It is common knowledge that one has to be careful when comparing floating point values. Usually, instead of using ==, we use some epsilon or ULP based equality testing.
However, I wonder, are there any cases, when using == is perfectly fine?
Look at this simple snippet, which cases are guaranteed to succeed?
void fn(float a, float b) {
float l1 = a/b;
float l2 = a/b;
if (l1==l1) { } // case a)
if (l1==l2) { } // case b)
if (l1==a/b) { } // case c)
if (l1==5.0f/3.0f) { } // case d)
}
int main() {
fn(5.0f, 3.0f);
}
Note: I've checked this and this, but they don't cover (all of) my cases.
Note2: It seems that I have to add some plus information, so answers can be useful in practice: I'd like to know:
what the C++ standard says
what happens, if a C++ implementation follows IEEE-754
This is the only relevant statement I found in the current draft standard:
The value representation of floating-point types is implementation-defined. [ Note: This document imposes no requirements on the accuracy of floating-point operations; see also [support.limits]. — end note ]
So, does this mean, that even "case a)" is implementation defined? I mean, l1==l1 is definitely a floating-point operation. So, if an implementation is "inaccurate", then could l1==l1 be false?
I think this question is not a duplicate of Is floating-point == ever OK?. That question doesn't address any of the cases I'm asking. Same subject, different question. I'd like to have answers specifically to case a)-d), for which I cannot find answers in the duplicated question.
However, I wonder, are there any cases, when using == is perfectly fine?
Sure there are. One category of examples are usages that involve no computation, e.g. setters that should only execute on changes:
void setRange(float min, float max)
{
if(min == m_fMin && max == m_fMax)
return;
m_fMin = min;
m_fMax = max;
// Do something with min and/or max
emit rangeChanged(min, max);
}
See also Is floating-point == ever OK? and Is floating-point == ever OK?.
Contrived cases may "work". Practical cases may still fail. One additional issue is that often optimisation will cause small variations in the way the calculation is done so that symbolically the results should be equal but numerically they are different. The example above could, theoretically, fail in such a case. Some compilers offer an option to produce more consistent results at a cost to performance. I would advise "always" avoiding the equality of floating point numbers.
Equality of physical measurements, as well as digitally stored floats, is often meaningless. So if your comparing if floats are equal in your code you are probably doing something wrong. You usually want greater than or less that or within a tolerance. Often code can be rewritten so these types of issues are avoided.
Only a) and b) are guaranteed to succeed in any sane implementation (see the legalese below for details), as they compare two values that have been derived in the same way and rounded to float precision. Consequently, both compared values are guaranteed to be identical to the last bit.
Case c) and d) may fail because the computation and subsequent comparison may be carried out with higher precision than float. The different rounding of double should be enough to fail the test.
Note that the cases a) and b) may still fail if infinities or NANs are involved, though.
Legalese
Using the N3242 C++11 working draft of the standard, I find the following:
In the text describing the assignment expression, it is explicitly stated that type conversion takes place, [expr.ass] 3:
If the left operand is not of class type, the expression is implicitly converted (Clause 4) to the cv-unqualified type of the left operand.
Clause 4 refers to the standard conversions [conv], which contain the following on floating point conversions, [conv.double] 1:
A prvalue of floating point type can be converted to a prvalue of another floating point type. If the
source value can be exactly represented in the destination type, the result of the conversion is that exact
representation. If the source value is between two adjacent destination values, the result of the conversion
is an implementation-defined choice of either of those values. Otherwise, the behavior is undefined.
(Emphasis mine.)
So we have the guarantee that the result of the conversion is actually defined, unless we are dealing with values outside the representable range (like float a = 1e300, which is UB).
When people think about "internal floating point representation may be more precise than visible in code", they think about the following sentence in the standard, [expr] 11:
The values of the floating operands and the results of floating expressions may be represented in greater
precision and range than that required by the type; the types are not changed thereby.
Note that this applies to operands and results, not to variables. This is emphasized by the attached footnote 60:
The cast and assignment operators must still perform their specific conversions as described in 5.4, 5.2.9 and 5.17.
(I guess, this is the footnote that Maciej Piechotka meant in the comments - the numbering seems to have changed in the version of the standard he's been using.)
So, when I say float a = some_double_expression;, I have the guarantee that the result of the expression is actually rounded to be representable by a float (invoking UB only if the value is out-of-bounds), and a will refer to that rounded value afterwards.
