问如何在向量中找到与另一个向量最近(最近)的值？

EN

vectors = {[2.29 2.56 2.77 2.90 2.05] [2.34 2.62 2.67 2.44 2.52] [1 2 3 4]}; 
    % Cell array of data vectors; 3 in this example
s = cellfun(@numel, vectors); % Get vector lengths
v = [vectors{:}]; % Concatenate all vectors into a vector
D = abs(bsxfun(@minus, v, v.')); % Compute distances. This gives a matrix.
    % Distances within the same vector will have to be discarded. This will be
    % done by replacing those values with NaN, in blocks
bb = arrayfun(@(x) NaN(x), s, 'uniformoutput', false); % Cell array of blocks
B = blkdiag(bb{:}); % NaN mask with those blocks
[~, ind] = min(D(:) + B(:)); % Add that mask. Get arg min in linear index
[ii, jj] = ind2sub(size(D), ind); % Convert to row and column indices
result = v([ii jj]); % Index into concatenated vector

%// data
A = [2.29 2.56 2.77 2.90 2.05]
B = [2.34 2.62 2.67 2.44 2.52]

%// distance matrix 
dist = abs(bsxfun(@minus,A(:),B(:).'));

%// find row and col indices of minimum
[~,idx] = min(dist(:))
[ii,jj] = ind2sub( [numel(A), numel(B)], idx)

%// output 
a = A(ii)
b = B(jj)

dist = abs(bsxfun(@minus,A(:),B(:).'));

dist = pdist2( A(:), B(:) )

%// data
data{1} = [2.29 2.56 2.77 2.90 2.05];
data{2} = [2.34 2.62 2.67 2.44 2.52];
data{3} = [2.34 2.62 2.67 2.44 2.52].*2;
data{4} = [2.34 2.62 2.67 2.44 2.52].*4;
%// length of each vector
N = 5;

%// create Filter for distance matrix
nans(1:numel(data)) = {NaN(N)};
mask = blkdiag(nans{:}) + 1; 

%// create new input for bsxfun
X = [data{:}];

%// filtered distance matrix 
dist = mask.*abs(bsxfun(@minus,X(:),X(:).'));

%// find row and col indices of minimum
[~,idx] = min(dist(:))
[ii,jj] = ind2sub( size(dist), idx)

%// output 
a = X(ii)
b = X(jj)

% A and B could have any Dimension, just same number of columns (signal Dimension)
A = rand(1000,4);
B = rand(500,4);

% Use any distance you like, some of them are not supported for KDTreeSearcher,
% and you should use ExhaustiveSearcher
myKnnModel= KDTreeSearcher(A, 'Distance', 'minkowski');

% you can ask for many (K) Nearest Neighbors and you always have access to it for later uses
[Idx, D] = knnsearch(myKnnModel, B, 'K',2);

% and this is answer to your special case
[~, idxA] = min(D(:, 1))
idxB = Idx(idxA)