文件名称:mexSparseLogical0Diag
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Because of memory constraints, it is often impossible to change by subscr ipt all the elements of a large sparse matrix to zero. This leads to changing the elements in a loop, which is horrendously slow.
This mex solves that problem.
Usage: B = mexSparseLogical0Diag(A).
This problem is very common when dealing with adjacency matrices used in clustering - an adjacency matrix is a logical matrix, where the main diagonal is all zeros (no element is a neighbour of itself).-Because of memory constraints, it is often impossible to change by subscr ipt all the elements of a large sparse matrix to zero. This leads to changing the elements in a loop, which is horrendously slow.
This mex solves that problem.
Usage: B = mexSparseLogical0Diag(A).
This problem is very common when dealing with adjacency matrices used in clustering- an adjacency matrix is a logical matrix, where the main diagonal is all zeros (no element is a neighbour of itself).
This mex solves that problem.
Usage: B = mexSparseLogical0Diag(A).
This problem is very common when dealing with adjacency matrices used in clustering - an adjacency matrix is a logical matrix, where the main diagonal is all zeros (no element is a neighbour of itself).-Because of memory constraints, it is often impossible to change by subscr ipt all the elements of a large sparse matrix to zero. This leads to changing the elements in a loop, which is horrendously slow.
This mex solves that problem.
Usage: B = mexSparseLogical0Diag(A).
This problem is very common when dealing with adjacency matrices used in clustering- an adjacency matrix is a logical matrix, where the main diagonal is all zeros (no element is a neighbour of itself).
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下载文件列表
license.txt
mexLogicalSparse0Diag.cpp
mexLogicalSparse0Diag.m
mexLogicalSparse0Diag.cpp
mexLogicalSparse0Diag.m