mxnet.np.cross¶

cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None)

Return 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 (ndarray) – Components of the first vector(s).

• b (ndarray) – 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.

Returns

c – Vector cross product(s).

Return type

ndarray

Raises

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

Notes

Supports full broadcasting of the inputs.

Examples

Vector cross-product.

>>> x = np.array([1., 2., 3.])
>>> y = np.array([4., 5., 6.])
>>> np.cross(x, y)
array([-3.,  6., -3.])


One vector with dimension 2.

>>> x = np.array([1., 2.])
>>> y = np.array([4., 5., 6.])
>>> np.cross(x, y)
array([12., -6., -3.])


Equivalently:

>>> x = np.array([1., 2., 0.])
>>> y = np.array([4., 5., 6.])
>>> np.cross(x, y)
array([12., -6., -3.])


Both vectors with dimension 2.

>>> x = np.array([1., 2.])
>>> y = np.array([4., 5.])
>>> np.cross(x, y)
array(-3.)


Multiple vector cross-products. Note that the direction of the cross product vector is defined by the right-hand rule.

>>> x = np.array([[1., 2., 3.], [4., 5., 6.]])
>>> y = np.array([[4., 5., 6.], [1., 2., 3.]])
>>> np.cross(x, y)
array([[-3.,  6., -3.],
[ 3., -6.,  3.]])


The orientation of c can be changed using the axisc keyword.

>>> np.cross(x, y, axisc=0)
array([[-3.,  3.],
[ 6., -6.],
[-3.,  3.]])


Change the vector definition of x and y using axisa and axisb.

>>> x = np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]])
>>> y = np.array([[7., 8., 9.], [4., 5., 6.], [1., 2., 3.]])
>>> np.cross(x, y)
array([[ -6.,  12.,  -6.],
[  0.,   0.,   0.],
[  6., -12.,   6.]])
>>> np.cross(x, y, axisa=0, axisb=0)
array([[-24.,  48., -24.],
[-30.,  60., -30.],
[-36.,  72., -36.]])