mindspore.numpy.cross

mindspore.numpy.cross(a, b, axisa=- 1, axisb=- 1, axisc=- 1, axis=None)[source]

Returns the cross product of two (arrays of) vectors.

The cross product of a and b in \(R^3\) is a vector perpendicular to both a and b. If a and b are arrays of vectors, the vectors are defined by the last axis of a and b by default, and these axes can have dimensions 2 or 3. Where the dimension of either a or b is 2, the third component of the input vector is assumed to be zero and the cross product calculated accordingly. In cases where both input vectors have dimension 2, the z-component of the cross product is returned.

Parameters

a (Union[list, tuple, Tensor]) – Components of the first vector(s).
b (Union[list, tuple, Tensor]) – Components of the second vector(s).
axisa (int, optional) – Axis of a that defines the vector(s). By default, the last axis.
axisb (int, optional) – Axis of b that defines the vector(s). By default, the last axis.
axisc (int, optional) – Axis of c containing the cross product vector(s). Ignored if both input vectors have dimension 2, as the return is scalar. By default, the last axis.
axis (int, optional) – If defined, the axis of a, b and c that defines the vector(s) and cross product(s). Overrides axisa, axisb and axisc. Default: None .

Returns

Tensor, vector cross product(s).

Raises

ValueError – when the dimensions of the vector(s) in a and/or b does not equal 2 or 3.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> x = np.array([[1,2,3], [4,5,6]])
>>> y = np.array([[4,5,6], [1,2,3]])
>>> output = np.cross(x, y)
>>> print(output)
[[-3  6 -3]
[ 3 -6  3]]
>>> output = np.cross(x, y, axisc=0)
>>> print(output)
[[-3  3]
[ 6 -6]
[-3  3]]