim, A_inv, &dim, &info FCONE );
dposv_(&uplo, &dim, &dim, A, &dim, A_inv, &dim, &info);
}
double gnorm(int G[], int b, double D[], int p, int iter)
{
double result;
// get Ti
vector<double> Ti(p*p);
char uplo = 'U'; int info;
inverse(D, &Ti[0], p);
dpotrf_( &uplo, &p, &Ti[0], &p, &info );
vector<double> H(p*p);
int check_H = 1;
for (int i=0; i<p; i++)
for (int j=0; j<p; j++)
H[i*p + j] = Ti[i*p + j]/Ti[i*p+i];
int qp = p*(p-1)/2;
for (int i=1; i<p; i++)
{
for (int j=0; j<i; j++)
{
if(H[i*p + j] != 0)
{
check_H = 0;
break;
}
}
if (check_H == 0) break;
}
vector<int> nu(p, 0);
for (int i=0; i<p; i++) for (int j=i; j<p; j++) nu[i] += G[i*p + j];
int size_graph = 0; for (int i=0; i<p; i++) size_graph += nu[i];
if (size_graph == qp)
{
double sum_lgamma_bnu = 0;
for (int i=0; i<p; i++) sum_lgamma_bnu += lgamma((b + nu[i])/(double)2);
result = ( (double)size_graph / 2 ) * log( M_PI ) + ( (double)p * ( b + p - 1 ) / 2 ) * log( 2 ) +
sum_lgamma_bnu - ( ( b + p - 1 ) / (double)2 ) * log( determinant( D, p ) );
}
else if(size_graph == 0)
{
double sum_log_diag_D = 0;
for (int i=0; i<p; i++) sum_log_diag_D += log(D[i*(p+1)]);
result = ( (double)p * b / 2 ) * log( 2 ) + p * lgamma( (double)b / 2 ) - ( (double)b / 2 ) * sum_log_diag_D;
}
else
{
vector<double> f_T(iter);
log_exp_mc(G, &nu[0], b, &H[0], check_H, iter, p, &f_T[0]);
double log_Ef_T = 0;
for (int i=0; i<iter; i++) log_Ef_T += exp(-f_T[i]/2)/iter;
log_Ef_T = log(log_Ef_T);
double sum_lgamma_bnu = 0;
for (int i=0; i<p; i++) sum_lgamma_bnu += lgamma((b + nu[i])/(double)2);
double sum_bnu_log_diag_Ti = 0;
vector<double> colsums(p,0);
for (int j=0; j<p; j++) for (int i=0; i<=j; i++) colsums[j] += G[i*p + j];
for (int i=0; i<p; i++) sum_bnu_log_diag_Ti += (b + nu[i] + colsums[i])*log(Ti[i*(p+1)]);
double result = ( (double)size_graph / 2 ) * log( M_PI ) + ( (double)p * b / 2 + size_graph ) * log( 2 ) +
sum_lgamma_bnu + sum_bnu_log_diag_Ti;
result += log_Ef_T;
}
return(result);
}
int update_b(int b_old, int G[], double K[], double D[], int p, int L, int ns[])
{
double lambda = 1 ; //What is lambda
double q_ratio; // to be used in prior
int b_new;
if (b_old == 3)
{
b_new = 4;
q_ratio = 0.5;
}
else
{
if ((double)rand()/RAND_MAX > 0.5) b_new = b_old + 1;
else b_new = b_old - 1;
if(b_old == 4 && b_new == 3) q_ratio = 2; // the case if b goes from 4 to 3.
