Re: [eigen] Efficiently creating a matrix of pairwise vector differences.

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If I got your question right, you end up with a NxN symmetric matrix, so a 250k x 250k matrix, so about 500GB.... this does not sound right. Perhaps you want to compute the differences between the column vectors? or you inverted N and M? Anyway, for that task you can exploit that:

(a-b)^2 = a^2 + b^2 -2*

with something like:

VectorXd D2 = data.rowwise().squaredNorm();
MatrixXd dist = D2.rowwise().replicate(N) + D2.transpose().colwise().replicate(N);
dist -= 2.*data.transpose()*data;

that will be considerably faster.


On Thu, Dec 21, 2017 at 12:10 AM, Smith, Louis <Louis_Smith@xxxxxxxxxxxxxxxxxx> wrote:


I'm trying to use eigen to compute the distances between m length vectors i and j which are each rows in an NxM matrix (note that M is often much much smaller than N. In my test case N is about 250,000 and M is 6). What I'm currently working with is an _expression_ like:

Matrix Xd data = "" //This works when data is written to cout, so elided.

MatrixXd distances = (data.rowwise() - data.transpose().colwise().transpose()).norm();

Which gives me the following error:

error: no member named 'transpose' in 'Eigen::VectorwiseOp<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0, -1, -1> >, 0>'
  MatrixXd distances = (data.rowwise() - data.transpose().colwise().transpose()).norm();

When I get rid of the third transpose I'm then subtracting a column vector from a row vector, which also doesn't work. Calling transpose on the row vector gives a similar error.

What I expect distances to be is an NxN symmetric matrix containing the distances (norms of difference vectors) for each pair of row vectors in data.

Sorry for the newbie question, but I'd really appreciate some insight on this since it seems like there should be a more eigeny way to write this than the double-for loop over the data, which also works but is very slow.



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