jax.numpy.hstack#
- jax.numpy.hstack(tup, dtype=None)[source]#
Horizontally stack arrays.
JAX implementation of
numpy.hstack()
.For arrays of one or more dimensions, this is equivalent to
jax.numpy.concatenate()
withaxis=1
.- Parameters:
tup (np.ndarray | Array | Sequence[ArrayLike]) – a sequence of arrays to stack; each must have the same shape along all but the second axis. Input arrays will be promoted to at least rank 1. If a single array is given it will be treated equivalently to tup = unstack(tup), but the implementation will avoid explicit unstacking.
dtype (DTypeLike | None | None) – 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:
See also
jax.numpy.stack()
: stack along arbitrary axesjax.numpy.concatenate()
: concatenation along existing axes.jax.numpy.vstack()
: stack vertically, i.e. along axis 0.jax.numpy.dstack()
: stack depth-wise, i.e. along axis 2.
Examples
Scalar values:
>>> jnp.hstack([1, 2, 3]) Array([1, 2, 3], dtype=int32, weak_type=True)
1D arrays:
>>> x = jnp.arange(3) >>> y = jnp.ones(3) >>> jnp.hstack([x, y]) Array([0., 1., 2., 1., 1., 1.], dtype=float32)
2D arrays:
>>> x = x.reshape(3, 1) >>> y = y.reshape(3, 1) >>> jnp.hstack([x, y]) Array([[0., 1.], [1., 1.], [2., 1.]], dtype=float32)