I have a function that calculates the sin() of a radian angle. It takes two parameters, the value of the angle in radian and the terms. Just to make everything clear, this is how sin() is calculated:
sin(x) = x - (1/3! * X^3) + (1/5! * X^5) - (1/7! * X^7) + (1/9! * X^9) - ...
This is the function that do this calculation:
double sinx(double theta, int terms) //Theta is the angle x in radian
{
double result = 0;//this variable holds the value and it's updated with each term.
int i = 1;
int num = 3;
while(i <= terms-1)
{
if(i % 2 != 0){
result = result - ( (1.0/factorial(num)) * pow(theta, num) );
printf("if\n");//this is just for debugging
}
else if(i % 2 == 0){
result = result + ( (1.0/factorial(num)) * pow(theta, num) );
printf("else if\n");//this is for debugging too
}
printf("%lf\n", result);//debugging also
num = num + 2;
i = i + 1;
}
return theta + result; //this calculates the final term
}
The problem is the variable result's value doesn't change. Which also results in the final result not changing when using different number of terms.
Those are some outputs I get:
//with theta = 0.2 and terms = 6 ;;
if
-0.001333
else if
-0.001331
if
-0.001331
else if
-0.001331
if
-0.001331
Computed Sin<0.200000> = 0.198669. //this is the returned value. It's printed in the main
//with theta = 0.2 and terms = 7
if
-0.001333
else if
-0.001331
if
-0.001331
else if
-0.001331
if
-0.001331
else if
-0.001331
Computed Sin<0.200000> = 0.198669.
Any ideas?
Your code should be totally right. At least my calculator gives the same result.
If you change your printf("%lf\n", result); to printf("%.17f\n", result); you get this output:
if
-0.00133333333333333
else if
-0.00133066666666667
if
-0.00133066920634921
else if
-0.00133066920493827
if
-0.00133066920493878
else if
-0.00133066920493878
Now you can see, that it is still changing in every loop, but very little.
Really it converges fast so for double precision there is no difference between 6 and 7 terms. Here is the dump with better precision:
if
-0.00133333333333333350
else if
-0.00133066666666666680
if
-0.00133066920634920640
else if
-0.00133066920493827170
if
-0.00133066920493878470
Sin(0.2, 6) = 0.19866933079506122000
if
-0.00133333333333333350
else if
-0.00133066666666666680
if
-0.00133066920634920640
else if
-0.00133066920493827170
if
-0.00133066920493878470
else if
-0.00133066920493878450
Sin(0.2, 7) = 0.19866933079506122000
Everything looks correct here. The reason the result doesn't appear to change is to do with how quickly the Taylor series for sin converges for small angles. If you try with a bigger number say pi you should see the value updating slightly more often. You may also want to include something to limit theta from -pi to +pi as sin is a periodic function.
theta = mod(theta+pi, 2*pi) - pi
Including this restriction will alleviate the need for more terms if you start calculating values > pi or < -pi
If performance is important then you can reduce some of the calculations by removing repeats in calculating the factorials and large exponents
double sin(double theta, int terms = 7)
{
theta = mod(theta+pi, 2*pi) - pi;
double sum = x, term = x, fact = 3;
for (int i = 1; i < terms; i++)
{
term = -term * theta * theta /(fact * (fact - 1));
sum += term;
fact += 2;
}
return sum;
}
Your program as you have posted seems quite right IF you have your factorial written the right way. I have written factorial this way:
double factorial(int n) {
if(n <= 1) {
return 1.0;
}
else {
return n * factorial(n-1);
}
}
Try using this.
Using 0.785398 (approx. pi/4) and 10 terms, I get output 0.707107.
double d = sinx(0.785398, 10);
printf("%f\n", d); // prints 0.707107
Here are some runs:
printf("%.20f\n", sinx(3.1415926535897932, 100));
printf("%.20f\n", sinx(3.1415926535897932/2, 100));
printf("%.20f\n", sinx(3.1415926535897932/4, 100));
Outputs:
0.00000000000000044409
1.00000000000000000000
0.70710678118654746000
which seem accurate enough, given the pi used is only approximate.
What did you expect ?
