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) – The Lp norm to use for normalization. Default is L2 norm (p=2). (default: 2)
axis (Optional[int]) – Axis 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.]])
>>> ivy.lp_normalize(x, p=1, axis=1)
ivy.array([[0.3333, 0.6666],
           [0.75, 1.]])

Array.lp_normalize(self, /, *, p=2, axis=None, out=None)#

Normalize the array to have Lp norm.

Parameters:

self (Array) – Input array.
p (float) – p-norm to use for normalization. (default: 2)
axis (Optional[int]) – Axis along which to normalize. If None, the whole array (default: None) is normalized.
out (Optional[Array]) – optional output array, for writing the result to. It must have a (default: None) shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – The normalized array.

Examples

>>> x = ivy.array([[1., 2.], [3., 4.]])
>>> x.lp_normalize(p=2, axis=1)
ivy.array([[0.4472, 0.8944],
       [0.6, 0.8]])

Container.lp_normalize(self, p=2, axis=None, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)#

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 – 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[Container]) – optional output container, for writing the result to. It must have a shape (default: None) 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.static_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]])
}