value of variable in c language with 100 digits - c

So I'm new to c , and I have just learned about data type, what confuse me is that a value range of a double for example is from 2.3E-308 to 1.7E+308
mathematically a number of 100 digits ∈ [2.3E-308 , 1.7E+308].
Writing this simple program
#include <stdio.h>
int main()
{
double c = 5416751717547457918597197587615765157415671579185765176547645735175197857989185791857948797847984848;
printf("%le",c);
return 0;
}
the result is 7.531214e+18 by changing %le by %lf th result is 7531214226330737664.000000
which doesn't equal c.
So whats is the problem.

This long number is actually a numerical literal of type long long. But since this type cannot contain such a long number, it is truncated modulo (LLONG_MAX + 1) and resulting in 7531214226330737360.
Demo.
Edit:
#JohnBollinger: ... and then converted to double, with a resulting loss of a few (binary) digits of precision.
#rici: Demo2 - here the constant is of type double because of added decimal point

It might seem that, if we can store a number of up to 10 to the power 308, we are storing 308 digits or so but, in floating point arithmetic, that isn't the case. Floating point numbers are not stored as huge strings of digits.
Broadly, a floating-point number is stored as a mantissa -- typically a number between zero and one -- and an exponent -- some number raised to the power of some other number. The different kinds of floating point number (float, double, long double) each has a different number of bits allocated to the mantissa and exponent. These bit counts, particularly in the mantissa, control the precision with which the number can be represented.
A double on most platforms gives 16-17 decimal digits of precision, regardless of the magnitude (power of ten). It's possible to use libraries that will do arithmetic to any degree of precision required, although such features are not built into C.
An additional complication is that, in your example, the number you assign to c is not actually defined to be a floating point number at all. Lacking any indication that it should be so represented, the compiler will treat it as an integer and, as it's too large to fit even the largest integer type on most platforms, it gets truncated down to integer range.

You should get a proper compiler or enable warnings on it. A recent GCC, with just default settings will output the following warning:
% gcc float.c
float.c: In function ‘main’:
float.c:4:12: warning: integer constant is too large for its type
double c = 5416751717547457918597197587615765157415671579185765176547645735175197857989185791857948797847984848;
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Notice that it says integer, i.e. a whole number, not floating point. In C a constant of that form denotes an integer. Unless suffixed with U, it is additionally a signed integer, of the greatest type that it fits. However, neither standard C, nor common implementations, have a type that is big enough to fit this value. So what happens, is [(C11 6.4.4.1p6)[http://port70.net/~nsz/c/c11/n1570.html#6.4.4.1p6]) :
If an integer constant cannot be represented by any type in its list and has no extended integer type, then the integer constant has no type.
Use of such an integer constant without type in arithmetic leads to undefined behaviour, that is the whole execution of the program is now meaningless. You should have read the warnings.
The "fix" would have been to add a . after the number!
#include <stdio.h>
int main(void)
{
double c = 54167517175474579185971975876157651574156715791\
85765176547645735175197857989185791857948797847984848.;
printf("%le\n",c);
}
And running it:
% ./a.out
5.416752e+99
Notice that even then, a double is precise to average ~15 significant decimal digits only.

Related

Why does %f print large values when floating point constants are passed instead of variables?

In the given program why did I get different results for each of the printfs?
#include <stdio.h>
int main()
{
float c = 4.4e10;
printf("%f\n", c);
printf("%f\n", 4.4e10);
return 0;
}
And it shows the following output:
44000002048.000000
44000000000.000000
A float is a type that holds a 32-bit floating point number, while the constant 4.4e10 represents a double, which holds a 64-bit floating point number (i.e. a double-precision floating point number)
When you assign 4.4e10 to c, the value 4.4e10 cannot be represented precisely (a rounding error in a parameter called the mantissa), and the closest possible value (44000002048) is stored. When it is passed to printf, it is promoted back to double, including the rounding error.
In the second case, the value is a double the whole time, without narrowing and widening, and it happens to be the case that a double can represent the value exactly.
If this is undesirable behavior, you can declare c as a double for a bit more precision (but beware that you'll still hit precision limits eventually).
You're actually printing the values of two different types here.
In the first case you're assigning a value to a variable of type float. The precision of a float is roughly 6 or 7 decimal digits, so unless the value can be represented exactly you'll see the closest value that can be represented by that type.
In the second case you're passing the constant 4.4e10 which has type double. This type has around 16 decimal digits of precision, and the value is within that range, so the exact value is printed.

