Revision 1191, 1.1 kB
(checked in by smidl, 14 years ago)
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OO implementation of Kalman in Matlab
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[1191] | 1 | %> @file mexLaplace.m |
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| 2 | %> @brief Matlab implementation of Gaussian density |
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| 3 | % ====================================================================== |
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| 4 | %> @brief Unconditional Gaussian density |
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| 5 | % |
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| 6 | %> \f[ f(x|\mu,b) \propto \exp(-(x-\mu)'R^{-1}(x-\mu))\f] |
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| 7 | % ====================================================================== |
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| 8 | classdef mexGauss < mexEpdf |
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| 9 | properties |
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| 10 | mu % mean values |
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| 11 | R % variance |
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| 12 | end |
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| 13 | methods |
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| 14 | function m=mean(obj) |
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| 15 | m = obj.mu; |
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| 16 | end |
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| 17 | function obj=validate(obj) |
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| 18 | if size(obj.R,1)~=size(obj.R,2) |
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| 19 | error('matrix R is not square'); |
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| 20 | end |
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| 21 | if length(obj.mu)~=size(obj.R,1) |
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| 22 | error('incompatible mu and R'); |
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| 23 | end |
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| 24 | end |
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| 25 | function dim=dimension(obj) |
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| 26 | dim = size(obj.mu,1); |
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| 27 | end |
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| 28 | function v=variance(obj) |
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| 29 | v=diag(R); |
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| 30 | end |
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| 31 | function l=evallog(obj,x) |
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| 32 | l=-log(2*obj.b)-abs(x-obj.mu)/obj.b; |
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| 33 | end |
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| 34 | function s=sample(obj) |
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| 35 | z = randn(obj.dimension); |
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| 36 | s = obj.mu+chol(R)'*z; |
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| 37 | end |
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| 38 | end |
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| 39 | end |
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