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| author | Matti Picus <matti.picus@gmail.com> | 2019-03-03 11:42:26 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-03-03 11:42:26 +0200 |
| commit | dde92f126643ae0c6c33d584c6a620b06e208134 (patch) | |
| tree | 7d1e3d4b887e9833df17cbbbd8ab00941be40fdd /numpy/core/numeric.py | |
| parent | b5e2b029bbaaa9eca124cc8560f17c70e8a0e4e3 (diff) | |
| parent | 3e8818c0f3b19d76015d1ae0478a8b50701bd4b4 (diff) | |
| download | numpy-dde92f126643ae0c6c33d584c6a620b06e208134.tar.gz | |
Merge pull request #13071 from danielplawrence/test-docs-tensordot
DOC: Removed incorrect claim regarding shape constraints for np.tenso…
Diffstat (limited to 'numpy/core/numeric.py')
| -rw-r--r-- | numpy/core/numeric.py | 18 |
1 files changed, 8 insertions, 10 deletions
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index 386049410..42fee4eb7 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -1203,20 +1203,18 @@ def _tensordot_dispatcher(a, b, axes=None): @array_function_dispatch(_tensordot_dispatcher) def tensordot(a, b, axes=2): """ - Compute tensor dot product along specified axes for arrays >= 1-D. + Compute tensor dot product along specified axes. - Given two tensors (arrays of dimension greater than or equal to one), - `a` and `b`, and an array_like object containing two array_like - objects, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s - elements (components) over the axes specified by ``a_axes`` and - ``b_axes``. The third argument can be a single non-negative - integer_like scalar, ``N``; if it is such, then the last ``N`` - dimensions of `a` and the first ``N`` dimensions of `b` are summed - over. + Given two tensors, `a` and `b`, and an array_like object containing + two array_like objects, ``(a_axes, b_axes)``, sum the products of + `a`'s and `b`'s elements (components) over the axes specified by + ``a_axes`` and ``b_axes``. The third argument can be a single non-negative + integer_like scalar, ``N``; if it is such, then the last ``N`` dimensions + of `a` and the first ``N`` dimensions of `b` are summed over. Parameters ---------- - a, b : array_like, len(shape) >= 1 + a, b : array_like Tensors to "dot". axes : int or (2,) array_like |