I am about to lose my mind. I have been staring at this for hours, and I cannot figure out what is wrong. When I enter a value of "1", I just get that the sum is 1, meaning that it has only gone this through the iteration once, but I don't know why seeing that the abs(term) should be greater than "lesser".
I am trying to calculate sin(x) given the user inputs x.
double sum = 0.0;
double term = 0.0;
double n = 1.0;
double x = 0.0;
double lesser = 1.0e-15;
while (true)
{
std::cout << "\nEnter radian value of x:";
std::cin >> x;
if (x == 999)
return 0;
term = x;
sum = 0.0;
n = 1.0;
while (abs (term) >= lesser)
{
sum = sum + term;
n = n + 1.0;
term = -term * (x/n);
n = n + 1.0;
term = term * (x/n);
}
std::cout << "\nApproximation for sin(0) is is: " << sum;
}
return 0;
The std::abs function works on integral types. You probably should be using std::fabs:
while (fabs (term) >= lesser) {
...
}
There may be other errors in the code, but this one will probably cause the loop to exit early because abs will round values in the range (0, 1) down to 0. fabs avoids this.
Alternatively, use the <cmath> header instead of the <math.h> header, or explicitly call std::abs. <cmath> exports overloads of abs for floating-point values.
Hope this helps!
Use fabs, not abs. abs is for integers and is killing the fractional part of your number. Or you can use std::abs
Related
original outdated code:
Write an algorithm that compute the Euler's number until
My professor from Algorithms course gave me the following homework:
Write a C/C++ program that calculates the value of the Euler's number (e) with a given accuracy of eps > 0.
Hint: The number e = 1 + 1/1! +1/2! + ... + 1 / n! + ... = 2.7172 ... can be calculated as the sum of elements of the sequence x_0, x_1, x_2, ..., where x_0 = 1, x_1 = 1+ 1/1 !, x_2 = 1 + 1/1! +1/2 !, ..., the summation continues as long as the condition |x_(i+1) - x_i| >= eps is valid.
As he further explained, eps is the precision of the algorithm. For example, the precision could be 1/100 |x_(i + 1) - x_i| = absolute value of ( x_(i+1) - x_i )
Currently, my program looks in the following way:
#include<iostream>
#include<cstdlib>
#include<math.h>
// Euler's number
using namespace std;
double factorial(double n)
{
double result = 1;
for(double i = 1; i <= n; i++)
{
result = result*i;
}
return result;
}
int main()
{
long double euler = 2;
long double counter = 2;
long double epsilon = 1.0/1000;
long double moduloDifference;
do
{
euler+= 1 / factorial(counter);
counter++;
moduloDifference = (euler + 1 / factorial(counter+1) - euler);
} while(moduloDifference >= epsilon);
printf("%.35Lf ", euler );
return 0;
}
Issues:
It seems my epsilon value does not work properly. It is supposed to control the precision. For example, when I wish precision of 5 digits, I initialize it to 1.0/10000, and it outputs 3 digits before they get truncated after 8 (.7180).
When I use long double data type, and epsilon = 1/10000, my epsilon gets the value 0, and my program runs infinitely. Yet, if change the data type from long double to double, it works. Why epsilon becomes 0 when using long double data type?
How can I optimize the algorithm of finding Euler's number? I know, I can rid off the function and calculate the Euler's value on the fly, but after each attempt to do that, I receive other errors.
One problem with computing Euler's constant this way is pretty simple: you're starting with some fairly large numbers, but since the denominator in each term is N!, the amount added by each successive term shrinks very quickly. Using naive summation, you quickly reach a point where the value you're adding is small enough that it no longer affects the sum.
In the specific case of Euler's constant, since the numbers constantly decrease, one way we can deal with them quite a bit better is to compute and store all the terms, then add them up in reverse order.
Another possibility that's more general is to use Kahan's summation algorithm instead. This keeps track of a running error while it's doing the summation, and takes the current error into account as it's adding each successive term.
