clip_vector_norm#
- ivy.clip_vector_norm(x, max_norm, /, *, p=2.0, out=None)[source]#
Clips (limits) the vector p-norm of an array.
- Parameters:
x (
Union
[Array
,NativeArray
]) – Input array containing elements to clip.max_norm (
float
) – The maximum value of the array norm.p (
float
, default:2.0
) – The p-value for computing the p-norm. Default is 2.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:
- Returns:
ret – An array with the vector norm downscaled to the max norm if needed.
Examples
With
ivy.Array
input:>>> x = ivy.array([0., 1., 2.]) >>> y = ivy.clip_vector_norm(x, 2.0) >>> print(y) ivy.array([0. , 0.894, 1.79 ])
>>> x = ivy.array([0.5, -0.7, 2.4]) >>> y = ivy.clip_vector_norm(x, 3.0, p=1.0) >>> print(y) ivy.array([ 0.417, -0.583, 2. ])
>>> x = ivy.array([[[0., 0.], [1., 3.], [2., 6.]], ... [[3., 9.], [4., 12.], [5., 15.]]]) >>> y = ivy.zeros(((2, 3, 2))) >>> ivy.clip_vector_norm(x, 4.0, p=1.0, out=y) >>> print(y) ivy.array([[[0. , 0. ], [0.0667, 0.2 ], [0.133 , 0.4 ]], [[0.2 , 0.6 ], [0.267 , 0.8 ], [0.333 , 1. ]]])
>>> x = ivy.array([[1.1, 2.2, 3.3], ... [-4.4, -5.5, -6.6]]) >>> ivy.clip_vector_norm(x, 1.0, p=3.0, out=x) >>> print(x) ivy.array([[ 0.131, 0.263, 0.394], [-0.526, -0.657, -0.788]])
With
ivy.Container
input:>>> x = ivy.Container(a=ivy.array([0., 1., 2.]), ... b=ivy.array([3., 4., 5.])) >>> y = ivy.clip_vector_norm(x, 2.0) >>> print(y) { a: ivy.array([0., 0.894, 1.79]), b: ivy.array([0.849, 1.13, 1.41]) }
With multiple
ivy.Container
inputs:>>> x = ivy.Container(a=ivy.array([0., 1., 2.]), ... b=ivy.array([3., 4., 5.])) >>> max_norm = ivy.Container(a=2, b=3) >>> y = ivy.clip_vector_norm(x, max_norm) >>> print(y) { a: ivy.array([0., 0.894, 1.79]), b: ivy.array([2.449, 2.65, 2.83]) }
- Array.clip_vector_norm(self, max_norm, /, *, p=2.0, out=None)[source]#
ivy.Array instance method variant of ivy.clip_vector_norm. This method simply wraps the function, and so the docstring for ivy.clip_vector_norm also applies to this method with minimal changes.
- Parameters:
self (
Array
) – input arraymax_norm (
float
) – float, the maximum value of the array norm.p (
float
, default:2.0
) – optional float, the p-value for computing the p-norm. Default is 2.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 – An array with the vector norm downscaled to the max norm if needed.
Examples
With
ivy.Array
instance method:>>> x = ivy.array([0., 1., 2.]) >>> y = x.clip_vector_norm(2.0) >>> print(y) ivy.array([0., 0.894, 1.79])
- Container.clip_vector_norm(self, max_norm, /, *, p=2.0, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#
ivy.Container instance method variant of ivy.clip_vector_norm. This method simply wraps the function, and so the docstring for ivy.clip_vector_norm also applies to this method with minimal changes.
- Parameters:
self (
Container
) – input arraymax_norm (
Union
[float
,Container
]) – float, the maximum value of the array norm.p (
Union
[float
,Container
], default:2.0
) – optional float, the p-value for computing the p-norm. Default is 2.key_chains (
Optional
[Union
[List
[str
],Dict
[str
,str
],Container
]], default:None
) – The key-chains to apply or not apply the method to. Default isNone
.to_apply (
Union
[bool
,Container
], default:True
) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default isTrue
.prune_unapplied (
Union
[bool
,Container
], default:False
) – Whether to prune key_chains for which the function was not applied. Default isFalse
.map_sequences (
Union
[bool
,Container
], default:False
) – Whether to also map method to sequences (lists, tuples). Default isFalse
.out (
Optional
[Container
], default:None
) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.
- Return type:
Container
- Returns:
ret – An array with the vector norm downscaled to the max norm if needed.
Examples
With
ivy.Container
instance method:>>> x = ivy.Container(a=ivy.array([0., 1., 2.]), ... b=ivy.array([3., 4., 5.])) >>> y = x.clip_vector_norm(2.0, p=1.0) >>> print(y) { a: ivy.array([0., 0.667, 1.33]), b: ivy.array([0.5, 0.667, 0.833]) }