else q_ratio = 1;
}
//prior
double logr = log(lambda)*(b_new - b_old) + log((double)factorial(b_old-3)/factorial(b_new-3)) + log(q_ratio);
for (int l=0; l<L; l++)
{
logr += ns[l]*(gnorm(&G[l*p*p], b_old, D, p, 100)-gnorm(&G[l*p*p], b_new, D, p, 100)+log(determinant(&K[l*p*p], p))*(b_new - b_old));
}
if (logr > log((double)rand()/RAND_MAX)) return(b_new);
else return(b_old);
}
void update_D(double D[], int G[], double K[], int b, int p, int L, int ns[])
{
double alpha = 2.0, beta = 2.0;
double c = 5;
vector<double> D_new(p*p);
memcpy(&D_new[0], D, p*p*sizeof(double));
default_random_engine generator;
vector<double> gnorm_old(L);
vector<double> gnorm_new(L);
//Calculate the first gnorm.
for (int l=0; l<L; l++) gnorm_old[l] = gnorm(&G[l*p*p], b, D, p, 100);
//Begin update.
for (int j=0; j<p; j++)
{
double d_old = D[j*(p+1)];
gamma_distribution<double> rgamma( c*d_old, 1/c );
double d_new = rgamma(generator); D_new[j*(p+1)] = d_new;
// Get q ratio first
double logr = c*log(c)*(d_new - d_old) - lgamma(c*d_new) + lgamma(c*d_old) + (c*d_new-1)*log(d_old) - (c*d_old-1)*log(d_new) +c*(d_new-d_old)
// Then prior.
+ (alpha-1)*log(d_new / d_old) - beta*(d_new - d_old);
for (int l=0; l<L; l++)
{
gnorm_new[l] = gnorm(&G[l*p*p], b, &D_new[0], p, 100);
logr += ns[l]*(gnorm_old[l] - gnorm_new[l]) +log(K[l*p*p + j*(p+1)]*(d_new-d_old)) ;
}
if (logr > log((double)rand()/RAND_MAX))
{
D[j*(p+1)] = d_new;
memcpy(&gnorm_old[0], &gnorm_new[0], L*sizeof(double));
}
else
{
D_new[j*(p+1)] = d_old; //if the last proposal is not accepted, we reload the original value from D.
}
}
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// Takes symmatric matrix A (p x p) and
// retrieves A12(1x(p-1)) and A22((p-1)x(p-1))
// Like A12=A[j, -j], and A22=A[-j, -j] in R
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void sub_matrices1( double A[], double A12[], double A22[], int psub, int pdim )
{
int i, ixpdim, ixp1, p1 = pdim - 1, subxp = psub * pdim, mpsub = pdim - psub - 1;
int size_psub = sizeof( double ) * psub;
int size_mpsub = sizeof( double ) * mpsub;
memcpy( A12, A + subxp, size_psub );
memcpy( A12 + psub, A + subxp + psub + 1, size_mpsub );
for( i = 0; i < psub; i++ )
{
ixpdim = i * pdim;
ixp1 = i * p1;
memcpy( A22 + ixp1 , A + ixpdim , size_psub );
memcpy( A22 + ixp1 + psub, A + ixpdim + psub + 1, size_mpsub );
}
for( i = psub + 1; i < pdim; i++ )
{
ixpdim = i * pdim;
ixp1 = ( i - 1 ) * p1;
memcpy( A22 + ixp1 , A + ixpdim , size_psub );
memcpy( A22 + ixp1 + psub, A + ixpdim + psub + 1, size_mpsub );
}
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// Takes symmetric matrix A (p x p) and
// retrieves upper part of sub_matrix B (p_sub x p_sub), dictated by vector sub
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void sub_matrix_upper( double A[], double sub_A[], int sub[], int psub, int pdim )