The third term is
0.2^5/120 = 0.000002
if you show the first six decimals, and the next terms are yet smaller.
Side remark:
It is much more efficient and more accurate to compute a term from the previous, using the recurrence
T*= Z/(N*(N-1))
where Z= -X*X (and this way, the alternating signs are automatically handled).
Related
I'm iterating over t like this
double dt = 0.1, t;
double tmax = 100;
for (t = 0; t <= tmax; t += dt) { /*do something*/ }
If dt is 0.1, as here, everything works as it should and the step is executed for t = 100. But if I decrease the step, for example dt = 0.001, the last step is not executed.
How should I properly iterate over doubles?
Iterate over an integer and use a start-step formula to get each double value.
double dt = 0.1, t;
double tmax = 100;
int i, n = (int)(tmax / dt); // n: check your rounding or specify explicitly.
for (i = 0; i <= n; i++) { t = 0/*start*/ + dt * i; /*do something*/ }
Your code is fine, and the behavior is expected. Ok, not expected, but explainable due to how floating point numbers work. Look here http://floating-point-gui.de/ In case the link disappears one day, google "what programmers should know about floating point" - I'm sure it'll be cached somewhere!
Instead of
for (t = 0; t <= tmax; t += dt) { /*do something*/ }
write:
double i;
for (i = 0; i <= 1000; i += 1.0) {
t = i / 10.0;
/*do something */
}
With this pattern, the loop body will be executed exactly 1001 times and each value of t will be the nearest double approximation of the number you intended (in particular, the 11th value will be 1 exactly, the 31th value will be 3 exactly, …).
Your version does not work well because simple decimal numbers such as 0.1 or 0.01 are not representable exactly in binary floating-point.
If you are willing to adapt the number of points to the fact that you are using binary floating-point, simply use a power of two and all will be well:
for (t = 0; t <= 100.0; t += 1.0 / 64.0) { /*do something*/ }
One of my C assignments was it to write an approximation of arctan(x) in the language C. The equation which I should base it on is
arctan(x)=\sum {k=0}^{\infty }(-1)^{k} \tfrac{x^{2k+1}}{2k+1}
In addition x is only defined as -1<=x<=1.
Here is my code.
#include <stdio.h>
#include <math.h>
double main(void) {
double x=1;
double k;
double sum;
double sum_old;
int count;
double pw(double y, double n) {
double i;
double number = 1;
for (i = 0; i < n; i++) {
number *= y;
}
return(number);
}
double fc (double y) {
double i;
double number = 1;
for (i = 1; i <= y; i++){
number *= i;
}
return(number);
}
if(x >= (-1) && x <= 1) {
for(k=0; sum!=sum_old; k++) {
sum_old = sum;
sum += pw((-1), k) * pw(x, (2*k) + 1)/((2*k) + 1);
count++;
printf("%d || %.17lf\n", count, sum);
}
printf("My result is: %.17lf\n",sum);
printf("atan(%f) is: %.17f\n", x, atan(x));
printf("My result minus atan(x) = %.17lf\n", sum - atan(x));
} else {
printf("x is not defined. Please choose an x in the intervall [-1, 1]\n");
}
return 0;
}
It seemingly works fine with every value, except value 1 and -1. If x=1, then the output ends with:
...
7207 || 0.78543285189457468
7208 || 0.78536
Whereas the output should look more like this. In this case x=0.5.
25 || 0.46364760900080587
26 || 0.46364760900080587
My result is: 0.46364760900080587
atan(0.500000) is: 0.46364760900080609
My result minus atan(x) atan(x) = -0.00000000000000022
How can I improve my code so that it can run with x=1 and x=-1.
Thanks in advance.
PS: I use my own created pw() function instead of pow(), because I wanted to bybass the restriction of not using pow() as we didn't had that in our lectures yet.
PPS: I'd appreciate any advice as to how to improve my code.
In each iteration, you add (-1)k • x2k+1 / (2k+1), and you stop when there is no change to the sum.
If this were calculated with ideal arithmetic (exact, infinitely precise arithmetic), it would never stop for non-zero x, since you are always changing the sum. When calculating with fixed-precision arithmetic, it stops when the term is so small it does not change the sum because of the limited precision.
