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, 88 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMstats/parzen.m b/sourcecodes/bnt-master/KPMstats/parzen.m
new file mode 100644
index 00000000..27a92783
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMstats/parzen.m
@@ -0,0 +1,88 @@
+function [B,B2,dist] = parzen(data, mu, Sigma, N)
+% EVAL_PDF_COND_PARZEN Evaluate the pdf of a conditional Parzen window
+% function B = eval_pdf_cond_parzen(data, mu, Sigma, N)
+%
+% B(q,t) = Pr(data(:,t) | Q=q) = sum_{m=1}^{N(q)} w(m,q)*K(data(:,t) - mu(:,m,q); sigma)
+% where K() is a Gaussian kernel with spherical variance sigma,
+% and w(m,q) = 1/N(q) if m<=N(q) and = 0 otherwise
+% where N(q) is the number of mxiture components for q 
+%
+% B2(m,q,t) =  K(data(:,t) - mu(:,m,q); sigma) for m=1:max(N)
+
+% This is like eval_pdf_cond_parzen, except mu is mu(:,m,q) instead of mu(:,q,m)
+% and we use 1/N(q) instead of mixmat(q,m)
+
+if nargout >= 2
+  keep_B2 = 1;
+else
+  keep_B2 = 0;
+end
+
+if nargout >= 3
+  keep_dist = 1;
+else
+  keep_dist = 0;
+end
+
+[d M Q] = size(mu);
+[d T] = size(data);
+
+M = max(N(:));
+
+B = zeros(Q,T);
+const1 = (2*pi*Sigma)^(-d/2);
+const2 = -(1/(2*Sigma));
+if T*Q*M>20000000 % not enough memory to call sqdist
+  disp('eval parzen for loop')
+  if keep_dist,
+    dist = zeros(M,Q,T);
+  end
+  if keep_B2
+    B2 = zeros(M,Q,T);
+  end
+  for q=1:Q
+    D = sqdist(mu(:,1:N(q),q), data); % D(m,t)
+    if keep_dist
+      dist(:,q,:) = D;
+    end
+    tmp = const1 * exp(const2*D);
+    if keep_B2,
+      B2(:,q,:) = tmp;
+    end
+    if N(q) > 0
+      %B(q,:) = (1/N(q)) * const1 * sum(exp(const2*D), 2);
+      B(q,:) = (1/N(q)) * sum(tmp,1);
+    end
+  end
+else
+  %disp('eval parzen vectorized')
+  dist = sqdist(reshape(mu(:,1:M,:), [d M*Q]), data); % D(mq,t)
+  dist = reshape(dist, [M Q T]);
+  B2 = const1 * exp(const2*dist); % B2(m,q,t)
+  if ~keep_dist
+    clear dist
+  end
+  
+  % weights(m,q) is the weight of mixture component m for q  
+  %    = 1/N(q) if m<=N(q) and = 0 otherwise
+  % e.g., N = [2   3   1], M = 3,
+  % weights = [1/2 1/3 1   = 1/2 1/3 1/1      2 3 1     1 1 1
+  %            1/2 1/3 0     1/2 1/3 1/1 .*   2 3 1 <=  2 2 2
+  %            0   1/3 0]    1/2 1/3 1/1      2 3 1     3 3 3
+   
+  Ns = repmat(N(:)', [M 1]);
+  ramp = 1:M;
+  ramp = repmat(ramp(:), [1 Q]);
+  n = N + (N==0); % avoid 1/0 by replacing with 0* 1/1m where 0 comes from mask
+  N1 = repmat(1 ./ n(:)', [M 1]);
+  mask = (ramp <= Ns);
+  weights = N1 .* mask;
+  B2 = B2 .* repmat(mask, [1 1 T]);
+  
+  % B(q,t) = sum_m B2(m,q,t) * P(m|q) = sum_m B2(m,q,t) * weights(m,q)
+  B = squeeze(sum(B2 .* repmat(weights, [1 1 T]), 1)); 
+  B = reshape(B, [Q T]); % undo effect of squeeze in case Q = 1
+end
+
+  
+