This question already has answers here:
Is floating point math broken?
(31 answers)
Closed 2 years ago.
I need to calculate the difference value between 2 string numbers by only taking only the first precision. I have to convert to double first then calculate the difference as below
#include <iostream>
#include <math.h>
#include <string>
using namespace std;
int main()
{
string v1 = "1568678435.244555";
string v2 = "1568678435.300111";
double s1 = atof(v1.substr(0,12).c_str()); // take upto first precision and convert to double
double s2 = atof(v2.substr(0,12).c_str()); // take upto first precision and convert to double
std::cout<<s1<<" "<<s2<<" "<<s2-s1<<endl;
if (s2-s1 >= 0.1)
cout<<"bigger";
else
cout<<"smaller";
return 0;
}
I expect the calculation would be 1568678435.3 - 1568678435.2 = 0.1 . But this program returns this value :
1.56868e+09 1.56868e+09 0.0999999
smaller
Why is that and how to get the value that I want properly?
Floating point format has limited precision. Not all values are representable. For example, the number 1568678435.2 is not representable (in IEEE-754 binary64 format). The closest representable value is:
1568678435.2000000476837158203125
1568678435.3 is also not a representable value. The closest reprecentable value is:
1568678435.2999999523162841796875
Given that the floating point values that you start with are not precise, it should be hardly surprising that the result of the calculation is also not precise. The floating point result of subtracting these numbers is:
0.099999904632568359375
Which very close to 0.1, but not quite. The error of the calculation was:
0.000000095367431640625
Also note that 0.1 is itself not a representable number, so there is no way to get that as the result of a floating point operation no matter what your inputs are.
how to get the value that I want properly?
To print the value 0.1, simply round the output to a sufficiently coarse precision:
std::cout << std::fixed << std::setprecision(1) << s2-s1;
This works as long as the error of the calculation doesn't exceed half of the desired precision.
If you don't want to deal with any accuracy error in your calculation, then you mustn't use floating point numbers.
You should round the difference between the values.
if (round((s2-s1) * 10) >= 1)
cout<<"bigger";
else
cout<<"smaller";
Related
This question already has answers here:
Is floating point math broken?
(31 answers)
Closed 3 years ago.
I had a program which requires one to search values from -100.00 to +100.00 with incrementation of 0.01 inside a for loop. But the if conditions arent working properly even if code is correct...
As an example I tried printing a small section i.e if(i==1.5){cout<<"yes...";}
it was not working even though the code was attaining the value i=1.5, i verified that by printing each of the values too.
#include<iostream>
#include<stdio.h>
using namespace std;
int main()
{
double i;
for(i=-1.00; i<1.00; i=i+0.01)
{
if(i>-0.04 && i<0.04)
{
cout<<i;
if(i==0.01)
cout<<"->yes ";
else
cout<<"->no ";
}
}
return 0;
}
Output:
-0.04->no -0.03->no -0.02->no -0.02->no -0.01->no 7.5287e-016->no 0.01->no 0.02->no 0.03->no
Process returned 0 (0x0) execution time : 1.391
(notice that 0.01 is being attained but still it prints 'no')
(also notice that 0.04 is being printed even if it wasn't instructed to do so)
use this if(abs(i - 0.01) < 0.00000000001) instead.
double - double precision floating point type. Usually IEEE-754 64 bit
floating point type
The crux of the problem is that numbers are represented in this format as a whole number times a power of two; rational numbers (such as 0.01, which is 1/100) whose denominator is not a power of two cannot be exactly represented.
In simple word, if the number can't be represented by a sum of 1/(2^n) you don't have the exact number you want to use. So to compare two double numbers calculate the absolute difference between them and use a tolerance value e.g. 0.00000000001 .
Doubles are stored in binary format. To cut things short fractional part is written as binary. Now let's imagine it's size is 1 bit. So you've two possible values (for fraction only): .0 and .5. With two bits you have: .0 .25 .5 .75. With three bits: .125 .25 .375 .5 .625 .75 .875. And so on. But you'll never get 0.1. So what computer does? It cheats. It lies to you, that 0.1 you see is 0.1. While it more looks like 0.1000000000000000002 or something like this. Why it looks like 0.1? Because formatting of floating point values has long standing tradition of rounding numbers, so 0.10000000000001 becomes 0.1. As a result 0.1 * 10 won't equal 1.0.
