Spearman.m

classdef Spearman < Metric
    %SPEARMAN static class to calculate Spearman's rank correlation coefficient
    %
    %   SPEARMAN methods:
    %      CALCULATEMETRIC            - Computes the evaluation metric
    %      CALCULATECROSSVALMETRIC    - Computes the evaluation metric as an error
    %
    %   References:
    %     [1] C. Spearman
    %         The proof and measurement of association between two things
    %         Am. J. Psychol., 15 (1904), pp. 72-101
    %     [2] M. Cruz-Ramírez, C. Hervás-Martínez, J. Sánchez-Monedero and
    %         P. A. Gutiérrez Metrics to guide a multi-objective evolutionary
    %         algorithm for ordinal classification, Neurocomputing, Vol. 135, July, 2014, pp. 21-31.
    %         https://doi.org/10.1016/j.neucom.2013.05.058
    %
    %   This file is part of ORCA: https://github.com/ayrna/orca
    %   Original authors: Pedro Antonio Gutiérrez, María Pérez Ortiz, Javier Sánchez Monedero
    %   Citation: If you use this code, please cite the associated paper http://www.uco.es/grupos/ayrna/orreview
    %   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
    methods
        function obj = MZE(obj)
            obj.name = 'Rho Spearman';
        end
    end
    
    methods(Static = true)
        
        function spearman = calculateMetric(argum1,argum2)
            %CALCULATEMETRIC Computes the evaluation metric
            %   METRIC = CALCULATEMETRIC(CM) returns calculated metric from confussion
            %   matrix CM
            %   METRIC = CALCULATEMETRIC(actual, pred) returns calculated metric from
            %   real labels (ACTUAL) labels and predicted labels (PRED)
            
            if nargin < 2
                [argum1, argum2] = getLabelsFromCM(argum1);
            end
            
            n = size(argum1,1);
            num = sum((argum1-repmat(mean(argum1),n,1)).*(argum2-repmat(mean(argum2),n,1)));
            div= sqrt(sum((argum1-repmat(mean(argum1),n,1)).^2)*sum((argum2-repmat(mean(argum2),n,1)).^2));
            if(num == 0)
                spearman = 0;
            else
                spearman = num/div;
            end
            
        end
        
        function value = calculateCrossvalMetric(argum1,argum2)
            %CALCULATECROSSVALMETRIC Computes the evaluation metric and returns
            %it as an error.
            %   METRIC = CALCULATECROSSVALMETRIC(CM) returns calculated metric from confussion
            %   matrix CM
            %   METRIC = CALCULATECROSSVALMETRIC(actual, pred) returns calculated metric from
            %   real labels (ACTUAL) labels and predicted labels (PRED)
            if nargin == 2
                value = 1 - Spearman.calculateMetric(argum1,argum2);
            else
                value = 1 - Spearman.calculateMetric(argum1);
            end
        end
        
    end
    
    
end