em_alg_GMM: Implements the EM algorithm on mvGMM data
In nwakim/nwREM: Implementation of an L0 penalized mvGMM EM algorithm

Implements the EM algorithm on mvGMM data

1	em_alg_GMM(x, k, lambda, max_it, tol)

`x`	A (n x d) matrix of observed data
`k`	The number of mixing components
`lambda`	Final value for regularization parameter - will go lambda^10 to lambda. Lambda will automatically equal infinity, if not specified. This is equivalent to a standard EM algorithm
`max_it`	Maximum number of iterations
`tol`	Relative tolerance of likelihood at convergence

A list containing the following values: * llk : Likelihood of final iteration * mus : (d x k) of estimated means * mix_prop : size k vector of estimated mixing proportions * covs : (d x d x k) vectors of estimated standard deviations * probs : Matrix (n x k) of the probabilties of observed values in each cluster * classification Size n vector of most likely cluster for each observed value * iter : number of iterations

nwakim/nwREM documentation built on May 22, 2019, 5:34 p.m.