This question already has answers here:
Round a float to a regular grid of predefined points
(11 answers)
Closed 4 years ago.
I am calculating the number of significant numbers past the decimal point. My program discards any numbers that are spaced more than 7 orders of magnitude apart after the decimal point. Expecting some error with doubles, I accounted for very small numbers popping up when subtracting ints from doubles, even when it looked like it should equal zero (To my knowledge this is due to how computers store and compute their numbers). My confusion is why my program does not handle this unexpected number given this random test value.
Having put many cout statements it would seem that it messes up when it tries to cast the final 2. Whenever it casts it casts to 1 instead.
bool flag = true;
long double test = 2029.00012;
int count = 0;
while(flag)
{
test = test - static_cast<int>(test);
if(test <= 0.00001)
{
flag = false;
}
test *= 10;
count++;
}
The solution I found was to cast only once at the beginning, as rounding may produce a negative and terminate prematurely, and to round thenceforth. The interesting thing is that both trunc and floor also had this issue, seemingly turning what should be a 2 into a 1.
My Professor and I were both quite stumped as I fully expected small numbers to appear (most were in the 10^-10 range), but was not expecting that casting, truncing, and flooring would all also fail.
It is important to understand that not all rational numbers are representable in finite precision. Also, it is important to understand that set of numbers which are representable in finite precision in decimal base, is different from the set of numbers that are representable in finite precision in binary base. Finally, it is important to understand that your CPU probably represents floating point numbers in binary.
2029.00012 in particular happens to be a number that is not representable in a double precision IEEE 754 floating point (and it indeed is a double precision literal; you may have intended to use long double instead). It so happens that the closest number that is representable is 2029.000119999999924402800388634204864501953125. So, you're counting the significant digits of that number, not the digits of the literal that you used.
If the intention of 0.00001 was to stop counting digits when the number is close to a whole number, it is not sufficient to check whether the value is less than the threshold, but also whether it is greater than 1 - threshold, as the representation error can go either way:
if(test <= 0.00001 || test >= 1 - 0.00001)
After all, you can multiple 0.99999999999999999999999999 with 10 many times until the result becomes close to zero, even though that number is very close to a whole number.
As multiple people have already commented, that won't work because of limitations of floating-point numbers. You had a somewhat correct intuition when you said that you expected "some error" with doubles, but that is ultimately not enough. Running your specific program on my machine, the closest representable double to 2029.00012 is 2029.0001199999999244 (this is actually a truncated value, but it shows the series of 9's well enough). For that reason, when you multiply by 10, you keep finding new significant digits.
Ultimately, the issue is that you are manipulating a base-2 real number like it's a base-10 number. This is actually quite difficult. The most notorious use cases for this are printing and parsing floating-point numbers, and a lot of sweat and blood went into that. For example, it wasn't that long ago that you could trick the official Java implementation into looping endlessly trying to convert a String to a double.
Your best shot might be to just reuse all that hard work. Print to 7 digits of precision, and subtract the number of trailing zeroes from the result:
#include <iostream>
#include <sstream>
#include <iomanip>
#include <string>
int main() {
long double d = 2029.00012;
auto double_string = (std::stringstream() << std::fixed << std::setprecision(7) << d).str();
auto first_decimal_index = double_string.find('.') + 1;
auto last_nonzero_index = double_string.find_last_not_of('0');
if (last_nonzero_index == std::string::npos) {
std::cout << "7 significant digits\n";
} else if (last_nonzero_index < first_decimal_index) {
std::cout << -(first_decimal_index - last_nonzero_index + 1) << " significant digits\n";
} else {
std::cout << (last_nonzero_index - first_decimal_index) << " significant digits\n";
}
}
It feels unsatisfactory, but:
it correctly prints 5;
the "satisfactory" alternative is possibly significantly harder to implement.