When |x| is less than one by any significant amount, this comes quickly because x2k+1 gets smaller. When |x| is one, the term becomes just 1 / (2k+1), which gets smaller very slowly. Not until k is around 253 would the sum stop changing.
You might consider changing your stopping condition to be when sum has not changed from sum_old very much rather than when it has not changed at all.
if(x >= (-1) && x <= 1) {
for(k=0; sum!=sum_old; k++) {
sum_old = sum;
sum += pw((-1), k) * pw(x, (2*k) + 1)/((2*k) + 1);
count++;
printf("%d || %.17lf\n", count, sum);
}
Comparing doubles can be tricky. The conventional way to compare doubles is to test within epsilon. There should be an epsilon value defined somewhere, but for your purposes how many digits are enough to approximate? If you only need like 3 or 4 digits you can instead have
#define EPSILON 0.0001 //make this however precise you need to approximate.
if(x >= (-1) && x <= 1) {
for(k=0; fabs(sum - sum_old) > EPSILON; k++) {
sum_old = sum;
sum += pw((-1), k) * pw(x, (2*k) + 1)/((2*k) + 1);
count++;
printf("%d || %.17lf\n", count, sum);
}
If the issue is that -1,1 iterate too many times either reduce the precision or increase the step per iteration. I am not sure that is what you're asking though, please clarify.
I think the cause of this is for a mathematical reason rather than a programming one.
Away from the little mistakes and adjustments that you should do to your code, putting x = 1 in the infinite series of arctan, is a boundary condition:
In this series, we add a negative value to a positive value then a negative value. This means the sum will be increasing, decreasing, increasing, ... and this will make some difference each iteration. This difference will be smaller until the preciseness of double won't catch it, so the program will stop and give us the value.
But in the sum equation. When we set z = 1 and n goes from 0 to ∞, this will make this term (-1^n) equal to 1 in one time and -1 in the next iteration. Also,
the value of the z-term will be one and the denominator value when n approaches infinity will = ∞ .
So the sum several iterations will be like +1/∞ -1/∞ +1/∞ -1/∞ ... (where ∞ here represents a big number). That way the series will not reach a specific number. This is because z = 1 is a boundary in this equation. And that is causing infinite iterations in your solution without reaching a number.
If you need to calculate arctan(1) I think you should use this formula:
All formulas are from this Wikipedia article.
Here is some modifications that make your code more compact and has less errors:
#include <stdio.h>
#include <math.h>
#define x 0.5 //here x is much easier to change
double pw(double, double); //declaration of the function should be done
int main() { //the default return type of main is int.
double k;
double sum = 0 ; //you should initiate your variables.
double sum_old = 1 ; //=1 only to pass the for condition first time.
//you don't need to define counter here
if(x < -1 || x > 1){
printf("x is not defined. Please choose an x in the interval [-1, 1]\n");
return 0;
}
for(k=0; sum!=sum_old; k++) {
sum_old = sum;
sum += pw((-1), k) * pw(x, (2*k) + 1)/((2*k) + 1);
printf("%.0f || %.17lf\n", k, sum);
}
printf("My result is: %.17lf\n",sum);
printf("atan(%f) is: %.17f\n", x, atan(x));
printf("My result minus atan(x) = %.17lf\n", sum - atan(x));
return 0;
}
double pw(double y, double n) { //functions should be declared out of the main function
double i;
double number = 1;
for (i = 0; i < n; i++) {
number *= y;
}
return(number);
}
double fc (double y) {
double i;
double number = 1;
for (i = 1; i <= y; i++){
number *= i;
}
return(number);
}
I've been working on a program that calculates sin(x), cos(x), and exp(x) without using math.h and compares them to the library values of their functions. I've been forbidden from actually using the basic power(x, n) and fact(n) functions. The only hint is that I have to do division before doing multiplication when combining the functions into one.
double power(double x, int n)
{
int i;
double prod=1.;
for(i=0;i<n;i++){
prod = prod*x;
}
return prod;
}
double fact(int n)
{
int i;
double prod=1.;
for(i=1;i<=n;i++) {
prod = prod*i;
}
return prod;
}
My idea is to somehow nest the for-loops together, and piecemeal the Taylor Expansion formula for each iteration of the loop, but I haven't had luck actually combining the two.
