mindspore.numpy.logaddexp

mindspore.numpy.logaddexp(x1, x2, dtype=None)[source]

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.

Note

Numpy arguments out, where, casting, order, subok, signature, and extobj are not supported.

Parameters

x1 (Tensor) – Input array.
x2 (Tensor) – Input array. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
dtype (mindspore.dtype) – Default: None. Overrides the dtype of the output Tensor.

Returns

Tensor or scalar. This is a scalar if both x1 and x2 are scalars.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> x1 = np.array([1, 2, 3]).astype('float16')
>>> x2 = np.array(2).astype('float16')
>>> output = np.logaddexp(x1, x2)
>>> print(output)
[2.312 2.693 3.312]