I'm trying to write a program that uses the series to compute the value of PI. The user will input how far it wants the program to compute the series and then the program should output its calculated value of PI. I believe I've successfully written the code for this, however it does not do well with large numbers and only gives me a few decimal places. When I tried to use cout << fixed << setprecision(42); It just gave me "nan" as the value of PI.
int main() {
long long seqNum; // sequence number users will input
long double val; // the series output
cout << "Welcome to the compute PI program." << endl; // welcome message
cout << "Please inter the sequence number in the form of an integer." << endl;
cin >> seqNum; // user input
while ( seqNum < 0) // validation, number must be positive
{
cout << "Please enter a positive number." << endl;
cin >> seqNum;
} // end while
if (seqNum > 0)
{
for ( long int i = 0; i < seqNum; i++ )
{
val = val + 4*(pow(-1.00,i)/(1 + 2*i)); // Gregory-Leibniz sum calculation
}// end for
cout << val;
} // end if
return 0;
}
Any help would be really appreciated. Thank you
Your problem involves an elementary, fundamental principle related to double values: a double, or any floating point type, can hold only a fixed upper limit of significant digits. There is no unlimited digits of precision with plain, garden-variety doubles. There's a hard, upper limit. The exact limit is implementation defined, but on modern C++ implementations the typical limit is just 16 or 17 digits of precision, not even close to your desired 42 digits of precision.
#include <limits>
#include <iostream>
int main()
{
std::cout << std::numeric_limits<double>::max_digits10 << std::endl;
return 0;
}
This gives you the maximum digits of precision with your platform/C++ compiler. This shows a maximum of 17 digits of precision with g++ 9.2 on Linux (max_digits10 is C++11 or later, use digits10 with old C++ compilers to show a closely-related metric).
Your desired 42 digits of precision likely far exceed what your modest doubles can handle. There are various special-purpose math libraries that can perform calculations with higher levels of precision, you can investigate those, if you wish.
You did not initialize or assign any value to val, but you are reading it when you get to the first iteration of
val = val + 4*(pow(-1.00,i)/(1 + 2*i));
This cause your program to have undefined behavior. Initialize val, probably to zero:
long double val = 0; // the series output
That aside, as mentioned in the answer of #SamVarshavchik there is a hard limit on the precision you can reach with the built-in floating point types and 42 places significance is almost certainly outside of that. Similarly the integer types that you are using are limited in size to probably at most 2^64 which is approximately 10^19.
Even if these limits weren't the problem, the series requires summation of roughly 10^42 terms to get PI to a precision of 42 places. It would take you longer than the universe has been around to calculate to that precision with all of earths current computing power combined.
Related
For a number a = 1.263839, we can do -
float a = 1.263839
cout << fixed << setprecision(2) << a <<endl;
output :- 1.26
But what if i want set precision of a number and store it, for example-
convert 1.263839 to 1.26 without printing it.
But what if i want set precision of a number and store it
You can store the desired precision in a variable:
int precision = 2;
You can then later use this stored precision when converting the float to a string:
std::cout << std::setprecision(precision) << a;
I think OP wants to convert from 1.263839 to 1.26 without printing the number.
If this is your goal, then you first must realise, that 1.26 is not representable by most commonly used floating point representation. The closest representable 32 bit binary IEEE-754 value is 1.2599999904632568359375.
So, assuming such representation, the best that you can hope for is some value that is very close to 1.26. In best case the one I showed, but since we need to calculate the value, keep in mind that some tiny error may be involved beyond the inability to precisely represent the value (at least in theory; there is no error with your example input using the algorithm below, but the possibility of accuracy loss should always be considered with floating point math).
The calculation is as follows:
Let P bet the number of digits after decimal point that you want to round to (2 in this case).
Let D be 10P (100 in this case).
Multiply input by D
std::round to nearest integer.
Divide by D.
P.S. Sometimes you might not want to round to the nearest, but instead want std::floor or std::ceil to the precision. This is slightly trickier. Simply std::floor(val * D) / D is wrong. For example 9.70 floored to two decimals that way would become 9.69, which would be undesirable.
What you can do in this case is multiply with one magnitude of precision, round to nearest, then divide the extra magnitude and proceed:
Let P bet the number of digits after decimal point that you want to round to (2 in this case).
Let D be 10P (100 in this case).
Multiply input by D * 10
std::round to nearest integer.
Divide by 10
std::floor or std::ceil
Divide by D.
You would need to truncate it. Possibly the easiest way is to multiply it by a factor (in case of 2 decimal places, by a factor of 100), then truncate or round it, and lastly divide by the very same factor.
Now, mind you, that floating-point precision issues might occur, and that even after those operations your float might not be 1.26, but 1.26000000000003 instead.
