dtw.m

function [Dist,D,k,w]=dtw(t,r)
    %Dynamic Time Warping Algorithm
    %Dist is unnormalized distance between t and r
    %D is the accumulated distance matrix
    %k is the normalizing factor
    %w is the optimal path
    %t is the vector you are testing against
    %r is the vector you are testing
    [rows,N]=size(t);
    [rows,M]=size(r);
    for n=1:N
        for m=1:M
            % Converted to do euclidian distance
            d(n,m)=sqrt(((t(n,1)-r(m,1)) + t(n,2)-r(m,2))^2);
        end
    end
    % d=(repmat(t(:),1,M)-repmat(r(:)',N,1)).^2; %this replaces the nested for loops from above Thanks Georg Schmitz 

    D=zeros(size(d));
    D(1,1)=d(1,1);

    for n=2:N
        D(n,1)=d(n,1)+D(n-1,1);
    end
    for m=2:M
        D(1,m)=d(1,m)+D(1,m-1);
    end
    for n=2:N
        for m=2:M
            D(n,m)=d(n,m)+min([D(n-1,m),D(n-1,m-1),D(n,m-1)]);
        end
    end

    Dist=D(N,M);
    n=N;
    m=M;
    k=1;
    w=[];
    w(1,:)=[N,M];
    while ((n+m)~=2)
        if (n-1)==0
            m=m-1;
        elseif (m-1)==0
            n=n-1;
        else 
          [values,number]=min([D(n-1,m),D(n,m-1),D(n-1,m-1)]);
          switch number
          case 1
            n=n-1;
          case 2
            m=m-1;
          case 3
            n=n-1;
            m=m-1;
          end
      end
        k=k+1;
        w=cat(1,w,[n,m]);
end