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, 65 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/Kalman/sample_lds.m b/sourcecodes/bnt-master/Kalman/sample_lds.m
new file mode 100644
index 00000000..a5b1aff4
--- /dev/null
+++ b/sourcecodes/bnt-master/Kalman/sample_lds.m
@@ -0,0 +1,65 @@
+function [x,y] = sample_lds(F, H, Q, R, init_state, T, models, G, u)
+% SAMPLE_LDS Simulate a run of a (switching) stochastic linear dynamical system.
+% [x,y] = switching_lds_draw(F, H, Q, R, init_state, models, G, u)
+% 
+%   x(t+1) = F*x(t) + G*u(t) + w(t),  w ~ N(0, Q),  x(0) = init_state
+%   y(t) =   H*x(t) + v(t),  v ~ N(0, R)
+%
+% Input:
+% F(:,:,i) - the transition matrix for the i'th model
+% H(:,:,i) - the observation matrix for the i'th model
+% Q(:,:,i) - the transition covariance for the i'th model
+% R(:,:,i) - the observation covariance for the i'th model
+% init_state(:,i) - the initial mean for the i'th model
+% T - the num. time steps to run for
+%
+% Optional inputs:
+% models(t) - which model to use at time t. Default = ones(1,T)
+% G(:,:,i) - the input matrix for the i'th model. Default = 0.
+% u(:,t)   - the input vector at time t. Default = zeros(1,T)
+%
+% Output:
+% x(:,t)    - the hidden state vector at time t.
+% y(:,t)    - the observation vector at time t.
+
+
+if ~iscell(F)
+  F = num2cell(F, [1 2]);
+  H = num2cell(H, [1 2]);
+  Q = num2cell(Q, [1 2]);
+  R = num2cell(R, [1 2]);
+end
+
+M = length(F);
+%T = length(models);
+
+if nargin < 7,
+  models = ones(1,T);
+end
+if nargin < 8,
+  G = num2cell(repmat(0, [1 1 M]));
+  u = zeros(1,T);
+end
+
+[os ss] = size(H{1});
+state_noise_samples = cell(1,M);
+obs_noise_samples = cell(1,M);
+for i=1:M
+  state_noise_samples{i} = sample_gaussian(zeros(length(Q{i}),1), Q{i}, T)';
+  obs_noise_samples{i} = sample_gaussian(zeros(length(R{i}),1), R{i}, T)';
+end
+
+x = zeros(ss, T);
+y = zeros(os, T);
+
+m = models(1);
+x(:,1) = init_state(:,m);
+y(:,1) = H{m}*x(:,1) + obs_noise_samples{m}(:,1);
+
+for t=2:T
+  m = models(t);
+  x(:,t) = F{m}*x(:,t-1) + G{m}*u(:,t-1) + state_noise_samples{m}(:,t);
+  y(:,t) = H{m}*x(:,t)  + obs_noise_samples{m}(:,t);
+end
+
+