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, 74 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/Kalman/tracking_demo.m b/sourcecodes/bnt-master/Kalman/tracking_demo.m
new file mode 100644
index 00000000..70cf6484
--- /dev/null
+++ b/sourcecodes/bnt-master/Kalman/tracking_demo.m
@@ -0,0 +1,74 @@
+% Make a point move in the 2D plane
+% State = (x y xdot ydot). We only observe (x y).
+
+% This code was used to generate Figure 15.9 of "Artificial Intelligence: a Modern Approach",
+% Russell and Norvig, 2nd edition, Prentice Hall, 2003.
+
+% X(t+1) = F X(t) + noise(Q)
+% Y(t) = H X(t) + noise(R)
+
+ss = 4; % state size
+os = 2; % observation size
+F = [1 0 1 0; 0 1 0 1; 0 0 1 0; 0 0 0 1]; 
+H = [1 0 0 0; 0 1 0 0];
+Q = 0.1*eye(ss);
+R = 1*eye(os);
+initx = [10 10 1 0]';
+initV = 10*eye(ss);
+
+seed = 9;
+rand('state', seed);
+randn('state', seed);
+T = 15;
+[x,y] = sample_lds(F, H, Q, R, initx, T);
+
+[xfilt, Vfilt, VVfilt, loglik] = kalman_filter(y, F, H, Q, R, initx, initV);
+[xsmooth, Vsmooth] = kalman_smoother(y, F, H, Q, R, initx, initV);
+
+dfilt = x([1 2],:) - xfilt([1 2],:);
+mse_filt = sqrt(sum(sum(dfilt.^2)))
+
+dsmooth = x([1 2],:) - xsmooth([1 2],:);
+mse_smooth = sqrt(sum(sum(dsmooth.^2)))
+
+
+figure(1)
+clf
+%subplot(2,1,1)
+hold on
+plot(x(1,:), x(2,:), 'ks-');
+plot(y(1,:), y(2,:), 'g*');
+plot(xfilt(1,:), xfilt(2,:), 'rx:');
+for t=1:T, plotgauss2d(xfilt(1:2,t), Vfilt(1:2, 1:2, t)); end
+hold off
+legend('true', 'observed', 'filtered', 3)
+xlabel('x')
+ylabel('y')
+
+
+
+% 3x3 inches
+set(gcf,'units','inches');
+set(gcf,'PaperPosition',[0 0 3 3])  
+%print(gcf,'-depsc','/home/eecs/murphyk/public_html/Bayes/Figures/aima_filtered.eps');
+%print(gcf,'-djpeg','-r100', '/home/eecs/murphyk/public_html/Bayes/Figures/aima_filtered.jpg');
+
+
+figure(2)
+%subplot(2,1,2)
+hold on
+plot(x(1,:), x(2,:), 'ks-');
+plot(y(1,:), y(2,:), 'g*');
+plot(xsmooth(1,:), xsmooth(2,:), 'rx:');
+for t=1:T, plotgauss2d(xsmooth(1:2,t), Vsmooth(1:2, 1:2, t)); end
+hold off
+legend('true', 'observed', 'smoothed', 3)
+xlabel('x')
+ylabel('y')
+
+
+% 3x3 inches
+set(gcf,'units','inches');
+set(gcf,'PaperPosition',[0 0 3 3])  
+%print(gcf,'-djpeg','-r100', '/home/eecs/murphyk/public_html/Bayes/Figures/aima_smoothed.jpg');
+%print(gcf,'-depsc','/home/eecs/murphyk/public_html/Bayes/Figures/aima_smoothed.eps');