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    Metadata details:
    filename: MF_hmm_CompareNStates.m
    path of file: ./repos/hctsa/Operations
    
    the code of the file until where you have to start completion: function out = MF_hmm_CompareNStates(y,trainp,nstater,randomSeed)
% MF_hmm_CompareNStates     Hidden Markov Model (HMM) fitting to a time series.
%
% Fits HMMs with different numbers of states, and compares the resulting
% test-set likelihoods.
%
% The code relies on Zoubin Gharamani's implementation of HMMs for real-valued
% Gassian-distributed observations, including the hmm and hmm_cl routines (
% renamed ZG_hmm and ZG_hmm_cl here).
% Implementation of HMMs for real-valued Gaussian observations:
% http://www.gatsby.ucl.ac.uk/~zoubin/software.html
% or, specifically:
% http://www.gatsby.ucl.ac.uk/~zoubin/software/hmm.tar.gz
%
%---INPUTS:
%
% y, the input time series
%
% trainp, the initial proportion of the time series to train the model on
%
% nstater, the vector of state numbers to compare. E.g., (2:4) compares a number
%               of states 2, 3, and 4.
%
%---OUTPUTS: statistics on how the log likelihood of the test data changes with
% the number of states n_{states}$. We implement the code for p_{train} = 0.6$
% as n_{states}$ varies across the range n_{states} = 2, 3, 4$.

% ------------------------------------------------------------------------------
% Copyright (C) 2020, Ben D. Fulcher <ben.d.fulcher@gmail.com>,
% <http://www.benfulcher.com>
%
% If you use this code for your research, please cite the following two papers:
%
% (1) B.D. Fulcher and N.S. Jones, "hctsa: A Computational Framework for Automated
% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).
% DOI: 10.1016/j.cels.2017.10.001
%
% (2) B.D. Fulcher, M.A. Little, N.S. Jones, "Highly comparative time-series
% analysis: the empirical structure of time series and their methods",
% J. Roy. Soc. Interface 10(83) 20130048 (2013).
% DOI: 10.1098/rsif.2013.0048
%
% This function is free software: you can redistribute it and/or modify it under
% the terms of the GNU General Public License as published by the Free Software
% Foundation, either version 3 of the License, or (at your option) any later
% version.
%
% This program is distributed in the hope that it will be useful, but WITHOUT
% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
% details.
%
% You should have received a copy of the GNU General Public License along with
% this program. If not, see <http://www.gnu.org/licenses/>.
% ------------------------------------------------------------------------------

% ------------------------------------------------------------------------------
%% Check Inputs
% ------------------------------------------------------------------------------
N = length(y); % number of samples in time series

    
function out = MF_hmm_CompareNStates(y,trainp,nstater,randomSeed)
% MF_hmm_CompareNStates     Hidden Markov Model (HMM) fitting to a time series.
%
% Fits HMMs with different numbers of states, and compares the resulting
% test-set likelihoods.
%
% The code relies on Zoubin Gharamani's implementation of HMMs for real-valued
% Gassian-distributed observations, including the hmm and hmm_cl routines (
% renamed ZG_hmm and ZG_hmm_cl here).
% Implementation of HMMs for real-valued Gaussian observations:
% http://www.gatsby.ucl.ac.uk/~zoubin/software.html
% or, specifically:
% http://www.gatsby.ucl.ac.uk/~zoubin/software/hmm.tar.gz
%
%---INPUTS:
%
% y, the input time series
%
% trainp, the initial proportion of the time series to train the model on
%
% nstater, the vector of state numbers to compare. E.g., (2:4) compares a number
%               of states 2, 3, and 4.
%
%---OUTPUTS: statistics on how the log likelihood of the test data changes with
% the number of states n_{states}$. We implement the code for p_{train} = 0.6$
% as n_{states}$ varies across the range n_{states} = 2, 3, 4$.

% ------------------------------------------------------------------------------
% Copyright (C) 2020, Ben D. Fulcher <ben.d.fulcher@gmail.com>,
% <http://www.benfulcher.com>
%
% If you use this code for your research, please cite the following two papers:
%
% (1) B.D. Fulcher and N.S. Jones, "hctsa: A Computational Framework for Automated
% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).
% DOI: 10.1016/j.cels.2017.10.001
%
% (2) B.D. Fulcher, M.A. Little, N.S. Jones, "Highly comparative time-series
% analysis: the empirical structure of time series and their methods",
% J. Roy. Soc. Interface 10(83) 20130048 (2013).
% DOI: 10.1098/rsif.2013.0048
%
% This function is free software: you can redistribute it and/or modify it under
% the terms of the GNU General Public License as published by the Free Software
% Foundation, either version 3 of the License, or (at your option) any later
% version.
%
% This program is distributed in the hope that it will be useful, but WITHOUT
% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
% details.
%
% You should have received a copy of the GNU General Public License along with
% this program. If not, see <http://www.gnu.org/licenses/>.
% ------------------------------------------------------------------------------

% ------------------------------------------------------------------------------
%% Check Inputs
% ------------------------------------------------------------------------------
N = length(y); % number of samples in time series

if nargin < 2 || isempty(trainp)
    fprintf(1,'Training the model on 60%% of the data by default\n');
    trainp = 0.6; % train on 60% of the data
end