I'm writing a program to calculate the result of numbers:
int main()
{
float a, b;
cin >> a >> b;
float result = b + a * a * 0.4;
cout << result;
}
but I have a warning at a * a and it said Warning C26451 Arithmetic overflow: Using operator '*' on a 4 byte value and then casting the result to a 8 byte value. Cast the value to the wider type before calling operator '*' to avoid overflow (io.2). Sorry if this a newbie question, can anyone help me with this? Thank you!
In the C language as described in the first edition of K&R, all floating-point operations were performed by converting operands to a common type (specifically double), performing operations with that type, and then if necessary converting the result to whatever type was needed. On many platforms, that was the most convenient and space-efficient way of handling floating-point math. While the Standard still allows implementations to behave that way, it also allows implementations to perform floating-point operations on smaller types to be performed using those types directly.
As written, the subexpression a * a * 0.5; would be performed by multiplying a * a together using float type, then multiply by a value 0.5 which is of type double. This latter multiplication would require converting the float result of a * a to double. If e.g. a had been equal to 2E19f, then performing the multiply using type float would yield a value too large to be represented using that type. Had the code instead performed the multiplication using type double, then the result 4E38 would be representable in that type, and the result of multiplying that by 0.5 (i.e. 2E38) would be within the range that is representable by float.
Note that in this particular situation, the use of float for the intermediate computations would only affect the result if a was within narrow ranges of very large or very small. If instead of multiplying by 0.5 one had multiplied by other values, however, the choice of whether to use float or double for the first multiplication could affect the accuracy of rounding. Generally, using double for both multiplies would yield slightly more accurate results, but at the expense of increased execution time. Using float for both may yield better execution speed, but at the result of reduced precision. If the floating-point constant had been something that isn't precisely representable in float, converting to double and multiplying by a double constant may yield slightly more accurate results than using float for everything, but in most cases where one would want that precision, one would also want the increased position that would be achieved by using double for the first multiply as well.
Let's look at the error message.
Using operator '*' on a 4 byte value
It is describing this code:
a * a
Your float is 4 bytes. The result of the multiplication is 4 bytes. And the result of a multiplication may overflow.
and then casting the result to a 8 byte value.
It is describing this code:
(result) * 0.4;
Your result is 4 bytes. 0.4 is a double, which is 8 bytes. C++ will promote your float result to a double before performing this multiplication.
So...
The compiler is observing that you are doing float math that could overflow and then immediately converting the result to a double, making the potential overflow unnecessary.
Change the code to this to remove the float to double to float conversions.
float result = b + a * a * 0.4f;
I read the question as "how to change the code to remove the warning?".
If you take the advice in the warning's text literally:
float result = b + (double)a * a * 0.4;
But this is nonsense — if an overflow happens, your result will probably not fit into float result.
It looks like in your case overflow is not possible, and you feel perfectly fine doing all calculations with float. If so, just write 0.4f instead of 0.4 — then the constant will have float type (instead of double), and the result will also be float.
If you want to "fix" the problem with overflow
double result = b + (double)a * a * 0.4;
But then you must also change the following code, which uses the result. And you don't remove the possibility of overflow, you just make it much less likely.
Related
I've read about the difference between double precision and single precision. However, in most cases, float and double seem to be interchangeable, i.e. using one or the other does not seem to affect the results. Is this really the case? When are floats and doubles interchangeable? What are the differences between them?
Huge difference.
As the name implies, a double has 2x the precision of float[1]. In general a double has 15 decimal digits of precision, while float has 7.
Here's how the number of digits are calculated:
double has 52 mantissa bits + 1 hidden bit: log(253)÷log(10) = 15.95 digits
float has 23 mantissa bits + 1 hidden bit: log(224)÷log(10) = 7.22 digits
This precision loss could lead to greater truncation errors being accumulated when repeated calculations are done, e.g.
float a = 1.f / 81;
float b = 0;
for (int i = 0; i < 729; ++ i)
b += a;
printf("%.7g\n", b); // prints 9.000023
while
double a = 1.0 / 81;
double b = 0;
for (int i = 0; i < 729; ++ i)
b += a;
printf("%.15g\n", b); // prints 8.99999999999996
Also, the maximum value of float is about 3e38, but double is about 1.7e308, so using float can hit "infinity" (i.e. a special floating-point number) much more easily than double for something simple, e.g. computing the factorial of 60.
During testing, maybe a few test cases contain these huge numbers, which may cause your programs to fail if you use floats.