For example, I've rewritten your code to use Kahan summation to compute to (approximately) the limit of precision of a typical (80-bit) long double:
#include<iostream>
#include<cstdlib>
#include<math.h>
#include <vector>
#include <iomanip>
#include <limits>
// Euler's number
using namespace std;
long double factorial(long double n)
{
long double result = 1.0L;
for(int i = 1; i <= n; i++)
{
result = result*i;
}
return result;
}
template <class InIt>
typename std::iterator_traits<InIt>::value_type accumulate(InIt begin, InIt end) {
typedef typename std::iterator_traits<InIt>::value_type real;
real sum = real();
real running_error = real();
for ( ; begin != end; ++begin) {
real difference = *begin - running_error;
real temp = sum + difference;
running_error = (temp - sum) - difference;
sum = temp;
}
return sum;
}
int main()
{
std::vector<long double> terms;
long double epsilon = 1e-19;
long double i = 0;
double term;
for (int i=0; (term=1.0L/factorial(i)) >= epsilon; i++)
terms.push_back(term);
int width = std::numeric_limits<long double>::digits10;
std::cout << std::setw(width) << std::setprecision(width) << accumulate(terms.begin(), terms.end()) << "\n";
}
Result: 2.71828182845904522
In fairness, I should actually add that I haven't checked what happens with your code using naive summation--it's possible the problem you're seeing is from some other source. On the other hand, this does fit fairly well with a type of situation where Kahan summation stands at least a reasonable chance of improving results.
#include<iostream>
#include<cmath>
#include<iomanip>
#define EPSILON 1.0/10000000
#define AMOUNT 6
using namespace std;
int main() {
long double e = 2.0, e0;
long double factorial = 1;
int counter = 2;
long double moduloDifference;
do {
e0 = e;
factorial *= counter++;
e += 1.0 / factorial;
moduloDifference = fabs(e - e0);
} while (moduloDifference >= EPSILON);
cout << "Wynik:" << endl;
cout << setprecision(AMOUNT) << e << endl;
return 0;
}
This an optimized version that does not have a separate function to calculate the factorial.
Issue 1: I am still not sure how EPSILON manages the precision.
Issue 2: I do not understand the real difference between long double and double. Regarding my code, why long double requires a decimal point (1.0/someNumber), and double doesn't (1/someNumber)
I'm working on this program that approximates a taylor series function. I have to approximate it so that the taylor series function stops approximating the sin function with a precision of .00001. In other words,the absolute value of the last approximation minus the current approximation equals less than or equal to 0.00001. It also approximates each angle from 0 to 360 degrees in 15 degree increments. My logic seems to be correct, but I cannot figure out why i am getting garbage values. Any help is appreciated!
#include <math.h>
#include <iomanip>
#include <iostream>
#include <string>
#include <stdlib.h>
#include <cmath>
double fact(int x){
int F = 1;
for(int i = 1; i <= x; i++){
F*=i;
}
return F;
}
double degreesToRadians(double angle_in_degrees){
double rad = (angle_in_degrees*M_PI)/180;
return rad;
}
using namespace std;
double mySine(double x){
int current =99999;
double comSin=x;
double prev=0;
int counter1 = 3;
int counter2 = 1;
while(current>0.00001){
prev = comSin;
if((counter2 % 2) == 0){
comSin += (pow(x,(counter1))/(fact(counter1)));
}else{
comSin -= (pow(x,(counter1))/(fact(counter1)));
}
current=abs(prev-comSin);
cout<<current<<endl;
counter1+=2;
counter2+=1;
}
return comSin;
}
using namespace std;
int main(){
cout<<"Angle\tSine"<<endl;
for (int i = 0; i<=360; i+=15){
cout<<i<<"\t"<<mySine(degreesToRadians(i));
}
}
Here is an example which illustrates how to go about doing this.
Using the pow function and calculating the factorial at each iteration is very inefficient -- these can often be maintained as running values which are updated alongside the sum during each iteration.
In this case, each iteration's addend is the product of two factors: a power of x and a (reciprocal) factorial. To get from one iteration's power factor to the next iteration's, just multiply by x*x. To get from one iteration's factorial factor to the next iteration's, just multiply by ((2*n+1) + 1) * ((2*n+1) + 2), before incrementing n (the iteration number).
And because these two factors are updated multiplicatively, they do not need to exist as separate running values, they can exists as a single running product. This also helps avoid precision problems -- both the power factor and the factorial can become large very quickly, but the ratio of their values goes to zero relatively gradually and is well-behaved as a running value.
So this example maintains these running values, updated at each iteration:
"sum" (of course)
"prod", the ratio: pow(x, 2n+1) / factorial 2n+1
"tnp1", the value of 2*n+1 (used in the factorial update)
The running update value, "prod" is negated every iteration in order to to factor in the (-1)^n.
