I've recently come across some code which has a loop of the form
for (int i = 0; i < 1e7; i++){
}
I question the wisdom of doing this since 1e7 is a floating point type, and will cause i to be promoted when evaluating the stopping condition. Should this be of cause for concern?
The elephant in the room here is that the range of an int could be as small as -32767 to +32767, and the behaviour on assigning a larger value than this to such an int is undefined.
But, as for your main point, indeed it should concern you as it is a very bad habit. Things could go wrong as yes, 1e7 is a floating point double type.
The fact that i will be converted to a floating point due to type promotion rules is somewhat moot: the real damage is done if there is unexpected truncation of the apparent integral literal. By the way of a "proof by example", consider first the loop
for (std::uint64_t i = std::numeric_limits<std::uint64_t>::max() - 1024; i ++< 18446744073709551615ULL; ){
std::cout << i << "\n";
}
This outputs every consecutive value of i in the range, as you'd expect. Note that std::numeric_limits<std::uint64_t>::max() is 18446744073709551615ULL, which is 1 less than the 64th power of 2. (Here I'm using a slide-like "operator" ++< which is useful when working with unsigned types. Many folk consider --> and ++< as obfuscating but in scientific programming they are common, particularly -->.)
Now on my machine, a double is an IEEE754 64 bit floating point. (Such as scheme is particularly good at representing powers of 2 exactly - IEEE754 can represent powers of 2 up to 1022 exactly.) So 18,446,744,073,709,551,616 (the 64th power of 2) can be represented exactly as a double. The nearest representable number before that is 18,446,744,073,709,550,592 (which is 1024 less).
So now let's write the loop as
for (std::uint64_t i = std::numeric_limits<std::uint64_t>::max() - 1024; i ++< 1.8446744073709551615e19; ){
std::cout << i << "\n";
}
On my machine that will only output one value of i: 18,446,744,073,709,550,592 (the number that we've already seen). This proves that 1.8446744073709551615e19 is a floating point type. If the compiler was allowed to treat the literal as an integral type then the output of the two loops would be equivalent.
It will work, assuming that your int is at least 32 bits.
However, if you really want to use exponential notation, you should better define an integer constant outside the loop and use proper casting, like this:
const int MAX_INDEX = static_cast<int>(1.0e7);
...
for (int i = 0; i < MAX_INDEX; i++) {
...
}
Considering this, I'd say it is much better to write
const int MAX_INDEX = 10000000;
or if you can use C++14
const int MAX_INDEX = 10'000'000;
1e7 is a literal of type double, and usually double is 64-bit IEEE 754 format with a 52-bit mantissa. Roughly every tenth power of 2 corresponds to a third power of 10, so double should be able to represent integers up to at least 105*3 = 1015, exactly. And if int is 32-bit then int has roughly 103*3 = 109 as max value (asking Google search it says that "2**31 - 1" = 2 147 483 647, i.e. twice the rough estimate).
So, in practice it's safe on current desktop systems and larger.
But C++ allows int to be just 16 bits, and on e.g. an embedded system with that small int, one would have Undefined Behavior.
If the intention to loop for a exact integer number of iterations, for example if iterating over exactly all the elements in an array then comparing against a floating point value is maybe not such a good idea, solely for accuracy reasons; since the implicit cast of an integer to float will truncate integers toward zero there's no real danger of out-of-bounds access, it will just abort the loop short.
Now the question is: When do these effects actually kick in? Will your program experience them? The floating point representation usually used these days is IEEE 754. As long as the exponent is 0 a floating point value is essentially an integer. C double precision floats 52 bits for the mantissa, which gives you integer precision to a value of up to 2^52, which is in the order of about 1e15. Without specifying with a suffix f that you want a floating point literal to be interpreted single precision the literal will be double precision and the implicit conversion will target that as well. So as long as your loop end condition is less 2^52 it will work reliably!
Now one question you have to think about on the x86 architecture is efficiency. The very first 80x87 FPUs came in a different package, and later a different chip and as aresult getting values into the FPU registers is a bit awkward on the x86 assembly level. Depending on what your intentions are it might make the difference in runtime for a realtime application; but that's premature optimization.
TL;DR: Is it safe to to? Most certainly yes. Will it cause trouble? It could cause numerical problems. Could it invoke undefined behavior? Depends on how you use the loop end condition, but if i is used to index an array and for some reason the array length ended up in a floating point variable always truncating toward zero it's not going to cause a logical problem. Is it a smart thing to do? Depends on the application.