It seems to me that your second-best alternative is to read on floating-point printing algorithms and implement just enough of it to get the length of the value that you're going to print, and that's not exactly an introductory-level task. If you decide to go this route, the current state of the art is the Grisu2 algorithm. Grisu2 has the notable benefit that it will always print the shortest base-10 string that will produce the given floating-point value, which is what you seem to be after.
If you want sane results, you can't just truncate the digits, because sometimes the floating point number will be a hair less than the rounded number. If you want to fix this via a fluke, change your initialization to be
long double test = 2029.00012L;
If you want to fix it for real,
bool flag = true;
long double test = 2029.00012;
int count = 0;
while (flag)
{
test = test - static_cast<int>(test + 0.000005);
if (test <= 0.00001)
{
flag = false;
}
test *= 10;
count++;
}
My apologies for butchering your haphazard indent; I can't abide by them. According to one of my CS professors, "ideally, a computer scientist never has to worry about the underlying hardware." I'd guess your CS professor might have similar thoughts.
Related
Here is my code :
#include <iostream>
#include <cmath>
using namespace std;
int main()
{
int n, i, num, m, k = 0;
cout << "Enter a number :\n";
cin >> num;
n = log10(num);
while (n > 0) {
i = pow(10, n);
m = num / i;
k = k + pow(m, 3);
num = num % i;
--n;
cout << m << endl;
cout << num << endl;
}
k = k + pow(num, 3);
return 0;
}
When I input 111 it gives me this
1
12
1
2
I am using codeblocks. I don't know what is wrong.
Whenever I use pow expecting an integer result, I add .5 so I use (int)(pow(10,m)+.5) instead of letting the compiler automatically convert pow(10,m) to an int.
I have read many places telling me others have done exhaustive tests of some of the situations in which I add that .5 and found zero cases where it makes a difference. But accurately identifying the conditions in which it isn't needed can be quite hard. Using it when it isn't needed does no real harm.
If it makes a difference, it is a difference you want. If it doesn't make a difference, it had a tiny cost.
In the posted code, I would adjust every call to pow that way, not just the one I used as an example.
There is no equally easy fix for your use of log10, but it may be subject to the same problem. Since you expect a non integer answer and want that non integer answer truncated down to an integer, adding .5 would be very wrong. So you may need to find some more complicated work around for the fundamental problem of working with floating point. I'm not certain, but assuming 32-bit integers, I think adding 1e-10 to the result of log10 before converting to int is both never enough to change log10(10^n-1) into log10(10^n) but always enough to correct the error that might have done the reverse.
pow does floating-point exponentiation.
Floating point functions and operations are inexact, you cannot ever rely on them to give you the exact value that they would appear to compute, unless you are an expert on the fine details of IEEE floating point representations and the guarantees given by your library functions.
(and furthermore, floating-point numbers might even be incapable of representing the integers you want exactly)
This is particularly problematic when you convert the result to an integer, because the result is truncated to zero: int x = 0.999999; sets x == 0, not x == 1. Even the tiniest error in the wrong direction completely spoils the result.
You could round to the nearest integer, but that has problems too; e.g. with sufficiently large numbers, your floating point numbers might not have enough precision to be near the result you want. Or if you do enough operations (or unstable operations) with the floating point numbers, the errors can accumulate to the point you get the wrong nearest integer.
If you want to do exact, integer arithmetic, then you should use functions that do so. e.g. write your own ipow function that computes integer exponentiation without any floating-point operations at all.
I am writing a function in c++ that is supposed to find the largest single digit in the number passed (inputValue). For example, the answer for .345 is 5. However, after a while, the program is changing the inputValue to something along the lines of .3449 (and the largest digit is then set to 9). I have no idea why this is happening. Any help to resolve this problem would be greatly appreciated.