{
int i, j, ixp, subixp;
for( i = 0; i < psub; i++ )
{
ixp = i * psub;
subixp = sub[ i ] * pdim;
for( j = 0; j <= i; j++ )
sub_A[ ixp + j ] = A[ subixp + sub[ j ] ];
}
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// Takes square matrix A (p x p) and
// retrieves vector sub_A which is 'sub' th row of matrix A, minus 'sub' element
// Likes A[j, -j] in R
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void sub_row_mins( double A[], double sub_A[], int subj, int pdim )
{
int subxp = subj * pdim;
memcpy( sub_A , A + subxp , sizeof( double ) * subj );
memcpy( sub_A + subj, A + subxp + subj + 1, sizeof( double ) * ( pdim - subj - 1 ) );
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// Takes square matrix A (p x p) and
// retrieves sub_matrix sub_A(p-2 x 2) which is sub cols of matrix A, minus two elements
// Likes A[-(i,j), (i,j)] in R
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void sub_cols_mins( double A[], double sub_A[], int subi, int subj, int pdim )
{
int p2 = pdim - 2, subixp = subi * pdim, subjxp = subj * pdim;
memcpy( sub_A , A + subixp , sizeof( double ) * subi );
memcpy( sub_A + subi , A + subixp + subi + 1, sizeof( double ) * ( subj - subi - 1 ) );
memcpy( sub_A + subj - 1, A + subixp + subj + 1, sizeof( double ) * ( pdim - subj - 1 ) );
memcpy( sub_A + p2 , A + subjxp , sizeof( double ) * subi );
memcpy( sub_A + p2 + subi , A + subjxp + subi + 1, sizeof( double ) * ( subj - subi - 1 ) );
memcpy( sub_A + p2 + subj - 1, A + subjxp + subj + 1, sizeof( double ) * ( pdim - subj - 1 ) );
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// Takes square matrix A (p x p) and
// retrieves A11_inv ( 2 x 2 ), A21 ( ( p - 2 ) x 2 ), and A22 ( ( p - 2 ) x ( p - 2 ) )
// Like A11_inv=inv ( A[ e, e ] ), A21 = A[ -e, e ], and A22 = A[ -e, -e ] in R
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void sub_matrices_inv( double A[], double A11_inv[], double A21[], double A22[], int sub0, int sub1, int pdim )
{
int i, ixp, ixp2, p2 = pdim - 2;
int sub0xp = sub0 * pdim, sub1xp = sub1 * pdim, sub0_plus = sub0 + 1, sub1_plus = sub1 + 1;
double a11 = A[ sub0 * pdim + sub0 ];
double a12 = A[ sub0 * pdim + sub1 ];
double a22 = A[ sub1 * pdim + sub1 ];
double det_A11 = a11 * a22 - a12 * a12;
A11_inv[ 0 ] = a22 / det_A11;
A11_inv[ 1 ] = - a12 / det_A11;
A11_inv[ 2 ] = A11_inv[ 1 ];
A11_inv[ 3 ] = a11 / det_A11;
int size_sub0 = sizeof( double ) * sub0;
int size_sub1_sub0 = sizeof( double ) * ( sub1 - sub0_plus );
int size_pdim_sub0 = sizeof( double ) * ( pdim - sub1_plus );
memcpy( A21 , A + sub0xp , size_sub0 );
memcpy( A21 + sub0 , A + sub0xp + sub0_plus, size_sub1_sub0 );
memcpy( A21 + sub1 - 1, A + sub0xp + sub1_plus, size_pdim_sub0 );
memcpy( A21 + p2 , A + sub1xp , size_sub0 );
memcpy( A21 + p2 + sub0 , A + sub1xp + sub0_plus, size_sub1_sub0 );