Of course, sometimes, even double isn't accurate enough, hence we sometimes have long double[1] (the above example gives 9.000000000000000066 on Mac), but all floating point types suffer from round-off errors, so if precision is very important (e.g. money processing) you should use int or a fraction class.
Furthermore, don't use += to sum lots of floating point numbers, as the errors accumulate quickly. If you're using Python, use fsum. Otherwise, try to implement the Kahan summation algorithm.
[1]: The C and C++ standards do not specify the representation of float, double and long double. It is possible that all three are implemented as IEEE double-precision. Nevertheless, for most architectures (gcc, MSVC; x86, x64, ARM) float is indeed a IEEE single-precision floating point number (binary32), and double is a IEEE double-precision floating point number (binary64).
Here is what the standard C99 (ISO-IEC 9899 6.2.5 §10) or C++2003 (ISO-IEC 14882-2003 3.1.9 §8) standards say:
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 C++ standard adds:
The value representation of floating-point types is implementation-defined.
I would suggest having a look at the excellent What Every Computer Scientist Should Know About Floating-Point Arithmetic that covers the IEEE floating-point standard in depth. You'll learn about the representation details and you'll realize there is a tradeoff between magnitude and precision. The precision of the floating point representation increases as the magnitude decreases, hence floating point numbers between -1 and 1 are those with the most precision.
Given a quadratic equation: x2 − 4.0000000 x + 3.9999999 = 0, the exact roots to 10 significant digits are, r1 = 2.000316228 and r2 = 1.999683772.
Using float and double, we can write a test program:
#include <stdio.h>
#include <math.h>
void dbl_solve(double a, double b, double c)
{
double d = b*b - 4.0*a*c;
double sd = sqrt(d);
double r1 = (-b + sd) / (2.0*a);
double r2 = (-b - sd) / (2.0*a);
printf("%.5f\t%.5f\n", r1, r2);
}
void flt_solve(float a, float b, float c)
{
float d = b*b - 4.0f*a*c;
float sd = sqrtf(d);
float r1 = (-b + sd) / (2.0f*a);
float r2 = (-b - sd) / (2.0f*a);
printf("%.5f\t%.5f\n", r1, r2);
}
int main(void)
{
float fa = 1.0f;
float fb = -4.0000000f;
float fc = 3.9999999f;
double da = 1.0;
double db = -4.0000000;
double dc = 3.9999999;
flt_solve(fa, fb, fc);
dbl_solve(da, db, dc);
return 0;
}
Running the program gives me:
2.00000 2.00000
2.00032 1.99968
Note that the numbers aren't large, but still you get cancellation effects using float.
(In fact, the above is not the best way of solving quadratic equations using either single- or double-precision floating-point numbers, but the answer remains unchanged even if one uses a more stable method.)
A double is 64 and single precision
(float) is 32 bits.
The double has a bigger mantissa (the integer bits of the real number).
Any inaccuracies will be smaller in the double.
I just ran into a error that took me forever to figure out and potentially can give you a good example of float precision.
#include <iostream>
#include <iomanip>
int main(){
for(float t=0;t<1;t+=0.01){
std::cout << std::fixed << std::setprecision(6) << t << std::endl;
}
}
The output is
0.000000
0.010000
0.020000
0.030000
0.040000
0.050000
0.060000
0.070000
0.080000
0.090000
0.100000
0.110000
0.120000
0.130000
0.140000
0.150000
0.160000
0.170000
0.180000
0.190000
0.200000
0.210000
0.220000
0.230000
0.240000
0.250000
0.260000
0.270000
0.280000
0.290000
0.300000
0.310000
0.320000
0.330000
0.340000
0.350000
0.360000
0.370000
0.380000
0.390000
0.400000
0.410000
0.420000
0.430000
0.440000
0.450000
0.460000
0.470000
0.480000
0.490000
0.500000
0.510000
0.520000
0.530000
0.540000
0.550000
0.560000
0.570000
0.580000
0.590000
0.600000
0.610000
0.620000
0.630000
0.640000
0.650000
0.660000
0.670000
0.680000
0.690000
0.700000
0.710000
0.720000
0.730000
0.740000
0.750000
0.760000
0.770000
0.780000
0.790000
0.800000
0.810000
0.820000
0.830000
0.839999
0.849999
0.859999
0.869999
0.879999
0.889999
0.899999
0.909999
0.919999
0.929999
0.939999
0.949999
0.959999
0.969999
0.979999
0.989999
0.999999
As you can see after 0.83, the precision runs down significantly.
