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, 116 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMstats/parzenC.c b/sourcecodes/bnt-master/KPMstats/parzenC.c
new file mode 100644
index 00000000..8fb07f1d
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMstats/parzenC.c
@@ -0,0 +1,116 @@
+/* C mex version of parzen.m
+[B,B2] = parzen(feat, mu,  Sigma, Nproto); 
+*/
+#include "mex.h"
+#include <stdio.h>
+#include <math.h>
+
+#define PI 3.141592654
+
+void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){
+  int    D, M, Q, T, d, m, q, t;
+  double *data, *mu, *SigmaPtr, *N, Sigma;
+  double *B, *dist, *B2, tmp;
+  const int* dim_mu;
+  double const1, const2, sum_m, sum_d, diff;
+  int Dt, DMq, Dm, MQt, Mq;
+  int dims_B2[3];
+
+  int ndim_mu, i, save_B2;
+  
+  data = mxGetPr(prhs[0]);
+  mu = mxGetPr(prhs[1]);
+  SigmaPtr = mxGetPr(prhs[2]);
+  Sigma = *SigmaPtr;
+  N = mxGetPr(prhs[3]);
+  
+  D = mxGetM(prhs[0]);
+  T = mxGetN(prhs[0]);
+
+  ndim_mu = mxGetNumberOfDimensions(prhs[1]);
+  dim_mu = mxGetDimensions(prhs[1]);
+  D = dim_mu[0];
+  M = dim_mu[1];
+  /* printf("parzenC: nlhs=%d, D=%d, M=%d, T=%d\n", nlhs, D, M, T);  */
+
+  /* If mu is mu(d,m,o,p), then [d M Q] = size(mu) in matlab sets Q=o*p,
+     i.e.. the size of all conditioning variabeles */
+  Q = 1;
+  for (i = 2; i < ndim_mu; i++) {
+    /* printf("dim_mu[%d]=%d\n", i, dim_mu[i]); */
+    Q = Q*dim_mu[i];
+  }
+
+  /* M = max(N) */
+  M = -1000000;
+  for (i=0; i < Q; i++) {
+    /* printf("N[%d]=%d\n", i, (int) N[i]); */
+    if (N[i] > M) {
+      M = (int) N[i];
+    }
+  }
+
+  /*  printf("parzenC: nlhs=%d, D=%d, Q=%d, M=%d, T=%d\n", nlhs, D, Q, M, T); */
+
+  plhs[0] = mxCreateDoubleMatrix(Q,T, mxREAL);
+  B = mxGetPr(plhs[0]);
+
+  if (nlhs >= 2)
+    save_B2 = 1;
+  else
+    save_B2 = 0;
+  
+  if (save_B2) {
+    /* printf("parzenC saving B2\n");  */
+    /*plhs[1] = mxCreateDoubleMatrix(M*Q*T,1, mxREAL);*/
+    dims_B2[0] = M;
+    dims_B2[1] = Q;
+    dims_B2[2] = T;
+    plhs[1] = mxCreateNumericArray(3, dims_B2, mxDOUBLE_CLASS, mxREAL);
+    B2 = mxGetPr(plhs[1]);
+  } else {
+    /* printf("parzenC not saving B2\n"); */
+  }
+  /*
+  plhs[2] = mxCreateDoubleMatrix(M*Q*T,1, mxREAL);
+  dist = mxGetPr(plhs[2]);
+  */
+  const1 = pow(2*PI*Sigma, -D/2.0);
+  const2 = -(1/(2*Sigma));
+ 
+  for (t=0; t < T; t++) {
+    /* printf("t=%d!\n",t); */
+    Dt  = D*t;
+    MQt = M*Q*t;
+    for (q=0; q < Q; q++) {
+      sum_m = 0;
+      DMq = D*M*q;
+      Mq = M*q;
+
+      for (m=0; m < (int)N[q]; m++) {
+	sum_d = 0;
+	Dm = D*m;
+	for (d=0; d < D; d++) {
+	  /* diff = data(d,t) - mu(d,m,q) */
+	  /*diff = data[d + D*t] - mu[d + D*m + D*M*q]; */
+	  diff = data[d + Dt] - mu[d + Dm + DMq];
+	  sum_d = sum_d + diff*diff;
+	}
+	/* dist[m,q,t] = dist[m + M*q + M*Q*t] = dist[m + Mq + MQt] = sum_d */
+	tmp = const1 * exp(const2*sum_d);
+	sum_m = sum_m + tmp;
+	if (save_B2)
+	  B2[m + Mq + MQt] = tmp;
+      }
+
+      if (N[q]>0) {
+	B[q + Q*t] = (1.0/N[q]) *  sum_m;
+      } else {
+	B[q + Q*t] = 0.0;
+      }
+    }
+  }
+}
+
+
+