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In Android Studio I had problems with calculating invoice totals because of the way java rounds. I know there are a lot of explanations, but many recommend methods that don't return reliable results.
For example:
1. Math.round((double)4.715 * (double)100 ) / (double)100 = 4.72 (expected 4.72)
2. Math.round((double)4.725 * (double)100 ) / (double)100 = 4.72 (but expected 4.73)
You can't put this code in an app for a client who calculates invoices. Because , in my case for example, the same invoice is calculated in another system and the result is different, meaning 4.72 respectively 4.73
I know that a double can't be represented exactly and the decimals are different than what we see. But we need a method that returns results as we expect.
Another example would be:
1. java.math.BigDecimal.valueOf(4.715).setScale(2,java.math.BigDecimal.ROUND_HALF_UP).doubleValue() = 4.72
2. new java.math.BigDecimal(4.715).setScale(2,java.math.BigDecimal.ROUND_HALF_UP).doubleValue() = 4.71
3. new java.math.BigDecimal( String.valueOf(4.715) ).setScale(2,java.math.BigDecimal.ROUND_HALF_UP).doubleValue() = 4.72
I think all these aspects could be well explained in Java documentation, but they should indicate a certain method for calculating rounds, a reliable method which returns results as we expected. I only wanted to round to 2 decimales.
In conclusion, which I hope will help some of the beginners, I think that the following method would return stable and good results:
java.math.BigDecimal.valueOf(4.715).setScale(2,java.math.BigDecimal.ROUND_HALF_UP).doubleValue() = 4.72
Or, at least, this is my observation after 3+ years of intensive usage of an app (500+ users every working day).
All practical explanations for these above are very welcome, so we can better understand how to avoid unexpected results.
For the BigDecimal examples the javadoc explains the difference.
BigDecimal(double value) ... is the exact decimal representation of the double's binary floating-point value.
Which we can check, by just printing the value.
System.out.println(new BigDecimal(4.715));
#4.714999999999999857891452847979962825775146484375
Which is barely less than 4.715, but enough such that it gets rounded down.
BigDecimal.valueOf(double value) uses the string representation of the double value from Double.toString(double value) which has quite a few rules.
System.out.println(Double.toString(4.715));
#4.715
The safest best is to just use BigDecimal for your calculations. Especially when dealing with arithmetic operations. It isn't clear when the value will switch to needing more decimal places. For example:
double d = 4.11547;
BigDecimal bd = BigDecimal.valueOf(d);
I this case, the string representation of d is 4.11547, so BigDecimal.valueOf returns the value that is written.
BigDecimal s1 = BigDecimal.valueOf(d-3);
BigDecimal s2 = bd.subtract(new BigDecimal(3));
It might be surprising to find s1 and s2 are different since '3' doesn't get rounded.
System.out.println(s1 + ", " + s2);
#1.1154700000000002, 1.11547
So it is best to use the BigDecimal methods for arithmetic too.
It's in the nature of binary floating point data types, like float and double in Java. double actually states this in his name. It has double precision compared to float - but it is not an exact representation of a decimal number.
Just adding some simplified math detail to the existing answer. This might help understand the seemingly strange behavior of Java floating point numbers.
The root cause of the problem is binary vs. decimal representation of numbers. You use decimal representation when you use a floating point literal in your code, e.g. double d = 1.5; or a String value, e.g. String s = "1.5";.
But the JVM uses a binary representation of the number. The mapping for integer numbers is easy (d for decimal, b for binary): 1 = 1b, 2d = 10b, 3d = 11b .... There is no issue with integer numbers. int and long work just the way you would expect. Except for the overflow...
But for floating point numbers things are different: 0.5d = 0.1b, 0.25d = 0.01b, 0.125d = 0.001b.... You are only able to add values for the series 1/2, 1/4, 1/8, 1/16... Now imagine, you want to show 0.1d in binary representation.
You start with 0.0001b = 0.0625d, which is the first binary value that is still less than 0.1d. 0.0375d remaining. You continue, and the next close value is 0.03125d, and so on. You'll acutally never get to exactly 0.1d. All you get is an approximation. You'll get closer and closer.
Consider the following piece of code. It does the approximation with the help of BigDecimal values:
public void approximate0dot1() {
BigDecimal destVal = new BigDecimal("0.1");
BigDecimal curVal = new BigDecimal("0");
BigDecimal inc = new BigDecimal("1");
BigDecimal div = new BigDecimal("2");
for (int step = 0; step < 20; step++) {
BigDecimal probeVal = curVal.add(inc);
int cmp = probeVal.compareTo(destVal);
if (cmp == 0) {
break;
} else if (cmp < 0) {
curVal = probeVal;
System.out.format("Added: %s, current value: %s, remaining: %s\n", inc, curVal, destVal.subtract(curVal));
}
inc = inc.divide(div);
}
System.out.format("Final value: %s\n", curVal);
}
And the output is:
Added: 0.0625, current value: 0.0625, remaining: 0.0375
Added: 0.03125, current value: 0.09375, remaining: 0.00625
Added: 0.00390625, current value: 0.09765625, remaining: 0.00234375
Added: 0.001953125, current value: 0.099609375, remaining: 0.000390625
Added: 0.000244140625, current value: 0.099853515625, remaining: 0.000146484375
Added: 0.0001220703125, current value: 0.0999755859375, remaining: 0.0000244140625
Added: 0.0000152587890625, current value: 0.0999908447265625, remaining: 0.0000091552734375
Added: 0.00000762939453125, current value: 0.09999847412109375, remaining: 0.00000152587890625
Final value: 0.09999847412109375
This is just a basic example to show the underlying issue. Internally, the JVM obviously does some optimization to get the best possible approximation for the available 64-bit precision, e.g.
