function j=mindist_classifier_type_final(tst,trn,type)
% This function used to calculate the minimum distance between the testing
% image and the training images using many metrics
% This code is edited by Eng. Alaa Tharwat Abd El. Monaaim Othman from Egypt
% Teaching assistant in El Sorouk Academy for Computer Science And Information Technology
% Please for any help send to me Engalaatharwat@hotmail.com
% Please if you used this code please refer this references
% "A novel ear recognition method using features combination" ,Atallah
% Hashad, Gouda I. Salama, Alaa Tharwat, Journal of AEIC, Vol. 10, Dec 2008.
% A version Dec. 2008
% Where : tst is the test image as a column
% trn is the training images as a columns
% type is the type of the minimum distance
[a1,b1]=size(tst);
[a2,b2]=size(trn);
X=zeros(b1+b2,a1);
X(1,:)=tst';
X(2:b1+b2,:)=trn';
switch (type)
case 'Euclidean'
Y=pdist(X,'Euclidean');
dif=Y(1,1:b2);
[p,j]=min(dif);
case 'minkowski1'
Y=pdist(X,'minkowski',3);
dif=Y(1,1:b2);
[p,j]=min(dif);
case 'minkowski2'
Y=pdist(X,'minkowski',9);
dif=Y(1,1:b2);
[p,j]=min(dif);
case 'cityblock'
Y=pdist(X,'cityblock');
dif=Y(1,1:b2);
[p,j]=min(dif);
case 'Hamming'
Y=pdist(X,'Hamming');
dif=Y(1,1:b2);
[p,j]=min(dif);
case 'Correlation'
Y=pdist(X,'Correlation');
dif=Y(1,1:b2);
[p,j]=min(dif);
case 'Jaccard'
Y=pdist(X,'Jaccard');
dif=Y(1,1:b2);
[p,j]=min(dif);
case 'Mahal'
Y=pdist(X,'mahal');
dif=Y(1,1:b2);
[p,j]=min(dif);
case 'cosine'
Y=pdist(X,'cosine');
dif=Y(1,1:b2);
[p1,j]=min(dif);
case 'Chebychev'
Y=pdist(X,'Chebychev');
dif=Y(1,1:b2);
[p1,j]=min(dif);
case 'seuclidean'
Y=pdist(X,'seuclidean');
dif=Y(1,1:b2);
[p1,j]=min(dif);
end
