Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/Kalman/learn_AR_diagonal.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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1 function [coef, C] = learn_AR_diagonal(y, k) | |
2 % Find the ML parameters for a collection of independent scalar AR processes. | |
3 | |
4 % sep_coef(1,1,t,i) is the coefficient to apply to compopnent i of the state vector t steps ago | |
5 % eg. consider two components L and R and let A = coef(:,:,1,:), B = coef(:,:,2,:) | |
6 % L3 (AL 0 BL 0) (L2) (CL 0 0 0) | |
7 % R3 = (0 AR 0 BR) (R2) (0 CR 0 0) | |
8 % L2 (1 0 0 0 ) (L1) + (0 0 0 0) | |
9 % R2 (0 1 0 0 ) (R1) (0 0 0 0) | |
10 | |
11 ss = size(y, 1); | |
12 sep_coef = zeros(1,1,k,ss); | |
13 for i=1:ss | |
14 [sep_coef(:,:,:,i), sep_cov(i)] = learn_AR(k, y(i,:)); | |
15 end | |
16 C = diag(sep_cov); | |
17 for t=1:k | |
18 x = sep_coef(1,1,t,:); | |
19 coef(:,:,t) = diag(x(:)); | |
20 end |