An implementation could indeed specify that the result of the rounding is random, and thus break the cases a) and b). Sane implementations won't do that, though.
Assuming IEEE 754 semantics, there are definitely some cases where you can do this. Conventional floating point number computations are exact whenever they can be, which for example includes (but is not limited to) all basic operations where the operands and the results are integers.
So if you know for a fact that you don't do anything that would result in something unrepresentable, you are fine. For example
float a = 1.0f;
float b = 1.0f;
float c = 2.0f;
assert(a + b == c); // you can safely expect this to succeed
The situation only really gets bad if you have computations with results that aren't exactly representable (or that involve operations which aren't exact) and you change the order of operations.
Note that the C++ standard itself doesn't guarantee IEEE 754 semantics, but that's what you can expect to be dealing with most of the time.
Case (a) fails if a == b == 0.0. In this case, the operation yields NaN, and by definition (IEEE, not C) NaN ≠ NaN.
Cases (b) and (c) can fail in parallel computation when floating-point round modes (or other computation modes) are changed in the middle of this thread's execution. Seen this one in practice, unfortunately.
Case (d) can be different because the compiler (on some machine) may choose to constant-fold the computation of 5.0f/3.0f and replace it with the constant result (of unspecified precision), whereas a/b must be computed at runtime on the target machine (which might be radically different). In fact, intermediate calculations may be performed in arbitrary precision. I've seen differences on old Intel architectures when intermediate computation was performed in 80-bit floating-point, a format that the language didn't even directly support.
In my humble opinion, you should not rely on the == operator because it has many corner cases. The biggest problem is rounding and extended precision. In case of x86, floating point operations can be done with bigger precision than you can store in variables (if you use coprocessors, IIRC SSE operations use same precision as storage).
This is usually good thing, but this causes problems like:
1./2 != 1./2 because one value is form variable and second is from floating point register. In the simplest cases, it will work, but if you add other floating point operations the compiler could decide to split some variables to the stack, changing their values, thus changing the result of the comparison.
To have 100% certainty you need look at assembly and see what operations was done before on both values. Even the order can change the result in non-trivial cases.
Overall what is point of using ==? You should use algorithms that are stable. This means they work even if values are not equal, but they still give the same results. The only place I know where == could be useful is serializing/deserializing where you know what result you want exactly and you can alter serialization to archive your goal.
Related
I am working on floating point determinism and having already studied so many surprising potential causes of indeterminism, I am starting to get paranoid about copying floats:
Does anything in the C++ standard or in general guarantee me that a float lvalue, after being copied to another float variable or when used as a const-ref or by-value parameter, will always be bitwise equivalent to the original value?
Can anything cause a copied float to be bitwise inquivalent to the original value, such as changing the floating point environment or passing it into a different thread?
Here is some sample code based on what I use to check for equivalence of floating point values in my test-cases, this one will fail because it expects FE_TONEAREST:
#include <cfenv>
#include <cstdint>
// MSVC-specific pragmas for floating point control
#pragma float_control(precise, on)
#pragma float_control(except, on)
#pragma fenv_access(on)
#pragma fp_contract(off)
// May make a copy of the floats
bool compareFloats(float resultValue, float comparisonValue)
{
// I was originally doing a bit-wise comparison here but I was made
// aware in the comments that this might not actually be what I want
// so I only check against the equality of the values here now
// (NaN values etc. have to be handled extra)
bool areEqual = (resultValue == comparisonValue);
// Additional outputs if not equal
// ...
return areEqual;
}
int main()
{
std::fesetround(FE_TOWARDZERO)
float value = 1.f / 10;
float expectedResult = 0x1.99999ap-4;
compareFloats(value, expectedResult);
}
Do I have to be worried that if I pass a float by-value into the comparison function it might come out differently on the other side, even though it is an lvalue?
No there is no such guarantee.
Subnormal, non-normalised floating points, and NaN are all cases where the bit patterns may differ.
I believe that signed negative zero is allowed to become a signed positive zero on assignment, although IEEE754 disallows that.
The C++ standard itself has virtually no guarantees on floating point math because it does not mandate IEEE-754 but leaves it up to the implementation (emphasis mine):
[basic.fundamental/12]
There are three floating-point types: float, double, and long double.