This is the function in my .hpp file
void LargeInput(const double inputValue)
//Function to find the largest value of the input
{
int tempMax = 0,//Value that the temporary max number is in loop
digit = 0,//Value of numbers after the decimal place
test = 0,
powerOten = 10;//Number multiplied by so that the next digit can be checked
double number = inputValue;//A variable that can be changed in the function
cout << "The number is still " << number << endl;
for (int k = 1; k <= 6; k++)
{
test = (number*powerOten);
cout << "test: " << test << endl;
digit = test % 10;
cout << (static_cast<int>(number*powerOten)) << endl;
if (tempMax < digit)
tempMax = digit;
powerOten *= 10;
}
return;
}
You cannot represent real numbers (doubles) precisely in a computer - they need to be approximated. If you change your function to work on longs or ints there won't be any inaccuracies. That seems natural enough for the context of your question, you're just looking at the digits and not the number, so .345 can be 345 and get the same result.
Try this:
int get_largest_digit(int n) {
int largest = 0;
while (n > 0) {
int x = n % 10;
if (x > largest) largest = x;
n /= 10;
}
return largest;
}
This is because the fractional component of real numbers is in the form of 1/2^n. As a result you can get values very close to what you want but you can never achieve exact values like 1/3.
It's common to instead use integers and have a conversion (like 1000 = 1) so if you had the number 1333 you would do printf("%d.%d", 1333/1000, 1333 % 1000) to print out 1.333.
By the way the first sentence is a simplification of how floating point numbers are actually represented. For more information check out; http://en.wikipedia.org/wiki/Floating_point#Representable_numbers.2C_conversion_and_rounding
This is how floating point number work, unfortunately. The core of the problem is that there are an infinite number of floating point numbers. More specifically, there are an infinite number of values between 0.1 and 0.2 and there are an infinite number of values between 0.01 and 0.02. Computers, however, have a finite number of bits to represent a floating point number (64 bits for a double precision number). Therefore, most floating point numbers have to be approximated. After any floating point operation, the processor has to round the result to a value it can represent in 64 bits.
Another property of floating point numbers is that as number get bigger they get less and less precise. This is because the same 64 bits have to be able to represent very big numbers (1,000,000,000) and very small numbers (0.000,000,000,001). Therefore, the rounding error gets larger when working with bigger numbers.
The other issue here is that you are converting from floating point to integer. This introduces even more rounding error. It appears that when (0.345 * 10000) is converted to an integer, the result is closer to 3449 than 3450.
I suggest you don't convert your numbers to integers. Write your program in terms of floating point numbers. You can't use the modulus (%) operator on floating point numbers to get a value for digit. Instead use the fmod function in the C math library (cmath.h).
As other answers have indicated, binary floating-point is incapable of representing most decimal numbers exactly. Therefore, you must reconsider your problem statement. Some alternatives are:
The number is passed as a double (specifically, a 64-bit IEEE-754 binary floating-point value), and you wish to find the largest digit in the decimal representation of the exact value passed. In this case, the solution suggested by user millimoose will work (provided the asprintf or snprintf function used is of good quality, so that it does not incur rounding errors that prevent it from producing correctly rounded output).
The number is passed as a double but is intended to represent a number that is exactly representable as a decimal numeral with a known number of digits. In this case, the solution suggested by user millimoose again works, with the format specification altered to convert the double to decimal with the desired number of digits (e.g., instead of “%.64f”, you could use “%.6f”).
The function is changed to pass the number in another way, such as with decimal floating-point, as a scaled integer, or as a string containing a decimal numeral.
Once you have clarified the problem statement, it may be interesting to consider how to solve it with floating-point arithmetic, rather than calling library functions for formatted output. This is likely to have pedagogical value (and incidentally might produce a solution that is computationally more efficient than calling a library function).
For example, this blog says 0.005 is not exactly 0.005, but rounding that number yields the right result.
I have tried all kinds of rounding in C++ and it fails when rounding numbers to certain decimal places. For example, Round(x,y) rounds x to a multiple of y. So Round(37.785,0.01) should give you 37.79 and not 37.78.
I am reopening this question to ask the community for help. The problem is with the impreciseness of floating point numbers (37,785 is represented as 37.78499999999).
The question is how does Excel get around this problem?