Related
I need to get a random value, after lots of operations. I see, that if I write, e.g. 1000000 and divide it to 10 for 100 times, I should get an almost random number.
double nump = 1000000000;
cout.precision(45);
for (int i = 1; i <= 100; i++) {
nump = nump / 10;
}
cout << nump;
But if I launch this code, I get every time the similar numbers. Where is inaccuracy which have machines? Why is this so accuracy? How to make such a calculation, that will lead to big inaccuracy?
You are misunderstanding what floating point accuracy means. It means that the value stored in a floating point variable (float or double) is not necessary exactly the same value as mathematically assigned/computed. This happens because not all mathematical real numbers can be represented in a floating point type.
It does not mean you will get different results when you perform the same instructions on the same values on the same build on the same machine.
For instance the integer value 16,777,217 cannot be represented in IEE 754 float. So in
float a = 16'777'217;
a will be "inaccurate" in the sense that it is not 16'777'217, but it will always have the same "inaccuracy".
I need to get a random value
Then use the C++11 random library.
There is no inaccuracy and therefore no randomness.
Just like the integers are an approximation for real numbers, the same can be said for floating point numbers. The distribution of floating point numbers across the reals is even in logarithmic space in the same way that the distribution of integers is even in real space.
When you write a number like 1.234 and assign that to a double, the closest double to 1.234 is picked. This is different to assigning that to an int where the nearest int towards 0 is picked. But the principle is the same.
When you compute nump / 10;, floating point standards (e.g. IEEE754) often require that the closest double to the result is picked.
If you need pseudo-random numbers then use appropriate functions that are part of the C++ standard library.
If you want true random numbers, and are very rich, then you can acquire some hardware for generating them, else you can use someone else's hardware: e.g. https://qrng.anu.edu.au/
I have a function doing some mathematical computation and returning a double. It ends up with different results under Windows and Android due to std::exp implementation beging different (Why do I get platform-specific result for std::exp?). The e-17 rounding difference gets propagated and in the end it's not just a rounding difference that I get (results can change 2.36 to 2.47 in the end). As I compare the result to some expected values, I want this function to return the same result on all platform.
So I need to round my result. The simpliest solution to do this is apparently (as far as I could find on the web) to do std::ceil(d*std::pow<double>(10,precision))/std::pow<double>(10,precision). However, I feel like this could still end up with different results depending on the platform (and moreover, it's hard to decide what precision should be).
I was wondering if hard-coding the least significant byte of the double could be a good rounding strategy.
This quick test seems to show that "yes":
#include <iostream>
#include <iomanip>
double roundByCast( double d )
{
double rounded = d;
unsigned char* temp = (unsigned char*) &rounded;
// changing least significant byte to be always the same
temp[0] = 128;
return rounded;
}
void showRoundInfo( double d, double rounded )
{
double diff = std::abs(d-rounded);
std::cout << "cast: " << d << " rounded to " << rounded << " (diff=" << diff << ")" << std::endl;
}
void roundIt( double d )
{
showRoundInfo( d, roundByCast(d) );
}
int main( int argc, char* argv[] )
{
roundIt( 7.87234042553191493141184764681 );
roundIt( 0.000000000000000000000184764681 );
roundIt( 78723404.2553191493141184764681 );
}
This outputs:
cast: 7.87234 rounded to 7.87234 (diff=2.66454e-14)
cast: 1.84765e-22 rounded to 1.84765e-22 (diff=9.87415e-37)
cast: 7.87234e+07 rounded to 7.87234e+07 (diff=4.47035e-07)
My question is:
Is unsigned char* temp = (unsigned char*) &rounded safe or is there an undefined behaviour here, and why?
If there is no UB (or if there is a better way to do this without UB), is such a round function safe and accurate for all input?
Note: I know floating point numbers are inaccurate. Please don't mark as duplicate of Is floating point math broken? or Why Are Floating Point Numbers Inaccurate?. I understand why results are different, I'm just looking for a way to make them be identical on all targetted platforms.
Edit, I may reformulate my question as people are asking why I have different values and why I want them to be the same.
Let's say you get a double from a computation that could end up with a different value due to platform specific implementations (like std::exp). If you want to fix those different double to end up having the exact same memory representation (1) on all platforms, and you want to loose the fewest precision as possible, then, is fixing the least significant byte a good approach? (because I feel that rounding to an arbitrary given precision is likely to loose more information than this trick).
(1) By "same representation", I mean that if you transform it to a std::bitset, you want to see the same bits sequence for all platform.
No, rounding is not a strategy for removing small errors, or guaranteeing agreement with calculations performed with errors.
For any slicing of the number line into ranges, you will successfully eliminate most slight deviations (by placing them in the same bucket and clamping to the same value), but you greatly increase the deviation if your original pair of values straddle a boundary.