The type double provides at least as much precision as float, and the type long double provides at least as much precision as double.
The set of values of the type float is a subset of the set of values of the type double; the set of values of the type double is a subset of the set of values of the type long double.
The value representation of floating-point types is implementation-defined.
[ Note: This document imposes no requirements on the accuracy of floating-point operations; see also [support.limits]. — end note ]
The C++ code you write is a high-level abstract description of what you want the abstract machine to do, and it is fully in the hands of the compiler what this gets translated to. "Assignments" is an aspect of the C++ standard, and as shown above, the C++ standard does not mandate the behavior of floating point operations. To verify the statement "assignments leave floating point values unchanged" your compiler would have to specify its floating point behavior in terms of the C++ abstract machine, and I've not seen any such documentation (especially not for MSVC).
In other words: Without nailing down the exact compiler, compiler version, compilation flags etc., it is impossible to say for sure what the floating point semantics of a C++ program are (especially regarding the difficult cases like rounding, NaNs or signed zero). Most compilers differentiate between strict IEEE conformance and relaxing some of those restrictions, but even then you are not necessarily guaranteed that the program has the same outputs in non-optimized vs optimized builds due to, say, constant folding, precision of intermediate results and so on.
Point in case: For gcc, even with -O0, your program in question does not compute 1.f / 10 at run-time but at compile-time and thus your rounding mode settings are ignored: https://godbolt.org/z/U8B6bc
You should not be paranoid about copying floats in particular but paranoid of compiler optimizations for floating point in general.
I am aware, that to compare two floating point values one needs to use some epsilon precision, as they are not exact. However, I wonder if there are edge cases, where I don't need that epsilon.
In particular, I would like to know if it is always safe to do something like this:
double foo(double x){
if (x < 0.0) return 0.0;
else return somethingelse(x); // somethingelse(x) != 0.0
}
int main(){
int x = -3.0;
if (foo(x) == 0.0) {
std::cout << "^- is this comparison ok?" << std::endl;
}
}
I know that there are better ways to write foo (e.g. returning a flag in addition), but I wonder if in general is it ok to assign 0.0 to a floating point variable and later compare it to 0.0.
Or more general, does the following comparison yield true always?
double x = 3.3;
double y = 3.3;
if (x == y) { std::cout << "is an epsilon required here?" << std::endl; }
When I tried it, it seems to work, but it might be that one should not rely on that.
Yes, in this example it is perfectly fine to check for == 0.0. This is not because 0.0 is special in any way, but because you only assign a value and compare it afterwards. You could also set it to 3.3 and compare for == 3.3, this would be fine too. You're storing a bit pattern, and comparing for that exact same bit pattern, as long as the values are not promoted to another type for doing the comparison.
However, calculation results that would mathematically equal zero would not always equal 0.0.
This Q/A has evolved to also include cases where different parts of the program are compiled by different compilers. The question does not mention this, my answer applies only when the same compiler is used for all relevant parts.
C++ 11 Standard,
§5.10 Equality operators
6 If both operands are of arithmetic or enumeration type, the usual
arithmetic conversions are performed on both operands; each of the
operators shall yield true if the specified relationship is true and
false if it is false.
The relationship is not defined further, so we have to use the common meaning of "equal".
§2.13.4 Floating literals
1 [...] If the scaled value is in the range of representable values
for its type, the result is the scaled value if representable, else
the larger or smaller representable value nearest the scaled value,
chosen in an implementation-defined manner. [...]
The compiler has to choose between exactly two values when converting a literal, when the value is not representable. If the same value is chosen for the same literal consistently, you are safe to compare values such as 3.3, because == means "equal".
Yes, if you return 0.0 you can compare it to 0.0; 0 is representable exactly as a floating-point value. If you return 3.3 you have to be a much more careful, since 3.3 is not exactly representable, so a conversion from double to float, for example, will produce a different value.
correction: 0 as a floating point value is not unique, but IEEE 754 defines the comparison 0.0==-0.0 to be true (any zero for that matter).
So with 0.0 this works - for every other number it does not. The literal 3.3 in one compilation unit (e.g. a library) and another (e.g. your application) might differ. The standard only requires the compiler to use the same rounding it would use at runtime - but different compilers / compiler settings might use different rounding.
It will work most of the time (for 0), but is very bad practice.
