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EM for HMM Multivariate Gaussian processes

version 1.8.0.0 (21.3 KB) by Sebastien PARIS
A fast implementation of the EM Algorithm for HMM Multivariate Gaussian Mixture

14.1K Downloads

Updated 18 May 2021

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em_ghmm : Expectation-Maximization algorithm for a HMM with Multivariate Gaussian measurement
Usage
-------
[logl , PI , A , M , S] = em_ghmm(Z , PI0 , A0 , M0 , S0 , [options]);
Inputs
-------
Z Measurements (m x K x n1 x ... x nl)
PI0 Initial probabilities (d x 1) : Pr(x_1 = i) , i=1,...,d. PI0 can be (d x 1 x v1 x ... x vr)
A0 Initial state transition probabilities matrix Pr(x_{k} = i| x_{k - 1} = j) such
sum_{x_k}(A0) = 1 => sum(A , 1) = 1. A0 can be (d x d x v1 x ... x vr).
M0 Initial mean vector. M0 can be (m x 1 x d x v1 x ... x vr)
S0 Initial covariance matrix. S0 can be (m x m x d x v1 x ... x vr)
options nb_ite Number of iteration (default [30])
update_PI Update PI (0/1 = no/[yes])
update_A Update PI (0/1 = no/[yes])
update_M Update M (0/1 = no/[yes])
update_S Update S (0/1 = no/[yes])
Outputs
-------
logl Final loglikelihood (n1 x ... x nl x v1 x ... x vr)
PI Estimated initial probabilities (d x 1 x n1 x ... x nl v1 x ... x vr)
A Estimated state transition probabilities matrix (d x d x n1 x ... x nl v1 x ... x vr)
M Estimated mean vector (m x 1 x d x n1 x ... x nl v1 x ... x vr)
S Estimated covariance vector (m x m x d x n1 x ... x nl v1 x ... x vr)
Please run mexme_em_ghmm to compile mex files on your platform.
Run test_em_ghmm for demo

Cite As

Sebastien PARIS (2021). EM for HMM Multivariate Gaussian processes (https://www.mathworks.com/matlabcentral/fileexchange/20712-em-for-hmm-multivariate-gaussian-processes), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2007b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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