give me a detailed code for LBG algorithm or give me the function d=disteu(v,c) in the below function
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function b=vqlbg(v,k) % VQLBG Vector quantization using the Linde-Buzo-Gray algorithm % % Inputs: % v contains training data vectors (one per column) % k is number of centroids required % % Outputs: % c contains the result VQ codebook (k columns, one for each centroids) c=mean(v,2); figure(8); plot(c(:,:),'.'); title('initial codebook'); %pause e=0.01; c(:,1)=c(:,1)+c(:,1)*e; figure(9); plot(c(:,:),'.'); title('codebook1'); %pause c(:,2)=c(:,1)-c(:,1)*e figure(10); plot(c(:,:),'.'); title('codebook2'); %pause % Nearest Neighbour Searching. % Given a current codebook 'c', assign each training vector in 'v' with the % closest codeword. Using the function disteu2, the distances between these % vectors (v and c) are computed. d=disteu(v,c); [m,id]=min(d,[],2); [rows,cols]=size(c); % The centroids of the vectors are found using the mean function. for j=1:cols c(:,j)=mean(v(:,find(id==j)),2); end figure(11); plot(c(:,:),'.'); title('new cluster'); %pause % for each training vector, find the closest codeword using the min % function. n=1;n=n*2; while cols<16 for i=1:cols c(:,i)=c(:,i)+c(:,i)*e; c(:,i+n)=c(:,i)-c(:,i)*e; d=disteu(v,c); [m,i]=min(d,[],2); [rows,cols]=size(c); end figure(12); plot(c(:,:),'.'); title('updated'); %pause % The centroids of the vectors are found using the mean function. for j=1:cols if find(i==j)~isempty(c); c(:,j)=mean(v(:,find(i==j)),2); end end n=n*2; end
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采纳的回答
Pedro Villena
2012-11-8
编辑:Pedro Villena
2012-11-9
function d = disteu(x, y)
% DISTEU Pairwise Euclidean distances between columns of two matrices
% Input:
% x, y: Two matrices whose each column is an a vector data.
% Output:
% d: Element d(i,j) will be the Euclidean distance between two
% column vectors X(:,i) and Y(:,j)
% Note:
% The Euclidean distance D between two vectors X and Y is:
% D = sum((x-y).^2).^0.5
[M, N] = size(x);
[M2, P] = size(y);
if(M ~= M2),
error('Matrix dimensions do not match.')
end
d = zeros(N, P);
if(N < P),
copies = zeros(1,P);
for n = 1:N,
d(n,:) = sum((x(:, n+copies) - y) .^2, 1);
end
else
copies = zeros(1,N);
for p = 1:P,
d(:,p) = sum((x - y(:, p+copies)) .^2, 1)';
end
end
d = d.^0.5;
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