jax.numpy.column_stack — JAX documentation (original) (raw)

jax.numpy.column_stack#

jax.numpy.column_stack(tup)[source]#

Stack arrays column-wise.

JAX implementation of numpy.column_stack().

For arrays of two or more dimensions, this is equivalent tojax.numpy.concatenate() with axis=1.

Parameters:

• tup (np.ndarray | Array | Sequence [ ArrayLike ]) – a sequence of arrays to stack; each must have the same leading dimension. Input arrays will be promoted to at least rank 2. If a single array is given it will be treated equivalently to tup = unstack(tup), but the implementation will avoid explicit unstacking.
• dtype – optional dtype of the resulting array. If not specified, the dtype will be determined via type promotion rules described in Type promotion semantics.

Returns:

the stacked result.

Return type:

Array

See also

• jax.numpy.stack(): stack along arbitrary axes
• jax.numpy.concatenate(): concatenation along existing axes.
• jax.numpy.vstack(): stack vertically, i.e. along axis 0.
• jax.numpy.hstack(): stack horizontally, i.e. along axis 1.
• jax.numpy.hstack(): stack depth=wise, i.e. along axis 2.

Examples

Scalar values:

jnp.column_stack([1, 2, 3]) Array([[1, 2, 3]], dtype=int32, weak_type=True)

1D arrays:

x = jnp.arange(3) y = jnp.ones(3) jnp.column_stack([x, y]) Array([[0., 1.], [1., 1.], [2., 1.]], dtype=float32)

2D arrays:

x = x.reshape(3, 1) y = y.reshape(3, 1) jnp.column_stack([x, y]) Array([[0., 1.], [1., 1.], [2., 1.]], dtype=float32)