The solution in this round() for float in C++ is incorrect for the above problem.
"Round(37.785,0.01) should give you 37.79 and not 37.78."
First off, there is no consensus that 37.79 rather than 37.78 is the "right" answer here? Tie-breakers are always a bit tough. While always rounding up in the case of a tie is a widely-used approach, it certainly is not the only approach.
Secondly, this isn't a tie-breaking situation. The numerical value in the IEEE binary64 floating point format is 37.784999999999997 (approximately). There are lots of ways to get a value of 37.784999999999997 besides a human typing in a value of 37.785 and happen to have that converted to that floating point representation. In most of these cases, the correct answer is 37.78 rather than 37.79.
Addendum
Consider the following Excel formulae:
=ROUND(37785/1000,2)
=ROUND(19810222/2^19+21474836/2^47,2)
Both cells will display the same value, 37.79. There is a legitimate argument over whether 37785/1000 should round to 37.78 or 37.79 with two place accuracy. How to deal with these corner cases is a bit arbitrary, and there is no consensus answer. There isn't even a consensus answer inside Microsoft: "the Round() function is not implemented in a consistent fashion among different Microsoft products for historical reasons." ( http://support.microsoft.com/kb/196652 ) Given an infinite precision machine, Microsoft's VBA would round 37.785 to 37.78 (banker's round) while Excel would yield 37.79 (symmetric arithmetic round).
There is no argument over the rounding of the latter formula. It is strictly less than 37.785, so it should round to 37.78, not 37.79. Yet Excel rounds it up. Why?
The reason has to do with how real numbers are represented in a computer. Microsoft, like many others, uses the IEEE 64 bit floating point format. The number 37785/1000 suffers from precision loss when expressed in this format. This precision loss does not occur with 19810222/2^19+21474836/2^47; it is an "exact number".
I intentionally constructed that exact number to have the same floating point representation as does the inexact 37785/1000. That Excel rounds this exact value up rather than down is the key to determining how Excel's ROUND() function works: It is a variant of symmetric arithmetic rounding. It rounds based on a comparison to the floating point representation of the corner case.
The algorithm in C++:
#include <cmath> // std::floor
// Compute 10 to some positive integral power.
// Dealing with overflow (exponent > 308) is an exercise left to the reader.
double pow10 (unsigned int exponent) {
double result = 1.0;
double base = 10.0;
while (exponent > 0) {
if ((exponent & 1) != 0) result *= base;
exponent >>= 1;
base *= base;
}
return result;
}
// Round the same way Excel does.
// Dealing with nonsense such as nplaces=400 is an exercise left to the reader.
double excel_round (double x, int nplaces) {
bool is_neg = false;
// Excel uses symmetric arithmetic round: Round away from zero.
// The algorithm will be easier if we only deal with positive numbers.
if (x < 0.0) {
is_neg = true;
x = -x;
}
// Construct the nearest rounded values and the nasty corner case.
// Note: We really do not want an optimizing compiler to put the corner
// case in an extended double precision register. Hence the volatile.
double round_down, round_up;
volatile double corner_case;
if (nplaces < 0) {
double scale = pow10 (-nplaces);
round_down = std::floor (x * scale);
corner_case = (round_down + 0.5) / scale;
round_up = (round_down + 1.0) / scale;
round_down /= scale;
}
else {
double scale = pow10 (nplaces);
round_down = std::floor (x / scale);
corner_case = (round_down + 0.5) * scale;
round_up = (round_down + 1.0) * scale;
round_down *= scale;
}
// Round by comparing to the corner case.
x = (x < corner_case) ? round_down : round_up;
// Correct the sign if needed.
if (is_neg) x = -x;
return x;
}
For very accurate arbitrary precision and rounding of floating point numbers to a fixed set of decimal places, you should take a look at a math library like GNU MPFR. While it's a C-library, the web-page I posted also links to a couple different C++ bindings if you want to avoid using C.
