I am confused about the behavior of floating point number after seeing the result of following code snippet.
float var1 = 5.4f;
float var2 = 5.5f;
if(var1 == 5.4)
System.out.println("Matched");
else
System.out.println("Oops!!");
if(var2 == 5.5)
System.out.println("Matched");
else
System.out.println("Oops!!");
Output:
Oops!!
Matched
Is this because of decimal number that can't be represent exactly in base 2 binary format?
OR
Is this because of precision as I comparing a float type variable with a double type? If yes then why it works fine for next variable?
Is this because of decimal number that can't be represent exactly in base 2 binary format?
Yes. Basically, 5.4f and 5.4d are not the same, because neither of them are exact representations of 5.4.
5.5f and 5.5d are the same because they're both exact representations of 5.5.
Note that 5.4 is implicitly the same as 5.4d - the default type for a floating point literal is double. Any use of a binary operator with operands of float and double will promote the float to a double and perform the operation on two double values.
It may make things easier to think of it in terms of decimal types. Suppose we had two types, Decimal5 and Decimal10 which are decimal numbers with 5 or 10 significant figures. Then consider "a third" and "a quarter":
A third:
Decimal5: 0.33333
Decimal10: 0.3333333333
A quarter (showing trailing zeroes just for clarity):
Decimal5: 0.25000
Decimal10: 0.2500000000
When comparing the Decimal5 value closest to a third with the Decimal10 value, the Decimal5 value would be converted to a Decimal10 value of 0.3333300000, which doesn't equal 0.3333333333. This is similar to your first example.
When comparing the values for a quarter, however, the Decimal5 value of 0.25000 is converted to 0.2500000000, which is the same as the Decimal10 value we have for a quarter. This is similar to your second example.
Of course the binary floating point types are a bit more complicated than that, with normalization, subnormal numbers etc - but for the purposes of your example, this analogy is close enough.
The difference is that 5.5 can be represented exactly in both float and double - whereas 5.4 can't be represented exactly.
reference http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html
Replace your if condition with:
if(var1 == 5.4f)
System.out.println("Matched");
else
System.out.println("Oops!!");
if(var2 == 5.5f)
System.out.println("Matched");
else
System.out.println("Oops!!");
Then it will print Matched both times. Reason is because without qualifier f in the end Java treats 5.4 as double which cannot be represented accurately in comparison to 5.5.
It's easy to see:
if(var1 == 5.4f)
System.out.println("Matched");
else
System.out.println("Oops!!");
if(var2 == 5.5f)
System.out.println("Matched");
else
System.out.println("Oops!!");
Output:
Matched
Matched
The binary representation of 5.4f and 5.4d is not exactly the same
If you want to compare floating point types, you must use comparing with epsilon. Here you can see all the ways you can use for comparing floating point types.
Related
Is there any possibility to calculate the highest error of the sum or subtraction of two numbers with 7 fractional digits?
For example:
a=#.#######
b=#.#######
a+/-b = #.####### + / - epsilon
a and b are random numbers. I need an equation for an if-case if a or b are equal to zero or equal to 1' <>epsilon. I thougt if I math.ceil 'a' and math.floor b I get the maximal error. But it does not work.
It seems like that the error is everytimes something with 1.#####...E-6. Can it get mathematically proofed?
Talking about IEEE 754:
The possible error (gap) depends on your floating point data type (i.e. float or double) and more importantly also depends on how far away your number is from zero. (nonuniform resolution)
Assuming you want to solve some programming problem:
In general, you deal with this issue by not using a simple equality comparer but define a domain specific epsilon in your comparisons:
// if(a == b)
if(Math.abs(a-b) < epsilon)
{
...
}
Does the following statement holds for any double (Java primitive double precision IEEE-754) except NaN:
Double.parseDouble(String.valueOf(d)) == d
Said otherwise, does parsing a serialized (using String.valueOf()) double value always yields the exact original double?
