erf#
- ivy.erf(x, /, *, out=None)[source]#
Compute the Gauss error function of
x
element-wise.- Parameters:
- Return type:
- Returns:
ret – The Gauss error function of x.
Examples
>>> x = ivy.array([0, 0.3, 0.7, 1.0]) >>> ivy.erf(x) ivy.array([0., 0.328, 0.677, 0.842])
- Array.erf(self, *, out=None)#
ivy.Array instance method variant of ivy.erf. This method simply wraps the function, and so the docstring for ivy.erf also applies to this method with minimal changes.
- Parameters:
self (
Array
) – input array to compute exponential for.out (
Optional
[Array
]) – optional output, for writing the result to. It must have a shape that the (default:None
) inputs broadcast to.
- Return type:
Array
- Returns:
ret – an array containing the Gauss error of
self
.
Examples
>>> x = ivy.array([0, 0.3, 0.7, 1.0]) >>> x.erf() ivy.array([0., 0.328, 0.677, 0.842])
- Container.erf(self, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)#
ivy.Container instance method variant of ivy.erf. This method simply wraps thefunction, and so the docstring for ivy.erf also applies to this method with minimal changes.
- Parameters:
self (
Container
) – input container to compute exponential for.key_chains (
Optional
[Union
[List
[str
],Dict
[str
,str
]]]) – The key-chains to apply or not apply the method to. Default isNone
. (default:None
)to_apply (
bool
) – If True, the method will be applied to key_chains, otherwise key_chains (default:True
) will be skipped. Default isTrue
.prune_unapplied (
bool
) – Whether to prune key_chains for which the function was not applied. (default:False
) Default isFalse
.map_sequences (
bool
) – Whether to also map method to sequences (lists, tuples). (default:False
) Default isFalse
.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 Gauss error of
self
.
Examples
>>> x = ivy.Container(a=ivy.array([-0.25, 4, 1.3]), ... b=ivy.array([12, -3.5, 1.234])) >>> y = x.erf() >>> print(y) { a: ivy.array([-0.27632612, 1., 0.934008]), b: ivy.array([1., -0.99999928, 0.91903949]) }