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| author | Charles Harris <charlesr.harris@gmail.com> | 2015-09-26 11:36:14 -0600 |
|---|---|---|
| committer | Charles Harris <charlesr.harris@gmail.com> | 2015-09-26 11:36:14 -0600 |
| commit | dbd2324d8e28dcdac53f800cc3d11158bac3ff7b (patch) | |
| tree | c583e5183d88ecf7637b63b1b919beb5a184e75a /numpy/linalg/linalg.py | |
| parent | 1bf73fa0119fc4ed9ab771d01e1cee5e9fd36c76 (diff) | |
| parent | ae56c58db4207bd11100a9d24c9edf7694e34d67 (diff) | |
| download | numpy-dbd2324d8e28dcdac53f800cc3d11158bac3ff7b.tar.gz | |
Merge pull request #6362 from seberg/issue6351
BUG,ENH: allow linalg.cond to work on a stack of matrices
Diffstat (limited to 'numpy/linalg/linalg.py')
| -rw-r--r-- | numpy/linalg/linalg.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index a2405c180..f5cb3cb77 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -1012,9 +1012,9 @@ def eig(a): w : (..., M) array The eigenvalues, each repeated according to its multiplicity. The eigenvalues are not necessarily ordered. The resulting - array will be of complex type, unless the imaginary part is - zero in which case it will be cast to a real type. When `a` - is real the resulting eigenvalues will be real (0 imaginary + array will be of complex type, unless the imaginary part is + zero in which case it will be cast to a real type. When `a` + is real the resulting eigenvalues will be real (0 imaginary part) or occur in conjugate pairs v : (..., M, M) array @@ -1382,7 +1382,7 @@ def cond(x, p=None): Parameters ---------- - x : (M, N) array_like + x : (..., M, N) array_like The matrix whose condition number is sought. p : {None, 1, -1, 2, -2, inf, -inf, 'fro'}, optional Order of the norm: @@ -1451,12 +1451,12 @@ def cond(x, p=None): 0.70710678118654746 """ - x = asarray(x) # in case we have a matrix + x = asarray(x) # in case we have a matrix if p is None: s = svd(x, compute_uv=False) - return s[0]/s[-1] + return s[..., 0]/s[..., -1] else: - return norm(x, p)*norm(inv(x), p) + return norm(x, p, axis=(-2, -1)) * norm(inv(x), p, axis=(-2, -1)) def matrix_rank(M, tol=None): |