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Closed 10 years ago.
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Is JavaScript's Math broken?
Why JS show 25.1+61.7+13.2 = 100.00000000000001 ? Its fixed if you just change the position of the number like 13.2+25.1+61.7 = 100. Can anyone explain this.
Because you are using floating point arithmetic.
Here is an explanation of what is happening.
JavaScript uses IEEE 754 floating point math, which is a base-2 floating point format. There are many many values that cannot be represented exactly in that format, and specifically among them are decimal fractions like x.7.
It's not a compiler issue. It's an issue with the fundamental way that JavaScript represents real numbers. Many other languages would have the exact same problem.
Computers don't handle real numbers in a rigorous way, generally.
Related
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How to deal with floating point number precision in JavaScript?
(47 answers)
Closed 1 year ago.
Sometimes simple algebra can result in floating accuracy errors.
in this case, I encountered 12 * 1.6 which resulted in 19.20000000003
Is there a way to catch this issue or prevent it from happening?
Accuracy is highly important so simply rounding or truncation would not be enough.
No. Its the nature of floating point arithmetic. You only have so much accuaracy.
https://en.wikipedia.org/wiki/Floating-point_arithmetic
This question already has answers here:
Is floating point math broken?
(31 answers)
Closed 5 years ago.
Can anyone answer me why javascript adds up 1.123460 + 0.112210 as 1.2356699999999998
while no way I can come up with that result by manual calculation or by any other compilers..
Am I going crazy or javascript, cannot figure out
Its just how JavaScript and many other languages deal with floats. Rounding them to 15 decimal places. Check this How to deal with floating point number precision in JavaScript? How to deal with floating point number precision in JavaScript?
This question already has answers here:
How to deal with floating point number precision in JavaScript?
(47 answers)
Closed 6 years ago.
I am trying to use eval() for calculator i am making, but if when i try this
console.log(eval("5.2-5"));
It returns
0.20000000000000018
Why is this happening.Thank you for your time.
This is due to how Javascript handles floating point precision. Please see How to deal with floating point number precision in JavaScript? for more information
Short answer: Due to the nature of how computers process floats, this means floating point accuracy actually breaks down past a certain point. This is what you're seeing.
Javascript evaluates "5.2-2" to a floating point number, which precision is not guaranteed.
If you need a fixed precision you could use
console.log(eval("5.2-5.0").toFixed(2)):
This question already has answers here:
What is JavaScript's highest integer value that a number can go to without losing precision?
(21 answers)
Closed 29 days ago.
So, for parseInt('10152547376283911', 10) I'm expecting 10152547376283911, but I'm getting 10152547376283912.
What I'm doing wrong?
Javascript native numbers do not have enough precision (significant digits) to hold the number you are expecting. See the question What is the standard solution in JavaScript for handling big numbers (BigNum)? for suggestions on how to deal with this problem.
Depending on your application, you may actually be able to use strings instead of numbers (for example, if your number represents something like a physical part number). You would only need a bigint library if you intend to do arithmetic on your numbers.
This question already has answers here:
Is floating point math broken?
(31 answers)
Closed 9 years ago.
test there in their consoles browser
1067.11-1000 = 67.1099999999999
but the correct thing 67.11
can even test the calculator windows ..
could someone explain this to me?
Floating point numbers are stored using base2, this creates small differences like the one you demonstrate above when converting to base10. The difference will be even greater if use the following numbers: 1000000067.11 - 1000000000 = 67.1100000143. This is because the level of precision decreases as the numbers calculated increases.
Lack of precision is the main disadvantage of the float type numbers - some real numbers can only be represented approximately.
You can follow this link to learn more about representation of floating point format