However, if I set up t as double, such an issue won't happen.
It took me five hours to realize this minor error, which ruined my program.
There are three floating point types:
float
double
long double
A simple Venn diagram will explain about:
The set of values of the types
The size of the numbers involved in the float-point calculations is not the most relevant thing. It's the calculation that is being performed that is relevant.
In essence, if you're performing a calculation and the result is an irrational number or recurring decimal, then there will be rounding errors when that number is squashed into the finite size data structure you're using. Since double is twice the size of float then the rounding error will be a lot smaller.
The tests may specifically use numbers which would cause this kind of error and therefore tested that you'd used the appropriate type in your code.
Type float, 32 bits long, has a precision of 7 digits. While it may store values with very large or very small range (+/- 3.4 * 10^38 or * 10^-38), it has only 7 significant digits.
Type double, 64 bits long, has a bigger range (*10^+/-308) and 15 digits precision.
Type long double is nominally 80 bits, though a given compiler/OS pairing may store it as 12-16 bytes for alignment purposes. The long double has an exponent that just ridiculously huge and should have 19 digits precision. Microsoft, in their infinite wisdom, limits long double to 8 bytes, the same as plain double.
Generally speaking, just use type double when you need a floating point value/variable. Literal floating point values used in expressions will be treated as doubles by default, and most of the math functions that return floating point values return doubles. You'll save yourself many headaches and typecastings if you just use double.
Floats have less precision than doubles. Although you already know, read What WE Should Know About Floating-Point Arithmetic for better understanding.
When using floating point numbers you cannot trust that your local tests will be exactly the same as the tests that are done on the server side. The environment and the compiler are probably different on you local system and where the final tests are run. I have seen this problem many times before in some TopCoder competitions especially if you try to compare two floating point numbers.
The built-in comparison operations differ as in when you compare 2 numbers with floating point, the difference in data type (i.e. float or double) may result in different outcomes.
If one works with embedded processing, eventually the underlying hardware (e.g. FPGA or some specific processor / microcontroller model) will have float implemented optimally in hardware whereas double will use software routines. So if the precision of a float is enough to handle the needs, the program will execute some times faster with float then double. As noted on other answers, beware of accumulation errors.
Quantitatively, as other answers have pointed out, the difference is that type double has about twice the precision, and three times the range, as type float (depending on how you count).
But perhaps even more important is the qualitative difference. Type float has good precision, which will often be good enough for whatever you're doing. Type double, on the other hand, has excellent precision, which will almost always be good enough for whatever you're doing.
The upshot, which is not nearly as well known as it should be, is that you should almost always use type double. Unless you have some particularly special need, you should almost never use type float.
As everyone knows, "roundoff error" is often a problem when you're doing floating-point work. Roundoff error can be subtle, and difficult to track down, and difficult to fix. Most programmers don't have the time or expertise to track down and fix numerical errors in floating-point algorithms — because unfortunately, the details end up being different for every different algorithm. But type double has enough precision such that, much of the time, you don't have to worry.
You'll get good results anyway. With type float, on the other hand, alarming-looking issues with roundoff crop up all the time.
And the thing that's not necessarily different between type float and double is execution speed. On most of today's general-purpose processors, arithmetic operations on type float and double take more or less exactly the same amount of time. Everything's done in parallel, so you don't pay a speed penalty for the greater range and precision of type double. That's why it's safe to make the recommendation that you should almost never use type float: Using double shouldn't cost you anything in speed, and it shouldn't cost you much in space, and it will almost definitely pay off handsomely in freedom from precision and roundoff error woes.
(With that said, though, one of the "special needs" where you may need type float is when you're doing embedded work on a microcontroller, or writing code that's optimized for a GPU. On those processors, type double can be significantly slower, or practically nonexistent, so in those cases programmers do typically choose type float for speed, and maybe pay for it in precision.)
Unlike an int (whole number), a float have a decimal point, and so can a double.
But the difference between the two is that a double is twice as detailed as a float, meaning that it can have double the amount of numbers after the decimal point.
Depending on this question Floating point division vs floating point multiplication. Division is slower than multiplication due to some reasons.
Will the compiler, usually, replace division by multiplication if it is possibe?