If your goal is to store a number with a small, fixed number of digits of precision after the decimal point, you can do that by storing it as an integer with an implicit power-of-ten multiplier:
#include <stdio.h>
#include <math.h>
// Given a floating point value and the number of digits
// after the decimal-point that you want to preserve,
// returns an integer encoding of the value.
int ConvertFloatToFixedPrecision(float floatVal, int numDigitsAfterDecimalPoint)
{
return (int) roundf(floatVal*powf(10.0f, numDigitsAfterDecimalPoint));
}
// Given an integer encoding of your value (as returned
// by the above function), converts it back into a floating
// point value again.
float ConvertFixedPrecisionBackToFloat(int fixedPrecision, int numDigitsAfterDecimalPoint)
{
return ((float) fixedPrecision) / powf(10.0f, numDigitsAfterDecimalPoint);
}
int main(int argc, char ** arg)
{
const float val = 1.263839;
int fixedTwoDigits = ConvertFloatToFixedPrecision(val, 2);
printf("fixedTwoDigits=%i\n", fixedTwoDigits);
float backToFloat = ConvertFixedPrecisionBackToFloat(fixedTwoDigits, 2);
printf("backToFloat=%f\n", backToFloat);
return 0;
}
When run, the above program prints this output:
fixedTwoDigits=126
backToFloat=1.260000
If you're talking about storing exactly 1.26 in your variable, chances are you can't (there may be an off chance that exactly 1.26 works, but let's assume it doesn't for a moment) because floating point numbers don't work like that. There are always little inaccuracies because of the way computers handle floating point decimal numbers. Even if you could get 1.26 exactly, the moment you try to use it in a calculation.
That said, you can use some math and truncation tricks to get very close:
int main()
{
// our float
float a = 1.263839;
// the precision we're trying to accomplish
int precision = 100; // 3 decimal places
// because we're an int, this will keep the 126 but lose everything else
int truncated = a * precision; // multiplying by the precision ensures we keep that many digits
// convert it back to a float
// Of course, we need to ensure we're doing floating point division
float b = static_cast<float>(truncated) / precision;
cout << "a: " << a << "\n";
cout << "b: " << b << "\n";
return 0;
}
Output:
a: 1.26384
b: 1.26
Note that this is not really 1.26 here. But is is very close.
This can be demonstrated by using setprecision():
cout << "a: " << std:: setprecision(10) << a << "\n";
cout << "b: " << std:: setprecision(10) << b << "\n";
Output:
a: 1.263839006
b: 1.25999999
So again, it's not exactly 1.26, but very close, and slightly closer than you were before.
Using a stringstream would be an easy way to achieve that:
#include <iostream>
#include <iomanip>
#include <sstream>
using namespace std;
int main() {
stringstream s("");
s << fixed << setprecision(2) << 1.263839;
float a;
s >> a;
cout << a; //Outputs 1.26
return 0;
}
I am trying to learn how to program in C++, so I created something that allowed to you enter a minimum, and maximum parameter, and it will compute k+(k+1)+(k+2)+...+(max), and compared it to the analytical value, using the standard formula (n(n+1)/2). It seems to work fine when I try small numbers, but when, for example, trying min=4, max=4*10^5 (400,000), I get a negative result for the sum, but a positive result checking with the analytical method, even after changing the type from 'int' to 'long'. Trying other combinations, I have achieved the opposite, with the analytical method resulting in a negative sum. I suspect this is related to the fact the type int can go up to a certain number of digits, but I wanted some confirmation on that, and if it isn't, what the actual problem is. The code is provided below:
#include <iostream>
// Values are inconsistent when paramin,parammax become large.
// For example, try (parammin,parammax)=(4,400,000)
int main() {
int parammax,parammin;
std::cout << "Input a minimum, then maximum parameter to sum up to" << std::endl;
std::cin >> parammin >> parammax;
int sum=0;
for (int iter = parammin; iter <= parammax; iter++){
sum += iter;
}
std::cout << "The sum is: " << sum << std::endl;
const int analyticalmethod = (parammax*(parammax+1)-parammin*(parammin-1))/2;
std::cout << "The analytical result for the sum is,"
" via (max*(max+1)-min*(min-1))/2: "
<< analyticalmethod << std::endl;
return 0;
}
Using very large numbers without control is dangerous in C++. The basic types int, long and long long are implementation dependant, with only the following requirements:
int is at least 16 bits large
long is at least as large as int and at least 32 bits large
long long is at least as large as long and at least 64 bits large
If you think you can need larger values, you should considere a multi precision library like the excellent gmp.