In your particular case of hardcoding the least significant byte, the very near values
0x1.mmmmmmm100
and
0x1.mmmmmmm0ff
have a deviation of only one ULP... but after your rounding, they differ by 256 ULP. Oops!
Is unsigned char* temp = (unsigned char*) &rounded safe or is there an undefined behaviour here, and why?
It is well defined, as aliasing through unsigned char is allowed.
is such a round function safe and accurate for all input?
No. You cannot perfectly fix this problem with truncating/rounding. Consider, that one implementation gives 0x.....0ff, and the other 0x.....100. Setting the lsb to 0x00 will make the original 1 ulp difference to 256 ulps.
No rounding algorithm can fix this.
You have two options:
don't use floating point, use some other way (for example, fixed point)
embed a floating point library into your application, which only uses basic floating point arithmetic (+, -, *, /, sqrt), and don't use -ffast-math, or any equivalent option. This way, if you're on a IEEE-754 compatible platform, floating point results should be the same, as IEEE-754 mandates that basic operations should be calculated "perfectly". It means as if the operation calculated at infinite precision, and then rounded to the resulting representation.
Btw, if an input 1e-17 difference means a huge output difference, then your problem/algorithm is ill-conditioned, which generally should be avoided, as it usually doesn't give you meaningful results.
What you are doing is totally, totally misguided.
Your problem is not that you are getting different results (2.36 vs. 2.47). Your problem is that at least one of these results, and likely both, have massive errors. Your Windows and Android results are not just different, they are WRONG. (At least one of them, and you have no idea which one).
Find out why you get these massive errors and change your algorithms to not increase tiny rounding errors massively. Or you have a problem that is inherently chaotic, in which case the difference between results is actually very useful information.
What you are trying just makes the rounding errors 256 times bigger, and if two different results end in ....1ff and ....200 hexadecimal, then you change these to ....180 and ....280, so even the difference between slightly different numbers can grow by a factor 256.
And on a bigendian machine your code will just go kaboom!!!
Your function won't work because of aliasing.
double roundByCast( double d )
{
double rounded = d;
unsigned char* temp = (unsigned char*) &rounded;
// changing least significant byte to be always the same
temp[0] = 128;
return rounded;
}
Casting to unsigned char* for temp is allowed, because char* casts are the exception to the aliasing rules. That's necessary for functions like read, write, memcpy, etc, so that they can copy values to and from byte representations.
However, you aren't allowed to write to temp[0] and then assume that rounded changed. You must create a new double variable (on the stack is fine) and memcpy temp back to it.
I have a class that internally represents some quantity in fixed point as 32-bit integer with somewhat arbitrary denominator (it is neither power of 2 nor power of 10).
For communicating with other applications the quantity is converted to plain old double on output and back on input. As code inside the class it looks like:
int32_t quantity;
double GetValue() { return double(quantity) / DENOMINATOR; }
void SetValue(double x) { quantity = x * DENOMINATOR; }
Now I need to ensure that if I output some value as double and read it back, I will always get the same value back. I.e. that
x.SetValue(x.GetValue());
will never change x.quantity (x is arbitrary instance of the class containing the above code).
The double representation has more digits of precision, so it should be possible. But it will almost certainly not be the case with the simplistic code above.
What rounding do I need to use and
How can I find the critical would-be corner cases to test that the rounding is indeed correct?
Any 32 bits will be represented exactly when you convert to a double, but when you divide then multiply by an arbitrary value you will get a similar value but not exactly the same. You should lose at most one bit per operations, which means your double will be almost the same, prior to casting back to an int.
However, since int casts are truncations, you will get the wrong result when very minor errors turn 2.000 into 1.999, thus what you need to do is a simple rounding task prior to casting back.
You can use std::lround() for this if you have C++11, else you can write you own rounding function.
You probably don't care about fairness much here, so the common int(doubleVal+0.5) will work for positives. If as seems likely, you have negatives, try this:
int round(double d) { return d<0?d-0.5:d+0.5; }
The problem you describe is the same problem which exists with converting between binary and decimal representation just with different bases. At least it exists if you want to have the double representation to be a good approximation of the original value (otherwise you could just multiply the 32 bit value you have with your fixed denominator and store the result in a double).
Assuming you want the double representation be a good approximation of your actual value the conversions are nontrivial! The conversion from your internal representation to double can be done using Dragon4 ("How to print floating point numbers accurately", Steele & White) or Grisu ("How to print floating point numbers quickly and accurately", Loitsch; I'm not sure if this algorithm is independent from the base, though). The reverse can be done using Bellerophon ("How to read floating point numbers accurately", Clinger). These algorithms aren't entirely trivial, though...
