evaluateFitnessError.m

%   COPYRIGHT
%   This file is part of TSSA: https://github.com/ayrna/tssa
%   Original authors: Antonio M. Duran Rosal, Pedro A. Gutierrez
%   Copyright:
%       This software is released under the The GNU General Public License v3.0 licence
%       available at http://www.gnu.org/licenses/gpl-3.0.html
%   Citation: If you use this code, please cite any of the following papers:
%     [1] A.M. Durán-Rosal, P.A. Gutiérrez, Á. Carmona-Poyato and C. Hervás-Martínez.
%         "A hybrid dynamic exploitation barebones particle swarm optimisation
%         algorithm for time series segmentation", Neurocomputing,
%         Vol. 353, August, 2019, pp. 45-55.
%		  https://doi.org/10.1016/j.neucom.2018.05.129
%     [2] A.M. Durán-Rosal, P.A. Gutiérrez, F.J. Martínez-Estudillo and C. Hervás-Martínez.
%         "Simultaneous optimisation of clustering quality and approximation error
%         for time series segmentation", Information Sciences, Vol. 442-443, May, 2018, pp. 186-201.
%         https://doi.org/10.1016/j.ins.2018.02.041
%     [3] A.M. Durán-Rosal, P.A. Gutiérrez, S. Salcedo-Sanz and C. Hervás-Martínez.
%         "A statistically-driven Coral Reef Optimization algorithm for optimal
%         size reduction of time series", Applied Soft Computing, 
%         Vol. 63. 2018, pp. 139-153.
%         https://doi.org/10.1016/j.asoc.2017.11.037
%
%% evaluateFitnessError
% Function: Evaluation method for approximation error
% 
% Input:
%     typeError:          type of error (RMSE, RMSEp, MAXe) (see function computeErrors)
%     population:         set of chromosomes
%     oldFitness:         fitness of population
%     serie:              time series
%     degree:             degree of approximation
%     
% Output:
%     fitness:            fitness of current population
function [fitness] = evaluateFitnessError(typeError,population,oldFitness,serie,degree)
    fitness = zeros(1,size(population,1));
    %fitness = oldFitness;
    for i=1:size(population,1),
        if isnan(oldFitness(i)),
            [errors] = computeErrors(population(i,:),serie,degree);
            fitness(i) = fitnessError(errors,typeError);
        elseif oldFitness(i) == -1,
            fitness(i) = -1;
        end 
    end
end