memcpy( A21 + p2 + sub1 - 1, A + sub1xp + sub1_plus, size_pdim_sub0 );
for( i = 0; i < sub0; i++ )
{
ixp = i * pdim;
ixp2 = i * p2;
memcpy( A22 + ixp2 , A + ixp , size_sub0 );
memcpy( A22 + ixp2 + sub0 , A + ixp + sub0_plus, size_sub1_sub0 );
memcpy( A22 + ixp2 + sub1 - 1, A + ixp + sub1_plus, size_pdim_sub0 );
}
for( i = sub0_plus; i < sub1; i++ )
{
ixp = i * pdim;
ixp2 = ( i - 1 ) * p2;
memcpy( A22 + ixp2 , A + ixp , size_sub0 );
memcpy( A22 + ixp2 + sub0 , A + ixp + sub0_plus, size_sub1_sub0 );
memcpy( A22 + ixp2 + sub1 - 1, A + ixp + sub1_plus, size_pdim_sub0 );
}
for( i = sub1_plus; i < pdim; i++ )
{
ixp = i * pdim;
ixp2 = ( i - 2 ) * p2;
memcpy( A22 + ixp2 , A + ixp , size_sub0 );
memcpy( A22 + ixp2 + sub0 , A + ixp + sub0_plus, size_sub1_sub0 );
memcpy( A22 + ixp2 + sub1 - 1, A + ixp + sub1_plus, size_pdim_sub0 );
}
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// computing birth/death rate or alpha for element (i,j)
// it is for double Metropolis-Hasting algorihtms
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void log_H_ij( double K[], double sigma[], double *log_Hij, int selected_edge_i, int selected_edge_j,
double Kj12[], double Kj12xK22_inv[], double K12[], double K12xK22_inv[], double K121[],
double sigmaj12[], double sigmaj22[], double sigma12[], double sigma22[], double sigma11_inv[], double sigma21xsigma11_inv[],
int dim, int p1, int p2, int jj,
double Dsijj, double Dsij, double Dsjj )
{
int one = 1, two = 2;
double alpha = 1.0, beta = 0.0, alpha1 = -1.0, beta1 = 1.0;
char transT = 'T', transN = 'N', sideL = 'L';
//double sigmaj11 = sigma[*jj]; // sigma[j, j]
sub_matrices1( sigma, sigmaj12, sigmaj22, selected_edge_j, dim );
// sigma[-j,-j] - ( sigma[-j, j] %*% sigma[j, -j] ) / sigma[j,j]
// Kj22_inv <- sigmaj22 = sigmaj22 - sigmaj12 * sigmaj12 / sigmaj11
double sigmajj_inv = - 1.0 / sigma[ selected_edge_j * ( dim + 1 ) ];
//F77_NAME(dsyr)( &sideL, p1, &sigmajj_inv, sigmaj12, &one, sigmaj22, p1 FCONE );
dsyr_( &sideL, &p1, &sigmajj_inv, sigmaj12, &one, sigmaj22, &p1 );
// For (i,j) = 0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
sub_row_mins( K, Kj12, selected_edge_j, dim ); // K12 = K[j, -j]
Kj12[ selected_edge_i ] = 0.0; // K12[1,i] = 0
// Kj12xK22_inv = Kj12 %*% Kj22_inv here sigmaj22 instead of Kj22_inv
//F77_NAME(dsymv)( &sideL, p1, &alpha, &sigmaj22[0], p1, Kj12, &one, &beta, Kj12xK22_inv, &one FCONE );
dsymv_( &sideL, &p1, &alpha, &sigmaj22[0], &p1, Kj12, &one, &beta, Kj12xK22_inv, &one );
// K022 = Kj12xK22_inv %*% t(Kj12)
//double K022 = F77_NAME(ddot)( p1, Kj12xK22_inv, &one, Kj12, &one );
double K022 = ddot_( &p1, Kj12xK22_inv, &one, Kj12, &one );
// For (i,j) = 1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
sub_cols_mins( K, K12, selected_edge_i, selected_edge_j, dim ); // K21 = K[-e, e]
