diff toolboxes/FullBNT-1.0.7/Kalman/kalman_filter.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/Kalman/kalman_filter.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,101 @@
+function [x, V, VV, loglik] = kalman_filter(y, A, C, Q, R, init_x, init_V, varargin)
+% Kalman filter.
+% [x, V, VV, loglik] = kalman_filter(y, A, C, Q, R, init_x, init_V, ...)
+%
+% INPUTS:
+% y(:,t)   - the observation at time t
+% A - the system matrix
+% C - the observation matrix 
+% Q - the system covariance 
+% R - the observation covariance
+% init_x - the initial state (column) vector 
+% init_V - the initial state covariance 
+%
+% OPTIONAL INPUTS (string/value pairs [default in brackets])
+% 'model' - model(t)=m means use params from model m at time t [ones(1,T) ]
+%     In this case, all the above matrices take an additional final dimension,
+%     i.e., A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m).
+%     However, init_x and init_V are independent of model(1).
+% 'u'     - u(:,t) the control signal at time t [ [] ]
+% 'B'     - B(:,:,m) the input regression matrix for model m
+%
+% OUTPUTS (where X is the hidden state being estimated)
+% x(:,t) = E[X(:,t) | y(:,1:t)]
+% V(:,:,t) = Cov[X(:,t) | y(:,1:t)]
+% VV(:,:,t) = Cov[X(:,t), X(:,t-1) | y(:,1:t)] t >= 2
+% loglik = sum{t=1}^T log P(y(:,t))
+%
+% If an input signal is specified, we also condition on it:
+% e.g., x(:,t) = E[X(:,t) | y(:,1:t), u(:, 1:t)]
+% If a model sequence is specified, we also condition on it:
+% e.g., x(:,t) = E[X(:,t) | y(:,1:t), u(:, 1:t), m(1:t)]
+
+[os T] = size(y);
+ss = size(A,1); % size of state space
+
+% set default params
+model = ones(1,T);
+u = [];
+B = [];
+ndx = [];
+
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i}
+   case 'model', model = args{i+1};
+   case 'u', u = args{i+1};
+   case 'B', B = args{i+1};
+   case 'ndx', ndx = args{i+1};
+   otherwise, error(['unrecognized argument ' args{i}])
+  end
+end
+
+x = zeros(ss, T);
+V = zeros(ss, ss, T);
+VV = zeros(ss, ss, T);
+
+loglik = 0;
+for t=1:T
+  m = model(t);
+  if t==1
+    %prevx = init_x(:,m);
+    %prevV = init_V(:,:,m);
+    prevx = init_x;
+    prevV = init_V;
+    initial = 1;
+  else
+    prevx = x(:,t-1);
+    prevV = V(:,:,t-1);
+    initial = 0;
+  end
+  if isempty(u)
+    [x(:,t), V(:,:,t), LL, VV(:,:,t)] = ...
+	kalman_update(A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m), y(:,t), prevx, prevV, 'initial', initial);
+  else
+    if isempty(ndx)
+      [x(:,t), V(:,:,t), LL, VV(:,:,t)] = ...
+	  kalman_update(A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m), y(:,t), prevx, prevV, ... 
+			'initial', initial, 'u', u(:,t), 'B', B(:,:,m));
+    else
+      i = ndx{t};
+      % copy over all elements; only some will get updated
+      x(:,t) = prevx;
+      prevP = inv(prevV);
+      prevPsmall = prevP(i,i);
+      prevVsmall = inv(prevPsmall);
+      [x(i,t), smallV, LL, VV(i,i,t)] = ...
+	  kalman_update(A(i,i,m), C(:,i,m), Q(i,i,m), R(:,:,m), y(:,t), prevx(i), prevVsmall, ...
+			'initial', initial, 'u', u(:,t), 'B', B(i,:,m));
+      smallP = inv(smallV);
+      prevP(i,i) = smallP;
+      V(:,:,t) = inv(prevP);
+    end    
+  end
+  loglik = loglik + LL;
+end
+
+
+
+
+