Compute the (batched) cross product of two arrays.
JAX implementation of numpy.cross()
.
This computes the 2-dimensional or 3-dimensional cross product,
\[c = a \times b\]
In 3 dimensions, c
is a length-3 array. In 2 dimensions, c
is a scalar.
a – N-dimensional array. a.shape[axisa]
indicates the dimension of the cross product, and must be 2 or 3.
b – N-dimensional array. Must have b.shape[axisb] == a.shape[axisb]
, and other dimensions of a
and b
must be broadcast compatible.
axisa (int) – specicy the axis of a
along which to compute the cross product.
axisb (int) – specicy the axis of b
along which to compute the cross product.
axisc (int) – specicy the axis of c
along which the cross product result will be stored.
axis (int | None) – if specified, this overrides axisa
, axisb
, and axisc
with a single value.
The array c
containing the (batched) cross product of a
and b
along the specified axes.
Examples
A 2-dimensional cross product returns a scalar:
>>> a = jnp.array([1, 2]) >>> b = jnp.array([3, 4]) >>> jnp.cross(a, b) Array(-2, dtype=int32)
A 3-dimensional cross product returns a length-3 vector:
>>> a = jnp.array([1, 2, 3]) >>> b = jnp.array([4, 5, 6]) >>> jnp.cross(a, b) Array([-3, 6, -3], dtype=int32)
With multi-dimensional inputs, the cross-product is computed along the last axis by default. Here’s a batched 3-dimensional cross product, operating on the rows of the inputs:
>>> a = jnp.array([[1, 2, 3], ... [3, 4, 3]]) >>> b = jnp.array([[2, 3, 2], ... [4, 5, 6]]) >>> jnp.cross(a, b) Array([[-5, 4, -1], [ 9, -6, -1]], dtype=int32)
Specifying axis=0 makes this a batched 2-dimensional cross product, operating on the columns of the inputs:
>>> jnp.cross(a, b, axis=0) Array([-2, -2, 12], dtype=int32)
Equivalently, we can independently specify the axis of the inputs a
and b
and the output c
:
>>> jnp.cross(a, b, axisa=0, axisb=0, axisc=0) Array([-2, -2, 12], dtype=int32)
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