cr.sparse.la.effective_rank

cr.sparse.la.effective_rank(A, rcond=None)[source]

Returns the effective rank of A based on its singular value decomposition

Parameters

  • A (jax.numpy.ndarray) – Input matrix of size (M, N) where M is the dimension of the ambient vector space and N is the number of vectors in A

  • rcond (float) – Relative condition number. Singular values s smaller than rcond * max(s) are considered zero. Default: floating point eps * max(M,N).

Returns

Returns the effective rank of A

Return type

(int)

Examples

>>> A = random.normal(key, (3, 5))
>>> r = svd_effective_rank(A)
>>> print(r)
3