With the exception of NaN as you've said, yes, that invariant should hold. If not, that's a JDK bug right there.
Double.toString says this in its Javadoc:
How many digits must be printed for the fractional part of m or a? There must be at least one digit to represent the fractional part, and beyond that as many, but only as many, more digits as are needed to uniquely distinguish the argument value from adjacent values of type double. That is, suppose that x is the exact mathematical value represented by the decimal representation produced by this method for a finite nonzero argument d. Then d must be the double value nearest to x; or if two double values are equally close to x, then d must be one of them and the least significant bit of the significand of d must be 0.
To summarize, it returns enough digits to identify this double uniquely, so Double.parseDouble should return the exact same double that was converted to a string.
The code review tool I use complains with the below when I start comparing two float values using equality operator. What is the correct way and how to do it? Is there a helper function (commons-*) out there which I can reuse?
Description
Cannot compare floating-point values using the equals (==) operator
Explanation
Comparing floating-point values by using either the equality (==) or inequality (!=) operators is not always accurate because of rounding errors.
Recommendation
Compare the two float values to see if they are close in value.
float a;
float b;
if(a==b)
{
..
}
IBM has a recommendation for comparing two floats, using division rather than subtraction - this makes it easier to select an epsilon that works for all ranges of input.
if (abs(a/b - 1) < epsilon)
As for the value of epsilon, I would use 5.96e-08 as given in this Wikipedia table, or perhaps 2x that value.
It wants you to compare them to within the amount of accuracy you need. For example if you require that the first 4 decimal digits of your floats are equal, then you would use:
if(-0.00001 <= a-b && a-b <= 0.00001)
{
..
}
Or:
if(Math.abs(a-b) < 0.00001){ ... }
Where you add the desired precision to the difference of the two numbers and compare it to twice the desired precision.
Whatever you think is more readable. I prefer the first one myself as it clearly shows the precision you are allowing on both sides.
a = 5.43421 and b = 5.434205 will pass the comparison
private static final float EPSILON = <very small positive number>;
if (Math.abs(a-b) < EPSILON)
...
As floating point offers you variable but uncontrollable precision (that is, you can't set the precision other than when you choose between using double and float), you have to pick your own fixed precision for comparisons.
Note that this isn't a true equivalence operator any more, as it isn't transitive. You can easily get a equals b and b equals c but a not equals c.
Edit: also note that if a is negative and b is a very large positive number, the subtraction can overflow and the result will be negative infinity, but the test will still work, as the absolute value of negative infinity is positive infinity, which will be bigger than EPSILON.
Use commons-lang
org.apache.commons.lang.math.NumberUtils#compare
Also commons-math (in your situation more appropriate solution):
http://commons.apache.org/math/apidocs/org/apache/commons/math/util/MathUtils.html#equals(double, double)
The float type is an approximate value - there's an exponent portion and a value portion with finite accuracy.
For example:
System.out.println((0.6 / 0.2) == 3); // false
The risk is that a tiny rounding error can make a comparison false, when mathematically it should be true.
The workaround is to compare floats allowing a minor difference to still be "equal":
static float e = 0.00000000000001f;
if (Math.abs(a - b) < e)
Apache commons-math to the rescue: MathUtils.(double x, double y, int maxUlps)
Returns true if both arguments are equal or within the range of allowed error (inclusive). Two float numbers are considered equal if there are (maxUlps - 1) (or fewer) floating point numbers between them, i.e. two adjacent floating point numbers are considered equal.
Here's the actual code form the Commons Math implementation:
private static final int SGN_MASK_FLOAT = 0x80000000;
public static boolean equals(float x, float y, int maxUlps) {
int xInt = Float.floatToIntBits(x);
int yInt = Float.floatToIntBits(y);
if (xInt < 0)
xInt = SGN_MASK_FLOAT - xInt;
if (yInt < 0)
yInt = SGN_MASK_FLOAT - yInt;
final boolean isEqual = Math.abs(xInt - yInt) <= maxUlps;
return isEqual && !Float.isNaN(x) && !Float.isNaN(y);
}
This gives you the number of floats that can be represented between your two values at the current scale, which should work better than an absolute epsilon.
