flopscope.

flopscope.numpy.linalg.cross

fnp.linalg.cross(x1: 'ArrayLike', x2: 'ArrayLike', /, *, axis: 'int' = -1) -> 'FlopscopeArray'[flopscope source][numpy source]

Returns the cross product of 3-element vectors.

Adapted from NumPy docs np.linalg.cross

Arealinalg
Typecustom
NumPy Refnp.linalg.cross
Cost
delegates to flops.cross
Flopscope Context

Delegates to `fnp.cross` which charges `numel(output)` FLOPs.

If x1 and/or x2 are multi-dimensional arrays, then the cross-product of each pair of corresponding 3-element vectors is independently computed.

This function is Array API compatible, contrary to flops.cross.

Parameters

x1:array_like

The first input array.

x2:array_like

The second input array. Must be compatible with x1 for all non-compute axes. The size of the axis over which to compute the cross-product must be the same size as the respective axis in x1.

axis:int, optional

The axis (dimension) of x1 and x2 containing the vectors for which to compute the cross-product. Default: -1.

Returns

out:ndarray

An array containing the cross products.

See also

Examples

Vector cross-product.

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

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

>>> x = flops.array([[1,2,3], [4,5,6]])
>>> y = flops.array([[4,5,6], [1,2,3]])
>>> flops.linalg.cross(x, y)
array([[-3,  6, -3],
       [ 3, -6,  3]])
>>> x = flops.array([[1, 2], [3, 4], [5, 6]])
>>> y = flops.array([[4, 5], [6, 1], [2, 3]])
>>> flops.linalg.cross(x, y, axis=0)
array([[-24,  6],
       [ 18, 24],
       [-6,  -18]])