System.out.println(new BigDecimal(0.1));
// prints 0.1000000000000000055511151231257827021181583404541015625
But this example shows, that there is already a rounding issue with decimal numbers a simple as a constant with the decimal value 0.1.
Some basic tips:
Do not use BigDecimal(double) constructor if you need exact decimal math, use BigDecimal(String) instead. Bad: new BigDecimal(0.1), Good: new BigDecimal("0.1")
Do not mix BigDecimal and floating point arithmetic, e.g. do not extract double value for further calculations like new BigDecimal("0.1").doubleValue();
public class doublePrecision {
public static void main(String[] args) {
double total = 0;
total += 5.6;
total += 5.8;
System.out.println(total);
}
}
The above code prints:
11.399999999999
How would I get this to just print (or be able to use it as) 11.4?
As others have mentioned, you'll probably want to use the BigDecimal class, if you want to have an exact representation of 11.4.
Now, a little explanation into why this is happening:
The float and double primitive types in Java are floating point numbers, where the number is stored as a binary representation of a fraction and a exponent.
More specifically, a double-precision floating point value such as the double type is a 64-bit value, where:
1 bit denotes the sign (positive or negative).
11 bits for the exponent.
52 bits for the significant digits (the fractional part as a binary).
These parts are combined to produce a double representation of a value.
(Source: Wikipedia: Double precision)
For a detailed description of how floating point values are handled in Java, see the Section 4.2.3: Floating-Point Types, Formats, and Values of the Java Language Specification.
The byte, char, int, long types are fixed-point numbers, which are exact representions of numbers. Unlike fixed point numbers, floating point numbers will some times (safe to assume "most of the time") not be able to return an exact representation of a number. This is the reason why you end up with 11.399999999999 as the result of 5.6 + 5.8.
When requiring a value that is exact, such as 1.5 or 150.1005, you'll want to use one of the fixed-point types, which will be able to represent the number exactly.
As has been mentioned several times already, Java has a BigDecimal class which will handle very large numbers and very small numbers.
From the Java API Reference for the BigDecimal class:
Immutable,
arbitrary-precision signed decimal
numbers. A BigDecimal consists of an
arbitrary precision integer unscaled
value and a 32-bit integer scale. If
zero or positive, the scale is the
number of digits to the right of the
decimal point. If negative, the
unscaled value of the number is
multiplied by ten to the power of the
negation of the scale. The value of
the number represented by the
BigDecimal is therefore (unscaledValue
× 10^-scale).
There has been many questions on Stack Overflow relating to the matter of floating point numbers and its precision. Here is a list of related questions that may be of interest:
Why do I see a double variable initialized to some value like 21.4 as 21.399999618530273?
How to print really big numbers in C++
How is floating point stored? When does it matter?
Use Float or Decimal for Accounting Application Dollar Amount?
If you really want to get down to the nitty gritty details of floating point numbers, take a look at What Every Computer Scientist Should Know About Floating-Point Arithmetic.
When you input a double number, for example, 33.33333333333333, the value you get is actually the closest representable double-precision value, which is exactly:
33.3333333333333285963817615993320941925048828125
Dividing that by 100 gives:
0.333333333333333285963817615993320941925048828125
which also isn't representable as a double-precision number, so again it is rounded to the nearest representable value, which is exactly:
0.3333333333333332593184650249895639717578887939453125
When you print this value out, it gets rounded yet again to 17 decimal digits, giving:
0.33333333333333326
If you just want to process values as fractions, you can create a Fraction class which holds a numerator and denominator field.
Write methods for add, subtract, multiply and divide as well as a toDouble method. This way you can avoid floats during calculations.
EDIT: Quick implementation,
public class Fraction {
private int numerator;
private int denominator;
public Fraction(int n, int d){
numerator = n;
denominator = d;
}
public double toDouble(){
return ((double)numerator)/((double)denominator);
}
public static Fraction add(Fraction a, Fraction b){
if(a.denominator != b.denominator){
double aTop = b.denominator * a.numerator;
double bTop = a.denominator * b.numerator;
return new Fraction(aTop + bTop, a.denominator * b.denominator);
}
else{
return new Fraction(a.numerator + b.numerator, a.denominator);
}
}
public static Fraction divide(Fraction a, Fraction b){
return new Fraction(a.numerator * b.denominator, a.denominator * b.numerator);
}
public static Fraction multiply(Fraction a, Fraction b){
return new Fraction(a.numerator * b.numerator, a.denominator * b.denominator);
}
public static Fraction subtract(Fraction a, Fraction b){
if(a.denominator != b.denominator){
double aTop = b.denominator * a.numerator;
double bTop = a.denominator * b.numerator;
return new Fraction(aTop-bTop, a.denominator*b.denominator);
}
else{
return new Fraction(a.numerator - b.numerator, a.denominator);
}
}
}
Observe that you'd have the same problem if you used limited-precision decimal arithmetic, and wanted to deal with 1/3: 0.333333333 * 3 is 0.999999999, not 1.00000000.
Unfortunately, 5.6, 5.8 and 11.4 just aren't round numbers in binary, because they involve fifths. So the float representation of them isn't exact, just as 0.3333 isn't exactly 1/3.
