mygrad.logaddexp

class mygrad.logaddexp(x1: ArrayLike, x2: ArrayLike, out: Optional[Union[Tensor, ndarray]] = None, *, where: Mask = True, dtype: DTypeLikeReals = None, constant: Optional[bool] = None)

Logarithm of the sum of exponentiations of the inputs.

Calculates log(exp(x1) + exp(x2)). This function is useful in statistics where the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases the logarithm of the calculated probability is stored. This function allows adding probabilities stored in such a fashion.

This docstring was adapted from that of numpy.logaddexp [1]

Parameters

x1, x2ArrayLike

Input values. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

outOptional[Union[Tensor, ndarray]]

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated tensor is returned.

constantOptional[bool]

If True, this tensor is treated as a constant, and thus does not facilitate back propagation (i.e. constant.grad will always return None).

Defaults to False for float-type data. Defaults to True for integer-type data.

Integer-type tensors must be constant.

whereMask

This condition is broadcast over the input. At locations where the condition is True, the out tensor will be set to the ufunc result. Elsewhere, the out tensor will retain its original value. Note that if an uninitialized out tensor is created via the default out=None, locations within it where the condition is False will remain uninitialized.

dtypeOptional[DTypeLikeReals]

The dtype of the resulting tensor.

Returns

logaddexpTensor

Logarithm of exp(x1) + exp(x2).

See also

logaddexp2

Logarithm of the sum of exponentiations of inputs in base 2.

References

1

Retrieved from https://numpy.org/doc/stable/reference/generated/numpy.logaddexp.html

Examples

>>> import mygrad as mg
>>> prob1 = mg.log(1e-50)
>>> prob2 = mg.log(2.5e-50)
>>> prob12 = mg.logaddexp(prob1, prob2)
>>> prob12
Tensor(-113.87649168120691)
>>> mg.exp(prob12)
Tensor(3.5000000000000057e-50)

Attributes

signature

Methods

accumulate([axis, dtype, out, constant])	Not implemented
at(indices[, b, constant])	Not implemented
outer(b, *[, dtype, out])	Not Implemented
reduce([axis, dtype, out, keepdims, ...])	Not Implemented
reduceat(indices[, axis, dtype, out])	Not Implemented

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
accumulate([axis, dtype, out, constant])	Not implemented
at(indices[, b, constant])	Not implemented
outer(b, *[, dtype, out])	Not Implemented
reduce([axis, dtype, out, keepdims, ...])	Not Implemented
reduceat(indices[, axis, dtype, out])	Not Implemented

Attributes

identity
nargs
nin
nout
ntypes
signature
types