Merge pull request #3539 from WarrenWeckesser/bug-norm-performance

BUG: linalg: norm was computing and then ignoring a product

1 files changed, 5 insertions, 7 deletions

diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py
index 4c4e51748..760ca31b0 100644
--- a/numpy/linalg/linalg.py
+++ b/numpy/linalg/linalg.py
@@ -2029,16 +2029,15 @@ def norm(x, ord=None, axis=None):
 
     # Check the default case first and handle it immediately.
     if ord is None and axis is None:
-        s = (x.conj() * x).real
-        return sqrt(add.reduce((x.conj() * x).ravel().real))
+        return sqrt(add.reduce((x.conj() * x).real, axis=None))
 
     # Normalize the `axis` argument to a tuple.
+    nd = x.ndim
     if axis is None:
-        axis = tuple(range(x.ndim))
+        axis = tuple(range(nd))
     elif not isinstance(axis, tuple):
         axis = (axis,)
 
-    nd = x.ndim
     if len(axis) == 1:
         if ord == Inf:
             return abs(x).max(axis=axis)
@@ -2067,11 +2066,10 @@ def norm(x, ord=None, axis=None):
             return add.reduce(absx**ord, axis=axis)**(1.0/ord)
     elif len(axis) == 2:
         row_axis, col_axis = axis
-        if not (-x.ndim <= row_axis < x.ndim and
-                -x.ndim <= col_axis < x.ndim):
+        if not (-nd <= row_axis < nd and -nd <= col_axis < nd):
             raise ValueError('Invalid axis %r for an array with shape %r' %
                              (axis, x.shape))
-        if row_axis % x.ndim == col_axis % x.ndim:
+        if row_axis % nd == col_axis % nd:
             raise ValueError('Duplicate axes given.')
         if ord == 2:
             return _multi_svd_norm(x, row_axis, col_axis, amax)