I also included the function "XlatedSine". When x is too far away from zero, the sum requires more iterations for an accurate result, which takes longer to run and also can require more precision than our floating-point values can provide. When the magnitude of x goes beyond PI, "XlatedSine" finds another x, close to zero, with an equivalent value for sin(x), then uses this shifted x in a call to MaclaurinSine.
#include <iostream>
#include <iomanip>
// Importing cmath seemed wrong LOL, so define Abs and PI
static double Abs(double x) { return x < 0 ? -x : x; }
const double PI = 3.14159265358979323846;
// Taylor series about x==0 for sin(x):
//
// Sum(n=[0...oo]) { ((-1)^n) * (x^(2*n+1)) / (2*n + 1)! }
//
double MaclaurinSine(double x) {
const double xsq = x*x; // cached constant x squared
int tnp1 = 3; // 2*n+1 | n==1
double prod = xsq*x / 6; // pow(x, 2*n+1) / (2*n+1)! | n==1
double sum = x; // sum after n==0
for(;;) {
prod = -prod;
sum += prod;
static const double MinUpdate = 0.00001; // try zero -- the factorial will always dominate the power of x, eventually
if(Abs(prod) <= MinUpdate) {
return sum;
}
// Update the two factors in prod
prod *= xsq; // add 2 to the power factor's exponent
prod /= (tnp1 + 1) * (tnp1 + 2); // update the factorial factor by two iterations
tnp1 += 2;
}
}
// XlatedSine translates x to an angle close to zero which will produce the equivalent result.
double XlatedSine(double x) {
if(Abs(x) >= PI) {
// Use int casting to do an fmod PI (but symmetric about zero).
// Keep in mind that a really big x could overflow the int,
// however such a large double value will have lost so much precision
// at a sub-PI-sized scale that doing this in a legit fashion
// would also disappoint.
const int p = static_cast<int>(x / PI);
x -= PI * p;
if(p % 2) {
x = -x;
}
}
return MaclaurinSine(x);
}
double DegreesToRadians(double angle_deg) {
return PI / 180 * angle_deg;
}
int main() {
std::cout<<"Angle\tSine\n" << std::setprecision(12);
for(int i = 0; i<=360; i+=15) {
std::cout << i << "\t" << MaclaurinSine(DegreesToRadians(i)) << "\n";
//std::cout << i << "\t" << XlatedSine(DegreesToRadians(i)) << "\n";
}
}
So I can calculate a sum of sequence without accuracy E.
int t=1, x, k;
float sum, a, result, factorial=1, E=0.0001;
for(k=0;k<=(n);k++){
while(t<=n){
factorial*=t;
t++;
}
sum=(pow(-x,k))/factorial;
sum+=sum;
//while(fabs(sum-???)<E){
// result=sum;
//}
}
So I know sum of sequence sum(k). But to calculate with accurace E, I must know sum of previous elements sum(k-1). How to get sum(k-1) from for loop?
Sorry for english.
is this a taylor series for e ^ (-x) ? if so you've written it out wrong. i don't think what you've got will converge.
http://www.efunda.com/math/taylor_series/exponential.cfm
e ^ (-x) is 1 + (-x) + (-x)^2/2! + (-x)^3/3! + ...
double calculate_power_of_e(double xx, double accuracy) {
double sum(1.0);
double term(1.0);
for (long kk=1; true; ++kk) {
term *= (-xx) / kk;
sum += term;
if (fabs(term) < accuracy)
break;
}
return sum;
}
printf("e^(-x)" = %.4f\n", calculate_power_of_e(5.0, .0001));
First a remark about the power formula that you apply: according to wikipedia you should add the terms pow(-x,k)/(k!) and not pow(-x,k)/(n!).
This leads to a small optimisation of your code: as k! = k * (k-1)! we can avoid the inner while loop and a lot of useless multiplications.
By the way, there is also an error in the way you build the sum: you always erase the previous result, and then add a second time the current term.