I took a stab at this based on the way java implements == for doubles. It converts to the IEEE 754 long integer form first and then does a bitwise compare. Double also provides the static doubleToLongBits() to get the integer form. Using bit fiddling you can 'round' the mantissa of the double by adding 1/2 (one bit) and truncating.
In keeping with supercat's observation, the function first tries a simple == comparison and only rounds if that fails. Here is what I came up with some (hopefully) helpful comments.
I did some limited testing, but can't say I've tried all edge cases. Also, I did not test performance. It shouldn't be too bad.
I just realized that this is essentially the same solution as the one offered by Dmitri. Perhaps a bit more concise.
static public boolean nearlyEqual(double lhs, double rhs){
// This rounds to the 6th mantissa bit from the end. So the numbers must have the same sign and exponent and the mantissas (as integers)
// need to be within 32 of each other (bottom 5 bits of 52 bits can be different).
// To allow 'n' bits of difference create an additive value of 1<<(n-1) and a mask of 0xffffffffffffffffL<<n
// e.g. 4 bits are: additive: 0x10L = 0x1L << 4 and mask: 0xffffffffffffffe0L = 0xffffffffffffffffL << 5
//int bitsToIgnore = 5;
//long additive = 1L << (bitsToIgnore - 1);
//long mask = ~0x0L << bitsToIgnore;
//return ((Double.doubleToLongBits(lhs)+additive) & mask) == ((Double.doubleToLongBits(rhs)+additive) & mask);
return lhs==rhs?true:((Double.doubleToLongBits(lhs)+0x10L) & 0xffffffffffffffe0L) == ((Double.doubleToLongBits(rhs)+0x10L) & 0xffffffffffffffe0L);
}
The following modification handles the change in sign case where the value is on either side of 0.
return lhs==rhs?true:((Double.doubleToLongBits(lhs)+0x10L) & 0x7fffffffffffffe0L) == ((Double.doubleToLongBits(rhs)+0x10L) & 0x7fffffffffffffe0L);
There are many cases where one wants to regard two floating-point numbers as equal only if they are absolutely equivalent, and a "delta" comparison would be wrong. For example, if f is a pure function), and one knows that q=f(x) and y===x, then one should know that q=f(y) without having to compute it. Unfortunately the == has two defects in this regard.
If one value is positive zero and the other is negative zero, they will compare as equal even though they are not necessarily equivalent. For example if f(d)=1/d, a=0 and b=-1*a, then a==b but f(a)!=f(b).
If either value is a NaN, the comparison will always yield false even if one value was assigned directly from the other.
Although there are many cases where checking floating-point numbers for exact equivalence is right and proper, I'm not sure about any cases where the actual behavior of == should be considered preferable. Arguably, all tests for equivalence should be done via a function that actually tests equivalence (e.g. by comparing bitwise forms).
First, a few things to note:
The "Standard" way to do this is to choose an constant epsilon, but constant epsilons do not work correctly for all number ranges.
If you want to use a constant epsilon sqrt(EPSILON) the square root of the epsilon from float.h is a generally considered a good value. (this comes from an infamous "orange book" who's name escapes me at the moment).
Floating point division is going to be slow, so you probably want to avoid it for comparisons even if it behaves like picking an epsilon that is custom made for the numbers' magnitudes.
What do you really want to do? something like this:
Compare how many representable floating point numbers the values differ by.
This code comes from this really great article by Bruce Dawson. The article has been since updated here. The main difference is the old article breaks the strict-aliasing rule. (casting floating pointers to int pointer, dereferencing, writing, casting back). While the C/C++ purist will quickly point out the flaw, in practice this works, and I consider the code more readable. However, the new article uses unions and C/C++ gets to keep its dignity. For brevity I give the code that breaks strict aliasing below.
