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, 164 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/SLAM/mk_linear_slam.m b/sourcecodes/bnt-master/BNT/examples/dynamic/SLAM/mk_linear_slam.m
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+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/SLAM/mk_linear_slam.m
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+function [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(varargin)
+
+% We create data from a linear system for testing SLAM algorithms.
+% i.e. , new robot pos = old robot pos + ctrl_signal, which is just a displacement vector.
+% and  observation = landmark_pos - robot_pos, which is just a displacement vector.
+%
+% The behavior is determined by the following optional arguments:
+%
+% 'nlandmarks' - num. landmarks
+% 'landmarks' - 'rnd' means random locations in the unit sqyare
+%               'square' means at [1 1], [4 1], [4 4] and [1 4]
+% 'T' - num steps to run
+% 'ctrl' - 'stationary' means the robot remains at [0 0],
+%          'leftright' means the robot receives a constant contol of [1 0],
+%          'square' means we navigate the robot around the square
+% 'data-assoc' - 'rnd' means we observe landmarks at random
+%                'nn' means we observe the nearest neighbor landmark
+%                'cycle' means we observe landmarks in order 1,2,.., 1, 2, ...
+
+args = varargin;
+% get mandatory params
+for i=1:2:length(args)
+  switch args{i},
+   case 'nlandmarks', nlandmarks = args{i+1};
+   case 'T', T = args{i+1};
+  end
+end
+
+% set defaults
+true_landmark_pos = rand(2,nlandmarks);
+true_data_assoc = [];
+
+% get args
+for i=1:2:length(args)
+  switch args{i},
+   case 'landmarks',
+    switch args{i+1},
+     case 'rnd',   true_landmark_pos = rand(2,nlandmarks);
+     case 'square',   true_landmark_pos = [1 1; 4 1; 4 4; 1 4]';
+    end
+   case 'ctrl',
+    switch args{i+1},
+     case 'stationary', ctrl_signal = repmat([0 0]', 1, T);
+     case 'leftright', ctrl_signal = repmat([1 0]', 1, T);
+     case 'square',   ctrl_signal = [repmat([1 0]', 1, T/4) repmat([0 1]', 1, T/4) ...
+		    repmat([-1 0]', 1, T/4) repmat([0 -1]', 1, T/4)];
+    end
+   case 'data-assoc', 
+    switch args{i+1},
+     case 'rnd', true_data_assoc  = sample_discrete(normalise(ones(1,nlandmarks)),1,T);
+     case 'cycle', true_data_assoc = wrap(1:T, nlandmarks);
+    end
+  end
+end
+if isempty(true_data_assoc)
+  use_nn = 1;
+else
+  use_nn = 0;
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%
+% generate data
+
+init_robot_pos = [0 0]';
+true_robot_pos = zeros(2, T);
+true_rel_dist = zeros(2, T);
+for t=1:T
+  if t>1
+    true_robot_pos(:,t) = true_robot_pos(:,t-1) + ctrl_signal(:,t);
+  else
+    true_robot_pos(:,t) = init_robot_pos + ctrl_signal(:,t);
+  end
+  nn = argmin(dist2(true_robot_pos(:,t)', true_landmark_pos'));
+  if use_nn
+    true_data_assoc(t) = nn;
+  end
+  true_rel_dist(:,t) = true_landmark_pos(:, nn) - true_robot_pos(:,t);
+end
+
+
+R = 1e-3*eye(2); % noise added to observation
+Q = 1e-3*eye(2); % noise added to robot motion
+
+% Create data set
+obs_noise_seq = sample_gaussian([0 0]', R, T)';
+obs_rel_pos = true_rel_dist + obs_noise_seq;
+%obs_rel_pos = true_rel_dist;
+
+%%%%%%%%%%%%%%%%%%
+% Create params
+
+
+% X(t) = A X(t-1) + B U(t) + noise(Q) 
+
+% [L1]  = [1     ]  * [L1]       + [0]  * Ut  + [0   ]
+% [L2]    [  1   ]    [L2]         [0]          [ 0  ]
+% [R ]t   [     1]    [R ]t-1      [1]          [   Q]
+
+% Y(t)|S(t)=s  = C(s) X(t) + noise(R)
+% Yt|St=1 = [1 0 -1]  * [L1]  + R
+%                       [L2]    
+%                       [R ]    
+
+% Create indices into block structure
+bs = 2*ones(1, nlandmarks+1); % sizes of blocks in state space
+robot_block =  block(nlandmarks+1, bs);
+for i=1:nlandmarks
+  landmark_block(:,i) = block(i, bs)';
+end
+Xsz = 2*(nlandmarks+1); % 2 values for each landmark plus robot
+Ysz = 2; % observe relative location
+Usz = 2; % input is (dx, dy)
+
+
+% create block-diagonal trans matrix for each switch
+A = zeros(Xsz, Xsz);
+for i=1:nlandmarks
+  bi = landmark_block(:,i);
+  A(bi, bi) = eye(2);
+end
+bi = robot_block;
+A(bi, bi) = eye(2);
+A = repmat(A, [1 1 nlandmarks]); % same for all switch values
+
+% create block-diagonal system cov
+
+
+Qbig = zeros(Xsz, Xsz);
+bi = robot_block;
+Qbig(bi,bi) = Q; % only add noise to robot motion
+Qbig = repmat(Qbig, [1 1 nlandmarks]);
+
+% create input matrix
+B = zeros(Xsz, Usz);
+B(robot_block,:) = eye(2); % only add input to robot position
+B = repmat(B, [1 1 nlandmarks]);
+
+% create observation matrix for each value of the switch node
+% C(:,:,i) = (0 ... I ... -I) where the I is in the i'th posn.
+% This computes L(i) - R
+C = zeros(Ysz, Xsz, nlandmarks);
+for i=1:nlandmarks
+  C(:, landmark_block(:,i), i) = eye(2); 
+  C(:, robot_block, i) = -eye(2);
+end
+
+% create observation cov for each value of the switch node
+Rbig = repmat(R, [1 1 nlandmarks]);
+
+% initial conditions
+init_x = zeros(Xsz, 1);
+init_v = zeros(Xsz, Xsz);
+bi = robot_block;
+init_x(bi) = init_robot_pos;
+%init_V(bi, bi) = 1e-5*eye(2); % very sure of robot posn
+init_V(bi, bi) = Q; % simualate uncertainty due to 1 motion step
+for i=1:nlandmarks
+  bi = landmark_block(:,i);
+  init_V(bi,bi)= 1e5*eye(2); % very uncertain of landmark psosns
+  %init_x(bi) = true_landmark_pos(:,i);
+  %init_V(bi,bi)= 1e-5*eye(2); % very sure of landmark psosns
+end