Any help or hint would be appreciated on how to combine these.
The other aspect of the program that confuses me is that there can only be a single input of X per iteration of the program, and therefore no dynamically defined 'n' for the loops.
Use Taylor series for the exponential:
e^x = 1 + x/1! + x^2/2! + x^3/3!...
and using Euler after that you can calculate sinx and cosx.
The trick is to look at the changes between each successive term in the Taylor series expansion. Let's start with ex:
e^x = 1 + x + x^2/2! + x^3/3! + x^4/4! + x^5/5! ...
Notice that each term is x / n times the prior term, where n is the term number. So start with a term of 1, then multiply by the above expression to get the next term.
That gives you the following implementation:
double etox(double x)
{
long double sum = 0;
// term starts at 1
long double term = 1;
// term number
int i = 1;
// continue until the term is below the precision of the current sum
while (sum + term != sum) {
sum += term;
// new term is x/i times the prior term, where i is the term number
term *= (long double)x / i;
i++;
}
return sum;
}
Note that with this implementation, you'll get some degree of error in the least significant digits. If you start adding from a higher term number and work your way back, this can be avoided.
Similarly for sin(x) and cos(x):
sin(x) = x - x^3/3! + x^5/5! - x^7/7! + x^9/9! ...
cos(x) = 1 - x^2/2! + x^4/4! - x^6/6! + x^8/8! ...
Each term is - (x*x) / ((2*n)*((2*n)-1)) times the prior term, where n is the term number.
I'll leave the the implementation of these two as an exercise for the reader.
Part of this comes from my answer for doing this in MIPS assembly: Taylor Series in MIPS assembly
You can do Taylor series on the fly without having to call sub-functions. In the series, each term can be calculated from the previous term in a loop. (i.e. no need to call fact and/or pow repeatedly, where each starts from the beginning). See https://en.wikipedia.org/wiki/Taylor_series
Anyway, here's code for sin and cos:
// mipstaylor/mipstaylor -- fast sine/cosine calculation
#include <stdio.h>
#include <math.h>
#define ITERMAX 10
// qcos -- calculate cosine
double
qcos(double x)
{
int iteridx;
double x2;
double cur;
int neg;
double xpow;
double n2m1;
double nfac;
double sum;
// square of x
x2 = x * x;
// values for initial terms where n==0:
xpow = 1.0;
n2m1 = 0.0;
nfac = 1.0;
neg = 1;
sum = 0.0;
iteridx = 0;
// NOTES:
// (1) with the setup above, we can just use the loop without any special
// casing
while (1) {
// calculate current value
cur = xpow / nfac;
// apply it to sum
if (neg < 0)
sum -= cur;
else
sum += cur;
// bug out when done
if (++iteridx >= ITERMAX)
break;
// now calculate intermediate values for _next_ sum term
// get _next_ power term
xpow *= x2;
// go from factorial(2n) to factorial(2n+1)
n2m1 += 1.0;
nfac *= n2m1;
// now get factorial(2n+1+1)
n2m1 += 1.0;
nfac *= n2m1;
// flip sign
neg = -neg;
}
return sum;
}
// qsin -- calculate sine
double
qsin(double x)
{
int iteridx;
double x2;
double cur;
int neg;
double xpow;
double n2m1;
double nfac;
double sum;
// square of x
x2 = x * x;
// values for initial terms where n==0:
xpow = x;
n2m1 = 1.0;
nfac = 1.0;
neg = 1;
sum = 0.0;
iteridx = 0;
// NOTES:
// (1) with the setup above, we can just use the loop without any special
// casing
while (1) {
// calculate current value
cur = xpow / nfac;
// apply it to sum
if (neg < 0)
sum -= cur;
else
sum += cur;
// bug out when done