sub_matrices_inv( sigma, sigma11_inv, sigma12, sigma22, selected_edge_i, selected_edge_j, dim );
// sigma21xsigma11_inv = sigma21 %*% sigma11_inv
//F77_NAME(dgemm)( &transN, &transN, p2, &two, &two, &alpha, sigma12, p2, sigma11_inv, &two, &beta, sigma21xsigma11_inv, p2 FCONE FCONE );
dgemm_( &transN, &transN, &p2, &two, &two, &alpha, sigma12, &p2, sigma11_inv, &two, &beta, sigma21xsigma11_inv, &p2 );
// sigma22 = sigma22 - sigma21xsigma11_inv %*% t( sigma21 )
//F77_NAME(dgemm)( &transN, &transT, p2, p2, &two, &alpha1, sigma21xsigma11_inv, p2, sigma12, p2, &beta1, sigma22, p2 FCONE FCONE );
dgemm_( &transN, &transT, &p2, &p2, &two, &alpha1, sigma21xsigma11_inv, &p2, sigma12, &p2, &beta1, sigma22, &p2 );
// K12xK22_inv = t( K21 ) %*% K22_inv here sigam12 = K22_inv
//F77_NAME(dgemm)( &transT, &transN, &two, p2, p2, &alpha, K12, p2, sigma22, p2, &beta, K12xK22_inv, &two FCONE FCONE );
dgemm_( &transT, &transN, &two, &p2, &p2, &alpha, K12, &p2, sigma22, &p2, &beta, K12xK22_inv, &two );
// K121 = K12xK22_inv %*% K21
//F77_NAME(dgemm)( &transN, &transN, &two, &two, p2, &alpha, K12xK22_inv, &two, K12, p2, &beta, K121, &two FCONE FCONE );
dgemm_( &transN, &transN, &two, &two, &p2, &alpha, K12xK22_inv, &two, K12, &p2, &beta, K121, &two );
// Finished (i,j) = 1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -|
double a11 = K[selected_edge_i *dim + selected_edge_i] - K121[0];
double sum_diag = Dsjj * ( K022 - K121[3] ) - Dsij * ( K121[1] + K121[2] );
// Dsijj = Dsii - Dsij * Dsij / Dsjj;
//*log_Hij = ( log( static_cast<double>(*Dsjj) ) - log( static_cast<double>(a11) ) + *Dsijj * a11 - sum_diag ) / 2;
*log_Hij = 0.5 * ( log( Dsjj / a11 ) + Dsijj * a11 - sum_diag );
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// rgwish ONLY for inside of MCMC algorithm
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void rgwish_sigma( int G[], int size_node[], double Ts[], double K[], double sigma[], int b_star,
int dim, double threshold,
double sigma_start[], double inv_C[], double beta_star[], double sigma_i[],
double sigma_start_N_i[], double sigma_N_i[], int N_i[] )
{
int i, i1, j, ij, ip, l, size_node_i, info, one = 1, pxp = dim * dim, dim1 = dim + 1, bKdim = b_star + dim - 1;
double alpha = 1.0, beta = 0.0;
char transT = 'T', transN = 'N', side = 'R', upper = 'U';
// - - STEP 1: sampling from wishart distributions - - - - - - - - - - - - - - - - - - - - - -|
// - - Sample values in Psi matrix
//GetRNGstate();
default_random_engine generator;
normal_distribution<double> distribution_2(0.0,1.0);
#pragma omp parallel for
for( i = 0; i < dim; i++ )
{
//sigma_start[ i * dim1 ] = sqrt( Rf_rgamma( ( bKdim - i ) * 0.5, 2.0 ) ); // i * dim1 = i * dim + i
gamma_distribution<double> distribution(( bKdim - i ) * 0.5, 2.0 ); //Rf_rgamma uses scale while C++ uses beta.
