We Keep Coding

double type Multiplication in C# giving me wrong values [duplicate]

This question already has answers here:
Is floating point math broken?
(31 answers)
Closed 7 years ago.
If I execute the following expression in C#:
double i = 10*0.69;
i is: 6.8999999999999995. Why?
I understand numbers such as 1/3 can be hard to represent in binary as it has infinite recurring decimal places but this is not the case for 0.69. And 0.69 can easily be represented in binary, one binary number for 69 and another to denote the position of the decimal place.
How do I work around this? Use the decimal type?

Because you've misunderstood floating point arithmetic and how data is stored.
In fact, your code isn't actually performing any arithmetic at execution time in this particular case - the compiler will have done it, then saved a constant in the generated executable. However, it can't store an exact value of 6.9, because that value cannot be precisely represented in floating point point format, just like 1/3 can't be precisely stored in a finite decimal representation.
See if this article helps you.

why doesn't the framework work around this and hide this problem from me and give me the
right answer,0.69!!!
Stop behaving like a dilbert manager, and accept that computers, though cool and awesome, have limits. In your specific case, it doesn't just "hide" the problem, because you have specifically told it not to. The language (the computer) provides alternatives to the format, that you didn't choose. You chose double, which has certain advantages over decimal, and certain downsides. Now, knowing the answer, you're upset that the downsides don't magically disappear.
As a programmer, you are responsible for hiding this downside from managers, and there are many ways to do that. However, the makers of C# have a responsibility to make floating point work correctly, and correct floating point will occasionally result in incorrect math.
So will every other number storage method, as we do not have infinite bits. Our job as programmers is to work with limited resources to make cool things happen. They got you 90% of the way there, just get the torch home.

And 0.69 can easily be represented in
binary, one binary number for 69 and
another to denote the position of the
decimal place.
I think this is a common mistake - you're thinking of floating point numbers as if they are base-10 (i.e decimal - hence my emphasis).
So - you're thinking that there are two whole-number parts to this double: 69 and divide by 100 to get the decimal place to move - which could also be expressed as:
69 x 10 to the power of -2.
However floats store the 'position of the point' as base-2.
Your float actually gets stored as:
68999999999999995 x 2 to the power of some big negative number
This isn't as much of a problem once you're used to it - most people know and expect that 1/3 can't be expressed accurately as a decimal or percentage. It's just that the fractions that can't be expressed in base-2 are different.

but why doesn't the framework work around this and hide this problem from me and give me the right answer,0.69!!!
Because you told it to use binary floating point, and the solution is to use decimal floating point, so you are suggesting that the framework should disregard the type you specified and use decimal instead, which is very much slower because it is not directly implemented in hardware.
A more efficient solution is to not output the full value of the representation and explicitly specify the accuracy required by your output. If you format the output to two decimal places, you will see the result you expect. However if this is a financial application decimal is precisely what you should use - you've seen Superman III (and Office Space) haven't you ;)
Note that it is all a finite approximation of an infinite range, it is merely that decimal and double use a different set of approximations. The advantage of decimal is it produces the same approximations that you would if you were performing the calculation yourself. For example if you calculated 1/3, you would eventually stop writing 3's when it was 'good enough'.

For the same reason that 1 / 3 in a decimal systems comes out as 0.3333333333333333333333333333333333333333333 and not the exact fraction, which is infinitely long.

To work around it (e.g. to display on screen) try this:
double i = (double) Decimal.Multiply(10, (Decimal) 0.69);
Everyone seems to have answered your first question, but ignored the second part.

Why doesn't 0.9 recurring always equal 1

Mathematically, 0.9 recurring can be shown to be equal to 1. This question however, is not about infinity, convergence, or the maths behind this.
The above assumption can be represented using doubles in C# with the following.
var oneOverNine = 1d / 9d;
var resultTimesNine = oneOverNine * 9d;
Using the code above, (resultTimesNine == 1d) evaluates to true.
When using decimals instead, the evaluation yields false, yet, my question is not about the disparate precision of double and decimal.
Since no type has infinite precision, how and why does double maintain such an equality where decimal does not? What is happening literally 'between the lines' of code above, with regards to the manner in which the oneOverNine variable is stored in memory?

It depends on the rounding used to get the closest representable value to 1/9. It could go either way. You can investigate the issue of representability at Rob Kennedy's useful page: http://pages.cs.wisc.edu/~rkennedy/exact-float
But don't think that somehow double is able to achieve exactness. It isn't. If you try with 2/9, 3/9 etc. you will find cases where the rounding goes the other way. The bottom line is that 1/9 is not exactly representable in binary floating point. And so rounding happens and your calculations are subject to rounding errors.

