mvpa2.clfs.ridge.lstsq¶
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mvpa2.clfs.ridge.lstsq(a, b, cond=None, overwrite_a=False, overwrite_b=False, check_finite=True, lapack_driver=None)¶ Compute least-squares solution to equation Ax = b.
Compute a vector x such that the 2-norm
|b - A x|is minimized.Parameters: a : (M, N) array_like
Left hand side matrix (2-D array).
b : (M,) or (M, K) array_like
Right hand side matrix or vector (1-D or 2-D array).
cond : float, optional
Cutoff for ‘small’ singular values; used to determine effective rank of a. Singular values smaller than
rcond * largest_singular_valueare considered zero.overwrite_a : bool, optional
Discard data in
a(may enhance performance). Default is False.overwrite_b : bool, optional
Discard data in
b(may enhance performance). Default is False.check_finite : bool, optional
Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.
lapack_driver: str, optional
Which LAPACK driver is used to solve the least-squares problem. Options are
'gelsd','gelsy','gelss'. Default ('gelsd') is a good choice. However,'gelsy'can be slightly faster on many problems.'gelss'was used historically. It is generally slow but uses less memory.New in version 0.17.0.
Returns: x : (N,) or (N, K) ndarray
Least-squares solution. Return shape matches shape of
b.residues : () or (1,) or (K,) ndarray
Sums of residues, squared 2-norm for each column in
b - a x. If rank of matrix a is< Nor> M, or'gelsy'is used, this is an empty array. If b was 1-D, this is an (1,) shape array, otherwise the shape is (K,).rank : int
Effective rank of matrix
a.s : (min(M,N),) ndarray or None
Singular values of
a. The condition number of a isabs(s[0] / s[-1]). None is returned when'gelsy'is used.Raises: LinAlgError
If computation does not converge.
ValueError
When parameters are wrong.
See also
optimize.nnls- linear least squares with non-negativity constraint
