Stack arrays column-wise.
JAX implementation of numpy.column_stack().
For arrays of two or more dimensions, this is equivalent to jax.numpy.concatenate() with axis=1.
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.
the stacked result.
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)
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4