sigma_start[ i * dim1 ] = sqrt( distribution(generator) ); // i * dim1 = i * dim + i
//sigma_start[i * dim1] = sqrt( rchisq( bKdim - i ) ); // i * dim1 = i * dim + i
}
#pragma omp parallel for
for( j = 1; j < dim; j++ )
for( int i = 0; i < j; i++ )
{
sigma_start[ j * dim + i ] = distribution_2(generator);
sigma_start[ i * dim + j ] = 0.0;
}
//PutRNGstate();
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// C = psi %*% Ts I used psi = psi %*% Ts Now is sigma_start = sigma_start %*% Ts
//F77_NAME(dtrmm)( &side, &upper, &transN, &transN, &dim, &dim, &alpha, Ts, &dim, &sigma_start[0], &dim FCONE FCONE FCONE FCONE );
dtrmm_(&side, &upper, &transN, &transN, &dim, &dim, &alpha, Ts, &dim, &sigma_start[0], &dim);
side = 'L';
// creating an identity matrix
#pragma omp parallel for
for( i = 0; i < dim; i++ )
for( int j = 0; j < dim; j++ )
inv_C[ j * dim + i ] = ( i == j );
// op( A )*X = alpha*B, or X*op( A ) = alpha*B,
//F77_NAME(dtrsm)( &side, &upper, &transN, &transN, &dim, &dim, &alpha, &sigma_start[0], &dim, &inv_C[0], &dim FCONE FCONE FCONE FCONE );
dtrsm_(&side, &upper, &transN, &transN, &dim, &dim, &alpha, &sigma_start[0], &dim, &inv_C[0], &dim);
// sigma_start = inv_C %*% t( inv_C )
//F77_NAME(dgemm)( &transN, &transT, &dim, &dim, &dim, &alpha, &inv_C[0], &dim, &inv_C[0], &dim, &beta, &sigma_start[0], &dim FCONE FCONE );
dgemm_(&transN, &transT, &dim, &dim, &dim, &alpha, &inv_C[0], &dim, &inv_C[0], &dim, &beta, &sigma_start[0], &dim);
memcpy( sigma, &sigma_start[0], sizeof( double ) * pxp );
// double temp, max_diff = 1.0, threshold_c = *threshold;
double mean_diff = 1.0;
int counter = 0;
while( ( mean_diff > threshold ) and ( counter < 5000 ) )
{
counter++;
mean_diff = 0.0;
for( i = 0; i < dim; i++ )
{
ip = i * dim;
size_node_i = size_node[ i ];
if( size_node_i > 0 )
{
l = 0;
for( j = 0; j < dim; j++ )
{
ij = ip + j;
if( G[ ij ] )
{
sigma_start_N_i[ l ] = sigma_start[ ij ];
N_i[ l++ ] = j;
}
else
beta_star[ j ] = 0.0;
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
sub_matrix_upper( sigma, &sigma_N_i[0], &N_i[0], size_node_i, dim );
// A * X = B for sigma_start_N_i := (sigma_N_i)^{-1} * sigma_start_N_i
//F77_NAME(dposv)( &upper, &size_node_i, &one, &sigma_N_i[0], &size_node_i, &sigma_start_N_i[0], &size_node_i, &info FCONE );
dposv_(&upper, &size_node_i, &one, &sigma_N_i[0], &size_node_i, &sigma_start_N_i[0], &size_node_i, &info);
for( j = 0; j < size_node_i; j++ ) beta_star[ N_i[ j ] ] = sigma_start_N_i[ j ];
// sigma_i = sigma %*% beta_star
//F77_NAME(dsymv)( &side, &dim, &alpha, sigma, &dim, &beta_star[0], &one, &beta, &sigma_i[0], &one FCONE );
dsymv_(&side, &dim, &alpha, sigma, &dim, &beta_star[0], &one, &beta, &sigma_i[0], &one);
memcpy( sigma + ip, sigma_i, sizeof( double ) * i );
for( j = 0; j < i; j++ )
{
ij = j * dim + i;
mean_diff += fabs( static_cast<double>( sigma[ ij ] - sigma_i[ j ] ) );
// temp = fabs( static_cast<double>( sigma[ ij ] - sigma_i[ j ] ) );
// max_diff = ( temp > max_diff ) ? temp : max_diff;
sigma[ ij ] = sigma_i[ j ];
}
i1 = i + 1;
memcpy( sigma + ip + i1, sigma_i + i1, sizeof( double ) * ( dim - i1 ) );
for( j = i1; j < dim; j++ )
{