As long as you are using the same compiler with the same settings (e.g. one compilation unit) it will work because the literal 0.0 or 0.0f will translate to the same bit pattern every time. The representation of zero is not unique though. So if foo is declared in a library and your call to it in some application the same function might fail.
You can rescue this very case by using std::fpclassify to check whether the returned value represents a zero. For every finite (non-zero) value you will have to use an epsilon-comparison though unless you stay within one compilation unit and perform no operations on the values.
As written in both cases you are using identical constants in the same file fed to the same compiler. The string to float conversion the compiler uses should return the same bit pattern so these should not only be equal as in a plus or minus cases for zero thing but equal bit by bit.
Were you to have a constant which uses the operating systems C library to generate the bit pattern then have a string to f or something that can possibly use a different C library if the binary is transported to another computer than the one compiled on. You might have a problem.
Certainly if you compute 3.3 for one of the terms, runtime, and have the other 3.3 computed compile time again you can and will get failures on the equal comparisons. Some constants obviously are more likely to work than others.
Of course as written your 3.3 comparison is dead code and the compiler just removes it if optimizations are enabled.
You didnt specify the floating point format nor standard if any for that format you were interested in. Some formats have the +/- zero problem, some dont for example.
It is a common misconception that floating point values are "not exact". In fact each of them is perfectly exact (except, may be, some special cases as -0.0 or Inf) and equal to s·2e – (p – 1), where s, e, and p are significand, exponent, and precision correspondingly, each of them integer. E.g. in IEEE 754-2008 binary32 format (aka float32) p = 24 and 1 is represented as 0x800000·20 – 23. There are two things that are really not exact when you deal with floating point values:
Representation of a real value using a FP one. Obviously, not all real numbers can be represented using a given FP format, so they have to be somehow rounded. There are several rounding modes, but the most commonly used is the "Round to nearest, ties to even". If you always use the same rounding mode, which is almost certainly the case, the same real value is always represented with the same FP one. So you can be sure that if two real values are equal, their FP counterparts are exactly equal too (but not the reverse, obviously).
Operations with FP numbers are (mostly) inexact. So if you have some real-value function φ(ξ) implemented in the computer as a function of a FP argument f(x), and you want to compare its result with some "true" value y, you need to use some ε in comparison, because it is very hard (sometimes even impossible) to white a function giving exactly y. And the value of ε strongly depends on the nature of the FP operations involved, so in each particular case there may be different optimal value.
For more details see D. Goldberg. What Every Computer Scientist Should Know About Floating-Point Arithmetic, and J.-M. Muller et al. Handbook of Floating-Point Arithmetic. Both texts you can find in the Internet.
Say I have the following:
int i = 23;
float f = 3.14;
if (i == f) // do something
i will be promoted to a float and the two float numbers will be compared, but can a float represent all int values? Why not promote both the int and the float to a double?
When int is promoted to unsigned in the integral promotions, negative values are also lost (which leads to such fun as 0u < -1 being true).
Like most mechanisms in C (that are inherited in C++), the usual arithmetic conversions should be understood in terms of hardware operations. The makers of C were very familiar with the assembly language of the machines with which they worked, and they wrote C to make immediate sense to themselves and people like themselves when writing things that would until then have been written in assembly (such as the UNIX kernel).
Now, processors, as a rule, do not have mixed-type instructions (add float to double, compare int to float, etc.) because it would be a huge waste of real estate on the wafer -- you'd have to implement as many times more opcodes as you want to support different types. That you only have instructions for "add int to int," "compare float to float", "multiply unsigned with unsigned" etc. makes the usual arithmetic conversions necessary in the first place -- they are a mapping of two types to the instruction family that makes most sense to use with them.
From the point of view of someone who's used to writing low-level machine code, if you have mixed types, the assembler instructions you're most likely to consider in the general case are those that require the least conversions. This is particularly the case with floating points, where conversions are runtime-expensive, and particularly back in the early 1970s, when C was developed, computers were slow, and when floating point calculations were done in software. This shows in the usual arithmetic conversions -- only one operand is ever converted (with the single exception of long/unsigned int, where the long may be converted to unsigned long, which does not require anything to be done on most machines. Perhaps not on any where the exception applies).
