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Real rank is 1 (rows are linearly dependant). Function returns 2.
I think the problem with svd function. It's documentation says:
(svd A) computes the sparse singular value decomposition of A
returning a map with keys {:values L :left U :right V} such that
A = U (diag L) V. If (size A) is [n m] and the rank of A is
k, we have the size of U as [n k], (diag L) as [k k],
and (t V) as [k m].
Example:
Real rank is 1 (rows are linearly dependant). Function returns 2.
I think the problem with
svd
function. It's documentation says:But it's not true. In jblas size of L calculated as
min(n, m)
, not as rank of a matrix. Check this: https://github.com/mikiobraun/jblas/blob/master/src/main/java/org/jblas/Singular.java#L26I think correct solution is to calculate number of non zero elements of L.
Thanks to Kevin for bug: https://groups.google.com/forum/#!topic/incanter/eINeIuV2Ktw
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