dpnp.logaddexp2

dpnp.logaddexp2(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)

Calculates the logarithm of the sum of exponents in base-2 for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

This function calculates log2(2**x1 + 2**x2). It is useful in machine learning when the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases the base-2 logarithm of the calculated probability can be used instead. This function allows adding probabilities stored in such a fashion.

For full documentation refer to numpy.logaddexp2.

Parameters:

x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have a real-valued floating-point data type. Both inputs x1 and x2 can not be scalars at the same time.
x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have a real-valued floating-point data type. Both inputs x1 and x2 can not be scalars at the same time. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
out ({None, dpnp.ndarray, usm_ndarray}, optional) -- Output array to populate. Array must have the correct shape and the expected data type. Default: None.
order ({"C", "F", "A", "K"}, optional) -- Memory layout of the newly output array, if parameter out is None. Default: "K".

Returns:

out -- An array containing the element-wise results. The data type of the returned array is determined by the Type Promotion Rules.

Return type:

dpnp.ndarray

Limitations

Parameters where and subok are supported with their default values. Keyword arguments kwargs are currently unsupported. Otherwise NotImplementedError exception will be raised.