mars.tensor.logaddexp2#

mars.tensor.logaddexp2(x1, x2, out=None, where=None, **kwargs)[source]#

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

Calculates log2(2**x1 + 2**x2). This function 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.

Parameters

  • x1 (array_like) – Input values.

  • x2 (array_like) – Input values.

  • out (Tensor, None, or tuple of Tensor and None, optional) – 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. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

  • where (array_like, optional) – Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.

  • **kwargs –

Returns

result – Base-2 logarithm of 2**x1 + 2**x2.

Return type

Tensor

See also

logaddexp

Logarithm of the sum of exponentiations of the inputs.

Examples

>>> import mars.tensor as mt

>>> prob1 = mt.log2(1e-50)
>>> prob2 = mt.log2(2.5e-50)
>>> prob12 = mt.logaddexp2(prob1, prob2)
>>> prob1.execute(), prob2.execute(), prob12.execute()
(-166.09640474436813, -164.77447664948076, -164.28904982231052)
>>> (2**prob12).execute()
3.4999999999999914e-50