％ EM algorithm for k multidimensional Gaussian mixture estimation

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％ Inputs:

％ X(n,d) - input data, n=number of observations, d=dimension of variable

％ k - maximum number of Gaussian components allowed

％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)

％ maxiter - maximum number of iteration allowed ([] for none)

％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)

％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)

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％ Ouputs:

％ W(1,k) - estimated weights of GM

％ M(d,k) - estimated mean vectors of GM

％ V(d,d,k) - estimated covariance matrices of GM

％ L - log likelihood of estimates

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