I'm working with currency values, so it's important to calculate accurately.
My current code breaks down a string into tokens then evaluates them. For decimal values, it first converts them to integers, does the calculation then converts back to a decimal.
For example if I had the expression
"0.1 * 0.2"
The first step would be to break it down into the tokens 0.1, * and 0.2. It then does some other malarky and figures it needs to multiple 0.1 and 0.2 together. The calculation would be
1 * 2 / 100
The calculation is done as integers to prevent JavaScript rounding error, i.e.
0.1 * 0.2 == 0.020000000000000004
My colleges argument is that by converting to a float from a string initially you've already lost precision. So my question is what's the upper bound and lower bound either side of 0 where a number cannot be represented by JavaScript exactly? So that I can check for this and handle it, if that's the right approach.
The problem you describe isn't a bounds problem. IEEE-754 double-precision binary floating point numbers can represent the value 0.5 perfectly, but cannot represent, say, 0.1 perfectly. Note that those have the same number of digits. The issue isn't the number of places of precision, it's the fact that the number type uses a different number base than we do. It uses base 2, rather than our base 10.
Just as we can't accurately represent 1 / 3 in our base 10 system, certain numbers cannot be accurately represented in IEEE-754's base 2 system.
In 2008, the IEEE came out with a revision adding a new format to IEEE-754 (it defines several formats; the "double-precision binary" one used by JS is just one of them) called "decimal64" which uses base 10 rather than base 2, for applications that need to handle rounding the same way we do (financial apps and such). That may start seeping into programming languages and such; for now, IEEE-754 single-precision and double-precision are the typical ones used, and others not based on the recent IEEE-754 standard like C#'s decimal.
In the meantime, there are "big decimal" libraries for JavaScript, such as big.js (haven't used it, no affiliation). If you search for "bignumber in JavaScript" or "JavaScript exact floating point" you should find multiple options.
I dont think that there is any such number which you can say that it is the first number that JavaScript can't represent accurately. It is all about decimal numbers and loss of precision.
Also to add there is no decimal data type in JavaScript - the only numeric data type is floating-point. JavaScript uses 64-bit floating point representation.
Floating point rounding errors. 0.1 cannot be represented as accurately in base-2 as in base-10 due to the missing prime factor of 5. Also to note that every floating point math is like this and is based on the IEEE 754 standard.
If I'm not mistaken, all JS engines uses IEEE 754 double to handle floating point numbers.
Take a look at the http://en.wikipedia.org/wiki/Double-precision_floating-point_format page, section 'Double-precision examples'. In the case numbers are close to 0, take a closer look at subnormals formula:
So, the closest to zero number, that can be represented in JavaScript floating point datatype is 21-1023 * 2-52 = 2-1022 * 2-52 = 2-1074.
Empirically:
Math.pow(2,-1074) = 5e-324
Math.pow(2,-1075) = 0
I notice you are doing currency calculations. You may find it better to use integers here. Use an integer to represent the price in cents. This eliminates problems with floating point which are more suitable for scientific calculations. This would basically be equivalent to using a BigDecimal in java.
Related
I'm trying to write a function that would fetch me the number of decimal places after the decimal point in a floating-point literal using the answer as a reference from here.
Although this seems to work fine when tried in the browser consoles, in the Node.js environment while running test cases, the precision is truncated only up to 14 digits.
let data = 123.834756380650877834678
console.log(data) // 123.83475638065088
And the function returns 14 as the answer.
Why is the rounding off happening at rest? Is it a default behavior?
The floating-point format used in JavaScript (and presumably Node.js) is IEEE-754 binary64. When 123.834756380650877834678 is used in source code, it is converted to the nearest representable value, which is 123.834756380650873097692965529859066009521484375.
When this is converted to a string with default formatting, JavaScript uses just enough digits to uniquely distinguish the value. For 123.834756380650873097692965529859066009521484375, this should produce “123.83475638065087”. If you are getting “123.83475638065088”, which differs in the last digit, then the software you are using does not conform to the JavaScript specification (ECMAScript).
In any case, the binary64 format does not have sufficient precision to preserve the information that the original numeral, “123.834756380650877834678”, has 21 digits after the decimal point.