For example:
float a;
// During runtime a=5.4f
float b = a/10.f;
Will it be:
float a;
// During runtime a=5.4f
float b = a*0.1f;
If it is considered a compiler dependable question, I am using VS2013 default compiler. However, it would be nice if I got a generic answer (theoretical validity of this optimization)
No, the compiler is not allowed to do that for the general case: the two operations could produce results that are not bit-identical due to the representation error of the reciprocal.
In your example, 0.1 does not have an exact representation as float. This causes the results of multiplication by 0.1 and division by 10 to differ:
float f = 21736517;
float a = f / 10.f;
float b = f * 0.1f;
cout << (a == b) << endl; // Prints zero
Demo.
Note: As njuffa correctly notes in the comment below, there are situations when the compiler could make some optimizations for a wide set of numbers, as described in this paper. For example, multiplying or dividing by a power of two is equivalent to addition to the exponent portion of the IEEE-754 float representation.
a complete newbie here. For my school homework, I was given to write a program that displays -
s= 1 + 1/2 + 1/3 + 1/4 ..... + 1/n
Here's what I did -
#include<iostream.h>
#include<conio.h>
void main()
{
clrscr();
int a;
float s=0, n;
cin>>a;
for(n=1;n<=a;n++)
{
s+=1/n;
}
cout<<s;
getch();
}
It perfectly displays what it should. However, in the past I have only written programs which uses int data type. To my understanding, int data type does not contain any decimal place whereas float does. So I don't know much about float yet. Later that night, I was watching some video on YouTube in which he was writing the exact same program but in a little different way. The video was in some foreign language so I couldn't understand it. What he did was declared 'n' as an integer.
int a, n;
float s=0;
instead of
int a
float s=0, n;
But this was not displaying the desired result. So he went ahead and showed two ways to correct it. He made changes in the for loop body -
s+=1.0f/n;
and
s+=1/(float)n;
To my understanding, he declared 'n' a float data type later in the program(Am I right?). So, my question is, both display the same result but is there any difference between the two? As we are declaring 'n' a float, why he has written 1.0f instead of n.f or f.n. I tried it but it gives error. And in the second method, why we can't write 1(float)/n instead of 1/(float)n? As in the first method we have added float suffix with 1. Also, is there a difference between 1.f and 1.0f?
I tried to google my question but couldn't find any answer. Also, another confusion that came to my mind after a few hours is - Why are we even declaring 'n' a float? As per the program, the sum should come out as a real number. So, shouldn't we declare only 's' a float. The more I think the more I confuse my brain. Please help!
Thank You.
The reason is that integer division behaves different than floating point division.
4 / 3 gives you the integer 1. 10 / 3 gives you the integer 3.
However, 4.0f / 3 gives you the float 1.3333..., 10.0f / 3 gives you the float 3.3333...
So if you have:
float f = 4 / 3;
4 / 3 will give you the integer 1, which will then be stored into the float f as 1.0f.
You instead have to make sure either the divisor or the dividend is a float:
float f = 4.0f / 3;
float f = 4 / 3.0f;
If you have two integer variables, then you have to convert one of them to a float first:
int a = ..., b = ...;
float f = (float)a / b;
float f = a / (float)b;
The first is equivalent to something like:
float tmp = a;
float f = tmp / b;
Since n will only ever have an integer value, it makes sense to define it as as int. However doing so means that this won't work as you might expect:
s+=1/n;
In the division operation both operands are integer types, so it performs integer division which means it takes the integer part of the result and throws away any fractional component. So 1/2 would evaluate to 0 because dividing 1 by 2 results in 0.5, and throwing away the fraction results in 0.
This in contrast to floating point division which keeps the fractional component. C will perform floating point division if either operand is a floating point type.
In the case of the above expression, we can force floating point division by performing a typecast on either operand:
s += (float)1/n
Or:
s += 1/(float)n
You can also specify the constant 1 as a floating point constant by giving a decimal component:
s += 1.0/n
Or appending the f suffix:
s += 1.0f/n
The f suffix (as well as the U, L, and LL suffixes) can only be applied to numerical constants, not variables.
What he is doing is something called casting. I'm sure your school will mention it in new lectures. Basically n is set as an integer for the entire program. But since integer and double are similar (both are numbers), the c/c++ language allows you to use them as either as long as you tell the compiler what you want to use it as. You do this by adding parenthesis and the data type ie
(float) n
he declared 'n' a float data type later in the program(Am I right?)
No, he defined (thereby also declared) n an int and later he explicitly converted (casted) it into a float. Both are very different.
both display the same result but is there any difference between the two?