When does a float constant overflow if it is implicitly converted to int type

I have two code snippets and both produce different results. I am using TDM-GCC 4.9.2 compiler and my compiler is 32-bit version
( Size of int is 4 bytes and Minimum value in float is -3.4e38 )
Code 1:
int x;
x=2.999999999999999; // 15 '9s' after decimal point
printf("%d",x);
Output:
2
Code 2:
int x;
x=2.9999999999999999; // 16 '9s' after decimal point
printf("%d",x);
Output:
3
Why is the implicit conversion different in these cases?
Is it due to some overflow in the Real constant specified and if so how does it happen?
(Restricting this answer to IEEE754).
When you assign a constant to a floating point, the IEEE754 standard requires the closest possible floating point number to be picked. Both the numbers you present cannot be represented exactly.
The nearest IEEE754 double precision floating point number to 2.999999999999999
is 2.99999999999999911182158029987476766109466552734375 whereas the nearest one to 2.9999999999999999 is 3.
Hence the output. Converting to an integral type truncates the value towards zero.
Using round is one way to obviate this effect.
Further reading: Is floating point math broken?

Nonintuitive result of the assignment of a double precision number to an int variable in C

Could someone give me an explanation why I get two different
numbers, resp. 14 and 15, as an output from the following code?
#include <stdio.h>
int main()
{
double Vmax = 2.9;
double Vmin = 1.4;
double step = 0.1;
double a =(Vmax-Vmin)/step;
int b = (Vmax-Vmin)/step;
int c = a;
printf("%d %d",b,c); // 14 15, why?
return 0;
}
I expect to get 15 in both cases but it seems I'm missing some fundamentals of the language.
I am not sure if it's relevant but I was doing the test in CodeBlocks. However, if I type the same lines of code in some on-line compiler ( this one for example) I get an answer of 15 for the two printed variables.
... why I get two different numbers ...
Aside from the usual float-point issues, the computation paths to b and c are arrived in different ways. c is calculated by first saving the value as double a.
double a =(Vmax-Vmin)/step;
int b = (Vmax-Vmin)/step;
int c = a;
C allows intermediate floating-point math to be computed using wider types. Check the value of FLT_EVAL_METHOD from <float.h>.
Except for assignment and cast (which remove all extra range and precision), ...
-1 indeterminable;
0 evaluate all operations and constants just to the range and precision of the
type;
1 evaluate operations and constants of type float and double to the
range and precision of the double type, evaluate long double
operations and constants to the range and precision of the long double
type;
2 evaluate all operations and constants to the range and precision of the
long double type.
C11dr §5.2.4.2.2 9
OP reported 2
By saving the quotient in double a = (Vmax-Vmin)/step;, precision is forced to double whereas int b = (Vmax-Vmin)/step; could compute as long double.
This subtle difference results from (Vmax-Vmin)/step (computed perhaps as long double) being saved as a double versus remaining a long double. One as 15 (or just above), and the other just under 15. int truncation amplifies this difference to 15 and 14.
On another compiler, the results may both have been the same due to FLT_EVAL_METHOD < 2 or other floating-point characteristics.
Conversion to int from a floating-point number is severe with numbers near a whole number. Often better to round() or lround(). The best solution is situation dependent.
This is indeed an interesting question, here is what happens precisely in your hardware. This answer gives the exact calculations with the precision of IEEE double precision floats, i.e. 52 bits mantissa plus one implicit bit. For details on the representation, see the wikipedia article.