The code you link to also does not and cannot compute the number of digits in an original numeral. It computes the number of digits needed to uniquely distinguish the value represented after conversion to binary64. For sufficiently short numerals without trailing zeros after the decimal point, this is the same as the number of digits after the decimal point in the original numeral. For others, it may not be.
it is default behavior of JavaScript.I think it will be same in node.js.
in JS The maximum number of decimals is 17.
for more details take a look at here
Hello guys I'm working on javascript file about astronomical calculations so I need more precisions.
I have a lot operations for each line and I want like result more precision (also ten or twenty decimal after comma).
In JavaScript if I do not declare number of decimal (for example using .ToFixed(n)), how many positions after the comma, the language consider during the calculation?
For example:
var a= 1.123456789*3.5; // result 3.9320987615
var b= a/0.321;
var c= b*a;
// c => 48.16635722800571
It will be the result?
Like using all decimal after comma for each variables of javascript does approximations ?
Why in other questions users suggests to use decimal.js or other libraries?
I'm sorry if my question seems stupid but for me is important.
I hope you can help me.
Sorry for my english !
Javascript uses IEEE-754 double-precision, that means it only calculates numbers to around 15 decimal digits of precision, any more than that gets cut off. If you need more precision you have to use decimal.js or another similar library. Other questions recommend decimal.js or other libraries is because it is easy to put into your program and can provide as much precision as you want.
The reason it isn't implemented in the computer by default is that it takes a lot more effort for the computer to calculate to 20 digits over 15 digits because the computer is built to compute only to 15 decimal digits. If you want to read more on it I would recommend reading Arbitrary Precision Arithmetic on Wikipedia.
Simply because the toFixed() method converts a number into a string, keeping a specified number of decimals. So, the returned value will be a string not a number.
I've written a JS math library for very large numbers that preserves precision and can be used for any base. The entire library is custom because none of the default math functions can handle numbers of these magnitude.
This means the power handler has to be custom as well. Simple enough for integer powers, but the only method for calculating decimal powers I've found is to do do sqrt(1^0*num^1) and (0+1)/2 and repeating the process until the second number equals the decimal portion of the power. Is this the basic concept behind most decimal powers or is there a non guess and check method?
Decimal powers use logarithms, since bx = ex ln b. Naturally this requires arbitrary-precision logarithm and exponentiation capability, but choosing a suitable logarithm base can make this manageable.
I'm using a server-side calculation which needs to generate (with * and + operations) and compare 40-bit integers. I'm aware that at that point the V8 engine stores the numbers as Double rather than int. Can I rely on these numbers to be generated and compared correctly?
My intuition says yes - doubles shouldn't have trouble with that - but I'm not sure how to check or where to find information on this.
Yes.
A JavaScript Number, which is a 64-bit IEEE 754 floating point value, can store integers from -253 to 253 without loss of precision, since doubles can store up to 53 bits of mantissa (52 explictly).
References:
ECMA-262: 4.3.19 Number value
Double-precision floating point numbers (Wikipedia)
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Possible Duplicate:
Is JavaScript's Math broken?
...and what can I do against that? After the calculation I want to have a String representation of the result. Because of that the zeros (plus the one) are desturburbing. "toFixed()" is not a perfect solution because I want to have all (correct) decimals of the potential result and I do not know the result (and the number of decimals) before. So the calculation shown above is only an example.
The short answer is: computers represent numbers in binary, so they can't represent all base 10 fractions perfectly as JavaScript numbers.
The (slightly) longer answer is that, because JavaScript numbers are 64-bit floating-point (equivalent to the double type in Java, C#, etc.), as Wikipedia describes here, there is a limited number of significand bits. For this reason, the precision of this base-2 number is limited.
As an analogy, consider representing the fraction 1/3 in base 10. Say that you only have so many digits to use. That means that you can never ever ever represent 1/3 exactly in base 10, because 1/3 requires an infinite number of digits to represent in base 10. Similarly, you can never represent 1/10 perfectly in a finite number of bits, because 1/10 requires an infinite number of bits to represent exactly. What you're seeing here is a fraction (58/10) that a computer can't represent exactly in a limited number of bits, so the computer is coming as close as it can.
Squeezing infinitely many real numbers into a finite number of bits requires an approximate representation. Although there are infinitely many integers, in most programs the result of integer computations can be stored in 32 bits. In contrast, given any fixed number of bits, most calculations with real numbers will produce quantities that cannot be exactly represented using that many bits. Therefore the result of a floating-point calculation must often be rounded in order to fit back into its finite representation. This rounding error is the characteristic feature of floating-point computation.
Above from http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html