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Diffstat (limited to 'numpy/linalg/linalg.py')
| -rw-r--r-- | numpy/linalg/linalg.py | 7 |
1 files changed, 5 insertions, 2 deletions
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index 287b38d9b..0b7aa132e 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -1674,7 +1674,7 @@ def norm(x, ord=None): Notes ----- - For values of ``ord < 0``, the result is, strictly speaking, not a + For values of ``ord <= 0``, the result is, strictly speaking, not a mathematical 'norm', but it may still be useful for various numerical purposes. @@ -1687,6 +1687,7 @@ def norm(x, ord=None): 'fro' Frobenius norm -- inf max(sum(abs(x), axis=1)) max(abs(x)) -inf min(sum(abs(x), axis=1)) min(abs(x)) + 0 -- sum(x != 0) 1 max(sum(abs(x), axis=0)) as below -1 min(sum(abs(x), axis=0)) as below 2 2-norm (largest sing. value) as below @@ -1754,15 +1755,17 @@ def norm(x, ord=None): """ x = asarray(x) - nd = len(x.shape) if ord is None: # check the default case first and handle it immediately return sqrt(add.reduce((x.conj() * x).ravel().real)) + nd = x.ndim if nd == 1: if ord == Inf: return abs(x).max() elif ord == -Inf: return abs(x).min() + elif ord == 0: + return (x != 0).sum() # Zero norm elif ord == 1: return abs(x).sum() # special case for speedup elif ord == 2: |