Nope. They're the same in this context. When an arithmetic operator has int and float operands, the former is implicitly converted into the latter and thereby the result will also be a float. He's just shown you two ways to do it. When both the operands are integers, you'd get an integer value as a result which may be incorrect, when proper mathematical division would give you a non-integer quotient. To avoid this, usually one of the operands are made into a floating-point number so that the actual result is closer to the expected result.
why he has written 1.0f instead of n.f or f.n. I tried it but it gives error. [...] Also, is there a difference between 1.f and 1.0f?
This is because the language syntax is defined thus. When you're declaring a floating-point literal, the suffix is to use .f. So 5 would be an int while 5.0f or 5.f is a float; there's no difference when you omit any trailing 0s. However, n.f is syntax error since n is a identifier (variable) name and not a constant number literal.
And in the second method, why we can't write 1(float)/n instead of 1/(float)n?
(float)n is a valid, C-style casting of the int variable n, while 1(float) is just syntax error.
s+=1.0f/n;
and
s+=1/(float)n;
... So, my question is, both display the same result but is there any difference between the two?
Yes.
In both C and C++, when a calculation involves expressions of different types, one or more of those expressions will be "promoted" to the type with greater precision or range. So if you have an expression with signed and unsigned operands, the signed operand will be "promoted" to unsigned. If you have an expression with float and double operands, the float operand will be promoted to double.
Remember that division with two integer operands gives an integer result - 1/2 yields 0, not 0.5. To get a floating point result, at least one of the operands must have a floating point type.
In the case of 1.0f/n, the expression 1.0f has type float1, so the n will be "promoted" from type int to type float.
In the case of 1/(float) n, the expression n is being explicitly cast to type float, so the expression 1 is promoted from type int to float.
Nitpicks:
Unless your compiler documentation explicitly lists void main() as a legal signature for the main function, use int main() instead. From the online C++ standard:
3.6.1 Main function
...
2 An implementation shall not predefine the main function. This function shall not be overloaded. It shall have a declared return type of type int, but otherwise its type is implementation-defined...
Secondly, please format your code - it makes it easier for others to read and debug. Whitespace and indentation are your friends - use them.
1. The constant expression 1.0 with no suffix has type double. The f suffix tells the compiler to treat it as float. 1.0/n would result in a value of type double.
This question already has answers here:
Comparing float and double
(3 answers)
Closed 7 years ago.
According to this post, when comparing a float and a double, the float should be treated as double.
The following program, does not seem to follow this statement. The behaviour looks quite unpredictable.
Here is my program:
void main(void)
{
double a = 1.1; // 1.5
float b = 1.1; // 1.5
printf("%X %X\n", a, b);
if ( a == b)
cout << "success " <<endl;
else
cout << "fail" <<endl;
}
When I run the following program, I get "fail" displayed.
However, when I change a and b to 1.5, it displays "success".
I have also printed the hex notations of the values. They are different in both the cases. My compiler is Visual Studio 2005
Can you explain this output ? Thanks.
float f = 1.1;
double d = 1.1;
if (f == d)
In this comparison, the value of f is promoted to type double. The problem you're seeing isn't in the comparison, but in the initialization. 1.1 can't be represented exactly as a floating-point value, so the values stored in f and d are the nearest value that can be represented. But float and double are different sizes, so have a different number of significant bits. When the value in f is promoted to double, there's no way to get back the extra bits that were lost when the value was stored, so you end up with all zeros in the extra bits. Those zero bits don't match the bits in d, so the comparison is false. And the reason the comparison succeeds with 1.5 is that 1.5 can be represented exactly as a float and as a double; it has a bunch of zeros in its low bits, so when the promotion adds zeros the result is the same as the double representation.
I found a decent explanation of the problem you are experiencing as well as some solutions.
See How dangerous is it to compare floating point values?
Just a side note, remember that some values can not be represented EXACTLY in IEEE 754 floating point representation. Your same example using a value of say 1.5 would compare as you expect because there is a perfect representation of 1.5 without any loss of data. However, 1.1 in 32-bit and 64-bit are in fact different values because the IEEE 754 standard can not perfectly represent 1.1.
See http://www.binaryconvert.com
double a = 1.1 --> 0x3FF199999999999A
Approximate representation = 1.10000000000000008881784197001
float b = 1.1 --> 0x3f8ccccd
Approximate representation = 1.10000002384185791015625
As you can see, the two values are different.