So, the usual arithmetic conversions are written to do what an assembly coder would do most of the time: you have two types that don't fit, convert one to the other so that it does. This is what you'd do in assembler code unless you had a specific reason to do otherwise, and to people who are used to writing assembler code and do have a specific reason to force a different conversion, explicitly requesting that conversion is natural. After all, you can simply write
if((double) i < (double) f)
It is interesting to note in this context, by the way, that unsigned is higher in the hierarchy than int, so that comparing int with unsigned will end in an unsigned comparison (hence the 0u < -1 bit from the beginning). I suspect this to be an indicator that people in olden times considered unsigned less as a restriction on int than as an extension of its value range: We don't need the sign right now, so let's use the extra bit for a larger value range. You'd use it if you had reason to expect that an int would overflow -- a much bigger worry in a world of 16-bit ints.
Even double may not be able to represent all int values, depending on how much bits does int contain.
Why not promote both the int and the float to a double?
Probably because it's more costly to convert both types to double than use one of the operands, which is already a float, as float. It would also introduce special rules for comparison operators incompatible with rules for arithmetic operators.
There's also no guarantee how floating point types will be represented, so it would be a blind shot to assume that converting int to double (or even long double) for comparison will solve anything.
The type promotion rules are designed to be simple and to work in a predictable manner. The types in C/C++ are naturally "sorted" by the range of values they can represent. See this for details. Although floating point types cannot represent all integers represented by integral types because they can't represent the same number of significant digits, they might be able to represent a wider range.
To have predictable behavior, when requiring type promotions, the numeric types are always converted to the type with the larger range to avoid overflow in the smaller one. Imagine this:
int i = 23464364; // more digits than float can represent!
float f = 123.4212E36f; // larger range than int can represent!
if (i == f) { /* do something */ }
If the conversion was done towards the integral type, the float f would certainly overflow when converted to int, leading to undefined behavior. On the other hand, converting i to f only causes a loss of precision which is irrelevant since f has the same precision so it's still possible that the comparison succeeds. It's up to the programmer at that point to interpret the result of the comparison according to the application requirements.
Finally, besides the fact that double precision floating point numbers suffer from the same problem representing integers (limited number of significant digits), using promotion on both types would lead to having a higher precision representation for i, while f is doomed to have the original precision, so the comparison will not succeed if i has a more significant digits than f to begin with. Now that is also undefined behavior: the comparison might succeed for some couples (i,f) but not for others.
can a float represent all int values?
For a typical modern system where both int and float are stored in 32 bits, no. Something's gotta give. 32 bits' worth of integers doesn't map 1-to-1 onto a same-sized set that includes fractions.
The i will be promoted to a float and the two float numbers will be compared…
Not necessarily. You don't really know what precision will apply. C++14 §5/12:
The values of the floating operands and the results of floating expressions may be represented in greater precision and range than that required by the type; the types are not changed thereby.
Although i after promotion has nominal type float, the value may be represented using double hardware. C++ doesn't guarantee floating-point precision loss or overflow. (This is not new in C++14; it's inherited from C since olden days.)
Why not promote both the int and the float to a double?
If you want optimal precision everywhere, use double instead and you'll never see a float. Or long double, but that might run slower. The rules are designed to be relatively sensible for the majority of use-cases of limited-precision types, considering that one machine may offer several alternative precisions.
Most of the time, fast and loose is good enough, so the machine is free to do whatever is easiest. That might mean a rounded, single-precision comparison, or double precision and no rounding.
But, such rules are ultimately compromises, and sometimes they fail. To precisely specify arithmetic in C++ (or C), it helps to make conversions and promotions explicit. Many style guides for extra-reliable software prohibit using implicit conversions altogether, and most compilers offer warnings to help you expunge them.
To learn about how these compromises came about, you can peruse the C rationale document. (The latest edition covers up to C99.) It is not just senseless baggage from the days of the PDP-11 or K&R.
It is fascinating that a number of answers here argue from the origin of the C language, explicitly naming K&R and historical baggage as the reason that an int is converted to a float when combined with a float.
This is pointing the blame to the wrong parties. In K&R C, there was no such thing as a float calculation. All floating point operations were done in double precision. For that reason, an integer (or anything else) was never implicitly converted to a float, but only to a double. A float also could not be the type of a function argument: you had to pass a pointer to float if you really, really, really wanted to avoid conversion into a double. For that reason, the functions
int x(float a)
{ ... }
and
int y(a)
float a;
{ ... }
have different calling conventions. The first gets a float argument, the second (by now no longer permissable as syntax) gets a double argument.
