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jax.numpy.c_

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jax.numpy.c_#

jax.numpy.c_ = <jax._src.numpy.index_tricks.CClass object>#

Concatenate slices, scalars and array-like objects along the last axis.

LAX-backend implementation of numpy.c_.

See also

jnp.r_: Concatenates slices, scalars and array-like objects along the first axis.

Examples

>>> a = jnp.arange(6).reshape((2,3))
>>> jnp.c_[a,a]
Array([[0, 1, 2, 0, 1, 2],
       [3, 4, 5, 3, 4, 5]], dtype=int32)

Use a string directive of the form "axis:dims:trans1d" as the first argument to specify concatenation axis, minimum number of dimensions, and the position of the upgraded array’s original dimensions in the resulting array’s shape tuple:

>>> jnp.c_['0,2', [1,2,3], [4,5,6]]
Array([[1],
       [2],
       [3],
       [4],
       [5],
       [6]], dtype=int32)

>>> jnp.c_['0,2,-1', [1,2,3], [4,5,6]]
Array([[1, 2, 3],
       [4, 5, 6]], dtype=int32)

Use the special directives "r" or "c" as the first argument on flat inputs to create an array with inputs stacked along the last axis:

>>> jnp.c_['r',[1,2,3], [4,5,6]]
Array([[1, 4],
       [2, 5],
       [3, 6]], dtype=int32)
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