What is happening literally 'between the lines' of code above, with regards to the manner in which the oneOverNine variable is stored in memory?
What you're asking about is called IEEE 754. This is the spec that C#, it's underlying .Net runtime, and most other programming platforms use to store and manipulate decimal values. This is because support for IEEE 754 is typically implemented directly at the CPU/chipset-level, making it both far more performant than an alternative implemented solely in software and far easier when building compilers, because the operations will map almost directly to specific CPU instructions.

c# floating point for loop, unexpected results

Can anyone explain to me why this program:
for(float i = -1; i < 1; i += .1F)
Console.WriteLine(i);
Outputs this:
-1
-0.9
-0.8
-0.6999999
-0.5999999
-0.4999999
-0.3999999
-0.2999999
-0.1999999
-0.99999993
7.450581E-08
0.1000001
0.2000001
0.3000001
0.4000001
0.5000001
0.6000001
0.7000001
0.8000001
0.9000002
Where is the rounding error coming from??

I'm sure this question must have been asked in some form before but I can't find it anywhere quickly. :)
The answer comes down to the way that floating point numbers are represented. You can go into the technical detail via wikipedia but it is simply put that a decimal number doesn't necessarily have an exact floating point representation...
The way floating point numbers (base 2 floating point anyway like doubles and floats) work [0]is by adding up powers of 1/2 to get to what you want. So 0.5 is just 1/2. 0.75 is 1/2+1/4 and so on.
the problem comes that you can never represent 0.1 in this binary system without an unending stream of increasingly smaller powers of 2 so the best a computer can do is store a number that is very close to but not quite 0.1.
Usually you don't notice these differences but they are there and sometimes you can make them manifest themselves. There are a lot of ways to deal with these issues and which one you use is very much dependant on what you are actually doing with it.
[0] in the slightly handwavey close enough kind of way

Floating point numbers are not correct, they are always approximated because they must be rounded!!
They are precise in binary representation.
Every CPU or pc could lead to different results.
Take a look at Wikipedia page

The big issue is that 0.1 cannot be represented in binary, just like 1 / 3 or 1 / 7 cannot be represented in decimal. So since the computer has to cut off at some point, it will accumulate a rounding error.
Try doing 0.1 + 0.7 == 0.8 in pretty much any programming language, you'll get false as a result.
In C# to get around this, use the decimal type to get better precision.

This will explain everything about floating-point:
http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html

The rounding error comes from the fact that Float is not a precise data type (when converted to decimal), it is an approxomation, note in the C# Reference Float is specified as having 7 digits of decimal precision.

It is fundamental to the any floating point variable. The reasons are complex but there is plenty of information if you google it.
Try using Decimal instead.

As other posters have intimated, the problem stems from the assumption that floating point numbers are a precise decimal representation. They are not- they are a precise binary (base-2) representation of a number. The problem you are experiencing is that you cannot always express a precise binary number in decimal format- just like you cannot express 1/3 in decimal format (.33333333...). At some point, rounding must occur.
In your example, rounding is occurring when you express .1F (because that is not a value that can be expressed precisely in base-2).

C# loss of precision when dividing doubles

I know this has been discussed time and time again, but I can't seem to get even the most simple example of a one-step division of doubles to result in the expected, unrounded outcome in C# - so I'm wondering if perhaps there's i.e. some compiler flag or something else strange I'm not thinking of. Consider this example:
double v1 = 0.7;
double v2 = 0.025;
double result = v1 / v2;
When I break after the last line and examine it in the VS debugger, the value of "result" is 27.999999999999996. I'm aware that I can resolve it by changing to "decimal," but that's not possible in the case of the surrounding program. Is it not strange that two low-precision doubles like this can't divide to the correct value of 28? Is the only solution really to Math.Round the result?

Is it not strange that two low-precision doubles like this can't divide to the correct value of 28?
No, not really. Neither 0.7 nor 0.025 can be exactly represented in the double type. The exact values involved are:
0.6999999999999999555910790149937383830547332763671875
0.025000000000000001387778780781445675529539585113525390625
Now are you surprised that the division doesn't give exactly 28? Garbage in, garbage out...
As you say, the right result to represent decimal numbers exactly is to use decimal. If the rest of your program is using the wrong type, that just means you need to work out which is higher: the cost of getting the wrong answer, or the cost of changing the whole program.

Precision is always a problem, in case you are dealing with float or double.
Its a known issue in Computer Science and every programming language is affected by it. To minimize these sort of errors, which are mostly related to rounding, a complete field of Numerical Analysis is dedicated to it.
For instance, let take the following code.
What would you expect?
You will expect the answer to be 1, but this is not the case, you will get 0.9999907.
float v = .001f;
float sum = 0;
for (int i = 0; i < 1000; i++ )
{
sum += v;
}

It has nothing to do with how 'simple' or 'small' the double numbers are. Strictly speaking, neither 0.7 or 0.025 may be stored as exactly those numbers in computer memory, so performing calculations on them may provide interesting results if you're after heavy precision.
So yes, use decimal or round.