You may also want to read a paper entitled "What every computer scientist should know about floating point arithmetic" by David Goldberg at the Xerox Palo Alto Research Center. It's an excellent article demonstrating the underlying process that allows floating point numbers to be approximated in a computer that represents everything in binary data, and how rounding errors and other problems can creep up in FPU-based floating point math.
I don't know how Excel does it, but printing floating point numbers nicely is a hard problem: http://www.serpentine.com/blog/2011/06/29/here-be-dragons-advances-in-problems-you-didnt-even-know-you-had/
So your actual question seems to be, how to get correctly rounded floating point -> string conversions. By googling for those terms you'll get a bunch of articles, but if you're interested in something to use, most platforms provide reasonably competent implementations of sprintf()/snprintf(). So just use those, and if you find bugs, file a report to the vendor.
A function that takes a floating point number as argument and returns another floating point number, rounded exactly to a given number of decimal digits cannot be written, because there are many numbers with a finite decimal representation that have an infinite binary representation; one of the simplest examples is 0.1 .
To achieve what you want you must accept to use a different type as a result of your rounding function. If your immediate need is printing the number you can use a string and a formatting function: the problem becomes how to obtain exactly the formatting you expect. Otherwise if you need to store this number in order to perform exact calculations on it, for instance if you are doing accounting, you need a library that's capable of representing decimal numbers exactly. In this case the most common approach is to use a scaled representation: an integer for the value together with the number of decimal digits. Dividing the value by ten raised to the scale gives you the original number.
If any of these approaches is suitable, I'll try and expand my answer with practical suggestions.
Excel rounds numbers like this "correctly" by doing WORK. They started in 1985, with a fairly "normal" set of floating-point routines, and added some scaled-integer fake floating point, and they've been tuning those things and adding special cases ever since. The app DID used to have most of the same "obvious" bugs that everybody else did, it's just that it mostly had them a long time ago. I filed a couple myself, back when I was doing tech support for them in the early 90s.
I believe the following C# code rounds numbers as they are rounded in Excel. To exactly replicate the behavior in C++ you might need to use a special decimal type.
In plain English, the double-precision number is converted to a decimal and then rounded to fifteen significant digits (not to be confused with fifteen decimal places). The result is rounded a second time to the specified number of decimal places.
That might seem weird, but what you have to understand is that Excel always displays numbers that are rounded to 15 significant figures. If the ROUND() function weren't using that display value as a starting point, and used the internal double representation instead, then there would be cases where ROUND(A1,N) did not seem to correspond to the actual value in A1. That would be very confusing to a non-technical user.
The double which is closest to 37.785 has an exact decimal value of 37.784999999999996589394868351519107818603515625. (Any double can be represented precisely by a finite base ten decimal because one quarter, one eighth, one sixteenth, and so forth all have finite decimal expansions.) If that number were rounded directly to two decimal places, there would be no tie to break and the result would be 37.78. If you round to 15 significant figures first you get 37.7850000000000. If this is further rounded to two decimal places, then you get 37.79, so there is no real mystery after all.
// Convert to a floating decimal point number, round to fifteen
// significant digits, and then round to the number of places
// indicated.
static decimal SmartRoundDouble(double input, int places)
{
int numLeadingDigits = (int)Math.Log10(Math.Abs(input)) + 1;
decimal inputDec = GetAccurateDecimal(input);
inputDec = MoveDecimalPointRight(inputDec, -numLeadingDigits);
decimal round1 = Math.Round(inputDec, 15);
round1 = MoveDecimalPointRight(round1, numLeadingDigits);
decimal round2 = Math.Round(round1, places, MidpointRounding.AwayFromZero);
return round2;
}
static decimal MoveDecimalPointRight(decimal d, int n)
{
if (n > 0)
for (int i = 0; i < n; i++)
d *= 10.0m;
else
for (int i = 0; i > n; i--)
d /= 10.0m;
return d;
}
// The constructor for decimal that accepts a double does
// some rounding by default. This gets a more exact number.