// Usable AlmostEqual function
bool AlmostEqual2sComplement(float A, float B, int maxUlps)
{
// Make sure maxUlps is non-negative and small enough that the
// default NAN won't compare as equal to anything.
assert(maxUlps > 0 && maxUlps < 4 * 1024 * 1024);
int aInt = *(int*)&A;
// Make aInt lexicographically ordered as a twos-complement int
if (aInt < 0)
aInt = 0x80000000 - aInt;
// Make bInt lexicographically ordered as a twos-complement int
int bInt = *(int*)&B;
if (bInt < 0)
bInt = 0x80000000 - bInt;
int intDiff = abs(aInt - bInt);
if (intDiff <= maxUlps)
return true;
return false;
}
The basic idea in the code above is to first notice that given the IEEE 754 floating point format, {sign-bit, biased-exponent, mantissa}, that the numbers are lexicographically ordered if interpreted as signed magnitude ints. That is the sign bit becomes the sign bit, the and the exponent always completely outranks the mantissa in defining magnitude of the float and because it comes first in determining the magnitude of the number interpreted as an int.
So, we interpret the bit representation of the floating point number as a signed-magnitude int. We then convert the signed-magnitude ints to a two's complement ints by subtracting them from 0x80000000 if the number is negative. Then we just compare the two values as we would any signed two's complement ints, and seeing how many values they differ by. If this amount is less than the threshold you choose for how many representable floats the values may differ by and still be considered equal, then you say that they are "equal." Note that this method correctly lets "equal" numbers differ by larger values for larger magnitude floats, and by smaller values for smaller magnitude floats.
Is it safe if I use comparision like this (a is int, b and c is float/double):
a == b
b == c
It may hear ridiculous, but in my old programing language, sometimes 1 + 2 == 3 is false (because left side returns 2.99999999999...). And, what about this:
Math.sqrt(b) == Math.sqrt(c)
b / 3 == 10 / 3 //In case b = 10, does it return true?
In general, no it is not safe due to the fact that so many decimal numbers cannot be precisely represented as float or double values. The often stated solution is test if the difference between the numbers is less than some "small" value (often denoted by a greek 'epsilon' character in the maths literature).
However - you need to be a bit careful how you do the test. For instance, if you write:
if (Math.abs(a - b) < 0.000001) {
System.err.println("equal");
}
where a and b are supposed to be "the same", you are testing the absolute error. If you do this, you can get into trouble if a and b are (say_ 1,999,999.99 and 2,000,000.00 respectively. The difference between these two numbers is less than the smallest representable value at that scale for a float, and yet it is much bigger than our chosen epsilon.
Arguably, a better approach is to use the relative error; e.g. coded (defensively) as
if (a == b ||
Math.abs(a - b) / Math.max(Math.abs(a), Math.abs(b)) < 0.000001) {
System.err.println("close enough to be equal");
}
But even this is not the complete answer, because it does not take account of the way that certain calculations cause the errors to build up to unmanageable proportions. Take a look at this Wikipedia link for more details.
The bottom line is that dealing with errors in floating point calculations is a lot more difficult than it appears at first glance.
The other point to note is (as others have explained) integer arithmetic behaves very differently to floating point arithmetic in a couple of respects:
integer division will truncate if the result is not integral
integer addition subtraction and multiplication will overflow.
Both of these happen without any warning, either at compile time or at runtime.
You do need to exercise some care.
1.0 + 2.0 == 3.0
is true because integers are exactly representable.
Math.sqrt(b) == Math.sqrt(c)
if b == c.
b / 3.0 == 10.0 / 3.0
if b == 10.0 which is what I think you meant.
The last two examples compare two different instances of the same calculation. When you have different calculations with non representable numbers then exact equality testing fails.
If you are testing the results of a calculation that is subject to floating point approximation then equality testing should be done up to a tolerance.
