BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 181 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/potentials/Tables/marg_sparse_table.c b/sourcecodes/bnt-master/BNT/potentials/Tables/marg_sparse_table.c
new file mode 100644
index 00000000..f6f7aa26
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/potentials/Tables/marg_sparse_table.c
@@ -0,0 +1,181 @@
+/* marg_sparse_table.c ../potential/tables*/
+
+/******************************************/
+/* 5 input & 1 output                     */
+/* Big sparse table                       */
+/* Big domain                             */
+/* Big sizes                              */
+/* onto                                   */
+/* maximize, if missed, maximize=0        */
+/*                                        */
+/* small sparse table                     */
+/******************************************/
+
+#include <math.h>
+#include <stdlib.h>
+#include "mex.h"
+
+int compare(const void* src1, const void* src2){
+	int i1 = *(int*)src1 ;
+	int i2 = *(int*)src2 ;
+	return i1-i2 ;
+}
+
+void ind_subv(int index, const int *cumprod, int n, int *bsubv){
+	int i;
+
+	for (i = n-1; i >= 0; i--) {
+		bsubv[i] = ((int)floor(index / cumprod[i]));
+		index = index % cumprod[i];
+	}
+}
+
+int subv_ind(const int n, const int *cumprod, const int *subv){
+	int i, index=0;
+
+	for(i=0; i<n; i++){
+		index += subv[i] * cumprod[i];
+	}
+	return index;
+}
+
+mxArray* convert_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){
+	mxArray *spTable;
+	int     i, temp, *irs, *jcs, count=0;
+	double  *sr;
+
+	spTable = mxCreateSparse(N, 1, nzCounts, mxREAL);
+    sr  = mxGetPr(spTable);
+    irs = mxGetIr(spTable);
+    jcs = mxGetJc(spTable);
+
+	jcs[0] = 0;
+	jcs[1] = nzCounts;
+
+	for(i=0; i<nzCounts; i++){
+		irs[i] = sequence[count];
+		count++;
+		temp = sequence[count];
+		sr[i] = Table[temp];
+		count++;
+	}
+	return spTable;
+}
+
+void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){
+	int        i, j, count, bdim, sdim, NS, NZB, position, bindex, sindex, maximize, nzCounts=0;
+	int        *mask, *sequence, *result, *bir, *bjc, *ssize, *bCumprod, *sCumprod, *bsubv, *ssubv;
+	double     *sTable, *pbDomain, *psDomain, *pbSize, *bpr, *spr;
+	const char *field_names[] = {"domain", "T", "sizes"};
+
+	if(nrhs < 5) maximize = 0;
+	else maximize = (int)mxGetScalar(prhs[4]);
+
+	bdim = mxGetNumberOfElements(prhs[1]);
+	sdim = mxGetNumberOfElements(prhs[3]);
+	pbSize = mxGetPr(prhs[2]);
+	pbDomain = mxGetPr(prhs[1]);
+	psDomain = mxGetPr(prhs[3]);
+	bpr = mxGetPr(prhs[0]);
+	bir = mxGetIr(prhs[0]);
+	bjc = mxGetJc(prhs[0]);
+	NZB = bjc[1];
+
+	if(sdim == 0){
+		plhs[0] = mxCreateSparse(1, 1, 1, mxREAL);
+		spr = mxGetPr(plhs[0]);
+		bir = mxGetIr(plhs[0]);
+		bjc = mxGetJc(plhs[0]);
+		*spr = 0;
+		*bir = 0;
+		bjc[0] = 0;
+		bjc[1] = 1;
+		if(maximize){
+			for(i=0; i<NZB; i++){
+				*spr = (*spr < bpr[i])? bpr[i] : *spr;
+			}
+		}
+		else{
+			for(i=0; i<NZB; i++){
+				*spr += bpr[i];
+			}
+		}	
+		return;
+	}
+
+	mask = malloc(sdim * sizeof(int));
+	count = 0;
+	for(i=0; i<sdim; i++){
+		for(j=0; j<bdim; j++){
+			if(psDomain[i] == pbDomain[j]){
+				mask[count] = j;
+				count++;
+				break;
+			}
+		}
+	}
+		
+	sTable = malloc(NZB * sizeof(double));
+	sequence = malloc(NZB * 2 * sizeof(double));
+	bCumprod = malloc(bdim * sizeof(int));
+	sCumprod = malloc(sdim * sizeof(int));
+	bsubv = malloc(bdim * sizeof(int));
+	ssubv = malloc(sdim * sizeof(int));
+	ssize = malloc(sdim * sizeof(int));
+
+	NS = 1;
+	for(i=0; i<count; i++){
+		ssize[i] = (int)pbSize[mask[i]];
+		NS *= ssize[i];
+	}
+
+	for(i=0; i<NZB; i++)sTable[i] = 0;
+	
+	bCumprod[0] = 1;
+	for(i=0; i<bdim-1; i++){
+		bCumprod[i+1] = bCumprod[i] * (int)pbSize[i];
+	}
+	sCumprod[0] = 1;
+	for(i=0; i<sdim-1; i++){
+		sCumprod[i+1] = sCumprod[i] * ssize[i];
+	}
+
+	count = 0;
+	for(i=0; i<NZB; i++){
+		bindex = bir[i];
+		ind_subv(bindex, bCumprod, bdim, bsubv);
+		for(j=0; j<sdim; j++){
+			ssubv[j] = bsubv[mask[j]];
+		}
+		sindex = subv_ind(sdim, sCumprod, ssubv);
+		result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare);
+		if(result){
+			position = (result - sequence) / 2;
+			if(maximize) 
+				sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position];
+			else sTable[position] += bpr[i];
+		}
+		else {
+			if(maximize) 
+				sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts];
+			else sTable[nzCounts] += bpr[i];
+			sequence[count] = sindex;
+			count++;
+			sequence[count] = nzCounts;
+			nzCounts++;
+			count++;
+		}
+	}
+	
+	qsort(sequence, nzCounts, sizeof(int) * 2, compare);
+	plhs[0] = convert_table_to_sparse(sTable, sequence, nzCounts, NS);
+
+	free(sTable);
+	free(sequence);
+	free(mask);
+	free(bCumprod);
+	free(sCumprod);
+	free(bsubv);
+	free(ssubv);
+	free(ssize);
+}