expm1#

ivy.expm1(x, /, *, out=None)[source]#

Calculate an implementation-dependent approximation to exp(x)-1, having domain [-infinity, +infinity] and codomain [-1, +infinity], for each element x_i of the input array x.

Note

The purpose of this function is to calculate exp(x)-1.0 more accurately when x is close to zero. Accordingly, conforming implementations should avoid implementing this function as simply exp(x)-1.0. See FDLIBM, or some other IEEE 754-2019 compliant mathematical library, for a potential reference implementation.

Special cases

For floating-point operands,

  • If x_i is NaN, the result is NaN.

  • If x_i is +0, the result is +0.

  • If x_i is -0, the result is -0.

  • If x_i is +infinity, the result is +infinity.

  • If x_i is -infinity, the result is -1.

Parameters:
  • x (Union[Array, NativeArray]) – input array. Should have a numeric data type.

  • 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 – an array containing the evaluated result for each element in x. The returned array must have a floating-point data type determined by type-promotion.

This function conforms to the Array API Standard. This docstring is an extension of the docstring # noqa in the standard.

Both the description and the type hints above assumes an array input for simplicity, but this function is nestable, and therefore also accepts ivy.Container instances in place of any of the arguments.

Examples

With ivy.Array inputs:

>>> x = ivy.array([[0, 5, float('-0'), ivy.nan]])
>>> ivy.expm1(x)
ivy.array([[  0., 147.,  -0.,  nan]])
>>> x = ivy.array([ivy.inf, 1, float('-inf')])
>>> y = ivy.zeros(3)
>>> ivy.expm1(x, out=y)
ivy.array([  inf,  1.72, -1.  ])

With ivy.Container inputs:

>>> x = ivy.Container(a=ivy.array([-1, 0,]),
...                   b=ivy.array([10, 1]))
>>> ivy.expm1(x)
{
    a: ivy.array([-0.632, 0.]),
    b: ivy.array([2.20e+04, 1.72e+00])
}
Array.expm1(self, *, out=None)#

ivy.Array instance method variant of ivy.expm1. This method simply wraps the function, and so the docstring for ivy.expm1 also applies to this method with minimal changes.

Parameters:
  • self (Array) – input array. Should have a numeric data type.

  • out (Optional[Array]) – optional output array, for writing the result to. It must have (default: None) a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the evaluated result for each element in x. The returned array must have a floating-point data type determined by type-promotion.

Examples

>>> x = ivy.array([5.5, -2.5, 1.5, -0])
>>> y = x.expm1()
>>> print(y)
ivy.array([244.   ,  -0.918,   3.48 ,   0.   ])
>>> y = ivy.array([0., 0.])
>>> x = ivy.array([5., 0.])
>>> _ = x.expm1(out=y)
>>> print(y)
ivy.array([147.,   0.])
Container.expm1(self, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)#

ivy.Container instance method variant of ivy.expm1. This method simply wraps the function, and so the docstring for ivy.expm1 also applies to this method with minimal changes.

Parameters:
  • self (Container) – input container. Should have a floating-point data type.

  • 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 evaluated result for each element in self. The returned array must have a real-valued floating-point data type determined by type-promotion.

Examples

>>> x = ivy.Container(a=ivy.array([2.5, 0.5]),
...                   b=ivy.array([5.4, -3.2]))
>>> y = x.expm1()
>>> print(y)
{
    a: ivy.array([11.2, 0.649]),
    b: ivy.array([220., -0.959])
}
>>> y = ivy.Container(a=ivy.array([0., 0.]))
>>> x = ivy.Container(a=ivy.array([4., -2.]))
>>> x.expm1(out=y)
>>> print(y)
{
    a: ivy.array([53.6, -0.865])
}