Ok, so you first define some variables:
double Vmax = 2.9;
double Vmin = 1.4;
double step = 0.1;
The respective values in binary will be
Vmax = 10.111001100110011001100110011001100110011001100110011
Vmin = 1.0110011001100110011001100110011001100110011001100110
step = .00011001100110011001100110011001100110011001100110011010
If you count the bits, you will see that I have given the first bit that is set plus 52 bits to the right. This is exactly the precision at which your computer stores a double. Note that the value of step has been rounded up.
Now you do some math on these numbers. The first operation, the subtraction, results in the precise result:
10.111001100110011001100110011001100110011001100110011
- 1.0110011001100110011001100110011001100110011001100110
--------------------------------------------------------
1.1000000000000000000000000000000000000000000000000000
Then you divide by step, which has been rounded up by your compiler:
1.1000000000000000000000000000000000000000000000000000
/ .00011001100110011001100110011001100110011001100110011010
--------------------------------------------------------
1110.1111111111111111111111111111111111111111111111111100001111111111111
Due to the rounding of step, the result is a tad below 15. Unlike before, I have not rounded immediately, because that is precisely where the interesting stuff happens: Your CPU can indeed store floating point numbers of greater precision than a double, so rounding does not take place immediately.
So, when you convert the result of (Vmax-Vmin)/step directly to an int, your CPU simply cuts off the bits after the fractional point (this is how the implicit double -> int conversion is defined by the language standards):
1110.1111111111111111111111111111111111111111111111111100001111111111111
cutoff to int: 1110
However, if you first store the result in a variable of type double, rounding takes place:
1110.1111111111111111111111111111111111111111111111111100001111111111111
rounded: 1111.0000000000000000000000000000000000000000000000000
cutoff to int: 1111
And this is precisely the result you got.
The "simple" answer is that those seemingly-simple numbers 2.9, 1.4, and 0.1 are all represented internally as binary floating point, and in binary, the number 1/10 is represented as the infinitely-repeating binary fraction 0.00011001100110011...[2] . (This is analogous to the way 1/3 in decimal ends up being 0.333333333... .) Converted back to decimal, those original numbers end up being things like 2.8999999999, 1.3999999999, and 0.0999999999. And when you do additional math on them, those .0999999999's tend to proliferate.
And then the additional problem is that the path by which you compute something -- whether you store it in intermediate variables of a particular type, or compute it "all at once", meaning that the processor might use internal registers with greater precision than type double -- can end up making a significant difference.
The bottom line is that when you convert a double back to an int, you almost always want to round, not truncate. What happened here was that (in effect) one computation path gave you 15.0000000001 which truncated down to 15, while the other gave you 14.999999999 which truncated all the way down to 14.
See also question 14.4a in the C FAQ list.
An equivalent problem is analyzed in analysis of C programs for FLT_EVAL_METHOD==2.
If FLT_EVAL_METHOD==2:
double a =(Vmax-Vmin)/step;
int b = (Vmax-Vmin)/step;
int c = a;
computes b by evaluating a long double expression then truncating it to a int, whereas for c it's evaluating from long double, truncating it to double and then to int.
So both values are not obtained with the same process, and this may lead to different results because floating types does not provides usual exact arithmetic.

How does ' %f ' work in C?