Correct solution is to avoid floating point numbers, unless you don't care for precision. If your program breaks, once your floating point value "changes" by miniscule amount, then you need to find another way. In your case using non-fractional values will be enough:
for(auto ii=-100; ii<100; ++ii)
{
if(ii>-4 && ii<4)
{
cout << (ii / 100.0);
if(ii==1)
cout<<"->yes ";
else
cout<<"->no ";
}
}
This question already has answers here:
Is floating point math broken?
(31 answers)
Closed 6 years ago.
I want to calculate the sum of three double numbers and I expect to get 1.
double a=0.0132;
double b=0.9581;
double c=0.0287;
cout << "sum= "<< a+b+c <<endl;
if (a+b+c != 1)
cout << "error" << endl;
The sum is equal to 1 but I still get the error! I also tried:
cout<< a+b+c-1
and it gives me -1.11022e-16
I could fix the problem by changing the code to
if (a+b+c-1 > 0.00001)
cout << "error" << endl;
and it works (no error). How can a negative number be greater than a positive number and why the numbers don't add up to 1?
Maybe it is something basic with summation and under/overflow but I really appreciate your help.
Thanks
Rational numbers are infinitely precise. Computers are finite.
Precision loss is a well known problem in computer programming.
The real question is, how can you remedy it?
Consider using an approximation function when comparing floats for equality.
#include <iostream>
#include <cmath>
#include <limits>
using namespace std;
template <typename T>
bool ApproximatelyEqual(const T dX, const T dY)
{
return std::abs(dX - dY) <= std::max(std::abs(dX), std::abs(dY))
* std::numeric_limits<T>::epsilon();
}
int main() {
double a=0.0132;
double b=0.9581;
double c=0.0287;
//Evaluates to true and does not print error.
if (!ApproximatelyEqual(a+b+c,1.0)) cout << "error" << endl;
}
Floating point numbers in C++ have a binary representation. This means that most numbers that can exactly represented by a decimal fraction with only a few digits cannot be exactly represented by floating point numbers. That's where your error comes from.
One example: 0.1 (decimal) is a periodic fraction in binary:
0.000110011001100110011001100...
Therefore it cannot be exactly be represented with any number of bits with binary encoding.
In order to avoid this type of error, you can use BCD (binary coded decimal) numbers which are supported by some special libraries. The drawbacks are slower calculation speed (not directly supported by the CPU) and slightly higher memory usage.
ANother option is to represent the number by a general fraction and store numerator and denomiator as separate integers.
This question already has answers here:
If operator< works properly for floating-point types, why can't we use it for equality testing?
(5 answers)
Closed 9 years ago.
Let:
double d = 0.1;
float f = 0.1;
should the expression
(f > d)
return true or false?
Empirically, the answer is true. However, I expected it to be false.
As 0.1 cannot be perfectly represented in binary, while double has 15 to 16 decimal digits of precision, and float has only 7. So, they both are less than 0.1, while the double is more close to 0.1.
I need an exact explanation for the true.
I'd say the answer depends on the rounding mode when converting the double to float. float has 24 binary bits of precision, and double has 53. In binary, 0.1 is:
0.1₁₀ = 0.0001100110011001100110011001100110011001100110011…₂
^ ^ ^ ^
1 10 20 24
So if we round up at the 24th digit, we'll get
0.1₁₀ ~ 0.000110011001100110011001101
which is greater than the exact value and the more precise approximation at 53 digits.
The number 0.1 will be rounded to the closest floating-point representation with the given precision. This approximation might be either greater than or less than 0.1, so without looking at the actual values, you can't predict whether the single precision or double precision approximation is greater.
Here's what the double precision value gets rounded to (using a Python interpreter):
>>> "%.55f" % 0.1
'0.1000000000000000055511151231257827021181583404541015625'
And here's the single precision value:
>>> "%.55f" % numpy.float32("0.1")
'0.1000000014901161193847656250000000000000000000000000000'
So you can see that the single precision approximation is greater.
If you convert .1 to binary you get:
0.000110011001100110011001100110011001100110011001100...
repeating forever
Mapping to data types, you get:
float(.1) = %.00011001100110011001101
^--- note rounding
double(.1) = %.0001100110011001100110011001100110011001100110011010
Convert that to base 10:
float(.1) = .10000002384185791015625
double(.1) = .100000000000000088817841970012523233890533447265625
This was taken from an article written by Bruce Dawson. it can be found here:
Doubles are not floats, so don’t compare them
I think Eric Lippert's comment on the question is actually the clearest explanation, so I'll repost it as an answer:
Suppose you are computing 1/9 in 3-digit decimal and 6-digit decimal. 0.111 < 0.111111, right?