if (++iteridx >= ITERMAX)
break;
// now calculate intermediate values for _next_ sum term
// get _next_ power term
xpow *= x2;
// go from factorial(2n+1) to factorial(2n+1+1)
n2m1 += 1.0;
nfac *= n2m1;
// now get factorial(2n+1+1+1)
n2m1 += 1.0;
nfac *= n2m1;
// flip sign
neg = -neg;
}
return sum;
}
// testfnc -- test function
void
testfnc(int typ,const char *sym)
{
double (*efnc)(double);
double (*qfnc)(double);
double vale;
double valq;
double x;
double dif;
int iter;
switch (typ) {
case 0:
efnc = cos;
qfnc = qcos;
break;
case 1:
efnc = sin;
qfnc = qsin;
break;
default:
efnc = NULL;
qfnc = NULL;
break;
}
iter = 0;
for (x = 0.0; x <= M_PI_2; x += 0.001, ++iter) {
vale = efnc(x);
valq = qfnc(x);
dif = vale - valq;
dif = fabs(dif);
printf("%s: %d x=%.15f e=%.15f q=%.15f dif=%.15f %s\n",
sym,iter,x,vale,valq,dif,(dif < 1e-14) ? "PASS" : "FAIL");
}
}
// main -- main program
int
main(int argc,char **argv)
{
testfnc(0,"cos");
testfnc(1,"sin");
return 0;
}
I'm looking for some nice C code that will accomplish effectively:
while (deltaPhase >= M_PI) deltaPhase -= M_TWOPI;
while (deltaPhase < -M_PI) deltaPhase += M_TWOPI;
What are my options?
Edit Apr 19, 2013:
Modulo function updated to handle boundary cases as noted by aka.nice and arr_sea:
static const double _PI= 3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348;
static const double _TWO_PI= 6.2831853071795864769252867665590057683943387987502116419498891846156328125724179972560696;
// Floating-point modulo
// The result (the remainder) has same sign as the divisor.
// Similar to matlab's mod(); Not similar to fmod() - Mod(-3,4)= 1 fmod(-3,4)= -3
template<typename T>
T Mod(T x, T y)
{
static_assert(!std::numeric_limits<T>::is_exact , "Mod: floating-point type expected");
if (0. == y)
return x;
double m= x - y * floor(x/y);
// handle boundary cases resulted from floating-point cut off:
if (y > 0) // modulo range: [0..y)
{
if (m>=y) // Mod(-1e-16 , 360. ): m= 360.
return 0;
if (m<0 )
{
if (y+m == y)
return 0 ; // just in case...
else
return y+m; // Mod(106.81415022205296 , _TWO_PI ): m= -1.421e-14
}
}
else // modulo range: (y..0]
{
if (m<=y) // Mod(1e-16 , -360. ): m= -360.
return 0;
if (m>0 )
{
if (y+m == y)
return 0 ; // just in case...
else
return y+m; // Mod(-106.81415022205296, -_TWO_PI): m= 1.421e-14
}
}
return m;
}
// wrap [rad] angle to [-PI..PI)
inline double WrapPosNegPI(double fAng)
{
return Mod(fAng + _PI, _TWO_PI) - _PI;
}
// wrap [rad] angle to [0..TWO_PI)
inline double WrapTwoPI(double fAng)
{
return Mod(fAng, _TWO_PI);
}
// wrap [deg] angle to [-180..180)
inline double WrapPosNeg180(double fAng)
{
return Mod(fAng + 180., 360.) - 180.;
}
// wrap [deg] angle to [0..360)
inline double Wrap360(double fAng)
{
return Mod(fAng ,360.);
}
One-liner constant-time solution:
Okay, it's a two-liner if you count the second function for [min,max) form, but close enough — you could merge them together anyways.
/* change to `float/fmodf` or `long double/fmodl` or `int/%` as appropriate */
/* wrap x -> [0,max) */
double wrapMax(double x, double max)
{
/* integer math: `(max + x % max) % max` */
return fmod(max + fmod(x, max), max);
}
/* wrap x -> [min,max) */
double wrapMinMax(double x, double min, double max)
{
return min + wrapMax(x - min, max - min);
}
Then you can simply use deltaPhase = wrapMinMax(deltaPhase, -M_PI, +M_PI).
The solutions is constant-time, meaning that the time it takes does not depend on how far your value is from [-PI,+PI) — for better or for worse.
