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    Metadata details:
    filename: varMicro.m
    path of file: ./repos/Microstate-EEGlab-toolbox/Microstate0.3
    
    the code of the file until where you have to start completion: function [A_opt,L_opt,Res] = varMicro(X,K_range,opts)
%VARMICRO Variational Microstates.
%
%  Note - Early untested version.
%
%  Implementation of variational microstates, with optional smoothing of
%  segments as described in [1].
%  Free energy is used to choose amongst multiple restarts, for the same 
%  number of microstates (K). Free energy is also used to find the optimal
%  number of microstates.
%
%  [1] - (unpublished manuscript). Variational microstate analysis.
%  [2] - Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1995).
%        Segmentation of brain electrical activity into microstates: model
%        estimation and validation. IEEE Transactions on Biomedical
%        Engineering.

    
function [A_opt,L_opt,Res] = varMicro(X,K_range,opts)
%VARMICRO Variational Microstates.
%
%  Note - Early untested version.
%
%  Implementation of variational microstates, with optional smoothing of
%  segments as described in [1].
%  Free energy is used to choose amongst multiple restarts, for the same 
%  number of microstates (K). Free energy is also used to find the optimal
%  number of microstates.
%
%  [1] - (unpublished manuscript). Variational microstate analysis.
%  [2] - Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1995).
%        Segmentation of brain electrical activity into microstates: model
%        estimation and validation. IEEE Transactions on Biomedical
%        Engineering.
%
%  Please cite this toolbox as:
%  Poulsen, A. T., Pedroni, A., Langer, N., &  Hansen, L. K. (unpublished
%  manuscript). Microstate EEGlab toolbox: An introductionary guide.
%
%  Andreas Trier Poulsen, atpo@dtu.dk
%  Technical University of Denmark, Cognitive systems - February 2017
%
%  Inputs
%  X       - EEG (channels x samples).
%  K_range - A priori number of microstates. Can be a vector.
%  
%  Optional inputs
%  opts. 
%       sig2_0         - Prior variance of activations (default: average
%                        EEG channel variance).
%       p0             - Probability for having same microstate as last 
%                        timepoint (default: 0). Setting to zero turns
%                        smoothing off.
%       reps           - Number of random initialisations of algorithm
%                        (default: 10).
%       max_iterations - Maximum number of iterations of algorithm
%                        (default: 1000).
%       thresh         - Threshold of convergence based on relative change
%                        in free energy (default: 1e-6). 
%       verbose        - Print status messages to command window?
%                        (default:1).
%
%  Outputs
%  A_opt   - Spatial distribution of microstates (channels x K).
%  L_opt   - Label of the most active microstate at each timepoint (1 x samples).
%  Res     - Struct containing additional results:
%     .Z_all    - Cell containing the microstate activations for each number of
%                 microstates defined in K_range. The dimensions of each cell
%                 is (K x samples).
%     .A_all    - Cell containing the spatial distribution for each number of
%                 microstates defined in K_range. The dimensions of each cell
%                 is (channels x K).
%     .L_all    - Cell containing the labels for each number of microstates
%                 defined in K_range. The dimensions of each cell is
%                 (1 x samples).
%     .S_opt         - Sparsity parameters (K x samples) of optimal solution.
%     .beta          - Noise precision for the best solution of each K in K_range.
%     .sig2_z_opt    - Variance of microstate activations (K x samples) of 
%                      optimal solution.
%     .K_act         - Number of microstates for optimal solution.
%     .Free_energy   - Free energy for the best solution of each K in K_range.
%     .sig2_modk     - Noise variance as calculated in [2] for the best 
%                      solution of each K in K_range.
%     .sig2_modk_mcv - Modified predictive residual variance as calculated 
%                      in [2] for the best solution of each K in K_range.
%     .R2            - Explained variance as calculated in [2] for the best
%                      solution of each K in K_range.
%     .MSE           - Mean squared error for the best solution of each K in K_range.
%     .opts          - Settings used to run the algorithm.


%% Checking inputs and setting default settings, where needed.
if nargin<3; opts = []; end