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, 39 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/Kalman/AR_to_SS.m b/sourcecodes/bnt-master/Kalman/AR_to_SS.m
new file mode 100644
index 00000000..3a60e492
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+++ b/sourcecodes/bnt-master/Kalman/AR_to_SS.m
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+function [F,H,Q,R,initx, initV] = AR_to_SS(coef, C, y)
+%
+% Convert a vector auto-regressive model of order k to state-space form.
+% [F,H,Q,R] = AR_to_SS(coef, C, y)
+% 
+% X(i) = A(1) X(i-1) + ... + A(k) X(i-k+1) + v, where v ~ N(0, C)
+% and A(i) = coef(:,:,i) is the weight matrix for i steps ago.
+% We initialize the state vector with [y(:,k)' ... y(:,1)']', since
+% the state vector stores [X(i) ... X(i-k+1)]' in order.
+
+[s s2 k] = size(coef); % s is the size of the state vector
+bs = s * ones(1,k); % size of each block
+
+F = zeros(s*k);
+for i=1:k
+   F(block(1,bs), block(i,bs)) = coef(:,:,i);
+end
+for i=1:k-1
+  F(block(i+1,bs), block(i,bs)) = eye(s);
+end
+
+H = zeros(1*s, k*s);
+% we get to see the most recent component of the state vector 
+H(block(1,bs), block(1,bs)) = eye(s); 
+%for i=1:k
+%  H(block(1,bs), block(i,bs)) = eye(s);
+%end
+
+Q = zeros(k*s);
+Q(block(1,bs), block(1,bs)) = C;
+
+R = zeros(s);
+
+initx = zeros(k*s, 1);
+for i=1:k
+  initx(block(i,bs)) = y(:, k-i+1); % concatenate the first k observation vectors
+end
+
+initV = zeros(k*s); % no uncertainty about the state (since perfectly observable)