If all the numbers you use are non-recurring decimals, and you want exact results, use BigDecimal. Or as others have said, if your values are like money in the sense that they're all a multiple of 0.01, or 0.001, or something, then multiply everything by a fixed power of 10 and use int or long (addition and subtraction are trivial: watch out for multiplication).
However, if you are happy with binary for the calculation, but you just want to print things out in a slightly friendlier format, try java.util.Formatter or String.format. In the format string specify a precision less than the full precision of a double. To 10 significant figures, say, 11.399999999999 is 11.4, so the result will be almost as accurate and more human-readable in cases where the binary result is very close to a value requiring only a few decimal places.
The precision to specify depends a bit on how much maths you've done with your numbers - in general the more you do, the more error will accumulate, but some algorithms accumulate it much faster than others (they're called "unstable" as opposed to "stable" with respect to rounding errors). If all you're doing is adding a few values, then I'd guess that dropping just one decimal place of precision will sort things out. Experiment.
You may want to look into using java's java.math.BigDecimal class if you really need precision math. Here is a good article from Oracle/Sun on the case for BigDecimal. While you can never represent 1/3 as someone mentioned, you can have the power to decide exactly how precise you want the result to be. setScale() is your friend.. :)
Ok, because I have way too much time on my hands at the moment here is a code example that relates to your question:
import java.math.BigDecimal;
/**
* Created by a wonderful programmer known as:
* Vincent Stoessel
* xaymaca#gmail.com
* on Mar 17, 2010 at 11:05:16 PM
*/
public class BigUp {
public static void main(String[] args) {
BigDecimal first, second, result ;
first = new BigDecimal("33.33333333333333") ;
second = new BigDecimal("100") ;
result = first.divide(second);
System.out.println("result is " + result);
//will print : result is 0.3333333333333333
}
}
and to plug my new favorite language, Groovy, here is a neater example of the same thing:
import java.math.BigDecimal
def first = new BigDecimal("33.33333333333333")
def second = new BigDecimal("100")
println "result is " + first/second // will print: result is 0.33333333333333
Pretty sure you could've made that into a three line example. :)
If you want exact precision, use BigDecimal. Otherwise, you can use ints multiplied by 10 ^ whatever precision you want.
As others have noted, not all decimal values can be represented as binary since decimal is based on powers of 10 and binary is based on powers of two.
If precision matters, use BigDecimal, but if you just want friendly output:
System.out.printf("%.2f\n", total);
Will give you:
11.40
You're running up against the precision limitation of type double.
Java.Math has some arbitrary-precision arithmetic facilities.
You can't, because 7.3 doesn't have a finite representation in binary. The closest you can get is 2054767329987789/2**48 = 7.3+1/1407374883553280.
Take a look at http://docs.python.org/tutorial/floatingpoint.html for a further explanation. (It's on the Python website, but Java and C++ have the same "problem".)
The solution depends on what exactly your problem is:
If it's that you just don't like seeing all those noise digits, then fix your string formatting. Don't display more than 15 significant digits (or 7 for float).
If it's that the inexactness of your numbers is breaking things like "if" statements, then you should write if (abs(x - 7.3) < TOLERANCE) instead of if (x == 7.3).
If you're working with money, then what you probably really want is decimal fixed point. Store an integer number of cents or whatever the smallest unit of your currency is.
(VERY UNLIKELY) If you need more than 53 significant bits (15-16 significant digits) of precision, then use a high-precision floating-point type, like BigDecimal.
private void getRound() {
// this is very simple and interesting
double a = 5, b = 3, c;
c = a / b;
System.out.println(" round val is " + c);
// round val is : 1.6666666666666667
// if you want to only two precision point with double we
// can use formate option in String
// which takes 2 parameters one is formte specifier which
// shows dicimal places another double value
String s = String.format("%.2f", c);
double val = Double.parseDouble(s);
System.out.println(" val is :" + val);
// now out put will be : val is :1.67
}
Use java.math.BigDecimal
Doubles are binary fractions internally, so they sometimes cannot represent decimal fractions to the exact decimal.
/*
0.8 1.2
0.7 1.3
0.7000000000000002 2.3
0.7999999999999998 4.2
*/
double adjust = fToInt + 1.0 - orgV;
// The following two lines works for me.
String s = String.format("%.2f", adjust);
double val = Double.parseDouble(s);
System.out.println(val); // output: 0.8, 0.7, 0.7, 0.8
Doubles are approximations of the decimal numbers in your Java source. You're seeing the consequence of the mismatch between the double (which is a binary-coded value) and your source (which is decimal-coded).
Java's producing the closest binary approximation. You can use the java.text.DecimalFormat to display a better-looking decimal value.
Short answer: Always use BigDecimal and make sure you are using the constructor with String argument, not the double one.
Back to your example, the following code will print 11.4, as you wish.
public class doublePrecision {
public static void main(String[] args) {
BigDecimal total = new BigDecimal("0");
total = total.add(new BigDecimal("5.6"));
total = total.add(new BigDecimal("5.8"));
System.out.println(total);
}
}
Multiply everything by 100 and store it in a long as cents.
Computers store numbers in binary and can't actually represent numbers such as 33.333333333 or 100.0 exactly. This is one of the tricky things about using doubles. You will have to just round the answer before showing it to a user. Luckily in most applications, you don't need that many decimal places anyhow.
Floating point numbers differ from real numbers in that for any given floating point number there is a next higher floating point number. Same as integers. There's no integer between 1 and 2.
