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lp_normalize#

ivy.lp_normalize(x, /, *, p=2, axis=None, out=None)[source]#

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

Parameters:

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

  • p (float, default: 2) – The Lp norm to use for normalization. Default is L2 norm (p=2).

  • axis (Optional[int], default: None) – Axis 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.]])
>>> y = ivy.lp_normalize(x, p=1, axis=1)
>>> print(y)
ivy.array([[0.33333334, 0.66666669],
       [0.42857143, 0.5714286 ]])
Array.lp_normalize(self, /, *, p=2, axis=None, out=None)[source]#

Normalize the array to have Lp norm.

Parameters:

  • self (Array) – Input array.

  • p (float, default: 2) – p-norm to use for normalization.

  • axis (Optional[int], default: None) – Axis 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.]])
>>> y = x.lp_normalize(p=2, axis=1)
>>> print(y)
ivy.array([[0.44721359, 0.89442718],
       [0.60000002, 0.80000001]])
Container.lp_normalize(self, p=2, axis=None, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container instance method variant of ivy.l2_normalize. This method simply wraps the function, and so the docstring for ivy.l2_normalize also applies to this method with minimal changes.

Parameters:

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

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

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

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

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

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

  • out (Optional[Container], default: None) – 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.

Examples

>>> x = ivy.Container(a=ivy.array([[0.5, 1.5, 2.5], [3.5, 4.5, 5.5]]),
...                    b=ivy.array([[-1., -1.], [-1., -0.5]]))
>>> y = x.lp_normalize(axis=1)
>>> print(y)
{
    a: ivy.array([[0.16903085, 0.50709254, 0.84515423],
                  [0.44183609, 0.56807494, 0.69431382]]),
    b: ivy.array([[-0.70710677, -0.70710677],
                  [-0.89442718, -0.44721359]])
}
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