diff options
Diffstat (limited to 'numpy/core/numeric.py')
| -rw-r--r-- | numpy/core/numeric.py | 39 |
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) |