function d=dist(X,W,type,para)% X: K x M% W: C x M% d: K x C  K俺狼 1 X M 氦磐客 C俺狼 1 X M 盔屈埃狼 芭府 % type = 0 (default) L2 norm, para=[]%      = 1 L1 norm, para=[]%      = 2 L_inf norm (box distance), para=[]%      = 3 distance taking covariance matrix into account%          para: c x 1, para{i}: M x M inverse of cov. matrixif nargin <3,   type=0; % default is L2 normendif type == 3,   if nargin < 4, error('must give covarince matrices'); endend[K,M]=size(X);[C,M1]=size(W);if M~=M1, error('X and W should have the same no of columns!'); endif type==0, % L2 norm   if M >1,      wnorm=sum((W.*W)');    % 1 by C vector      xnorm=sum((X.*X)');    % 1 by K vector   elseif M==1,      wnorm=(W.*W)';         % 1 by C vector      xnorm=(X.*X)';         % 1 by K vector   end   d=sqrt(xnorm'*ones(1,C)-2*X*W'+ones(K,1)*wnorm); % K by C  matrix elseif type==1, % L1 norm   d=[];   if C <=K, % compute by column      for i=1:C,         d=[d sum(abs(X'-W(i,:)'*ones(1,K)))'];      end   elseif K < C, % compute by row      for i=1:K,         d=[d; sum(abs(X(i,:)'*ones(1,C)-W'))];      end   endelseif type==2, % L_inf norm   d=[];   if C <=K, % compute by column      for i=1:C,         d=[d max(abs(X'-W(i,:)'*ones(1,K)))'];      end   elseif K < C, % compute by row      for i=1:K,         d=[d; max(abs(X(i,:)'*ones(1,C)-W'))];      end   endelseif type==3, % L2 norm with covariance matrix   % (x-w)*Cinv*(x-w)'   % calculate x-w   d=[];   for i=1:C,      dxw=X-ones(K,1)*w(i,:); % K by M      d = [d sum((para{i}*dxw').*dxw')'];   endend