Do you have any specific real world examples? I think you will find that it is rare to want to test equality with floating point.
b / 3 != 10 / 3 - if b is a floating point variable like b = 10.0f, so b / 3 is 3.3333, while 10 / 3 is integer division, so is equal to 3.
If b == c, then Math.sqrt(b) == Math.sqrt(c) - this is because the sqrt function returns double anyways.
In general, you shouldn't be comparing doubles/floats for equation, because they are floating point numbers so you might get errors. You almost always want to compare them with a given precision, i.e.:
b - c < 0.000001
The safest way to compare a float/double with something else is actually to use see if their difference is a small number.
e.g.
Math.abs(a - b) < EPS
where EPS can be something like 0.0000001.
In this way you make sure that precision errors do not affect your results.
== comparison is not particularly safe for doubles/floats in basically any language. An epsilon comparison method (where you check that the difference between two floats is reasonably small) is your best bet.
For:
Math.sqrt(b) == Math.sqrt(c)
I'm not sure why you wouldn't just compare b and c, but an epsilon comparison would work here too.
For:
b / 3 == 10 / 3
Since 10/3 = 3 because of integer division, this will not necessarily give the results you're looking for. You could use 10.0 / 3, though i'm still not sure why you wouldn't just compare b and 10 (using the epsilon comparison method in either case).
Float wrapper class's compare method can be used to compare two float values.
Float.compare(float1,float2)==0
It will compare integer bits corresponding to each float object.
In java you can compare a float with another float,and a double with another double.And you will get true when comparing a double with a float when their precision is equal
b is float and 10 is integer then if you compare both with your this critearia then it will give false because...
b = flaot (meance ans 3.33333333333333333333333)
10 is integer so that make sence.
If you want a more complete explanation, here there is a good one: http://download.oracle.com/docs/cd/E19957-01/806-3568/ncg_goldberg.html (but it is a little bit long)
i do the below java print command for this double variable
double test=58.15;
When i do a System.out.println(test); and System.out.println(new Double(test).toString()); It prints as 58.15.
When i do a System.out.println(new BigDecimal(test)) I get the below value
58.14999999999999857891452847979962825775146484375
I am able to understand "test" double variable value is internally stored as 58.1499999. But when i do the below two System.out.println i am getting the output as 58.15 and not 58.1499999.
System.out.println(test);
System.out.println(new Double(test).toString());
It prints the output as 58.15 for the above two.
Is the above System.out.println statements are doing some rounding of the value 58.1499999 and printing it as 58.15?
System.out.println(new BigDecimal("58.15"));
To construct a BigDecimal from a hard-coded constant, you must always use one of constants in the class (ZERO, ONE, or TEN) or one of the string constructors. The reason is that one you put the value in a double, you've already lost precision that can never be regained.
EDIT: polygenelubricants is right. Specifically, you're using Double.toString or equivalent. To quote from there:
How many digits must be printed for
the fractional part of m or a? There
must be at least one digit to
represent the fractional part, and
beyond that as many, but only as many,
more digits as are needed to uniquely
distinguish the argument value from
adjacent values of type double. That
is, suppose that x is the exact
mathematical value represented by the
decimal representation produced by
this method for a finite nonzero
argument d. Then d must be the double
value nearest to x; or if two double
values are equally close to x, then d
must be one of them and the least
significant bit of the significand of
d must be 0.
Yes, println (or more precisely, Double.toString) rounds. For proof, System.out.println(.1D); prints 0.1, which is impossible to represent in binary.
Also, when using BigDecimal, don't use the double constructor, because that would attempt to precisely represent an imprecise value. Use the String constructor instead.
out.println and Double.toString() use the format specified in Double.toString(double).
BigDecimal uses more precision by default, as described in the javadoc, and when you call toString() it outputs all of the characters up to the precision level available to a primitive double since .15 does not have an exact binary representation.