There's no way to represent 1/3 as a float. There's a float below it and there's a float above it, and there's a certain distance between them. And 1/3 is in that space.
Apfloat for Java claims to work with arbitrary precision floating point numbers, but I've never used it. Probably worth a look.
http://www.apfloat.org/apfloat_java/
A similar question was asked here before
Java floating point high precision library
Use a BigDecimal. It even lets you specify rounding rules (like ROUND_HALF_EVEN, which will minimize statistical error by rounding to the even neighbor if both are the same distance; i.e. both 1.5 and 2.5 round to 2).
Why not use the round() method from Math class?
// The number of 0s determines how many digits you want after the floating point
// (here one digit)
total = (double)Math.round(total * 10) / 10;
System.out.println(total); // prints 11.4
Check out BigDecimal, it handles problems dealing with floating point arithmetic like that.
The new call would look like this:
term[number].coefficient.add(co);
Use setScale() to set the number of decimal place precision to be used.
If you have no choice other than using double values, can use the below code.
public static double sumDouble(double value1, double value2) {
double sum = 0.0;
String value1Str = Double.toString(value1);
int decimalIndex = value1Str.indexOf(".");
int value1Precision = 0;
if (decimalIndex != -1) {
value1Precision = (value1Str.length() - 1) - decimalIndex;
}
String value2Str = Double.toString(value2);
decimalIndex = value2Str.indexOf(".");
int value2Precision = 0;
if (decimalIndex != -1) {
value2Precision = (value2Str.length() - 1) - decimalIndex;
}
int maxPrecision = value1Precision > value2Precision ? value1Precision : value2Precision;
sum = value1 + value2;
String s = String.format("%." + maxPrecision + "f", sum);
sum = Double.parseDouble(s);
return sum;
}
You can Do the Following!
System.out.println(String.format("%.12f", total));
if you change the decimal value here %.12f
So far I understand it as main goal to get correct double from wrong double.
Look for my solution how to get correct value from "approximate" wrong value - if it is real floating point it rounds last digit - counted from all digits - counting before dot and try to keep max possible digits after dot - hope that it is enough precision for most cases:
public static double roundError(double value) {
BigDecimal valueBigDecimal = new BigDecimal(Double.toString(value));
String valueString = valueBigDecimal.toPlainString();
if (!valueString.contains(".")) return value;
String[] valueArray = valueString.split("[.]");
int places = 16;
places -= valueArray[0].length();
if ("56789".contains("" + valueArray[0].charAt(valueArray[0].length() - 1))) places--;
//System.out.println("Rounding " + value + "(" + valueString + ") to " + places + " places");
return valueBigDecimal.setScale(places, RoundingMode.HALF_UP).doubleValue();
}
I know it is long code, sure not best, maybe someone can fix it to be more elegant. Anyway it is working, see examples:
roundError(5.6+5.8) = 11.399999999999999 = 11.4
roundError(0.4-0.3) = 0.10000000000000003 = 0.1
roundError(37235.137567000005) = 37235.137567
roundError(1/3) 0.3333333333333333 = 0.333333333333333
roundError(3723513756.7000005) = 3.7235137567E9 (3723513756.7)
roundError(3723513756123.7000005) = 3.7235137561237E12 (3723513756123.7)
roundError(372351375612.7000005) = 3.723513756127E11 (372351375612.7)
roundError(1.7976931348623157) = 1.797693134862316
Do not waste your efford using BigDecimal. In 99.99999% cases you don't need it. java double type is of cource approximate but in almost all cases, it is sufficiently precise. Mind that your have an error at 14th significant digit. This is really negligible!
To get nice output use:
System.out.printf("%.2f\n", total);
In my little project, I need to do something like Math.pow(7777.66, 5555.44) only with VERY big numbers. I came across a few solutions:
Use double - but the numbers are too big
Use BigDecimal.pow but no support for fractional
Use the X^(A+B)=X^A*X^B formula (B is the remainder of the second num), but again no support for big X or big A because I still convert to double
Use some kind of Taylor series algorithm or something like that - I'm not very good at math so this one is my last option if I don't find any solutions (some libraries or formula for (A+B)^(C+D)).
Does anyone know of a library or an easy solution? I figured that many people deal with the same problem...
p.s.
I found some library called ApFloat that claims to do it approximately, but the results I got were so approximate that even 8^2 gave me 60...