Verification:
Now, I don't expect you to take my word for it, so here are some examples, including boundary conditions. I'm using integers for clarity, but it works much the same with fmod() and floats:
Positive x:
wrapMax(3, 5) == 3: (5 + 3 % 5) % 5 == (5 + 3) % 5 == 8 % 5 == 3
wrapMax(6, 5) == 1: (5 + 6 % 5) % 5 == (5 + 1) % 5 == 6 % 5 == 1
Negative x:
Note: These assume that integer modulo copies left-hand sign; if not, you get the above ("Positive") case.
wrapMax(-3, 5) == 2: (5 + (-3) % 5) % 5 == (5 - 3) % 5 == 2 % 5 == 2
wrapMax(-6, 5) == 4: (5 + (-6) % 5) % 5 == (5 - 1) % 5 == 4 % 5 == 4
Boundaries:
wrapMax(0, 5) == 0: (5 + 0 % 5) % 5 == (5 + 0) % 5 == 5 % 5 == 0
wrapMax(5, 5) == 0: (5 + 5 % 5) % 5 == (5 + 0) % 5== 5 % 5 == 0
wrapMax(-5, 5) == 0: (5 + (-5) % 5) % 5 == (5 + 0) % 5 == 5 % 5 == 0
Note: Possibly -0 instead of +0 for floating-point.
The wrapMinMax function works much the same: wrapping x to [min,max) is the same as wrapping x - min to [0,max-min), and then (re-)adding min to the result.
I don't know what would happen with a negative max, but feel free to check that yourself!
If ever your input angle can reach arbitrarily high values, and if continuity matters, you can also try
atan2(sin(x),cos(x))
This will preserve continuity of sin(x) and cos(x) better than modulo for high values of x, especially in single precision (float).
Indeed, exact_value_of_pi - double_precision_approximation ~= 1.22e-16
On the other hand, most library/hardware use a high precision approximation of PI for applying the modulo when evaluating trigonometric functions (though x86 family is known to use a rather poor one).
Result might be in [-pi,pi], you'll have to check the exact bounds.
Personaly, I would prevent any angle to reach several revolutions by wrapping systematically and stick to a fmod solution like the one of boost.
There is also fmod function in math.h but the sign causes trouble so that a subsequent operation is needed to make the result fir in the proper range (like you already do with the while's). For big values of deltaPhase this is probably faster than substracting/adding `M_TWOPI' hundreds of times.
deltaPhase = fmod(deltaPhase, M_TWOPI);
EDIT:
I didn't try it intensively but I think you can use fmod this way by handling positive and negative values differently:
if (deltaPhase>0)
deltaPhase = fmod(deltaPhase+M_PI, 2.0*M_PI)-M_PI;
else
deltaPhase = fmod(deltaPhase-M_PI, 2.0*M_PI)+M_PI;
The computational time is constant (unlike the while solution which gets slower as the absolute value of deltaPhase increases)
I would do this:
double wrap(double x) {
return x-2*M_PI*floor(x/(2*M_PI)+0.5);
}
There will be significant numerical errors. The best solution to the numerical errors is to store your phase scaled by 1/PI or by 1/(2*PI) and depending on what you are doing store them as fixed point.
Instead of working in radians, use angles scaled by 1/(2π) and use modf, floor etc. Convert back to radians to use library functions.