I made a little program to determine the length of a user-provided integer:
#include <iostream>
using namespace std;
int main()
{
int c=0; //counter for loop
int q=1; //quotient of number upon division
cout << "Hello Cerberus! Please enter a number." << endl;
cin >> q;
if(q > -10 && q < 10)
{
cout << "The number you entered is 1 digit long." << endl;
}
else
{
while(q != 0)
{
q=q/10;
c++;
}
cout << "The number you entered is " << c << " digits long." << endl;
}
return 0;
}
It works quite nicely, unless the numbers get too big. Once the input is 13 digits long or so, the program defaults to "The number you entered is 1 digit long" (it shouldn't even present that solution unless the number is between -10 and 10).
Is there a length limit for user-input integers, or is this demonstrative of my computer's memory limits?
It's a limit in your computer's architecture. Every numeric type has a fixed upper limit, because the type describes data with a fixed size. For example, your int is likely to take up either four or eight bytes in memory (depending on CPU; based on your observations, I'd say the former), and there are only so many combinations of bits that can be stored in so many bytes of memory.
You can determine the range of int on your platform using std::numeric_limits, but personally I recommend sticking with the fixed-width type aliases (e.g. int32_t, int64_t) and picking whichever ones have sufficient range for your application.
Alternatively, there do exist so-called "bigint" libraries that are essentially classes wrapping integer arrays and adding clever functionality to make arbitrarily-large values work as if they were of arithmetic types. That's probably overkill for you here though.
Just don't be tempted to start using floating-point types (float, double) for their magic range-enhancing abilities; just like with the integral types, their precision is fundamentally limited, but using floating-point types adds additional problems and concerns on top.
There is no fundamental limit on user input, though. That's because your stream is converting text characters, and your stream can basically have as many text characters in it as you could possibly imagine. At that level, you're really only limited by available memory.
I have a seemingly simple c++ issue that's bothering me. The output of the code
#include <iostream>
using namespace std;
int main() {
// your code goes here
double c = 9.43827 * 0.105952 ;
cout << c << endl ;
return 0;
}
is 1. Just 1. I guess this is due to precision loss based on how doubles are stored in c++ but surely there must be a way in c++ to get some sort of precision (2 or 3 decimal places) in the result.
It's not precision loss in storage, it's precision loss in converting to text. The stream inserter for double defaults to six significant digits. The product here, 1.000003583, rounded to six significant digits, is 1.00000. In addition, if you haven't set showpoint, the trailing zeros and the decimal point will be suppressed, so you'll see a bare 1. To get the decimal point to show, use std::cout << std::showpoint << c << '\n';. To see more significant digits, use std::cout << std::setprecision(whatever) << c << '\n';, where whatever is the number of digits you want the formatter to use.
#include <stdio.h>
int main() {
// your code goes here
double c = ((double)9.43827) * 0.105952 ;
for(int i = (sizeof(double)*8)-1; i >= 0; i-- ) {
printf("%ld", (*(long*)&c>>i)&1);
}
}
If you run that, you can clearly see the bit representation of your double is not the integer value 1. You're not losing any data.
0011111111110000000000000000001111000001110100001010001001001001
but it is very close to 1, so that's what gets printed out.
Try using cout<<setprecision(12)<<c<<endl;
setprecision sets the decimal precision to be used to format floating-point values on output operations.
source
I'm using the tasks on code abbey to work my way through C++.
I'm trying to use the rounding function by importing math.h and it works for every value that I'm trying to input apart from one pair
when I divide 4991264 by 4 and round it, it outputs the answer as 1.24782e+06
#include <iostream>
#include <math.h>
using namespace std;
int getTotal(){
int total;
cin >> total;
return total;
}
void doMath(int total){
int count;
double holder;
double holder2;
double solution;
solution = 0;
count = 0;
while (count != total){
cout << "enter a number ";
cin >> holder;
cout << "enter a number ";
cin >> holder2;
solution = (holder / holder2);
cout << round(solution) << "\n";
++count;
}
}
int main(){
int total = getTotal();
doMath(total);
return 0;
}
http://ideone.com/f40E1s is the code and the inputs.
Thanks,
A floating point variable keeps a value of a given type (in memory).
This value "rests" there with its own precision, in binary format.
When this value has to be shown or output in someway, typically is converted to decimal format. This conversion can have loss of precision sometimes.
Anyway, when you are doing precise arithmetica operations, as in your example, the conversion to decimal is not, in general, an issue.
What it has to be understood here is that "printing" a value is not the same that "showing the exact value held in memory".
The object cout has predefined ways to show the values you are computing.
The exact value has not changed, it's not, in this case, a problem of bad computing.
Indeed, it's only a matter of how to show this value on screen.
The format used to print the value is: in exponential notation with "only" 6 decimal digits precision.
You need to increase the precision of values when printed, and to avoid exponential notation.
Take a look to this website: Output formatting in C++
Thus, for example, the following code do the job (for a precision of 8 decimal digits):
cout << setiosflags(ios::fixed) << setprecision(8) << round(solution) << "\n";
In general, you have to investigate and practice more about this formatting options.