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, 107 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/SLAM/slam_partial_kf.m b/sourcecodes/bnt-master/BNT/examples/dynamic/SLAM/slam_partial_kf.m
new file mode 100644
index 00000000..3fe998be
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/SLAM/slam_partial_kf.m
@@ -0,0 +1,107 @@
+% See how well partial Kalman filter updates work
+
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+nlandmarks = 6;
+T = 12;
+
+[A,B,C,Q,R,Qbig,Rbig,init_x,init_V,robot_block,landmark_block,...
+	  true_landmark_pos, true_robot_pos, true_data_assoc, ...
+	  obs_rel_pos, ctrl_signal] = mk_linear_slam(...
+	      'nlandmarks', nlandmarks, 'T', T, 'ctrl', 'leftright', 'data-assoc', 'cycle');
+
+% exact
+[xe, Ve] = kalman_filter(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ...
+				     'model', true_data_assoc, 'u', ctrl_signal, 'B', B);
+
+
+% approx
+%k = nlandmarks-1; % exact
+k = 3;
+ndx = {};
+for t=1:T
+  landmarks = unique(true_data_assoc(t:-1:max(t-k,1)));
+  tmp = [landmark_block(:, landmarks) robot_block'];
+  ndx{t} = tmp(:);
+end
+
+[xa, Va] = kalman_filter(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ...
+			 'model', true_data_assoc, 'u', ctrl_signal, 'B', B, ...
+		       'ndx', ndx);
+
+
+
+nrows = 10;
+stepsize = T/(2*nrows);
+ts = 1:stepsize:T;
+
+if 1 % plot
+  
+clim = [0 max(max(Va(:,:,end)))];
+
+figure(2)
+if 0
+  imagesc(Ve(1:2:end,1:2:end, T))
+  clim = get(gca,'clim');
+else
+  i = 1;
+  for t=ts(:)'
+    subplot(nrows,2,i)
+    i = i + 1;
+    imagesc(Ve(1:2:end,1:2:end, t))
+    set(gca, 'clim', clim)
+    colorbar
+  end
+end
+suptitle('exact')
+
+
+figure(3)
+if 0
+  imagesc(Va(1:2:end,1:2:end, T))
+  set(gca,'clim', clim)
+else
+  i = 1;
+  for t=ts(:)'
+    subplot(nrows,2,i)
+    i = i+1;
+    imagesc(Va(1:2:end,1:2:end, t))
+    set(gca, 'clim', clim)
+    colorbar
+  end
+end
+suptitle('approx')
+
+
+figure(4)
+i = 1;
+for t=ts(:)'
+  subplot(nrows,2,i)
+  i = i+1;
+  Vd = Va(1:2:end,1:2:end, t) - Ve(1:2:end,1:2:end,t);
+  imagesc(Vd)
+  set(gca, 'clim', clim)
+  colorbar
+end
+suptitle('diff')
+
+end % all plot
+
+
+for t=1:T
+  %err(t)=rms(xa(:,t), xe(:,t));
+  err(t)=rms(xa(1:end-2,t), xe(1:end-2,t)); % exclude robot
+end
+figure(5);plot(err)
+title('rms mean pos')
+
+
+for t=1:T
+  i = 1:2*nlandmarks;
+  denom = Ve(i,i,t) + (Ve(i,i,t)==0);
+  Vd =(Va(i,i,t)-Ve(i,i,t)) ./ denom;
+  Verr(t) = max(Vd(:));
+end
+figure(6); plot(Verr)
+title('max relative Verr')