Now suppose you are computing 6/9. 0.667 > 0.666667, right?
You can't have it that 6/9 in three digit decimal is 0.666 because that is not the closest 3-digit decimal to 6/9!
Since it can't be exactly represented, comparing 1/10 in base 2 is like comparing 1/7 in base 10.
1/7 = 0.142857142857... but comparing at different base 10 precisions (3 versus 6 decimal places) we have 0.143 > 0.142857.
Just to add to the other answers talking about IEEE-754 and x86: the issue is even more complicated than they make it seem. There is not "one" representation of 0.1 in IEEE-754 - there are two. Either rounding the last digit down or up would be valid. This difference can and does actually occur, because x86 does not use 64-bits for its internal floating-point computations; it actually uses 80-bits! This is called double extended-precision.
So, even among just x86 compilers, it sometimes happen that the same number is represented two different ways, because some computes its binary representation with 64-bits, while others use 80.
In fact, it can happen even with the same compiler, even on the same machine!
#include <iostream>
#include <cmath>
void foo(double x, double y)
{
if (std::cos(x) != std::cos(y)) {
std::cout << "Huh?!?\n"; //← you might end up here when x == y!!
}
}
int main()
{
foo(1.0, 1.0);
return 0;
}
See Why is cos(x) != cos(y) even though x == y? for more info.
The rank of double is greater than that of float in conversions. By doing a logical comparison, f is cast to double and maybe the implementation you are using is giving inconsistent results. If you suffix f so the compiler registers it as a float, then you get 0.00 which is false in double type. Unsuffixed floating types are double.
#include <stdio.h>
#include <float.h>
int main()
{
double d = 0.1;
float f = 0.1f;
printf("%f\n", (f > d));
return 0;
}
This question already has answers here:
strange output in comparison of float with float literal
(8 answers)
Closed 9 years ago.
EDIT: There are a lot of disgruntled members here because this question had a duplicate on the site. In my defense, I tried searching for the answer FIRST, and maybe I was using poor searching keywords, but I could not find a direct, clear answer to this specific code example. Little did I know there was one out there from **2009** that would then be linked to from here.
Here's a coded example:
#include <iostream>
using namespace std;
int main() {
float x = 0.1 * 7;
if (x == 0.7)
cout << "TRUE. \n";
else
cout << "FALSE. \n";
return 0;
}
This results in FALSE. However, when I output x, it does indeed output as 0.7. Explanation?
Please read What Every Computer Scientist Should Know About Floating-Point Arithmetic.
First of all, 0.1 is a literal of type double. The closest representable value to 0.1 in IEEE 754 double-precision is:
0.1000000000000000055511151231257827021181583404541015625
If you multiply that by 7, the closest representable value in IEE 754 single-precision (since you're storing it in a float) is:
0.699999988079071044921875
Which, as you can see, is almost 0.7, but not quite. This then gets converted to a double for the comparison, and you end up comparing the following two values:
0.699999988079071044921875 == 0.6999999999999999555910790149937383830547332763671875
Which of course evaluates to false.
This is because numbers are stored in binary. In binary, you cannot exactly represent the fraction .1 or .7 with finitely many places, because these have repeating expansions in binary. something like 1/2 can be represented exactly with the representation .1, but .1 in decimal for instance is .0001100110011.... So, when you cut off this number, you're bound to have roundoff error.
Doubles and floats should never be compared by the == operator. Numbers are stored in memory inaccurately because in binary they don't have to have finite representation (for example 0.1).
You will see it here:
#include <iostream>
using namespace std;
int main() {
float x = 0.1 * 7;
cout << x-0.7;
return 0;
}
The difference is NOT zero, but something very very close to zero.
Like every datatype a float is represented as binary number. For the exact representation see here: http://en.wikipedia.org/wiki/IEEE_floating_point
When converting a decimal number to a floating point number by hand, you first have to convert it to a fixed point number.
Converting 0.7 to base 2 (binary):
0.7 = 0.101100110011...
As you see it has infinite digits after the comma, so when representing it as a float datatype, some digits will get cut off. This results in the number not being EXACTLY 0.7 when converting it back to decimal.
In your example the multiplication results in a different number than the literal "0.7".
To fix this: Use a epsilon when comparing equality of floats:
if (x < 0.71f && x > 0.69f)
This question already has answers here:
Closed 11 years ago.
Possible Duplicate:
strange output in comparision of float with float literal
float f = 1.1;
double d = 1.1;
if(f == d) // returns false!
Why is it so?