The solution for arguments under 1.7976931348623157E308 (Double.MAX_VALUE) but supporting results with MILLIONS of digits:
Since double supports numbers up to MAX_VALUE (for example, 100! in double looks like this: 9.332621544394415E157), there is no problem to use BigDecimal.doubleValue(). But you shouldn't just do Math.pow(double, double) because if the result is bigger than MAX_VALUE you will just get infinity. SO: use the formula X^(A+B)=X^A*X^B to separate the calculation to TWO powers, the big, using BigDecimal.pow, and the small (remainder of the 2nd argument), using Math.pow, then multiply. X will be copied to DOUBLE - make sure it's not bigger than MAX_VALUE, A will be INT (maximum 2147483647 but the BigDecimal.pow doesn't support integers more than a billion anyway), and B will be double, always less than 1. This way you can do the following (ignore my private constants etc):
int signOf2 = n2.signum();
try {
// Perform X^(A+B)=X^A*X^B (B = remainder)
double dn1 = n1.doubleValue();
// Compare the same row of digits according to context
if (!CalculatorUtils.isEqual(n1, dn1))
throw new Exception(); // Cannot convert n1 to double
n2 = n2.multiply(new BigDecimal(signOf2)); // n2 is now positive
BigDecimal remainderOf2 = n2.remainder(BigDecimal.ONE);
BigDecimal n2IntPart = n2.subtract(remainderOf2);
// Calculate big part of the power using context -
// bigger range and performance but lower accuracy
BigDecimal intPow = n1.pow(n2IntPart.intValueExact(),
CalculatorConstants.DEFAULT_CONTEXT);
BigDecimal doublePow =
new BigDecimal(Math.pow(dn1, remainderOf2.doubleValue()));
result = intPow.multiply(doublePow);
} catch (Exception e) {
if (e instanceof CalculatorException)
throw (CalculatorException) e;
throw new CalculatorException(
CalculatorConstants.Errors.UNSUPPORTED_NUMBER_ +
"power!");
}
// Fix negative power
if (signOf2 == -1)
result = BigDecimal.ONE.divide(result, CalculatorConstants.BIG_SCALE,
RoundingMode.HALF_UP);
Results examples:
50!^10! = 12.50911317862076252364259*10^233996181
50!^0.06 = 7395.788659356498101260513
The big-math library released under MIT license has a simple static helper BigDecimalMath.log(BigDecimal, MathContext) for log and many other functions not included with BigDecimal. Very simple to use and has lots of benchmarking data to compare performance.
Exponents = logarithms.
Take a look at Logarithm of a BigDecimal
public class doublePrecision {
public static void main(String[] args) {
double total = 0;
total += 5.6;
total += 5.8;
System.out.println(total);
}
}
The above code prints:
11.399999999999
How would I get this to just print (or be able to use it as) 11.4?
As others have mentioned, you'll probably want to use the BigDecimal class, if you want to have an exact representation of 11.4.
Now, a little explanation into why this is happening:
The float and double primitive types in Java are floating point numbers, where the number is stored as a binary representation of a fraction and a exponent.
More specifically, a double-precision floating point value such as the double type is a 64-bit value, where:
1 bit denotes the sign (positive or negative).
11 bits for the exponent.
52 bits for the significant digits (the fractional part as a binary).
These parts are combined to produce a double representation of a value.
(Source: Wikipedia: Double precision)
For a detailed description of how floating point values are handled in Java, see the Section 4.2.3: Floating-Point Types, Formats, and Values of the Java Language Specification.
The byte, char, int, long types are fixed-point numbers, which are exact representions of numbers. Unlike fixed point numbers, floating point numbers will some times (safe to assume "most of the time") not be able to return an exact representation of a number. This is the reason why you end up with 11.399999999999 as the result of 5.6 + 5.8.
When requiring a value that is exact, such as 1.5 or 150.1005, you'll want to use one of the fixed-point types, which will be able to represent the number exactly.
As has been mentioned several times already, Java has a BigDecimal class which will handle very large numbers and very small numbers.
From the Java API Reference for the BigDecimal class:
Immutable,
arbitrary-precision signed decimal
numbers. A BigDecimal consists of an
arbitrary precision integer unscaled
value and a 32-bit integer scale. If
zero or positive, the scale is the
number of digits to the right of the
decimal point. If negative, the
unscaled value of the number is
multiplied by ten to the power of the
negation of the scale. The value of
the number represented by the
BigDecimal is therefore (unscaledValue
× 10^-scale).
There has been many questions on Stack Overflow relating to the matter of floating point numbers and its precision. Here is a list of related questions that may be of interest:
Why do I see a double variable initialized to some value like 21.4 as 21.399999618530273?
How to print really big numbers in C++
How is floating point stored? When does it matter?
Use Float or Decimal for Accounting Application Dollar Amount?
If you really want to get down to the nitty gritty details of floating point numbers, take a look at What Every Computer Scientist Should Know About Floating-Point Arithmetic.
When you input a double number, for example, 33.33333333333333, the value you get is actually the closest representable double-precision value, which is exactly:
33.3333333333333285963817615993320941925048828125
Dividing that by 100 gives:
0.333333333333333285963817615993320941925048828125
which also isn't representable as a double-precision number, so again it is rounded to the nearest representable value, which is exactly:
0.3333333333333332593184650249895639717578887939453125
When you print this value out, it gets rounded yet again to 17 decimal digits, giving:
0.33333333333333326
If you just want to process values as fractions, you can create a Fraction class which holds a numerator and denominator field.
Write methods for add, subtract, multiply and divide as well as a toDouble method. This way you can avoid floats during calculations.
EDIT: Quick implementation,
public class Fraction {
private int numerator;
private int denominator;
public Fraction(int n, int d){
numerator = n;
denominator = d;
}
public double toDouble(){
return ((double)numerator)/((double)denominator);
}
public static Fraction add(Fraction a, Fraction b){
if(a.denominator != b.denominator){
double aTop = b.denominator * a.numerator;
double bTop = a.denominator * b.numerator;
return new Fraction(aTop + bTop, a.denominator * b.denominator);
}
else{
return new Fraction(a.numerator + b.numerator, a.denominator);
}
}
public static Fraction divide(Fraction a, Fraction b){
return new Fraction(a.numerator * b.denominator, a.denominator * b.numerator);
}
public static Fraction multiply(Fraction a, Fraction b){
return new Fraction(a.numerator * b.numerator, a.denominator * b.denominator);
}
public static Fraction subtract(Fraction a, Fraction b){
if(a.denominator != b.denominator){
double aTop = b.denominator * a.numerator;
double bTop = a.denominator * b.numerator;
return new Fraction(aTop-bTop, a.denominator*b.denominator);
}
else{
return new Fraction(a.numerator - b.numerator, a.denominator);
}
}
}
Observe that you'd have the same problem if you used limited-precision decimal arithmetic, and wanted to deal with 1/3: 0.333333333 * 3 is 0.999999999, not 1.00000000.