Single-precision floating point arithmetic and function arguments were only introduced with ANSI C. Kernighan/Ritchie is innocent.
Now with the newly available single float expressions (single float previously was only a storage format), there also had to be new type conversions. Whatever the ANSI C team picked here (and I would be at a loss for a better choice) is not the fault of K&R.
Q1: Can a float represent all int values?
IEE754 can represent all integers exactly as floats, up to about 223, as mentioned in this answer.
Q2: Why not promote both the int and the float to a double?
The rules in the Standard for these conversions are slight modifications of those in K&R: the modifications accommodate the added types and the value preserving rules. Explicit license was added to perform calculations in a “wider” type than absolutely necessary, since this can sometimes produce smaller and faster code, not to mention the correct answer more often. Calculations can also be performed in a “narrower” type by the as if rule so long as the same end result is obtained. Explicit casting can always be used to obtain a value in a desired type.
Source
Performing calculations in a wider type means that given float f1; and float f2;, f1 + f2 might be calculated in double precision. And it means that given int i; and float f;, i == f might be calculated in double precision. But it isn't required to calculate i == f in double precision, as hvd stated in the comment.
Also C standard says so. These are known as the usual arithmetic conversions . The following description is taken straight from the ANSI C standard.
...if either operand has type float , the other operand is converted to type float .
Source and you can see it in the ref too.
A relevant link is this answer. A more analytic source is here.
Here is another way to explain this: The usual arithmetic conversions are implicitly performed to cast their values in a common type. Compiler first performs integer promotion, if operands still have different types then they are converted to the type that appears highest in the following hierarchy:
Source.
When a programming language is created some decisions are made intuitively.
For instance why not convert int+float to int+int instead of float+float or double+double? Why call int->float a promotion if it holds the same about of bits? Why not call float->int a promotion?
If you rely on implicit type conversions you should know how they work, otherwise just convert manually.
Some language could have been designed without any automatic type conversions at all. And not every decision during a design phase could have been made logically with a good reason.
JavaScript with it's duck typing has even more obscure decisions under the hood. Designing an absolutely logical language is impossible, I think it goes to Godel incompleteness theorem. You have to balance logic, intuition, practice and ideals.
The question is why: Because it is fast, easy to explain, easy to compile, and these were all very important reasons at the time when the C language was developed.
You could have had a different rule: That for every comparison of arithmetic values, the result is that of comparing the actual numerical values. That would be somewhere between trivial if one of the expressions compared is a constant, one additional instruction when comparing signed and unsigned int, and quite difficult if you compare long long and double and want correct results when the long long cannot be represented as double. (0u < -1 would be false, because it would compare the numerical values 0 and -1 without considering their types).
In Swift, the problem is solved easily by disallowing operations between different types.
The rules are written for 16 bit ints (smallest required size). Your compiler with 32 bit ints surely converts both sides to double. There are no float registers in modern hardware anyway so it has to convert to double. Now if you have 64 bit ints I'm not too sure what it does. long double would be appropriate (normally 80 bits but it's not even standard).
I would like to know if I can assume that the value of a float will not change if I just pass it around functions, without any further calculations. I would like to write some tests for that kind of functions using hardcoded values.
Example :
float identity(float f) { return f; }
Can I write the following test :
TEST() {
EXPECT(identity(1.8f) == 1.8f);
}
In general the C++ standard doesn't make guarantees if it's known that the guarantee will lead to sub-optimal code for some processor architecture.
The legacy x86 floating point processing uses 80-bit registers for calculations. The mere act of moving a value from one of those registers into 64 bits of memory causes rounding to occur.
If you're not performing any lossy operation and just passing the floating point data around it should be safe to assume (assuming there are no interferences or optimization bugs) that the values will remain the same. Just make sure you're not comparing floating point values with literal values interpreted as double (EXPECT(indentity(1.8f) == 1.8);) or vice-versa.
/paranoid_level on
However you should always check your target architecture behavior with floating point numbers, especially with respect to the IEEE 754 standard: on a system which allows IEEE 754 exceptions under specific circumstances (e.g. -ftz flags often used in GPUs) you might end up having results inconsistent with your expectations (possibly when combining smart compiler optimizations) since results might be handled internally in a different manner. An example is an architecture which applies to any floating point operation a denormals-are-zero (-daz) policy.
