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| author | ziejd2 | 2018-03-14 23:23:33 -0500 |
|---|---|---|
| committer | GitHub | 2018-03-14 23:23:33 -0500 |
| commit | 1ff6baa44e22b91eefb48aea6f3befa078c0489b (patch) | |
| tree | e0fd79d2e32fd2aedda2eadaed0f19af3514c520 /sourcecodes/bnt-master/Kalman/learn_AR.m | |
| parent | 6882395afdadf4e982b25b5215071a0932730950 (diff) | |
| parent | c80226899f5cdd9f11c163817d59445213f5bef0 (diff) | |
| download | BNW-1ff6baa44e22b91eefb48aea6f3befa078c0489b.tar.gz | |
Merge pull request #1 from ziejd2/octave_php_separate
Octave php separate
Diffstat (limited to 'sourcecodes/bnt-master/Kalman/learn_AR.m')
| -rw-r--r-- | sourcecodes/bnt-master/Kalman/learn_AR.m | 30 |
1 files changed, 30 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/Kalman/learn_AR.m b/sourcecodes/bnt-master/Kalman/learn_AR.m new file mode 100644 index 00000000..2d846c3a --- /dev/null +++ b/sourcecodes/bnt-master/Kalman/learn_AR.m @@ -0,0 +1,30 @@ +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); + |
