comparison toolboxes/FullBNT-1.0.7/Kalman/learn_AR.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [coef, C] = learn_AR(data, k)
+% Find the ML parameters of a vector autoregressive process of order k.
+% [coef, C] = learn_AR(k, data)
+% data{l}(:,t) = the observations at time t in sequence l
+
+warning('learn_AR seems to be broken');
+
+nex = length(data);
+obs = cell(1, nex);
+for l=1:nex
+  obs{l} = convert_to_lagged_form(data{l}, k);
+end
+
+% The initial parameter values don't matter, since this is a perfectly observable problem.
+% However, the size of F must be set correctly.
+y = data{1};
+[s T] = size(y);
+coef = rand(s,s,k);
+C = rand_psd(s);
+[F,H,Q,R,initx,initV] = AR_to_SS(coef, C, y);
+
+max_iter = 1;
+fully_observed = 1;
+diagQ = 0;
+diagR = 0;
+[F, H, Q, R, initx, initV, loglik] = ...
+    learn_kalman(obs, F, H, Q, R, initx, initV, max_iter, diagQ, diagR, fully_observed);
+
+[coef, C] = SS_to_AR(F, Q, k);
+