rounding-error

问题 I can't get it why the ff code returns 12.62 instead of 12.63 QDebug << QString::number(12.625, 'f', 2); and also what will be the solution for this? Currently my solution is this QDebug << QString::number(12.625 + 0.0001, 'f', 2); and it will return 12.63 . btw my OS is ubuntu 11.04 回答1: GCC uses "Round to Even" or "Banker's Rounding" as it's default mode while Visual Studio uses "Round away from Zero" From the GCC docs: Rounding This is the default mode. It should be used unless there is a

问题 I'm in the process of converting a program from Scilab code to C++. One loop in particular is producing a slightly different result than the original Scilab code (it's a long piece of code so I'm not going to include it in the question but I'll try my best to summarise the issue below). The problem is, each step of the loop uses calculations from the previous step. Additionally, the difference between calculations only becomes apparent around the 100,000th iteration (out of approximately 300

问题 I am implementing an Hybrid mobile application in which I have to represent our Desktop application written in C#. When rounding off a value, the value differs between Desktop and Mobile application. Example Code used in C#: Math.Round (7060.625, 2); // prints 7060.62 Math.Round (7060.624, 2); // prints 7060.62 Math.Round (7060.626, 2); // prints 7060.63 Code used in JS: +(7060.625).toFixed(2); // prints 7060.63 (value differs) +(7060.624).toFixed(2); // prints 7060.62 +(7060.626).toFixed(2);

问题 I am implementing an Hybrid mobile application in which I have to represent our Desktop application written in C#. When rounding off a value, the value differs between Desktop and Mobile application. Example Code used in C#: Math.Round (7060.625, 2); // prints 7060.62 Math.Round (7060.624, 2); // prints 7060.62 Math.Round (7060.626, 2); // prints 7060.63 Code used in JS: +(7060.625).toFixed(2); // prints 7060.63 (value differs) +(7060.624).toFixed(2); // prints 7060.62 +(7060.626).toFixed(2);

问题 I'm having an issue that I believe has to do with working with floats and precision but I'm not very well versed in the various intricacies involved. I'm a math person and in my mind I might as well still be just working with decimals on a chalkboard. I'll begin studying up on this, but in the mean time, I'm wondering if there are any general techniques for working with floats that might address the problem I'll outline below. I have a numpy array of decimals that I would like to round to the

问题 Given the following issue with rounding milliseconds under R. How do I get around it so that the times are correct? > options(digits.secs=3) > as.POSIXlt("13:29:56.061", format='%H:%M:%OS', tz='UTC') [1] "2012-06-07 13:29:56.060 UTC" > as.POSIXlt("13:29:56.062", format='%H:%M:%OS', tz='UTC') [1] "2012-06-07 13:29:56.061 UTC" > as.POSIXlt("13:29:56.063", format='%H:%M:%OS', tz='UTC') [1] "2012-06-07 13:29:56.063 UTC" I noticed that this URL provides background information but doesn't solve my

问题 Assume You're given two sets of floating point variables implemented according to IEEE754, meant to be treated as exact values calculated according to formulae present in standard. All legal values are possible. The amount of variables in set may be any natural number. What would be a good way to compare exact, in mathematical sense, sums of values represented by said variables. Due to domain's nature, the problem can easily be represented as comparing a single sum to zero. You can disregard

问题 We're currently using System.Decimals to represent numbers in a .NET application we're developing. I know that decimals are design to minimize errors due to rounding, but I also know that certain numbers, 1/3 for example, cannot be represented as a decimal so some calculations will have small rounding error. I believe the magnitude of this error will be very small and insignificant, however a colleague disagrees. I would therefore like to be able to estimate the order of magnitude of the

问题 I am curious about the existence of any "rounding" standards" when it comes to the calculation of financial data. My initial thoughts are to perform rounding only when the data is being presented to the user (presentation layer). If "rounded" data is then used for further calculations, should be use the "rounded" figure or the "raw" figure? Does anyone have any advice? Please note that I am aware of different rounding methods, i.e. Bankers Rounding etc. 回答1: The first and most important rule:

问题 I've tried big.js, bignumber.js, and decimal.js; they all work reasonably well up to a certain point, but fall short when I need to do arbitrary-precision calculations with large enough numbers of "odd" digits (my current test case is 31435517643980 * (1 / 31435517643980) === 1 ). I am open to any solution that allows me to process expressions like this, including calls to an external API. I'm currently looking at Wolfram|Alpha's API, but the 2000 calls/month limit is a restriction I'd like