l1_normalize#

ivy.l1_normalize(x, /, *, axis=None, out=None)[source]#

Normalize the input array along the given axis to have L1 norm equal to 1.

Parameters:

  • x (Union[Array, NativeArray]) – Input array.

  • axis (Optional[Union[int, Tuple[int, ...]]]) –

    (default: None) Axis or axes along which to normalize. If None,

    the whole array is normalized.

  • out (Optional[Array]) –

    (default: None) Optional output array, for writing the result to.

    It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – The normalized array.

Examples

>>> x = ivy.array([[1., 2.], [3., 4.]])
>>> ivy.l1_normalize(x, axis=1)
ivy.array([[0.3333, 0.6667],
           [0.4286, 0.5714]])
Array.l1_normalize(self, axis=None, out=None)#

Normalize the array to have unit L1 norm.

Parameters:

  • self (Array) – Input array.

  • axis (Optional[Union[int, Tuple[int, ...]]]) – Axis or axes along which to normalize. If None, the whole array is normalized. (default: None)

  • out (Optional[Array]) – Optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – The normalized array.

Examples

>>> x = ivy.array([[1., 2.], [3., 4.]])
>>> x.l1_normalize(axis=1)
ivy.array([[0.3333, 0.6667],
           [0.4286, 0.5714]])
Container.l1_normalize(self, axis=None, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)#

ivy.Container instance method variant of ivy.l1_normalize. This method simply wraps the function, and so the docstring for ivy.l1_normalize also applies to this method with minimal changes.

Parameters:

  • self – The input container with leaves to be normalized.

  • axis (Optional[int]) – The axis along which to normalize. (default: None)

  • key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)

  • to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains (default: True) will be skipped. Default is True.

  • prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. (default: False) Default is False.

  • map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.

  • out – Optional output container, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – A container containing the normalized leaves.