static decimal GetAccurateDecimal(double r)
{
string accurateStr = r.ToString("G17", CultureInfo.InvariantCulture);
return Decimal.Parse(accurateStr, CultureInfo.InvariantCulture);
}
What you NEED is this :
double f = 22.0/7.0;
cout.setf(ios::fixed, ios::floatfield);
cout.precision(6);
cout<<f<<endl;
How it can be implemented (just a overview for rounding last digit)
:
long getRoundedPrec(double d, double precision = 9)
{
precision = (int)precision;
stringstream s;
long l = (d - ((double)((int)d)))* pow(10.0,precision+1);
int lastDigit = (l-((l/10)*10));
if( lastDigit >= 5){
l = l/10 +1;
}
return l;
}
Just as base-10 numbers must be rounded as they are converted to base-2, it is possible to round a number as it is converted from base-2 to base-10. Once the number has a base-10 representation it can be rounded again in a straightforward manner by looking at the digit to the right of the one you wish to round.
While there's nothing wrong with the above assertion, there's a much more pragmatic solution. The problem is that the binary representation tries to get as close as possible to the decimal number, even if that binary is less than the decimal. The amount of error is within [-0.5,0.5] least significant bits (LSB) of the true value. For rounding purposes you'd rather it be within [0,1] LSB so that the error is always positive, but that's not possible without changing all the rules of floating point math.
The one thing you can do is add 1 LSB to the value, so the error is within [0.5,1.5] LSB of the true value. This is less accurate overall, but only by a very tiny amount; when the value is rounded for representation as a decimal number it is much more likely to be rounded to a proper decimal number because the error is always positive.
To add 1 LSB to the value before rounding it, see the answers to this question. For example in Visual Studio C++ 2010 the procedure would be:
Round(_nextafter(37.785,37.785*1.1),0.01);
There are many ways to optimize the result of a floating-point value using statistical, numerical... algorithms
The easiest one is probably searching for repetitive 9s or 0s in the range of precision. If there are any, maybe those 9s are redundant, just round them up. But this may not work in many cases. Here's an example for a float with 6 digits of precision:
2.67899999 → 2.679
12.3499999 → 12.35
1.20000001 → 1.2
Excel always limits the input range to 15 digits and rounds the output to maximum 15 digits so this might be one of the way Excel uses
Or you can include the precision along with the number. After each step, adjust the accuracy depend on the precision of operands. For example
1.113 → 3 decimal digits
6.15634 → 5 decimal digits
Since both number are inside the double's 16-17 digits precision range, their sum will be accurate to the larger of them, which is 5 digits. Similarly, 3+5 < 16, so their product will be precise to 8 decimal numbers
1.113 + 6.15634 = 7.26934 → 5 decimal digits
1.113 * 6.15634 = 6.85200642 → 8 decimal digits
But 4.1341677841 * 2.251457145 will only take double's accuracy because the real result exceed double's precision
Another efficient algorithm is Grisu but I haven't had an opportunity to try.
In 2010, Florian Loitsch published a wonderful paper in PLDI, "Printing floating-point numbers quickly and accurately with integers", which represents the biggest step in this field in 20 years: he mostly figured out how to use machine integers to perform accurate rendering! Why do I say "mostly"? Because although Loitsch's "Grisu3" algorithm is very fast, it gives up on about 0.5% of numbers, in which case you have to fall back to Dragon4 or a derivative
Here be dragons: advances in problems you didn’t even know you had
In fact I think Excel must combine many different methods to achieve the best result of all
Example When a Value Reaches Zero
In Excel 95 or earlier, enter the following into a new workbook:
A1: =1.333+1.225-1.333-1.225
Right-click cell A1, and then click Format Cells. On the Number tab, click Scientific under Category. Set the Decimal places to 15.
Rather than displaying 0, Excel 95 displays -2.22044604925031E-16.
