Add wording suggested by Eric. Remove extra example.

(The existing example already showcases the contracted axis stacking.)

1 files changed, 4 insertions, 35 deletions

diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index 4d99ef256..52c796ade 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -1131,8 +1131,7 @@ def outer(a, b, out=None):
 
 
 def tensordot(a, b, axes=2):
-    """
-    Compute tensor dot product along specified axes for arrays >= 1-D.
+    """Compute tensor dot product along specified axes for arrays >= 1-D.
 
     Given two tensors (arrays of dimension greater than or equal to one),
     `a` and `b`, and an array_like object containing two array_like
@@ -1175,42 +1174,12 @@ def tensordot(a, b, axes=2):
     (first) axes of `a` (`b`) - the argument `axes` should consist of
     two sequences of the same length, with the first axis to sum over given
     first in both sequences, the second axis second, and so forth.
-    
-    It is worth noting that the axes of result could be reversed sometimes. 
-    And please refer to the example below:
+
+    The shape of the result consists of the non-contracted axes of the
+    first tensor, followed by the non-contracted axes of the second.
 
     Examples
     --------
-    An axes shift test example:
-    >>> diagMat=np.diag([1,1,1])
-    >>> mat3D = np.array([[[ 0,  1,  2, 10],
-        [ 3,  4,  5, 0],
-        [ 6,  7,  8, 1]],
-       [[ 9, 10, 11, 2],
-        [12, 13, 14,3],
-        [15, 16, 17,0]]])
-    >>> newMat = np.tensordot(mat3D, diagMat, axes=([1],[1]))
-    >>> print mat3D
-    [[[ 0  1  2 10]
-      [ 3  4  5  0]
-      [ 6  7  8  1]]
-     [[ 9 10 11  2]
-      [12 13 14  3]
-      [15 16 17  0]]]
-    >>> print newMat
-    [[[ 0  3  6]
-      [ 1  4  7]
-      [ 2  5  8]
-      [10  0  1]]
-     [[ 9 12 15]
-      [10 13 16]
-      [11 14 17]
-      [ 2  3  0]]]
-    >>> print newMat.shape
-        (2, 4, 3)
-    >>> print mat3D.shape
-        (2, 3, 4)
-        
     A "traditional" example:
 
     >>> a = np.arange(60.).reshape(3,4,5)