orth(A, rcond=None)
Input array
Relative condition number. Singular values s
smaller than rcond * max(s)
are considered zero. Default: floating point eps * max(M,N).
Orthonormal basis for the range of A. K = effective rank of A, as determined by rcond
Construct an orthonormal basis for the range of A using SVD
null_space
Matrix null space
svd
Singular value decomposition of a matrix
>>> from scipy.linalg import orth
... A = np.array([[2, 0, 0], [0, 5, 0]]) # rank 2 array
... orth(A) array([[0., 1.], [1., 0.]])
>>> orth(A.T) array([[0., 1.], [1., 0.], [0., 0.]])See :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.linalg._decomp_svd.null_space
scipy.linalg._decomp_svd.orth
scipy.linalg._decomp_svd.subspace_angles
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