Excel 97, however, introduced an optimization that attempts to correct for this problem. Should an addition or subtraction operation result in a value at or very close to zero, Excel 97 and later will compensate for any error introduced as a result of converting an operand to and from binary. The example above when performed in Excel 97 and later correctly displays 0 or 0.000000000000000E+00 in scientific notation.
Floating-point arithmetic may give inaccurate results in Excel
As mjfgates says, Excel does hard work to get this "right". The first thing to do when you try to reimplement this, is define what you mean by "right". Obvious solutions:
implement rational arithmetic
Slow but reliable.
implement a bunch of heuristics
Fast but tricky to get right (think "years of bug reports").
It really depends on your application.
Most decimal fractions can't be accurately represented in binary.
double x = 0.0;
for (int i = 1; i <= 10; i++)
{
x += 0.1;
}
// x should now be 1.0, right?
//
// it isn't. Test it and see.
One solution is to use BCD. It's old. But, it's also tried and true. We have a lot of other old ideas that we use every day (like using a 0 to represent nothing...).
Another technique uses scaling upon input/output. This has the advantage of nearly all math being integer math.
This question already has answers here:
Closed 11 years ago.
Possible Duplicate:
Why does Visual Studio 2008 tell me .9 - .8999999999999995 = 0.00000000000000055511151231257827?
c++
Hey so i'm making a function to return the number of a digits in a number data type given, but i'm having some trouble with doubles.
I figure out how many digits are in it by multiplying it by like 10 billion and then taking away digits 1 by 1 until the double ends up being 0. however when putting in a double of value say .7904 i never exit the function as it keeps taking away digits which never end up being 0 as the resut of .7904 ends up being 7,903,999,988 and not 7,904,000,000.
How can i solve this problem?? Thanks =) ! oh and any other feed back on my code is WELCOME!
here's the code of my function:
/////////////////////// Numb_Digits() ////////////////////////////////////////////////////
enum{DECIMALS = 10, WHOLE_NUMBS = 20, ALL = 30};
template<typename T>
unsigned long int Numb_Digits(T numb, int scope)
{
unsigned long int length= 0;
switch(scope){
case DECIMALS: numb-= (int)numb; numb*=10000000000; // 10 bil (10 zeros)
for(; numb != 0; length++)
numb-=((int)(numb/pow((double)10, (double)(9-length))))* pow((double)10, (double)(9-length)); break;
case WHOLE_NUMBS: numb= (int)numb; numb*=10000000000;
for(; numb != 0; length++)
numb-=((int)(numb/pow((double)10, (double)(9-length))))* pow((double)10, (double)(9-length)); break;
case ALL: numb = numb; numb*=10000000000;
for(; numb != 0; length++)
numb-=((int)(numb/pow((double)10, (double)(9-length))))* pow((double)10, (double)(9-length)); break;
default: break;}
return length;
};
int main()
{
double test = 345.6457;
cout << Numb_Digits(test, ALL) << endl;
cout << Numb_Digits(test, DECIMALS) << endl;
cout << Numb_Digits(test, WHOLE_NUMBS) << endl;
return 0;
}
It's because of their binary representation, which is discussed in depth here:
http://en.wikipedia.org/wiki/IEEE_754-2008
Basically, when a number can't be represented as is, an approximation is used instead.
To compare floats for equality, check if their difference is lesser than an arbitrary precision.
The easy summary about floating point arithmetic :
http://floating-point-gui.de/
Read this and you'll see the light.
If you're more on the math side, Goldberg paper is always nice :
http://cr.yp.to/2005-590/goldberg.pdf
Long story short : real numbers are stored with a fixed, irregular precision, leading to non obvious behaviors. This is unrelated to the language but more a design choice of how to handle real numbers as a whole.
This is because C++ (like most other languages) can not store floating point numbers with infinte precision.
Floating points are stored like this:
sign * coefficient * 10^exponent if you're using base 10.
The problem is that both the coefficient and exponent are stored as finite integers.
This is a common problem with storing floating point in computer programs, you usually get a tiny rounding error.