Once this is corrected, you just have to take care of an additional variable:
double myexpo(double x, int n=100) {
int k;
double sum = 1.0, pvsum, factorial = 1.0, E = 0.0001;
for (k = 1; k <= (n); k++){ // start with 1
pvsum = sum;
factorial *= k; // don't calculate factorial for 0.
sum += (pow(-x, k)) / factorial;
if (k > 1 && fabs(sum - pvsum) < E) { // check if diff is small enough
cout << k << " iterations" << endl;
break; // interupt the for loop if it's precise enough
}
}
return sum; // at the end of the loop sum is the best approximation
}
You can test this function with this:
double x;
do {
cout << "Enter number: ";
cin >> x;
cout << myexpo(x) << endl;
cout << exp(-x) << endl;
} while (x > 0);
Remark: I'd suggest to either use double or to use the f suffix for the float litterals (e.g. 0.001f), even if it works as is.
Check when the absolute value of the term becomes smaller than your desired accuracy.
double sum = 0, x = 1, k = 0, E = 0.0001, fact = 1;
while(true){
double term = pow(-x, k) / fact;
if(fabs(term) < E)
break;
sum += term;
fact *= (++k);
}
printf("e^(-x) = %.4f", sum);
When the term is insignificant compare to 1.0, stop looping.
By using recursion, and |x| is not too big, the smallest terms are summed first.
e(x) = 1 + x/1! + x*x/2! + x*x*x/3! + ...
double my_exp_term(double x, double term, unsigned n) {
if (term + 1.0 == 1.0) return term;
n++;
return term + my_exp_term(x, term*x/n, n);
}
double my_exp(double x) {
return 1.0 + my_exp_term(x, x, 1);
}
double y = my_exp(-1);
Exponential function
Hello I am solving trigonometry functions like sin(x) and cos(x) with Taylor Series Expansions
Problem: My values are not wrong just not very precise
My question is whether I can improve the accuracy of these functions, I think I have tried everything but I need your suggestions.
double trig::funcsin(int value)
{
sum = 0;
//summation
factorial fac;
for(int i = 0; i < 7; i++)
{
sum += pow((-1), i)*(((double)pow(value, (double)2*i+1)/(double)fac.fact((double)2*i+ 1)));
}
return sum;
}
double trig::funccos(int value)
{
factorial fac;
sum = 0;
for(int i = 0;i < 7;i++)
{
sum += (pow((-1), i)*((double)pow(value, (double)2*i)/(double)fac.fact((double)2*i)));
}
return sum;
}
Example:
Real: -0.7568024953
Mine: -0.73207
Real: -0.27941549819
Mine: -0.501801
Aslo as x becomes larger the output values become less precise at an exponential rate.
I am on GCC compiler, please give me suggestions
The following code demonstrates the Taylor series (about x==0) for the sin() function.
As you know, the sine function repeats an identical cycle for every 2*pi interval.
But the Taylor series is just a polynomial -- it needs a lot of terms to approximate a wiggly function like sine. And trying to approximate the sine function at some point far away from the origin will require so many terms that accumulated errors will give an unsatisfactory result.
To avoid this problem, my function starts by remapping x into a single cycle's range centered around zero, between -pi and +pi.
It's best to avoid using pow and factorial functions if you can instead cheaply update components at each step in the summation. For example, I keep a running value for pow(x, 2*n+1): It starts off set to x (at n==0), then every time n is incremented, I multiply this by x*x. So it only costs a single multiplication to update this value at each step. A similar optimization is used for the factorial term.
This series alternates between positive and negative terms, so to avoid the hassle of keeping track of whether we need to add or subtract a term, the loop handles two terms on each iteration -- it adds the first and subtracts the second.
Each time a new sum is calculated, it is compared with the previous sum. If the two are equal (indicating the updates have surpassed the sum variable's precision), the function returns. This isn't a great way to test for a terminating condition, but it makes the function simpler.
#include <iostream>
#include <iomanip>
double mod_pi(double x) {
static const double two_pi = 3.14159265358979 * 2;
const int q = static_cast<int>(x / two_pi + 0.5);
return x - two_pi * q;
}
double func_sin(double x) {
x = mod_pi(x);
double sum = 0;
double a = 1; // 2*n+1 [1, 3, 5, 7, ...]
double b = x; // x^a
double c = 1; // (2*n+1)!
const double x_sq = x * x;
for(;;) {
const double tp = b / c;
// update for negative term
c *= (a+1) * (a+2);
a += 2;
b *= x_sq;
const double tn = b / c;
const double ns = tp - tn + sum;
if(ns == sum) return ns;
sum = ns;
// update for positive term (at top of loop)
c *= (a+1) * (a+2);
a += 2;
b *= x_sq;
}
}
int main() {
const double y = func_sin(-0.858407346398077);
std::cout << std::setprecision(13) << y << std::endl;
}
I wrote the following code to sum the series (-1)^i*(i/(i+1)). But when I run it I get -1 for any value of n.