The following C++ code:
union float2bin{
float f;
int i;
};
float2bin obj;
obj.f=2.243;
cout<<obj.i;
gives output as some garbage value .
But
union float2bin{
float f;
float i;
};
float2bin obj;
obj.f=2.243;
cout<<obj.i;
gives output same as the value of f i.e 2.243
Compiler GCC has int & float of same size i.e 4 but then what's the reason behind this output behaviour?
The reason is because it is undefined behavior. In practice,
you'll get away with reading an int from something that was
stored as a float on most machines, but you'll read garbage
values unless you know what to expect. Doing it in the other
direction will likely cause the program to crash for certain
values of int.
Under the hood, of course, integral values and floating point
values have different representations, at least on most
machines. (On some Unisys mainframes, your code would do what
you expect. But they're not the most common systems around, and
you probably don't have one on your desktop.) Basically,
regardless of the type, you have a sequence of bits, which will
be interpreted by the hardware in some way. C++ requires
integers to use a pure binary representation, which constrains
the representation somewhat. It also requires a very large
range for floating point values, and more or less requires some
form of exponential notation, with some bits representing the
exponent, and others the mantissa. With different encodings for
each.
The reason is because floating point values are stored in a more complicated way, partitioning the 32 bits into a sign, an exponent and a fraction. If these bits are read as an integer straight off, it will look like a very different value.
The important point here is that if you create a union, you are saying that it is one contiguous block of memory that can be interpreted in two different ways. No where in this mechanism does it account for a safe conversion between float and int, in which case some kind of rounding occurs.
Update: What you might want is
float f = 10.25f;
int i = (int)f;
// Will give you i = 10
However, the union approach is closer to this:
float f = 10.25f;
int i = *((int *)&f);
// Will give you some seemingly arbitrary value
Say I want a function that takes two floats (x and y), and I want to compare them using not their float representation but rather their bitwise representation as a 32-bit unsigned int. That is, a number like -495.5 has bit representation 0b11000011111001011100000000000000 or 0xC3E5C000 as a float, and I have an unsigned int with the same bit representation (corresponding to a decimal value 3286614016, which I don't care about). Is there any easy way for me to perform an operation like <= on these floats using only the information contained in their respective unsigned int counterparts?
You must do a signed compare unless you ensure that all the original values were positive. You must use an integer type that is the same size as the original floating point type. Each chip may have a different internal format, so comparing values from different chips as integers is most likely to give misleading results.
Most float formats look something like this: sxxxmmmm
s is a sign bit
xxx is an exponent
mmmm is the mantissa
The value represented will then be something like: 1mmm << (xxx-k)
1mmm because there is an implied leading 1 bit unless the value is zero.
If xxx < k then it will be a right shift. k is near but not equal to half the largest value that could be expressed by xxx. It is adjusted for the size of the mantissa.
All to say that, disregarding NaN, comparing floating point values as signed integers of the same size will yield meaningful results. They are designed that way so that floating point comparisons are no more costly than integer comparisons. There are compiler optimizations to turn off NaN checks so that the comparisons are straight integer comparisons if the floating point format of the chip supports it.
As an integer, NaN is greater than infinity is greater than finite values. If you try an unsigned compare, all the negative values will be larger than the positive values, just like signed integers cast to unsigned.
If you truly truly don't care about what the conversion yields, it isn't too hard. But the results are extremely non-portable, and you almost certainly won't get an ordering that at all resembles what you'd get by comparing the floats directly.
typedef unsigned int TypeWithSameSizeAsFloat; //Fix this for your platform
bool compare1(float one, float two)
union Convert {
float f;
TypeWithSameSizeAsFloat i;
}
Convert lhs, rhs;
lhs.f = one;
rhs.f = two;
return lhs.i < rhs.i;
}
bool compare2(float one, float two) {
return reinterpret_cast<TypeWithSameSizeAsFloat&>(one)
< reinterpret_cast<TypeWithSameSizeAsFloat&>(two);
}
Just understand the caveats, and chose your second type carefully. Its a near worthless excersize at any rate.
In a word, no. IEEE 754 might allow some kinds of hacks like this, but they do not work all the time and handle all cases, and some platforms do not use that floating point standard (such as doubles on x87 having 80 bit precision internally).
If you're doing this for performance reasons I suggest you strongly reconsider -- if it's faster to use the integer comparison the compiler will probably do it for you, and if it is not, you pay for a float to int conversion multiple times, when a simple comparison may be possible without moving the floats out of registers.
Maybe I'm misreading the question, but I suppose you could do this:
bool compare(float a, float b)
{
return *((unsigned int*)&a) < *((unsigned int*)&b);
}
But this assumes all kinds of things and also warrants the question of why you'd want to compare the bitwise representations of two floats.