Also, unless you are working in some limited memory type environment, it's somewhat pointless to use floats. Just use doubles and save yourself the headaches.
If you are not clear on why some values can not be accurately represented, consult a tutorial on how to covert a decimal to floating point.
Here's one: http://class.ece.iastate.edu/arun/CprE281_F05/ieee754/ie5.html
I would regard code which directly performs a comparison between a float and a double without a typecast to be broken; even if the language spec says that the float will be implicitly converted, there are two different ways that the comparison might sensibly be performed, and neither is sufficiently dominant to really justify a "silent" default behavior (i.e. one which compiles without generating a warning). If one wants to perform a conversion by having both operands evaluated as double, I would suggest adding an explicit type cast to make one's intentions clear. In most cases other than tests to see whether a particular double->float conversion will be reversible without loss of precision, however, I suspect that comparison between float values is probably more appropriate.
Fundamentally, when comparing floating-point values X and Y of any sort, one should regard comparisons as indicating that X or Y is larger, or that the numbers are "indistinguishable". A comparison which shows X is larger should be taken to indicate that the number that Y is supposed to represent is probably smaller than X or close to X. A comparison that says the numbers are indistinguishable means exactly that. If one views things in such fashion, comparisons performed by casting to float may not be as "informative" as those done with double, but are less likely to yield results that are just plain wrong. By comparison, consider:
double x, y;
float f = x;
If one compares f and y, it's possible that what one is interested in is how y compares with the value of x rounded to a float, but it's more likely that what one really wants to know is whether, knowing the rounded value of x, whether one can say anything about the relationship between x and y. If x is 0.1 and y is 0.2, f will have enough information to say whether x is larger than y; if y is 0.100000001, it will not. In the latter case, if both operands are cast to double, the comparison will erroneously imply that x was larger; if they are both cast to float, the comparison will report them as indistinguishable. Note that comparison results when casting both operands to double may be erroneous not only when values are within a part per million; they may be off by hundreds of orders of magnitude, such as if x=1e40 and y=1e300. Compare f and y as float and they'll compare indistinguishable; compare them as double and the smaller value will erroneously compare larger.
The reason why the rounding error occurs with 1.1 and not with 1.5 is due to the number of bits required to accurately represent a number like 0.1 in floating point format. In fact an accurate representation is not possible.
See How To Represent 0.1 In Floating Point Arithmetic And Decimal for an example, particularly the answer by #paxdiablo.
I just wanted to ask what happens number wise if i do not typecast integers to float when storing in a float variable like this:
int32 IntVar1 = 100
int32 IntVar2 = 200
float FloatVar = IntVar1/IntVar2;
Currently i am doing this:
int32 IntVar1 = 100
int32 IntVar2 = 200
float FloatVar = float(IntVar1)/float(IntVar2);
But in the amount of code i have, this looks really retarded. I thought about changing my int variables to float, but i guess that would be a performance hit. And since the integer values are not supposed to hold any decimals, it feels like a complete waste.
So i wonder, are there any way that option 1 could be working? Or do i have to typecast OR change variables to float? (All typecasting pretty much makes the code unreadable)
I wouldn't worry too much about premature optimization. If it makes more sense for your values to be expressed as float types, go for it. If your program doesn't run as fast as you need, and you've profiled it and know that the floating point operations are the problem, then start thinking about how to speed it up.
I'd value readability over all of the casting, which seems to be your instinct as well.
Also, since this question is tagged C++, I think it's (unfortunately?) more idiomatic to do:
float FloatVar = static_cast<float>(IntVar1)/IntVar2
Behold the magic of functions:
float div(int x, int y)
{
return float(x) / float(y);
}
Now you can say:
int32 IntVar1 = 100
int32 IntVar2 = 200
float FloatVar = div(IntVar1, IntVar2);
You need at least one of those operands to be float, otherwise the division will be truncated. I usually cast the first operand:
float FloatVar = (float)IntVar1/IntVar2;
which, elegance-wise, isn't that bad.
As per the ISO/IEC standard- N3797 - section 5.6
For integral operands the / operator yields the algebraic quotient
with any fractional part discarded; if the quotient a/b is
representable in the type of the result, (a/b)*b + a%b is equal to a;
otherwise, the behavior of both a/b and a%b is undefined
The discarding of the fractional part is called truncation towards zero.
There is no wonder if the fractional part is discarded in
22/7