To explain this by analogy:
Imagine that you are working in base 3. In base 3, 0.1 is (in decimal) 1/3, or 0.333333333'.
So you can EXACTLY represent 1/3 (decimal) in base 3, but you get rounding errors when trying to express it in decimal.
Well, you can get exactly the same thing with some decimal numbers: They can be exactly expressed in decimal, but they CAN'T be exactly expressed in binary; hence, you get rounding errors with them.

Short answer to your first question: No, it's not strange. Floating-point numbers are discrete approximations of the real numbers, which means that rounding errors will propagate and scale when you do arithmetic operations.
Theres' a whole field of mathematics called numerical analyis that basically deal with how to minimize the errors when working with such approximations.

It's the usual floating point imprecision. Not every number can be represented as a double, and those minor representation inaccuracies add up. It's also a reason why you should not compare doubles to exact numbers. I just tested it, and result.ToString() showed 28 (maybe some kind of rounding happens in double.ToString()?). result == 28 returned false though. And (int)result returned 27. So you'll just need to expect imprecisions like that.

Is a double really unsuitable for money?

I always tell in c# a variable of type double is not suitable for money. All weird things could happen. But I can't seem to create an example to demonstrate some of these issues. Can anyone provide such an example?
(edit; this post was originally tagged C#; some replies refer to specific details of decimal, which therefore means System.Decimal).
(edit 2: I was specific asking for some c# code, so I don't think this is language agnostic only)

Very, very unsuitable. Use decimal.
double x = 3.65, y = 0.05, z = 3.7;
Console.WriteLine((x + y) == z); // false
(example from Jon's page here - recommended reading ;-p)

You will get odd errors effectively caused by rounding. In addition, comparisons with exact values are extremely tricky - you usually need to apply some sort of epsilon to check for the actual value being "near" a particular one.
Here's a concrete example:
using System;
class Test
{
static void Main()
{
double x = 0.1;
double y = x + x + x;
Console.WriteLine(y == 0.3); // Prints False
}
}

Yes it's unsuitable.
If I remember correctly double has about 17 significant numbers, so normally rounding errors will take place far behind the decimal point. Most financial software uses 4 decimals behind the decimal point, that leaves 13 decimals to work with so the maximum number you can work with for single operations is still very much higher than the USA national debt. But rounding errors will add up over time. If your software runs for a long time you'll eventually start losing cents. Certain operations will make this worse. For example adding large amounts to small amounts will cause a significant loss of precision.
You need fixed point datatypes for money operations, most people don't mind if you lose a cent here and there but accountants aren't like most people..
edit
According to this site http://msdn.microsoft.com/en-us/library/678hzkk9.aspx Doubles actually have 15 to 16 significant digits instead of 17.
#Jon Skeet decimal is more suitable than double because of its higher precision, 28 or 29 significant decimals. That means less chance of accumulated rounding errors becoming significant. Fixed point datatypes (ie integers that represent cents or 100th of a cent like I've seen used) like Boojum mentions are actually better suited.

Since decimal uses a scaling factor of multiples of 10, numbers like 0.1 can be represented exactly. In essence, the decimal type represents this as 1 / 10 ^ 1, whereas a double would represent this as 104857 / 2 ^ 20 (in reality it would be more like really-big-number / 2 ^ 1023).
A decimal can exactly represent any base 10 value with up to 28/29 significant digits (like 0.1). A double can't.

My understanding is that most financial systems express currency using integers -- i.e., counting everything in cents.
IEEE double precision actually can represent all integers exactly in the range -2^53 through +2^53. (Hacker's Delight, pg. 262) If you use only addition, subtraction and multiplication, and keep everything to integers within this range then you should see no loss of precision. I'd be very wary of division or more complex operations, however.

Using double when you don't know what you are doing is unsuitable.
"double" can represent an amount of a trillion dollars with an error of 1/90th of a cent. So you will get highly precise results. Want to calculate how much it costs to put a man on Mars and get him back alive? double will do just fine.
But with money there are often very specific rules saying that a certain calculation must give a certain result and no other. If you calculate an amount that is very very very close to $98.135 then there will often be a rule that determines whether the result should be $98.14 or $98.13 and you must follow that rule and get the result that is required.
Depending on where you live, using 64 bit integers to represent cents or pennies or kopeks or whatever is the smallest unit in your country will usually work just fine. For example, 64 bit signed integers representing cents can represent values up to 92,223 trillion dollars. 32 bit integers are usually unsuitable.

No a double will always have rounding errors, use "decimal" if you're on .Net...

Actually floating-point double is perfectly well suited to representing amounts of money as long as you pick a suitable unit.
See http://www.idinews.com/moneyRep.html
So is fixed-point long. Either consumes 8 bytes, surely preferable to the 16 consumed by a decimal item.
Whether or not something works (i.e. yields the expected and correct result) is not a matter of either voting or individual preference. A technique either works or it doesn't.