Hey i need to know how %f works , that is how
printf("%f",number);
extract a floating point number from a series of bits in number.
Consider the code:
main()
{
int i=1;
printf("\nd %d\nf %f",i,i);
}
Output is :
d 1
f -0.000000
So ultimately it doesn't depend on variable 'i', but just depends on the usage of %d and %f(or whatever) i just need to know how %f extracts the float number corresponding to series of bits in 'i'
To all those who misunderstood my question i know that %f can't be used to an integer and would load garbage values if size of integer was smaller than float. As for my case the size of integer and float are 4 bytes.
Let me be clear if value of is 1 then the corresponding binary value of i will be this:
0000 0000 0000 0000 0000 0000 0000 0001 [32 bits]
How would %f extract -0.0000 as in this case from this series of bits.(How it knows where to put decimal point etc , i can't find it from IEEE 754)
[PLEASE DO CORRECT ME IF I AM WRONG IN MY EXPLANATION OR ASSUMPION]
It's undefined behavior to use "%f" to an int, so the answer to your question is: you don't need to know, and you shouldn't do it.
The output depends on the format specifier like "%f" instead of the type of the argument i is because variadic functions (like printf() or scanf()) have no way of knowing the type of variable argument part.
As others have said, giving mismatched "%" specifier and arguments is undefined behavior, and, according to the C standard, anything can happen.
What does happen, in this case, on most modern computers, is this:
printf looks at the place in memory where the data should have been, interprets whatever data it finds there as a floating-point number, and prints that number.
Since printf is a function that can take a variable number of arguments, all floats are converted to doubles before being sent to the function, so printf expects to find a double, which (on normal modern computers) is 64 bits. But you send an int, which is only 32 bits, so printf will look at the 32 bits from the int, and 32 more bits of garbage that just happened to be there. When you tried this, it seems that the combination was a bit pattern corresponding to the double floating-point value -0.0.
Well.
It's easy to see how an integer can be packed into bytes, but how do you represent decimals?
The simplest technique is fixed point: of the n bits, the first m are before the point and the rest after. This is not a very good representation, however. Bits are wasted on some numbers, and it has uniform precision, while in real life, most desired decimals are between 0 and 1.
Enter floating point. The IEEE 754 spec defines a way of interpreting bits that has, since then, been almost universally accepted. It has very high near-zero precision, is compact, expandable and allows for very large numbers as well.
The linked articles are a good read.
You can output a floating-point number (float x;) manually by treating the value as a "black box" and extracting the digits one-by-one.
First, check if x < 0. If so, output a minus-sign - and negate the number. Now we know that it is positive.
Next, output the integer portion. Assign the floating-point number to an integer variable, which will truncate it, ie. int integer = x;. Then determine how many digits there are using the base-10 logarithm log10(). Note, log10(0) is undefined, so you'll have to handle zero as a special case. Then iterate from 0 up to the number of digits, each time dividing by 10^digit_index to move the desired digit into the unit's position, and take the 10-residue (modulus).
for (i=digits; i>=0; i--)
dig = (integer / pow(10,i)) % 10;
Then, output the decimal point ..
For the fractional part, subtract the integer from the original (absolute-value, remember) floating-point number. And output each digit in a similar way, but this time multiplying by 10^frac_digits. You won't be able to predict the number of significant fractional digits this way, so just use a fixed precision (constant number of fractional digits).
I have C code to fill a string with the representation of a floating-point number here, although I make no claims as to its readability.
IEEE formats store the number as a normalized binary fraction. It's more similar to scientific notation, like 3.57×102 instead of 357.0. So it is stored as an exponent-mantissa pair. Being "normalized" means there's actually an implicit additional 1 bit at the front of the mantissa that is not stored. Hopefully that's enough to help you understand a more detailed description of the format from elsewhere.
Remember, we're in binary, so there's no "decimal point". And with the exponent-mantissa notation, there isn't even a binary point in the format. It's implicitly represented in the exponent.
On the tangentially-related issue of passing floats to printf, remember that this is a variadic function. So it does not declare types of arguments that it receives, and all arguments passed undergo automatic conversions. So, float will automatically promote to double. So what you're doing is (substituting hex for brevity), passing 2 64-bit values:
double f, double f
0xabcdefgh 0xijklmnop 0xabcdefgh 0xijklmnop
Then you tell printf to interpret this sequence of words as an int followed by a double. So the 32-bit int seen by printf is only the first half of the floating-point number, and then the floating-point number seem by printf has its words reversed. The fourth word is never used.
To get the integer representation, you'll need to use type-punning with a pointer.
printf("%d %f\n", *(int *)&f, f);
Which reads (from right-to-left): take the address of the float, treat it as a pointer-to-int, follow the pointer.

Calculations with long double in clang – Compiler bug?

