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, 71 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/Kalman/kalman_update.m b/sourcecodes/bnt-master/Kalman/kalman_update.m
new file mode 100644
index 00000000..ed8fc283
--- /dev/null
+++ b/sourcecodes/bnt-master/Kalman/kalman_update.m
@@ -0,0 +1,71 @@
+function [xnew, Vnew, loglik, VVnew] = kalman_update(A, C, Q, R, y, x, V, varargin)
+% KALMAN_UPDATE Do a one step update of the Kalman filter
+% [xnew, Vnew, loglik] = kalman_update(A, C, Q, R, y, x, V, ...)
+%
+% INPUTS:
+% A - the system matrix
+% C - the observation matrix 
+% Q - the system covariance 
+% R - the observation covariance
+% y(:)   - the observation at time t
+% x(:) - E[X | y(:, 1:t-1)] prior mean
+% V(:,:) - Cov[X | y(:, 1:t-1)] prior covariance
+%
+% OPTIONAL INPUTS (string/value pairs [default in brackets])
+% 'initial' - 1 means x and V are taken as initial conditions (so A and Q are ignored) [0]
+% 'u'     - u(:) the control signal at time t [ [] ]
+% 'B'     - the input regression matrix
+%
+% OUTPUTS (where X is the hidden state being estimated)
+%  xnew(:) =   E[ X | y(:, 1:t) ] 
+%  Vnew(:,:) = Var[ X(t) | y(:, 1:t) ]
+%  VVnew(:,:) = Cov[ X(t), X(t-1) | y(:, 1:t) ]
+%  loglik = log P(y(:,t) | y(:,1:t-1)) log-likelihood of innovatio
+
+% set default params
+u = [];
+B = [];
+initial = 0;
+
+args = varargin;
+for i=1:2:length(args)
+  switch args{i}
+   case 'u', u = args{i+1};
+   case 'B', B = args{i+1};
+   case 'initial', initial = args{i+1};
+   otherwise, error(['unrecognized argument ' args{i}])
+  end
+end
+
+%  xpred(:) = E[X_t+1 | y(:, 1:t)]
+%  Vpred(:,:) = Cov[X_t+1 | y(:, 1:t)]
+
+if initial
+  if isempty(u)
+    xpred = x;
+  else
+    xpred = x + B*u;
+  end
+  Vpred = V;
+else
+  if isempty(u)
+    xpred = A*x;
+  else
+    xpred = A*x + B*u;
+  end
+  Vpred = A*V*A' + Q;
+end
+
+e = y - C*xpred; % error (innovation)
+n = length(e);
+ss = length(A);
+S = C*Vpred*C' + R;
+Sinv = inv(S);
+ss = length(V);
+loglik = gaussian_prob(e, zeros(1,length(e)), S, 1);
+K = Vpred*C'*Sinv; % Kalman gain matrix
+% If there is no observation vector, set K = zeros(ss).
+xnew = xpred + K*e;
+Vnew = (eye(ss) - K*C)*Vpred;
+VVnew = (eye(ss) - K*C)*A*V;
+