Can some one please point out what I am doing wrong? Thank you in advance!
#include <iostream>
using namespace std;
int main()
{
int sum = 0;
int i = 1.0;
int n = 5.0;
for(i=1;i<=n;i++)
sum = (-1)^i*(i/(i+1));
cout << "Sum" <<" = "<< sum << endl;
return 0;
}
Problem #1: The C++ ^ operator isn't the math power operator. It's a bitwise XOR.
You should use pow() instead.
Problem #2:
You are storing floating-point types into an integer type. So the following will result in integer division (truncated division):
i/(i+1)
Problem #3:
You are not actually summing anything up:
sum = ...
should be:
sum += ...
A corrected version of the code is as follows:
double sum = 0;
int i = 1;
int n = 5;
for(i = 1; i <= n; i++)
sum += pow(-1.,(double)i) * ((double)i / (i + 1));
Although you really don't need to use pow in this case. A simple test for odd/even will do.
double sum = 0;
int i = 1;
int n = 5;
for(i = 1; i <= n; i++){
double val = (double)i / (i + 1);
if (i % 2 != 0){
val *= -1.;
}
sum += val;
}
You need too put sum += pow(-1,i)*(i/(i+1));
Otherwise you lose previous result each time.
Use pow function for pow operation.
edit : as said in other post, use double or float instead of int to avoid truncated division.
How about this
((i % 2) == 0 ? 1 : -1)
instead of
std::pow(-1, i)
?
Full answer:
double sum = 0;
int i = 1.0;
int n = 5.0;
for (i = 1; i <= n; ++i) {
signed char sign = ((i % 2) == 0 ? 1 : -1);
sum += sign * (i / (i+1));
}
Few problems:
^ is teh bitwise exclusive or in c++ not "raised to power". Use pow() method.
Remove the dangling opening bracket from the last line
Use ints not floats when assigning to ints.
You seem to have a few things wrong with your code:
using namespace std;
This is not directly related to your problem at hand, but don't ever say using namespace std; It introduces subtle bugs.
int i = 1.0;
int n = 5.0;
You are initializaing integral variables with floating-point constants. Try
int i = 1;
int n = 5;
sum = (-1)^i*(i/(i+1));
You have two problems with this expression. First, the quantity (i/(i+1)) is always zero. Remember dividing two ints rounds the result. Second, ^ doesn't do what you think it does. It is the exclusive-or operator, not the exponentiation operator. Third, ^ binds less tightly than *, so your expression is:
-1 xor (i * (i/(i+1)))
-1 xor (i * 0)
-1 xor 0
-1
^ does not do what you think it does. Also there are some other mistakes in your code.
What it should be:
#include <iostream>
#include <cmath>
int main( )
{
long sum = 0;
int i = 1;
int n = 5;
for( i = 1; i <= n; i++ )
sum += std::pow( -1.f, i ) * ( i / ( i + 1 ) );
std::cout << "Sum = " << sum << std::endl;
return 0;
}
To take a power of a value, use std::pow (see here). Also you can not assign int to a decimal value. For that you need to use float or double.
The aforementioned ^ is a bitwise-XOR, not a mark for an exponent.
Also be careful of Integer Arithmetic as you may get unexpected results. You most likely want to change your variables to either float or double.
There are a few issues with the code:
int sum = 0;
The intermediate results are not integers, this should be a double
int i = 1.0;
Since you will use this in a division, it should be a double, 1/2 is 0 if calculated in integers.
int n = 5.0;
This is an int, not a floating point value, no .0 is needed.
for(i=1;i<=n;i++)
You've already initialized i to 1, why do it again?
sum = (-1)^i*(i/(i+1));
Every iteration you lose the previous value, you should use sum+= 'new values'
Also, you don't need pow to calculate (-1)^i, all this does is switch between +1 and -1 depending on the odd/even status of i. You can do this easier with an if statement or with 2 for's, one for odd i one for even ones... Many choices really.