ij = j * dim + i;
mean_diff += fabs( static_cast<double>( sigma[ ij ] - sigma_i[ j ] ) );
// temp = fabs( static_cast<double>( sigma[ ij ] - sigma_i[ j ] ) );
// max_diff = ( temp > max_diff ) ? temp : max_diff;
sigma[ ij ] = sigma_i[ j ];
}
}else{
for( j = 0; j < i; j++ )
{
ij = j * dim + i;
mean_diff += fabs( static_cast<double>( sigma[ ij ] ) );
// temp = fabs( static_cast<double>( sigma[ ij ] ) );
// max_diff = ( temp > max_diff ) ? temp : max_diff;
sigma[ ij ] = 0.0;
sigma[ ip + j ] = 0.0;
}
for( j = i + 1; j < dim; j++ )
{
ij = j * dim + i;
mean_diff += fabs( static_cast<double>( sigma[ ij ] ) );
// temp = fabs( static_cast<double>( sigma[ ij ] ) );
// max_diff = ( temp > max_diff ) ? temp : max_diff;
sigma[ ij ] = 0.0;
sigma[ ip + j ] = 0.0;
}
}
}
mean_diff /= pxp;
}
memcpy( &sigma_start[0], sigma, sizeof( double ) * pxp );
inverse( &sigma_start[0], K, dim );
// creating an identity matrix
//#pragma omp parallel for
//for( i = 0; i < dim; i++ )
// for( int j = 0; j < dim; j++ )
// K[ j * dim + i ] = ( i == j );
// LAPACK function: computes solution to A * X = B, where A is symmetric positive definite matrix
//F77_NAME(dposv)( &upper, &dim, &dim, &sigma_start[0], &dim, K, &dim, &info );
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// Parallel Computation for birth-death rates for double BD-MCMC algorithm
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void rates_bdmcmc_dmh_parallel( double rates[], double log_ratio_g_prior[], int G[], int index_row[], int index_col[], int *sub_qp, double Ds[], double D[],
double sigma[], double K[], double sigma_dmh[],
double K_dmh[], int L, int dim )
{
int p1 = dim - 1, p2 = dim - 2, p2x2 = ( dim - 2 ) * 2;
#pragma omp parallel
{
int pxp = dim*dim;
int index_rate_j, i, j, ij, jj;
double Dsjj, Dsij, Dsijj, Dij, Dijj, Djj, log_rate;
double *K121 = new double[ 4 ];
double *Kj12 = new double[ p1 ];
double *sigmaj12 = new double[ p1 ];
double *sigmaj22 = new double[ p1 * p1 ];
double *Kj12xK22_inv = new double[ p1 ];
double *K21 = new double[ p2x2 ];
double *sigma12 = new double[ p2x2 ];
double *sigma22 = new double[ p2 * p2 ];
double *sigma11_inv = new double[ 4 ];
double *sigma21xsigma11_inv = new double[ p2x2 ];
double *K12xK22_inv = new double[ p2x2 ];
double *K12 = new double[ p2x2 ];
for (int l=0; l<L; l++)
{
#pragma omp for
for( j = 1; j < dim; j++ )
{
index_rate_j = l*dim*(dim - 1)/2 + ( j * ( j - 1 ) ) / 2;
jj = j * dim + j;
Dsjj = Ds[ l*dim*dim + jj ];
Djj = D[ jj ];
for( i = 0; i < j; i++ )
{
ij = j * dim + i;
int lij = ij + l*dim*dim;
Dsij = Ds[ lij ];
Dsijj = - Dsij * Dsij / Dsjj;
Dij = D[ ij ];
Dijj = - Dij * Dij / Djj;
double logH_ij, logI_p;
log_H_ij( &K[l*pxp], &sigma[l*pxp], &logH_ij, i, j,
&Kj12[0], &Kj12xK22_inv[0], &K12[0], &K12xK22_inv[0], &K121[0],
&sigmaj12[0], &sigmaj22[0], &sigma12[0], &sigma22[0], &sigma11_inv[0], &sigma21xsigma11_inv[0],
dim, p1, p2, jj,
Dsijj, Dsij, Dsjj );
log_H_ij( &K_dmh[l*pxp], &sigma_dmh[l*pxp], &logI_p, i, j,
&Kj12[0], &Kj12xK22_inv[0], &K12[0], &K12xK22_inv[0], &K121[0],
&sigmaj12[0], &sigmaj22[0], &sigma12[0], &sigma22[0], &sigma11_inv[0], &sigma21xsigma11_inv[0],
dim, p1, p2, jj,