Unfortunately, 5.6, 5.8 and 11.4 just aren't round numbers in binary, because they involve fifths. So the float representation of them isn't exact, just as 0.3333 isn't exactly 1/3.
If all the numbers you use are non-recurring decimals, and you want exact results, use BigDecimal. Or as others have said, if your values are like money in the sense that they're all a multiple of 0.01, or 0.001, or something, then multiply everything by a fixed power of 10 and use int or long (addition and subtraction are trivial: watch out for multiplication).
However, if you are happy with binary for the calculation, but you just want to print things out in a slightly friendlier format, try java.util.Formatter or String.format. In the format string specify a precision less than the full precision of a double. To 10 significant figures, say, 11.399999999999 is 11.4, so the result will be almost as accurate and more human-readable in cases where the binary result is very close to a value requiring only a few decimal places.
The precision to specify depends a bit on how much maths you've done with your numbers - in general the more you do, the more error will accumulate, but some algorithms accumulate it much faster than others (they're called "unstable" as opposed to "stable" with respect to rounding errors). If all you're doing is adding a few values, then I'd guess that dropping just one decimal place of precision will sort things out. Experiment.
You may want to look into using java's java.math.BigDecimal class if you really need precision math. Here is a good article from Oracle/Sun on the case for BigDecimal. While you can never represent 1/3 as someone mentioned, you can have the power to decide exactly how precise you want the result to be. setScale() is your friend.. :)
Ok, because I have way too much time on my hands at the moment here is a code example that relates to your question:
import java.math.BigDecimal;
/**
* Created by a wonderful programmer known as:
* Vincent Stoessel
* xaymaca#gmail.com
* on Mar 17, 2010 at 11:05:16 PM
*/
public class BigUp {
public static void main(String[] args) {
BigDecimal first, second, result ;
first = new BigDecimal("33.33333333333333") ;
second = new BigDecimal("100") ;
result = first.divide(second);
System.out.println("result is " + result);
//will print : result is 0.3333333333333333
}
}
and to plug my new favorite language, Groovy, here is a neater example of the same thing:
import java.math.BigDecimal
def first = new BigDecimal("33.33333333333333")
def second = new BigDecimal("100")
println "result is " + first/second // will print: result is 0.33333333333333
Pretty sure you could've made that into a three line example. :)
If you want exact precision, use BigDecimal. Otherwise, you can use ints multiplied by 10 ^ whatever precision you want.
As others have noted, not all decimal values can be represented as binary since decimal is based on powers of 10 and binary is based on powers of two.
If precision matters, use BigDecimal, but if you just want friendly output:
System.out.printf("%.2f\n", total);
Will give you:
11.40
You're running up against the precision limitation of type double.
Java.Math has some arbitrary-precision arithmetic facilities.
You can't, because 7.3 doesn't have a finite representation in binary. The closest you can get is 2054767329987789/2**48 = 7.3+1/1407374883553280.
Take a look at http://docs.python.org/tutorial/floatingpoint.html for a further explanation. (It's on the Python website, but Java and C++ have the same "problem".)
The solution depends on what exactly your problem is:
If it's that you just don't like seeing all those noise digits, then fix your string formatting. Don't display more than 15 significant digits (or 7 for float).
If it's that the inexactness of your numbers is breaking things like "if" statements, then you should write if (abs(x - 7.3) < TOLERANCE) instead of if (x == 7.3).
If you're working with money, then what you probably really want is decimal fixed point. Store an integer number of cents or whatever the smallest unit of your currency is.
(VERY UNLIKELY) If you need more than 53 significant bits (15-16 significant digits) of precision, then use a high-precision floating-point type, like BigDecimal.
private void getRound() {
// this is very simple and interesting
double a = 5, b = 3, c;
c = a / b;
System.out.println(" round val is " + c);
// round val is : 1.6666666666666667
// if you want to only two precision point with double we
// can use formate option in String
// which takes 2 parameters one is formte specifier which
// shows dicimal places another double value
String s = String.format("%.2f", c);
double val = Double.parseDouble(s);
System.out.println(" val is :" + val);
// now out put will be : val is :1.67
}
Use java.math.BigDecimal
Doubles are binary fractions internally, so they sometimes cannot represent decimal fractions to the exact decimal.
/*
0.8 1.2
0.7 1.3
0.7000000000000002 2.3
0.7999999999999998 4.2
*/
double adjust = fToInt + 1.0 - orgV;
// The following two lines works for me.
String s = String.format("%.2f", adjust);
double val = Double.parseDouble(s);
System.out.println(val); // output: 0.8, 0.7, 0.7, 0.8
Doubles are approximations of the decimal numbers in your Java source. You're seeing the consequence of the mismatch between the double (which is a binary-coded value) and your source (which is decimal-coded).
Java's producing the closest binary approximation. You can use the java.text.DecimalFormat to display a better-looking decimal value.
Short answer: Always use BigDecimal and make sure you are using the constructor with String argument, not the double one.
