float.as_integer_ratio()的实现限制 [英] Implementation limitations of float.as_integer_ratio()
本文介绍了float.as_integer_ratio()的实现限制的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
最近,一名通讯员提到 float.as_integer_ratio()
,Python 2.6中的新增功能,指出典型的浮点实现本质上是实数的有理近似.出于好奇,我不得不尝试π:
Recently, a correspondent mentioned float.as_integer_ratio()
, new in Python 2.6, noting that typical floating point implementations are essentially rational approximations of real numbers. Intrigued, I had to try π:
>>> float.as_integer_ratio(math.pi);
(884279719003555L, 281474976710656L)
由于有马,:
(428224593349304L, 136308121570117L)
例如,此代码:
#! /usr/bin/env python
from decimal import *
getcontext().prec = 36
print "python: ",Decimal(884279719003555) / Decimal(281474976710656)
print "Arima: ",Decimal(428224593349304) / Decimal(136308121570117)
print "Wiki: 3.14159265358979323846264338327950288"
产生以下输出:
python: 3.14159265358979311599796346854418516
Arima: 3.14159265358979323846264338327569743
Wiki: 3.14159265358979323846264338327950288
当然,鉴于64位浮点数提供的精度,结果是正确的,但它使我问:如何找到有关as_integer_ratio()
的实现限制的更多信息?感谢您的指导.
Certainly, the result is correct given the precision afforded by 64-bit floating-point numbers, but it leads me to ask: How can I find out more about the implementation limitations of as_integer_ratio()
? Thanks for any guidance.
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