How to avoid math rounding errors in Javascsript Browser environments [duplicate] - javascript

This question already has answers here:
How to deal with floating point number precision in JavaScript?
(47 answers)
Closed last month.
I´ve come across this oddity when writing some JS code. It happens in both Chrome and Firefox:
10.04 + 0.01 = 10.049999999999999
Not sure if I´m just missing something obvious that causes this?
Right now I am handling it with rounding the number afterwards - Is there a better way to avoid this bug/feature?

This is a consequence of the way numbers are stored internally in IEEE 754 form.
To avoid this, use a BigDecimal library such as this one: https://www.npmjs.com/package/js-big-decimal

Related

Solution to the infamous 64-bit floating point math error in JavaScript [duplicate]

This question already has answers here:
Is floating point math broken?
(31 answers)
Closed 6 years ago.
Is there a solution to the infamous floating point math error in JavaScript due to the 64-bit floating point representation?
Is floating point math broken?
Im trying to make a math intensive application based on JavaScript, however, due to this error in JavaScript, it always results in inaccurate output.
For ex.:
0.1 + 0.2 = 0.30000000000000004
whereas the expected is
0.1 + 0.2 = 0.3
I wonder how the financial organizations like (PayPal) are still up with there javascript applications.
The "solution" to the (non-)error is to ignore it, and ensure that you use an appropriate function, i.e. toFixed(n), to present the number with the desired number of decimal places.
It's important to know the difference between the internal representation of a value, and how to present that value to the end user.
You can use https://github.com/ekg/fraction.js library for the intermediate values and convert the final result back to decimal value to minimize error.

JavaScript does not like 10151920335784069 and refuses to accept it [duplicate]

This question already has answers here:
javascript large integer round because precision? (why?)
(2 answers)
Closed 8 years ago.
I've just run into a peculiar issue with Javascript.
An API call returns some JSON as it normally does. One of the ids returned is the long number "10151920335784069".
However, in Javascript world that becomes "10151920335784068" (one subtracted).
A quick test in the (Chrome) console demonstrates it:
x = 10151920335784069;
console.log(x);
10151920335784068
x==10151920335784069;
true
Further more:
x==10151920335784067;
true
x==10151920335784066;
false
What is going on here?
JavaScript (ECMA 262 5th Edition) uses double-precision 64bit numbers in IEEE 754 format. That representation cannot store your value in question exactly so it must round it to the nearest value per the IEEE 754 specification.
Authors and users of APIs that use JSON data should keep this limitation in mind. Many runtime environments (such as JavaScript) have potentially unexpected behavior regarding such numerical values even though the JSON format doesn't impose any such limitations.
All numerical variables in Javascript are stored as 64-bit floating point integers, so at high levels of precision, with numbers above 32 bits, it will round and give you slightly inaccurate numbers
If you want to check if two numbers are roughly even, you can use this
if(Math.abs(num-check)/check<1e-8)){
alert("For most practical intents and purposes, they are equal!");
}

Javascript doesn't like 16.1 value? [duplicate]

This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
Is JavaScript’s Math broken?
Funny question, but why at 16.1 javascript become "crazy"? :)
Code :
var value1=16.1;
var value2=16.2;
console.log(value1 * 1000);
console.log(value2 * 1000);
Output :
16100.000000000002
16200
Why?
That's because javascript casts everything to a double internally. As a result, all calculations pick up some noise due to floating point inaccuracy: Floating point inaccuracy examples
One way to fix this issue, is to just round down to the nearest int after all intermediate calculations.
Answer Copy From Here
It's not a javascript problem, it's a problem related to any programming language using floating point numbers, see
Is floating point math broken?
for explanation of the root problem and for some useful workarounds too.

javascript round problems [duplicate]

This question already has answers here:
Closed 11 years ago.
Possible Duplicate:
Is JavaScript's Math broken?
I've some problems with javascript.
If I do this:
alert(6.000 * 1.050);
I expect 6.3, but I get 6.30000001
can anybody help me ? or explain why this happens?
Here you can simple use the method toFixed() in java-script
alert(parseFloat(6.000 * 1.050).toFixed(1));
They're called floats, and sometimes have a little bit of inaccuracy.
http://en.wikipedia.org/wiki/Floating_point#Accuracy_proble
Standard problem; decimals can't be stored with infinite precision in general, so most programming languages have data types that approximates them and show a rounded version. Problem is that multiplication or subtraction can cause the inaccuracies to show.
In the end, you'll just have to round probably.

185.3 + 12.37 = 197.67000000000002? [duplicate]

This question already has answers here:
Understanding floating point problems
(4 answers)
Closed 11 months ago.
This page has a simple alert:
alert(185.3 + 12.37);
To me, that should equal 197.67
However, in the browsers I've tested (Chrome/Safari on OSX, FF on Win7) the answer is:
197.67000000000002
Why is that? Is this just a known bug or is there more to JavaScript addition than I realize?
javascript uses the double datatype, which can't, due to restricted binary places, express all decimal numbers accurately (not all numbers can be expressed with finite binary places). You can read more at wikipedia.
You should read this:
http://download.oracle.com/docs/cd/E19957-01/806-3568/ncg_goldberg.html
It's not a bug; it's just a well-known fact of floating point numbers for every language.
In binary, this is the infinitely repeating binary fraction 11000101.10(10101110000101000111) - which cannot be represented in a finite number of bits, so it is rounded to an approximation.

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