Source code for mars.tensor.arithmetic.log1p

#!/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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import numpy as np

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

class TensorLog1p(TensorUnaryOp):
    _op_type_ = OperandDef.LOG1P
    _func_name = "log1p"

[docs]@infer_dtype(np.log1p) def log1p(x, out=None, where=None, **kwargs): """ Return the natural logarithm of one plus the input tensor, element-wise. Calculates ``log(1 + x)``. Parameters ---------- x : 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 ------- y : Tensor Natural logarithm of `1 + x`, element-wise. See Also -------- expm1 : ``exp(x) - 1``, the inverse of `log1p`. Notes ----- For real-valued input, `log1p` is accurate also for `x` so small that `1 + x == 1` in floating-point accuracy. Logarithm is a multivalued function: for each `x` there is an infinite number of `z` such that `exp(z) = 1 + x`. The convention is to return the `z` whose imaginary part lies in `[-pi, pi]`. For real-valued input data types, `log1p` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `log1p` is a complex analytical function that has a branch cut `[-inf, -1]` and is continuous from above on it. `log1p` handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard. References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", 10th printing, 1964, pp. 67. .. [2] Wikipedia, "Logarithm". Examples -------- >>> import mars.tensor as mt >>> mt.log1p(1e-99).execute() 1e-99 >>> mt.log(1 + 1e-99).execute() 0.0 """ op = TensorLog1p(**kwargs) return op(x, out=out, where=where)