This also has the effect that rotating ten thousand and a half revolutions is the same as rotating half then ten thousand revolutions, which is not guaranteed if your angles are in radians, as you have an exact representation in the floating point value rather than summing approximate representations:
#include <iostream>
#include <cmath>
float wrap_rads ( float r )
{
while ( r > M_PI ) {
r -= 2 * M_PI;
}
while ( r <= -M_PI ) {
r += 2 * M_PI;
}
return r;
}
float wrap_grads ( float r )
{
float i;
r = modff ( r, &i );
if ( r > 0.5 ) r -= 1;
if ( r <= -0.5 ) r += 1;
return r;
}
int main ()
{
for (int rotations = 1; rotations < 100000; rotations *= 10 ) {
{
float pi = ( float ) M_PI;
float two_pi = 2 * pi;
float a = pi;
a += rotations * two_pi;
std::cout << rotations << " and a half rotations in radians " << a << " => " << wrap_rads ( a ) / two_pi << '\n' ;
}
{
float pi = ( float ) 0.5;
float two_pi = 2 * pi;
float a = pi;
a += rotations * two_pi;
std::cout << rotations << " and a half rotations in grads " << a << " => " << wrap_grads ( a ) / two_pi << '\n' ;
}
std::cout << '\n';
}}
Here is a version for other people finding this question that can use C++ with Boost:
#include <boost/math/constants/constants.hpp>
#include <boost/math/special_functions/sign.hpp>
template<typename T>
inline T normalizeRadiansPiToMinusPi(T rad)
{
// copy the sign of the value in radians to the value of pi
T signedPI = boost::math::copysign(boost::math::constants::pi<T>(),rad);
// set the value of rad to the appropriate signed value between pi and -pi
rad = fmod(rad+signedPI,(2*boost::math::constants::pi<T>())) - signedPI;
return rad;
}
C++11 version, no Boost dependency:
#include <cmath>
// Bring the 'difference' between two angles into [-pi; pi].
template <typename T>
T normalizeRadiansPiToMinusPi(T rad) {
// Copy the sign of the value in radians to the value of pi.
T signed_pi = std::copysign(M_PI,rad);
// Set the value of difference to the appropriate signed value between pi and -pi.
rad = std::fmod(rad + signed_pi,(2 * M_PI)) - signed_pi;
return rad;
}
I encountered this question when searching for how to wrap a floating point value (or a double) between two arbitrary numbers. It didn't answer specifically for my case, so I worked out my own solution which can be seen here. This will take a given value and wrap it between lowerBound and upperBound where upperBound perfectly meets lowerBound such that they are equivalent (ie: 360 degrees == 0 degrees so 360 would wrap to 0)
Hopefully this answer is helpful to others stumbling across this question looking for a more generic bounding solution.
double boundBetween(double val, double lowerBound, double upperBound){
if(lowerBound > upperBound){std::swap(lowerBound, upperBound);}
val-=lowerBound; //adjust to 0
double rangeSize = upperBound - lowerBound;
if(rangeSize == 0){return upperBound;} //avoid dividing by 0
return val - (rangeSize * std::floor(val/rangeSize)) + lowerBound;
}
A related question for integers is available here:
Clean, efficient algorithm for wrapping integers in C++
A two-liner, non-iterative, tested solution for normalizing arbitrary angles to [-π, π):
double normalizeAngle(double angle)
{
double a = fmod(angle + M_PI, 2 * M_PI);
return a >= 0 ? (a - M_PI) : (a + M_PI);
}
Similarly, for [0, 2π):
double normalizeAngle(double angle)
{
double a = fmod(angle, 2 * M_PI);
return a >= 0 ? a : (a + 2 * M_PI);
}
In the case where fmod() is implemented through truncated division and has the same sign as the dividend, it can be taken advantage of to solve the general problem thusly:
For the case of (-PI, PI]:
if (x > 0) x = x - 2PI * ceil(x/2PI) #Shift to the negative regime
return fmod(x - PI, 2PI) + PI
And for the case of [-PI, PI):
if (x < 0) x = x - 2PI * floor(x/2PI) #Shift to the positive regime
return fmod(x + PI, 2PI) - PI
[Note that this is pseudocode; my original was written in Tcl, and I didn't want to torture everyone with that. I needed the first case, so had to figure this out.]
deltaPhase -= floor(deltaPhase/M_TWOPI)*M_TWOPI;
The way suggested you suggested is best. It is fastest for small deflections. If angles in your program are constantly being deflected into the proper range, then you should only run into big out of range values rarely. Therefore paying the cost of a complicated modular arithmetic code every round seems wasteful. Comparisons are cheap compared to modular arithmetic (http://embeddedgurus.com/stack-overflow/2011/02/efficient-c-tip-13-use-the-modulus-operator-with-caution/).