That is perfectly fine and the value will be identical.
As pointed out in the comments, if you were to write:
TEST() {
EXPECT(indentity(1.8) == 1.8f);
EXPECT(indentity(1.8l) == 1.8f);
}
you could end up having problems, due to implicit conversions from int/double.
however if you compare floats with floats, you're perfectly safe.
reference
When comparing doubles for equality, we need to give a tolerance level, because floating-point computation might introduce errors. For example:
double x;
double y;
x = f();
y = g();
if (fabs(x-y)<epsilon) {
// they are equal!
} else {
// they are not!
}
However, if I simply assign a constant value, without any computation, do I still need to check the epsilon?
double x = 1;
double y = 1;
if (x==y) {
// they are equal!
} else {
// no they are not!
}
Is == comparison good enough? Or I need to do fabs(x-y)<epsilon again? Is it possible to introduce error in assigning? Am I too paranoid?
How about casting (double x = static_cast<double>(100))? Is that gonna introduce floating-point error as well?
I am using C++ on Linux, but if it differs by language, I would like to understand that as well.
Actually, it depends on the value and the implementation. The C++ standard (draft n3126) has this to say in 2.14.4 Floating literals:
If the scaled value is in the range of representable values for its type, the result is the scaled value if representable, else the larger or smaller representable value nearest the scaled value, chosen in an implementation-defined manner.
In other words, if the value is exactly representable (and 1 is, in IEEE754, as is 100 in your static cast), you get the value. Otherwise (such as with 0.1) you get an implementation-defined close match (a). Now I'd be very worried about an implementation that chose a different close match based on the same input token but it is possible.
(a) Actually, that paragraph can be read in two ways, either the implementation is free to choose either the closest higher or closest lower value regardless of which is actually the closest, or it must choose the closest to the desired value.
If the latter, it doesn't change this answer however since all you have to do is hardcode a floating point value exactly at the midpoint of two representable types and the implementation is once again free to choose either.
For example, it might alternate between the next higher and next lower for the same reason banker's rounding is applied - to reduce the cumulative errors.
No if you assign literals they should be the same :)
Also if you start with the same value and do the same operations, they should be the same.
Floating point values are non-exact, but the operations should produce consistent results :)
Both cases are ultimately subject to implementation defined representations.
Storage of floating point values and their representations take on may forms - load by address or constant? optimized out by fast math? what is the register width? is it stored in an SSE register? Many variations exist.
If you need precise behavior and portability, do not rely on this implementation defined behavior.
IEEE-754, which is a standard common implementations of floating point numbers abide to, requires floating-point operations to produce a result that is the nearest representable value to an infinitely-precise result. Thus the only imprecision that you will face is rounding after each operation you perform, as well as propagation of rounding errors from the operations performed earlier in the chain. Floats are not per se inexact. And by the way, epsilon can and should be computed, you can consult any numerics book on that.
Floating point numbers can represent integers precisely up to the length of their mantissa. So for example if you cast from an int to a double, it will always be exact, but for casting into into a float, it will no longer be exact for very large integers.
There is one major example of extensive usage of floating point numbers as a substitute for integers, it's the LUA scripting language, which has no integer built-in type, and floating-point numbers are used extensively for logic and flow control etc. The performance and storage penalty from using floating-point numbers turns out to be smaller than the penalty of resolving multiple types at run time and makes the implementation lighter. LUA has been extensively used not only on PC, but also on game consoles.
Now, many compilers have an optional switch that disables IEEE-754 compatibility. Then compromises are made. Denormalized numbers (very very small numbers where the exponent has reached smallest possible value) are often treated as zero, and approximations in implementation of power, logarithm, sqrt, and 1/(x^2) can be made, but addition/subtraction, comparison and multiplication should retain their properties for numbers which can be exactly represented.
The easy answer: For constants == is ok.
There are two exceptions which you should be aware of:
First exception:
0.0 == -0.0
There is a negative zero which compares equal for the IEEE 754 standard. This means
1/INFINITY == 1/-INFINITY which breaks f(x) == f(y) => x == y
Second exception:
NaN != NaN
This is a special caveat of NotaNumber which allows to find out if a number is a NaN
on systems which do not have a test function available (Yes, that happens).