Is this a bug in clang?
This prints out the maximum double value:
long double a = DBL_MAX;
printf("%Lf\n", a);
It is:
179769313486231570814527423731704356798070567525844996598917476803157260780028538760589558632766878171540458953514382464234321326889464182768467546703537516986049910576551282076245490090389328944075868508455133942304583236903222948165808559332123348274797826204144723168738177180919299881250404026184124858368.000000
This prints out the maximum long double value:
long double a = LDBL_MAX;
printf("%Lf\n", a);
It is:
/* … bigger, but not displayed here. For a good reason. ;-) */
This is quite clear.
But when I use an arithmetic expression, that is compile time computable as an initializer, I get a surprising result:
long double a = 1.L + DBL_MAX + 1.L;
printf("%Lf\n", a);
This still prints out DBL_MAX and not DBL_MAX + 2!?
It is the same, if the computation is done at runtime:
long double b = 2.L;
long double a = DBL_MAX;
printf("%Lf\n", a+b);
Still DBL_MAX.
$ clang --version
Apple clang version 4.1 (tags/Apple/clang-421.11.66) (based on LLVM 3.1svn)
Target: x86_64-apple-darwin12.4.0
Thread model: posix
Not a bug. long double in clang/x86_64 has 64 bits of precision, and results are rounded to fit in that format.
This will all be clearer if we use hex instead of binary. DBL_MAX is:
0xfffffffffffff800000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
The exact mathematical result of 1.L + DBL_MAX is therefore:
0xfffffffffffff800000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001
... but that is not representable as a long double, so the computed result is rounded to the closest representable long double, which is just DBL_MAX; adding 1 does not (and should not) change the value.
(It rounds down instead of up because the next larger representable number is
0xfffffffffffff801000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
which is much farther away from the mathematically precise result than DBL_MAX is).
The IEE754 floating-point double has mantissa of 53 bits wide (52 physical + 1 implicit bit). That means that double can accurately represent contiguous integers in -2^53...+2^53 range (i.e. from -9007199254740992 to +9007199254740992). After that, the type can no longer represent contiguous integers precisely. Instead, the type can represent only even integer values. Any odd value will be rounded to an adjacent even value in accordance with some implementation-specific rules. So, it is perfectly expected that adding 1 to 9007199254740992 within double might result in nothing due to rounding. Starting from that limit you'll have to add at least 2 to see the change in the value (until you reach the point where adding 2 will cease to have any effect either and you'll have to add at least 4, and so on).
The same logic applies to long double, if it is larger than double on your platform. On x86 long double might refer to hardware 80-bit floating-point type with 64-bit mantissa. It means that even with that type your range for precise representation of contiguous integers is limited to a mere -2^64...+2^64.
The value of DBL_MAX is far, FAR, FAAAAR! outside that range. Which means that trying to add 1 to DBL_MAX will not have any effect on the value. Adding 2 will not have any effect either. Neither will 4, nor 1024, nor even 4294967296. You have to add something in 2^960 area (actually nextafter(2^959)) in order to make an impact on a DBL_MAX value stored in a 80-bit long double format.
This is expected behavior.
long double a = 1.L + DBL_MAX + 1.L;
The long double type is floating point: it has a finite amount of precision. The result of most operations is rounded to the nearest representable value.
See What Every Programmer Should Know About Floating-Point Arithmetic.
A not quite technically correct answer that hopefully helps:
The number is represented by a sign, an exponent, and a fraction.
On this page, information about the C data types is given (https://en.wikipedia.org/wiki/C_data_types). The chart claims that long double is not guaranteed to be a "larger" data type than double; however, since C99 this is guaranteed if it exists on the target architecture (Annex F IEC 60559 floating-point arithmetic). Your results from DBL_MAX and LDBL_MAX show that on your implementation it does in fact use more bits.
So here's what's happening:
you have a number in the following format:
in double that would be
<1 bit><11 bits><52 bits>
in long, you have this 80 bit representation (https://en.wikipedia.org/wiki/Extended_precision)
<1 bit><15 bits><64 bits>
You can fit the double type into the long double type so this causes no problems. However, notice that the decimal point is "floating" (hence the name) not all digits in the number are represented. The computer represents the most significant digits, and then and exponent (so it would be like me writing 1234567 E 234 for example, notice that I'm not writing all 234 digits of that number). When you try to add 1 to this, the digit in the one's place is not being represented (due to the size of the exponent), so this will be ignored after rounding.
For more details, read up on floating point here (https://en.wikipedia.org/wiki/Double_precision_floating-point_format)

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