The important factors under consideration with float or double numbers are:
Precision & Rounding
Precision:
The precision of a floating point number is how many digits it can represent without losing any information it contains.
Consider the fraction 1/3. The decimal representation of this number is 0.33333333333333… with 3′s going out to infinity. An infinite length number would require infinite memory to be depicted with exact precision, but float or double data types typically only have 4 or 8 bytes. Thus Floating point & double numbers can only store a certain number of digits, and the rest are bound to get lost. Thus, there is no definite accurate way of representing float or double numbers with numbers that require more precision than the variables can hold.
Rounding:
There is a non-obvious differences between binary and decimal (base 10) numbers.
Consider the fraction 1/10. In decimal, this can be easily represented as 0.1, and 0.1 can be thought of as an easily representable number. However, in binary, 0.1 is represented by the infinite sequence: 0.00011001100110011…
An example:
#include <iomanip>
int main()
{
using namespace std;
cout << setprecision(17);
double dValue = 0.1;
cout << dValue << endl;
}
This output is:
0.10000000000000001
And not
0.1.
This is because the double had to truncate the approximation due to it’s limited memory, which results in a number that is not exactly 0.1. Such an scenario is called a Rounding error.
Whenever comparing two close float and double numbers such rounding errors kick in and eventually the comparison yields incorrect results and this is the reason you should never compare floating point numbers or double using ==.
The best you can do is to take their difference and check if it is less than an epsilon.
abs(x - y) < epsilon
Try running this code, the results will make the reason obvious.
#include <iomanip>
#include <iostream>
int main()
{
std::cout << std::setprecision(100) << (double)1.1 << std::endl;
std::cout << std::setprecision(100) << (float)1.1 << std::endl;
std::cout << std::setprecision(100) << (double)((float)1.1) << std::endl;
}
The output:
1.100000000000000088817841970012523233890533447265625
1.10000002384185791015625
1.10000002384185791015625
Neither float nor double can represent 1.1 accurately. When you try to do the comparison the float number is implicitly upconverted to a double. The double data type can accurately represent the contents of the float, so the comparison yields false.
Generally you shouldn't compare floats to floats, doubles to doubles, or floats to doubles using ==.
The best practice is to subtract them, and check if the absolute value of the difference is less than a small epsilon.
if(std::fabs(f - d) < std::numeric_limits<float>::epsilon())
{
// ...
}
One reason is because floating point numbers are (more or less) binary fractions, and can only approximate many decimal numbers. Many decimal numbers must necessarily be converted to repeating binary "decimals", or irrational numbers. This will introduce a rounding error.
From wikipedia:
For instance, 1/5 cannot be represented exactly as a floating point number using a binary base but can be represented exactly using a decimal base.
In your particular case, a float and double will have different rounding for the irrational/repeating fraction that must be used to represent 1.1 in binary. You will be hard pressed to get them to be "equal" after their corresponding conversions have introduced different levels of rounding error.
The code I gave above solves this by simply checking if the values are within a very short delta. Your comparison changes from "are these values equal?" to "are these values within a small margin of error from each other?"
Also, see this question: What is the most effective way for float and double comparison?
There are also a lot of other oddities about floating point numbers that break a simple equality comparison. Check this article for a description of some of them:
http://www.cygnus-software.com/papers/comparingfloats/comparingfloats.htm
The IEEE 754 32-bit float can store: 1.1000000238...
The IEEE 754 64-bit double can store: 1.1000000000000000888...
See why they're not "equal"?
In IEEE 754, fractions are stored in powers of 2:
2^(-1), 2^(-2), 2^(-3), ...
1/2, 1/4, 1/8, ...
Now we need a way to represent 0.1. This is (a simplified version of) the 32-bit IEEE 754 representation (float):
2^(-4) + 2^(-5) + 2^(-8) + 2^(-9) + 2^(-12) + 2^(-13) + ... + 2^(-24) + 2^(-25) + 2^(-27)
00011001100110011001101
1.10000002384185791015625
With 64-bit double, it's even more accurate. It doesn't stop at 2^(-25), it keeps going for about twice as much. (2^(-48) + 2^(-49) + 2^(-51), maybe?)
Resources
IEEE 754 Converter (32-bit)
Floats and doubles are stored in a binary format that can not represent every number exactly (it's impossible to represent the infinitely many possible different numbers in a finite space).
As a result they do rounding. Float has to round more than double, because it is smaller, so 1.1 rounded to the nearest valid Float is different to 1.1 rounded to the nearest valud Double.
To see what numbers are valid floats and doubles see Floating Point