diff options
Diffstat (limited to 'numpy/linalg/linalg.py')
| -rw-r--r-- | numpy/linalg/linalg.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index 8dec67318..e0b3268a6 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -661,11 +661,11 @@ Singular values less than s[0]*rcond are treated as zero. if one_eq: x = array(ravel(bstar)[:n], dtype=result_t, copy=True) if results['rank']==n and m>n: - resids = array([sum((ravel(bstar)[n:])**2)], dtype=result_t) + resids = array([sum((ravel(bstar)[n:])**2,axis=0)], dtype=result_t) else: x = array(transpose(bstar)[:n,:], dtype=result_t, copy=True) if results['rank']==n and m>n: - resids = sum((transpose(bstar)[n:,:])**2).astype(result_t) + resids = sum((transpose(bstar)[n:,:])**2,axis=0).astype(result_t) st = s[:min(n,m)].copy().astype(_realType(result_t)) return wrap(x), resids, results['rank'], st @@ -687,7 +687,7 @@ def norm(x, ord=None): For vectors ord can be any real number including Inf or -Inf. ord = Inf, computes the maximum of the magnitudes ord = -Inf, computes minimum of the magnitudes - ord is finite, computes sum(abs(x)**ord)**(1.0/ord) + ord is finite, computes sum(abs(x)**ord,axis=0)**(1.0/ord) For matrices ord can only be one of the following values: ord = 2 computes the largest singular value @@ -696,7 +696,7 @@ def norm(x, ord=None): ord = -1 computes the smallest column sum of absolute values ord = Inf computes the largest row sum of absolute values ord = -Inf computes the smallest row sum of absolute values - ord = 'fro' computes the frobenius norm sqrt(sum(diag(X.H * X))) + ord = 'fro' computes the frobenius norm sqrt(sum(diag(X.H * X),axis=0)) For values ord < 0, the result is, strictly speaking, not a mathematical 'norm', but it may still be useful for numerical purposes. |