Back to your example, the following code will print 11.4, as you wish.
public class doublePrecision {
public static void main(String[] args) {
BigDecimal total = new BigDecimal("0");
total = total.add(new BigDecimal("5.6"));
total = total.add(new BigDecimal("5.8"));
System.out.println(total);
}
}
Multiply everything by 100 and store it in a long as cents.
Computers store numbers in binary and can't actually represent numbers such as 33.333333333 or 100.0 exactly. This is one of the tricky things about using doubles. You will have to just round the answer before showing it to a user. Luckily in most applications, you don't need that many decimal places anyhow.
Floating point numbers differ from real numbers in that for any given floating point number there is a next higher floating point number. Same as integers. There's no integer between 1 and 2.
There's no way to represent 1/3 as a float. There's a float below it and there's a float above it, and there's a certain distance between them. And 1/3 is in that space.
Apfloat for Java claims to work with arbitrary precision floating point numbers, but I've never used it. Probably worth a look.
http://www.apfloat.org/apfloat_java/
A similar question was asked here before
Java floating point high precision library
Use a BigDecimal. It even lets you specify rounding rules (like ROUND_HALF_EVEN, which will minimize statistical error by rounding to the even neighbor if both are the same distance; i.e. both 1.5 and 2.5 round to 2).
Why not use the round() method from Math class?
// The number of 0s determines how many digits you want after the floating point
// (here one digit)
total = (double)Math.round(total * 10) / 10;
System.out.println(total); // prints 11.4
Check out BigDecimal, it handles problems dealing with floating point arithmetic like that.
The new call would look like this:
term[number].coefficient.add(co);
Use setScale() to set the number of decimal place precision to be used.
If you have no choice other than using double values, can use the below code.
public static double sumDouble(double value1, double value2) {
double sum = 0.0;
String value1Str = Double.toString(value1);
int decimalIndex = value1Str.indexOf(".");
int value1Precision = 0;
if (decimalIndex != -1) {
value1Precision = (value1Str.length() - 1) - decimalIndex;
}
String value2Str = Double.toString(value2);
decimalIndex = value2Str.indexOf(".");
int value2Precision = 0;
if (decimalIndex != -1) {
value2Precision = (value2Str.length() - 1) - decimalIndex;
}
int maxPrecision = value1Precision > value2Precision ? value1Precision : value2Precision;
sum = value1 + value2;
String s = String.format("%." + maxPrecision + "f", sum);
sum = Double.parseDouble(s);
return sum;
}
You can Do the Following!
System.out.println(String.format("%.12f", total));
if you change the decimal value here %.12f
So far I understand it as main goal to get correct double from wrong double.
Look for my solution how to get correct value from "approximate" wrong value - if it is real floating point it rounds last digit - counted from all digits - counting before dot and try to keep max possible digits after dot - hope that it is enough precision for most cases:
public static double roundError(double value) {
BigDecimal valueBigDecimal = new BigDecimal(Double.toString(value));
String valueString = valueBigDecimal.toPlainString();
if (!valueString.contains(".")) return value;
String[] valueArray = valueString.split("[.]");
int places = 16;
places -= valueArray[0].length();
if ("56789".contains("" + valueArray[0].charAt(valueArray[0].length() - 1))) places--;
//System.out.println("Rounding " + value + "(" + valueString + ") to " + places + " places");
return valueBigDecimal.setScale(places, RoundingMode.HALF_UP).doubleValue();
}
I know it is long code, sure not best, maybe someone can fix it to be more elegant. Anyway it is working, see examples:
roundError(5.6+5.8) = 11.399999999999999 = 11.4
roundError(0.4-0.3) = 0.10000000000000003 = 0.1
roundError(37235.137567000005) = 37235.137567
roundError(1/3) 0.3333333333333333 = 0.333333333333333
roundError(3723513756.7000005) = 3.7235137567E9 (3723513756.7)
roundError(3723513756123.7000005) = 3.7235137561237E12 (3723513756123.7)
roundError(372351375612.7000005) = 3.723513756127E11 (372351375612.7)
roundError(1.7976931348623157) = 1.797693134862316
Do not waste your efford using BigDecimal. In 99.99999% cases you don't need it. java double type is of cource approximate but in almost all cases, it is sufficiently precise. Mind that your have an error at 14th significant digit. This is really negligible!
To get nice output use:
System.out.printf("%.2f\n", total);
This question already has answers here:
Retain precision with double in Java
(24 answers)
Closed 4 years ago.
Seems like the subtraction is triggering some kind of issue and the resulting value is wrong.
double tempCommission = targetPremium.doubleValue()*rate.doubleValue()/100d;
78.75 = 787.5 * 10.0/100d
double netToCompany = targetPremium.doubleValue() - tempCommission;
708.75 = 787.5 - 78.75
double dCommission = request.getPremium().doubleValue() - netToCompany;
877.8499999999999 = 1586.6 - 708.75
The resulting expected value would be 877.85.
What should be done to ensure the correct calculation?
To control the precision of floating point arithmetic, you should use java.math.BigDecimal. Read The need for BigDecimal by John Zukowski for more information.
Given your example, the last line would be as following using BigDecimal.
import java.math.BigDecimal;
BigDecimal premium = BigDecimal.valueOf("1586.6");
BigDecimal netToCompany = BigDecimal.valueOf("708.75");
BigDecimal commission = premium.subtract(netToCompany);
System.out.println(commission + " = " + premium + " - " + netToCompany);
This results in the following output.
