# erf#

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

Compute the Gauss error function of `x` element-wise.

Parameters:
• x (`Union`[`Array`, `NativeArray`]) – input array.

• 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 Gauss error function of x.

Examples

With `ivy.Array` inputs:

```>>> x = ivy.array([0, 0.3, 0.7])
>>> y = ivy.erf(x)
>>> print(y)
ivy.array([0., 0.32862675, 0.67780113])
```
```>>> x = ivy.array([0.1, 0.3, 0.4, 0.5])
>>> ivy.erf(x, out=x)
>>> print(x)
ivy.array([0.11246294, 0.32862675, 0.42839241, 0.52050018])
```
```>>> x = ivy.array([[0.15, 0.28], [0.41, 1.75]])
>>> y = ivy.zeros((2, 2))
>>> ivy.erf(x, out=y)
>>> print(y)
ivy.array([[0.16799599, 0.30787992], [0.43796915, 0.98667163]])
```

With `ivy.Container` input:

```>>> x = ivy.Container(a=ivy.array([0.9, 1.1, 1.2]), b=ivy.array([1.3, 1.4, 1.5]))
>>> y = ivy.erf(x)
>>> print(y)
{
a: ivy.array([0.79690808, 0.88020504, 0.91031402]),
b: ivy.array([0.934008, 0.95228523, 0.96610528])
}
```
Array.erf(self, *, out=None)[source]#

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`], default: `None`) – optional output, for writing the result to. It must have a shape that the 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)[source]#

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`], `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 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])
}
```