In C99:
float unwindRadians( float radians )
{
const bool radiansNeedUnwinding = radians < -M_PI || M_PI <= radians;
if ( radiansNeedUnwinding )
{
if ( signbit( radians ) )
{
radians = -fmodf( -radians + M_PI, 2.f * M_PI ) + M_PI;
}
else
{
radians = fmodf( radians + M_PI, 2.f * M_PI ) - M_PI;
}
}
return radians;
}
If linking against glibc's libm (including newlib's implementation) you can access
__ieee754_rem_pio2f() and __ieee754_rem_pio2() private functions:
extern __int32_t __ieee754_rem_pio2f (float,float*);
float wrapToPI(float xf){
const float p[4]={0,M_PI_2,M_PI,-M_PI_2};
float yf[2];
int q;
int qmod4;
q=__ieee754_rem_pio2f(xf,yf);
/* xf = q * M_PI_2 + yf[0] + yf[1] /
* yf[1] << y[0], not sure if it could be ignored */
qmod4= q % 4;
if (qmod4==2)
/* (yf[0] > 0) defines interval (-pi,pi]*/
return ( (yf[0] > 0) ? -p[2] : p[2] ) + yf[0] + yf[1];
else
return p[qmod4] + yf[0] + yf[1];
}
Edit: Just realised that you need to link to libm.a, I couldn't find the symbols declared in libm.so
I have used (in python):
def WrapAngle(Wrapped, UnWrapped ):
TWOPI = math.pi * 2
TWOPIINV = 1.0 / TWOPI
return UnWrapped + round((Wrapped - UnWrapped) * TWOPIINV) * TWOPI
c-code equivalent:
#define TWOPI 6.28318531
double WrapAngle(const double dWrapped, const double dUnWrapped )
{
const double TWOPIINV = 1.0/ TWOPI;
return dUnWrapped + round((dWrapped - dUnWrapped) * TWOPIINV) * TWOPI;
}
notice that this brings it in the wrapped domain +/- 2pi so for +/- pi domain you need to handle that afterward like:
if( angle > pi):
angle -= 2*math.pi
I created this function CalculateCos:
int Factorial (long int n)
{
long int r = 1;
for (int i = 2; i<=n; i++)
{
r = r*i;
}
return r;
}
float CalculateVariable(int CVnumber, int CVloopCounter)
{
float CVresult = 0;
CVresult = pow(CVnumber, (CVloopCounter*2)) / (long int)Factorial(CVnumber*2);
return CVresult;
}
float CalculateCos(int number)
{
float result = 1;
int loopCounter = 1;
int minusOrPlus = 1;
while(loopCounter <= precision && loopCounter <= 8)
{
if(!minusOrPlus)
{
result = result - CalculateVariable(number, loopCounter);
printf("%f\n", result);
minusOrPlus = 1;
}
else
{
result = result + CalculateVariable(number, loopCounter);
printf("%f\n", result);
minusOrPlus = 0;
}
loopCounter++;
}
return result;
}
The reason why I printf after the subtraction or adding, is because it gives me strange output, like:
Enter a number, for the cos function
6
1.000000
0.999997
1.000095
0.996588
1.122822
-3.421593
160.177368
-5729.385254
Result is: -5729.3852539
Official function result is: 0.9601703
Can you help me to get correct results on this?
UPDATE:
Now my solution is:
float CalculateCos(float number)
{
float result = 0;
float step = 1;
int loopCounter = 1;
while(loopCounter <= 5)
{
step = step * (-number) * number / (((2*loopCounter)-1)*((2*loopCounter)-2));
result += step;
loopCounter++;
}
return result;
}
Current problem:
since your Factorial function returns int and you casts it to long int, its result is going to overflow even before the input goes to 16 in your case (14! > max_int).
You're calculating cos using Taylor series:
cos(x) = 1 - x2/2! + x4/4! - x6/6!
+ ...
I'm not going to write code. But there are some things wrong in your program, which can be fixed easily:
The input is in radian, so number should be a float.
Calculating each step of Taylor series using exponentiation and factorial separately leads to overflow very soon. The correct way is maintaining a float variable: step = 1 at first and in kth loop iteration step = step * (- x) * x / ((2*k-1)*(2*k)). In this way, you simply add step to result in the loop and don't need minusOrPlus anymore.
The number of loop iterations is bounded by 8 which is too small, so the result could be not precise enough.
I don't see you use precision variable anywhere. It could be used to check precision of the result. For example, when abs(step) < precision, we're going to terminate the loop.