def iddp_aid(eps, A):
"""
Compute ID of a real matrix to a specified relative precision using random
sampling.
:param eps:
Relative precision.
:type eps: float
:param A:
Matrix.
:type A: :class:`numpy.ndarray`
:return:
Rank of ID.
:rtype: int
:return:
Column index array.
:rtype: :class:`numpy.ndarray`
:return:
Interpolation coefficients.
:rtype: :class:`numpy.ndarray`
"""
A = np.asfortranarray(A)
m, n = A.shape
n2, w = idd_frmi(m)
proj = np.empty(n*(2*n2 + 1) + n2 + 1, order='F')
k, idx, proj = _id.iddp_aid(eps, A, w, proj)
proj = proj[:k*(n-k)].reshape((k, n-k), order='F')
return k, idx, proj
def iddp_aid(eps, A):
"""
Compute ID of a real matrix to a specified relative precision using random
sampling.
:param eps:
Relative precision.
:type eps: float
:param A:
Matrix.
:type A: :class:`numpy.ndarray`
:return:
Rank of ID.
:rtype: int
:return:
Column index array.
:rtype: :class:`numpy.ndarray`
:return:
Interpolation coefficients.
:rtype: :class:`numpy.ndarray`
"""
A = np.asfortranarray(A)
m, n = A.shape
n2, w = idd_frmi(m)
proj = np.empty(n*(2*n2 + 1) + n2 + 1, order='F')
k, idx, proj = _id.iddp_aid(eps, A, w, proj)
proj = proj[:k*(n-k)].reshape((k, n-k), order='F')
return k, idx, proj