📄 mindisclassifier.m
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function k=MinDisClassifier(n,m,classcenter,x)
% MINDISCLASSIFIER is the implementation of minimum-distance classifier
% n denotes the dimension of the problem
% m is the number of classes
% classcenter is a matrix(n*m),classcenter(i,j) represents the i-th
% component of the j-th class
% x is a incoming pattern
%
% the function will return the number k into which x is classified
if nargout>1
error('Too many output arguments.');
end
if nargin~=4
error('Wrong number of input arguments.');
end
[cn,cm]=size(classcenter);
if cm~=m | cn~=n
error('Wrong input data.');
end
k=1;
max=dot(x,classcenter(:,1))-0.5*dot(classcenter(:,1),classcenter(:,1));
for j=2:m
if (dot(x,classcenter(:,j))-0.5*dot(classcenter(:,j),classcenter(:,j)))>max
max=dot(x,classcenter(:,j))-0.5*dot(classcenter(:,j),classcenter(:,j));
k=j;
end
end
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