This question already has answers here:
Is floating point math broken?
(31 answers)
Closed 4 years ago.
Hi I am doing following operation in Javascript and I am getting weird results, can someone tell me what is going on.
5.62-6.18+0.56 = 4.440892098500626e-16
Because of floating point inaccuracies, the result isn't exactly zero. JavaScript uses scientific notation to display numbers as small as this one. You might be interested in the toPrecision() method.
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This question already has answers here:
How to deal with floating point number precision in JavaScript?
(47 answers)
Is floating point math broken?
(31 answers)
Closed 5 years ago.
I'm trying to run the following calculation in JavaScript, 78.98 * 10 and the result returned is always 789.8000000000001 My question is where did that 0.0000000000001 come from?
I tried on several calculators, and that 0.0000000000001 should not be there. I did inclusive tests in other programming languages.
My question is, is there a logical explanation for this? If it is an error in the JavaScript engine where I notify?
Thank you.
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:
Is floating point math broken?
(31 answers)
Closed 7 years ago.
I just noticed the strangest problem with simple addition in javascript.
I have a simple equation that I am doing:
-1.000+(1.001) and the answer should be .001 however I am getting 0.0009999999999998899 instead. I can't understand why this is happening and there doesn't seem to be any way to get the right answer. I've check with multiple calculators and they all give me .001 but javascript gives me this crazy number.
What is going on? This isn't right. The number is very close but it is completely wrong. I've tried with other decimals values and I get strange results. How can javascript not do math properly?
Here is the simple alert box I used:
alert(1.000-(1.001));
try it with toFixed() method.
var erg = 1.000-(1.001)
alert(erg.toFixed(3));
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