在matlab中执行此功能的函数称为pdist.不幸的是，它非常缓慢，并且没有考虑Matlab的矢量化功能.

以下是我为项目编写的代码.让我知道您能获得什么样的速度.

   Qx=repmat(dot(x,x,2),1,size(x,1));
   D=sqrt(Qx+Qx'-2*x*x');

请注意，这仅在数据点位于行中且尺寸标注为列时才有效.例如，假设我有256个数据点和100000个维，然后在我的Mac上使用x = rand(256,100000)，上面的代码在大约半秒钟内生成了256x256矩阵.

I am computing a similarity matrix based on Euclidean distance in MATLAB. My code is as follows:

for i=1:N % M,N is the size of the matrix x for whose elements I am computing similarity matrix
 for j=1:N
  D(i,j) = sqrt(sum(x(:,i)-x(:,j)).^2)); % D is the similarity matrix
 end
end

Can any help with optimizing this = reducing the for loops as my matrix x is of dimension 256x30000.

Thanks a lot!

--Aditya

The function to do so in matlab is called pdist. Unfortunately it is painfully slow and doesnt take Matlabs vectorization abilities into account.

The following is code I wrote for a project. Let me know what kind of speed up you get.

   Qx=repmat(dot(x,x,2),1,size(x,1));
   D=sqrt(Qx+Qx'-2*x*x');

Note though that this will only work if your data points are in the rows and your dimensions the columns. So for example lets say I have 256 data points and 100000 dimensions then on my mac using x=rand(256,100000) and the above code produces a 256x256 matrix in about half a second.