Dijj, Dij, Djj );
//log_rate = ( G[ ij ] ) ? ( logH_ij - logI_p ) : ( logI_p - logH_ij );
log_rate = ( G[ lij ] ) ? ( logH_ij - logI_p ) - log_ratio_g_prior[ lij ] : ( logI_p - logH_ij ) + log_ratio_g_prior[ lij ];
rates[ index_rate_j + i ] = ( log_rate < 0.0 ) ? exp( log_rate ) : 1.0;
}
}
}
delete[] K121;
delete[] Kj12;
delete[] sigmaj12;
delete[] sigmaj22;
delete[] Kj12xK22_inv;
delete[] K21;
delete[] sigma12;
delete[] sigma22;
delete[] sigma11_inv;
delete[] sigma21xsigma11_inv;
delete[] K12xK22_inv;
delete[] K12;
}
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// To select an edge for BDMCMC algorithm
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void select_edge( double rates[], int *index_selected_edge, double *sum_rates, int qp_star )
{
// rates = sum_sort_rates
vector<double>cumulative_rates( qp_star, 0.0 );
cumulative_rates[ 0 ] = rates[ 0 ];
for( int i = 1; i < qp_star; i++ )
cumulative_rates[ i ] = cumulative_rates[ i - 1 ] + rates[ i ];
*sum_rates = cumulative_rates[ qp_star - 1 ];
// GetRNGstate();
//double random_value = *sum_rates * unif_rand(); // Rf_runif( 0.0, *sum_rates );
double random_value = *sum_rates * (double)rand()/RAND_MAX; // Rf_runif( 0.0, *sum_rates );
// PutRNGstate();
//int counter = 0;
//while( random_value > cumulative_rates[ counter ] ) ++counter;
//*index_selected_edge = counter;
// To start, find the subscript of the middle position.
int lower_bound = 0;
int upper_bound = qp_star - 1;
int position = upper_bound / 2; // ( lower_bound + upper_bound ) / 2;
while( upper_bound - lower_bound > 1 )
{
//if ( rates[position] > random_value ) { upper_bound = position; } else { lower_bound = position; }
( cumulative_rates[ position ] > random_value ) ? upper_bound = position : lower_bound = position;
position = ( lower_bound + upper_bound ) / 2;
}
*index_selected_edge = ( cumulative_rates[ position ] < random_value ) ? ++position : position;
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
// birth-death MCMC for Gaussian Graphical models
// Based on Double Metropolis-Hastings
// it is for Bayesian model averaging
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
void ggm_DMH_bdmcmc_ma( int iteration, int burn_in, int G[], double g_prior[],
double K[], int dim, double threshold, double K_hat[], double p_links[],
int b1, int ns[], double S_dot[], double D[], int L, int print_c,
double beta0[], double beta1[], double X[], double Y_bar[])
{
int index_selected_edge, selected_edge_l, selected_edge_i, selected_edge_j, selected_edge_ij;
int ip, i, j, ij, pxp = dim * dim, Lxpxp = L * pxp, one = 1;
int qp = L * dim * ( dim - 1 ) / 2;
double sum_weights = 0.0, weight_C, sum_rates;
vector<double> sigma( Lxpxp );
for(int l=0; l<L; l++)
inverse( &K[l*pxp], &sigma[l*pxp], dim );
vector<double> p_links_Cpp( Lxpxp, 0.0 );
vector<double> K_hat_Cpp( Lxpxp, 0.0 );
// - - for rgwish_sigma
vector<double> sigma_start( Lxpxp );
vector<double> inv_C( Lxpxp );
vector<double> beta_star( L*dim );
vector<double> sigma_i( L*dim );
vector<double> sigma_start_N_i( L*dim ); // For dynamic memory used
vector<double> sigma_N_i( Lxpxp ); // For dynamic memory used