Source code for mars.tensor.arithmetic.logaddexp2

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# distributed under the License is distributed on an "AS IS" BASIS,
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import numpy as np

from ... import opcodes as OperandDef
from ..utils import infer_dtype
from .core import TensorBinOp
from .utils import arithmetic_operand

class TensorLogAddExp2(TensorBinOp):
    _op_type_ = OperandDef.LOGADDEXP2
    _func_name = "logaddexp2"

[docs]@infer_dtype(np.logaddexp2) def logaddexp2(x1, x2, out=None, where=None, **kwargs): """ 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, 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 : Tensor Base-2 logarithm of ``2**x1 + 2**x2``. 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 """ op = TensorLogAddExp2(**kwargs) return op(x1, x2, out=out, where=where)