877.85 = 1586.6 - 708.75
As the previous answers stated, this is a consequence of doing floating point arithmetic.
As a previous poster suggested, When you are doing numeric calculations, use java.math.BigDecimal.
However, there is a gotcha to using BigDecimal. When you are converting from the double value to a BigDecimal, you have a choice of using a new BigDecimal(double) constructor or the BigDecimal.valueOf(double) static factory method. Use the static factory method.
The double constructor converts the entire precision of the double to a BigDecimal while the static factory effectively converts it to a String, then converts that to a BigDecimal.
This becomes relevant when you are running into those subtle rounding errors. A number might display as .585, but internally its value is '0.58499999999999996447286321199499070644378662109375'. If you used the BigDecimal constructor, you would get the number that is NOT equal to 0.585, while the static method would give you a value equal to 0.585.
double value = 0.585;
System.out.println(new BigDecimal(value));
System.out.println(BigDecimal.valueOf(value));
on my system gives
0.58499999999999996447286321199499070644378662109375
0.585
Another example:
double d = 0;
for (int i = 1; i <= 10; i++) {
d += 0.1;
}
System.out.println(d); // prints 0.9999999999999999 not 1.0
Use BigDecimal instead.
EDIT:
Also, just to point out this isn't a 'Java' rounding issue. Other languages exhibit
similar (though not necessarily consistent) behaviour. Java at least guarantees consistent behaviour in this regard.
I would modify the example above as follows:
import java.math.BigDecimal;
BigDecimal premium = new BigDecimal("1586.6");
BigDecimal netToCompany = new BigDecimal("708.75");
BigDecimal commission = premium.subtract(netToCompany);
System.out.println(commission + " = " + premium + " - " + netToCompany);
This way you avoid the pitfalls of using string to begin with.
Another alternative:
import java.math.BigDecimal;
BigDecimal premium = BigDecimal.valueOf(158660, 2);
BigDecimal netToCompany = BigDecimal.valueOf(70875, 2);
BigDecimal commission = premium.subtract(netToCompany);
System.out.println(commission + " = " + premium + " - " + netToCompany);
I think these options are better than using doubles. In webapps numbers start out as strings anyways.
Any time you do calculations with doubles, this can happen. This code would give you 877.85:
double answer = Math.round(dCommission * 100000) / 100000.0;
Save the number of cents rather than dollars, and just do the format to dollars when you output it. That way you can use an integer which doesn't suffer from the precision issues.
See responses to this question. Essentially what you are seeing is a natural consequence of using floating point arithmetic.
You could pick some arbitrary precision (significant digits of your inputs?) and round your result to it, if you feel comfortable doing that.
This is a fun issue.
The idea behind Timons reply is you specify an epsilon which represents the smallest precision a legal double can be. If you know in your application that you will never need precision below 0.00000001 then what he suggests is sufficient to get a more precise result very close to the truth. Useful in applications where they know up front their maximum precision (for in instance finance for currency precisions, etc)
However the fundamental problem with trying to round it off is that when you divide by a factor to rescale it you actually introduce another possibility for precision problems. Any manipulation of doubles can introduce imprecision problems with varying frequency. Especially if you're trying to round at a very significant digit (so your operands are < 0) for instance if you run the following with Timons code:
System.out.println(round((1515476.0) * 0.00001) / 0.00001);
Will result in 1499999.9999999998 where the goal here is to round at the units of 500000 (i.e we want 1500000)
In fact the only way to be completely sure you've eliminated the imprecision is to go through a BigDecimal to scale off. e.g.
System.out.println(BigDecimal.valueOf(1515476.0).setScale(-5, RoundingMode.HALF_UP).doubleValue());
Using a mix of the epsilon strategy and the BigDecimal strategy will give you fine control over your precision. The idea being the epsilon gets you very close and then the BigDecimal will eliminate any imprecision caused by rescaling afterwards. Though using BigDecimal will reduce the expected performance of your application.
It has been pointed out to me that the final step of using BigDecimal to rescale it isn't always necessary for some uses cases when you can determine that there's no input value that the final division can reintroduce an error. Currently I don't know how to properly determine this so if anyone knows how then I'd be delighted to hear about it.
So far the most elegant and most efficient way to do that in Java:
double newNum = Math.floor(num * 100 + 0.5) / 100;
Better yet use JScience as BigDecimal is fairly limited (e.g., no sqrt function)
double dCommission = 1586.6 - 708.75;
System.out.println(dCommission);
> 877.8499999999999
Real dCommissionR = Real.valueOf(1586.6 - 708.75);
System.out.println(dCommissionR);
> 877.850000000000
double rounded = Math.rint(toround * 100) / 100;
Although you should not use doubles for precise calculations the following trick helped me if you are rounding the results anyway.
public static int round(Double i) {
return (int) Math.round(i + ((i > 0.0) ? 0.00000001 : -0.00000001));
}
Example:
Double foo = 0.0;
for (int i = 1; i <= 150; i++) {
foo += 0.00010;
}
System.out.println(foo);
System.out.println(Math.round(foo * 100.0) / 100.0);
System.out.println(round(foo*100.0) / 100.0);
Which prints:
0.014999999999999965
0.01
0.02
More info: http://en.wikipedia.org/wiki/Double_precision
It's quite simple.
Use the %.2f operator for output. Problem solved!
For example:
int a = 877.8499999999999;
System.out.printf("Formatted Output is: %.2f", a);
The above code results in a print output of:
877.85
The %.2f operator defines that only TWO decimal places should be used.