The most common way of dealing with this is:
Store the number as a fraction (x/y)
Use a delta that allows small deviations (if abs(x-y) < delta)
Use a third party library such as GMP that can store floating point with perfect precision.
Regarding your question about counting decimals.
There is no way of dealing with this if you get a double as input. You cannot be sure that the user actually sent 1.819999999645634565360 and not 1.82.
Either you have to change your input or change the way your function works.
More info on floating point can be found here: http://en.wikipedia.org/wiki/Floating_point
This is because of the way the IEEE floating point standard is implemented, which will vary depending on operations. It is an approximation of precision. Never use logic of if(float == float), ever!
Float numbers are represented in the form Significant digits × baseexponent(IEEE 754). In your case, float 1.82 = 1 + 0.5 + 0.25 + 0.0625 + ...
Since only a limited digits could be stored, therefore there will be a round error if the float number cannot be represented as a terminating expansion in the relevant base (base 2 in the case).
You should always check relative differences with floating point numbers, not absolute values.
You need to read this, too.
Computers don't store floating point numbers exactly. To accomplish what you are doing, you could store the original input as a string, and count the number of characters.
This question already has answers here:
Closed 12 years ago.
Possible Duplicate:
Floating point inaccuracy examples
double a = 0.3;
std::cout.precision(20);
std::cout << a << std::endl;
result: 0.2999999999999999889
double a, b;
a = 0.3;
b = 0;
for (char i = 1; i <= 50; i++) {
b = b + a;
};
std::cout.precision(20);
std::cout << b << std::endl;
result: 15.000000000000014211
So.. 'a' is smaller than it should be.
But if we take 'a' 50 times - result will be bigger than it should be.
Why is this?
And how to get correct result in this case?
To get the correct results, don't set precision greater than available for this numeric type:
#include <iostream>
#include <limits>
int main()
{
double a = 0.3;
std::cout.precision(std::numeric_limits<double>::digits10);
std::cout << a << std::endl;
double b = 0;
for (char i = 1; i <= 50; i++) {
b = b + a;
};
std::cout.precision(std::numeric_limits<double>::digits10);
std::cout << b << std::endl;
}
Although if that loop runs for 5000 iterations instead of 50, the accumulated error will show up even with this approach -- it's just how floating-point numbers work.
Why is this?
Because floating-point numbers are stored in binary, in which 0.3 is 0.01001100110011001... repeating just like 1/3 is 0.333333... is repeating in decimal. When you write 0.3, you actually get 0.299999999999999988897769753748434595763683319091796875 (the infinite binary representation rounded to 53 significant digits).
Keep in mind that for the applications for which floating-point is designed, it's not a problem that you can't represent 0.3 exactly. Floating-point was designed to be used with:
Physical measurements, which are often measured to only 4 sig figs and never to more than 15.
Transcendental functions like logarithms and the trig functions, which are only approximated anyway.
For which binary-decimal conversions are pretty much irrelevant compared to other sources of error.
Now, if you're writing financial software, for which $0.30 means exactly $0.30, it's different. There are decimal arithmetic classes designed for this situation.
And how to get correct result in this case?
Limiting the precision to 15 significant digits is usually enough to hide the "noise" digits. Unless you actually need an exact answer, this is usually the best approach.
Computers store floating point numbers in binary, not decimal.
Many numbers that look ordinary in decimal, such as 0.3, have no exact representation of finite length in binary.
Therefore, the compiler picks the closest number that has an exact binary representation, just like you write 0.33333 for 1⁄3.
If you add many floating-point numbers, these tiny difference add up, and you get unexpected results.
It's not that it's bigger or smaller, it's just that it's physically impossible to store "0.3" as an exact value inside a binary floating point number.
The way to get the "correct" result is to not display 20 decimal places.
To get the "correct" result, try
List of Arbitrary-precision arithmetic Libraries from Wikipedia:
http://en.wikipedia.org/wiki/Arbitrary-precision
or
http://speleotrove.com/decimal