TOPSIS code part of good and bad solution distance method

1 Copy data to workspace , And named it X

2 Determine whether you need to forward

[n,m]=size(X);
disp([' share 'num2str(n)' Two evaluation objects ,'num2str(m)' Evaluation indicators '])
judge=input([' this 'num2str(m)' Whether the indicators need to be processed forward , Please enter 1, No, please enter 0：']);
if judge == 1
    Position = input(' Please enter the column that needs forward processing , for example ：[2,3,6]');
    disp(' Please enter the type to be processed (1： Very small ,2： The middle type ,3： Interval type )')
    Type = input(' for example ： The first 2 Columns are very small , The first 3 Columns are interval type , The first 6 Columns are intermediate , enter [1,3,2]:')
    for i = 1:size(Position,2)
        X(:,Position(i)) = Positivization(X(:,Position(i)),Type(i),Position(i));
    end
    disp(' The forward matrix  X = ')
    disp(X)    
end

  Definition Positivization function

% Definition Positivization
function[posit_x] = Positivization(x,type,i)
    if type == 1
        disp([' The first 'num2str(i)' Columns are very small , Is moving forward '])
        posit_x = Min2Max(x);
        disp([' The first 'num2str(i)' Column miniaturization forward processing is complete '])
        disp('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
    elseif type == 2
        disp([' The first 'num2str(i)' Columns are intermediate , Is moving forward '])
        best = input(' Please enter the best value ：')
        posit_x = Mid2Max(x,best);
        disp([' The first 'num2str(i)' The forward processing of column intermediate type is completed '])
        disp('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
    elseif type == 3
        disp([' The first 'num2str(i)' Columns are interval type , Is moving forward '])
        posit_x = Inter2Max(x,a,b);
        disp([' The first 'num2str(i)' Column interval type forward processing completed '])
        disp('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
    else
        disp(' error ')
    end
end

  Definition Min2Max function

% Definition Min2Max function 
function[posit_x] = Min2Max(x)
    posit_x = max(x) - x;
end

Definition Mid2Max function

% Definition Mid2Max function 
function[posit_x] = Mid2Max(x,best)
    M = max(abs(x-best));
    posit_x = 1-abs(x-best)/M;
end

Definition Inter2Max function  

% Definition Inter2Max function 
function [posit_x] = Inter2Max(x,a,b)
    r_x = size(x,1);
    M = max([a-min(x),max(x)-b]);
    posit_x = zeros(r_x,1);
    for i = 1:r_x
        if x(i) < a
            posit_x(i) = 1-(a-x(i))/M;
        elseif x(i) > b
            posit_x(i) = 1-(x(i)-b)/M;
        else
            posit_x(i) = 1;
        end
    end
end
            

3 Normalize the forward matrix

Z=X./repmat(sum(X.*X).^0.5,n,1);
disp(' Standardized matrix Z=')
disp(Z)

4 Calculate the distance from the maximum and the distance from the minimum , And calculate the score

D_P = sum([(Z-repmat(max(Z),n,1)).^2],2).^0.5
D_N = sum([(Z-repmat(min(Z),n,1)).^2],2).^0.5
S=D_N./(D_P+D_N);% Non normalized score 
disp(' The final score is ：')
stand_S = S/sum(S)
[sorted_S,index] = sort(stand_S,'descend')