I am getting back into programming after twenty years. I was a good C programmer then, so I am jumping in with C++. For my first project, I am writing a function to calculate a cube root using Newton's Method.
Instead of converging on the answer, it just keeps approaching zero. Since it was so small, I figured it was easier to print out step by step values rather than use the debugger.
I am using Visual C++, with Visual Studio.
Here is the entire function:
double CalcCube(double xx, double guess)
{
int ii = 0;
double result, resbuff;
for (ii = 0; ii < 10; ii++)
{
resbuff = guess - (pow(guess,3) / (3*guess*guess));
cout << "Resbuff = " << resbuff << endl;
cout << "Guess = " << guess << endl;
guess = resbuff;
}
result = guess;
return result;
}`
`
Wrong equation used
Newton's Method finds the value of x such that f(x) = 0.
Code is solving 0 = x*x*x, which the answer is x = 0.
// x*x*x
// resbuff = guess - (pow(guess,3) / (3*guess*guess));
The zero being sought is the solution to 0 = y - x*x*x
resbuff = guess - (xx - pow(guess,3) / (3*guess*guess));
// y - x*x*x
The problem is the math:
pow(guess,3) / (3*guess*guess) = guess^3 / 3*guess^2 = guess/3
=> resbuf= guess - guess/3
So in each iteration you get an smaller value, tending to 0
The equation for computing cube root using the Newton Raphson method is (from https://socratic.org/questions/how-do-you-use-newton-s-method-to-approximate-the-value-of-cube-root)
xn1 = xn - (x - xn^3)/3*xn^2
By using
resbuff = guess - (pow(guess,3) / (3*guess*guess));
you are computing the cube root of zero. That's why you are getting zero as your answer.
Change that line to
resbuff = guess - (xx - pow(guess,3)) / (3*guess*guess));
to get the correct answer.
Related
I'm running a simple for loop with some if statements. In this for loop, 3 variables are to be given a value depending on the index value in the for loop. It seems fairly simple, however, when I run the code, the values always come out as zero and I have no idea why this is happening. My for loop is provided below. I appreciate any suggestions.
double A [N+1];
double r;
double s;
double v;
for(int i = 2; i < N+1; i++)
{
if(i == 2)
{
r = 1/2/i/(i-1);
s = -1/2/(i*i - 1);
v = 1/4/i/(i+1);
}
else if(i <= N-2 && i > 2)
{
r = 1/4/i/(i-1);
s = -1/2/(i*i - 1);
v = 1/4/i/(i+1);
}
else if(i <= N-4 && i > N-2)
{
r = 1/4/i/(i-1);
s = 0;
v = 1/4/i/(i+1);
}
else
{
r = 1/4/i/(i-1);
s = 0;
v = 0;
}
A[i] = r*F[i-2] + s*F[i] + v*F[i+2];
cout << r << s << v << endl;
}
It’s happening because you’re using integer division. An example:
r = 1/2/i/(i-1);
This is the same as:
r = ((1 / 2) / i) / (i - 1);
Which is the same as:
r = (0 / i) / (i - 1);
… which is the same as:
r = 0 / (i - 1);
… which is 0.
Because 1 / 2 is 0 in integer arithmetic. To fix this, use floating point values.
Three things:
else if(i <= N-4 && i > N-2) makes no sense, that condition cannot hold
all your divisions are integer divisions - to fix, convert one of the numbers to a double.
as a result of 1, when i = N-1, and i = N, then the last branch is taken where you force two variables to 0 anyway!
1, 2 and 4 are integers. In integerland 1/2 = 0 and 1/4 = 0
With integers, 1/2 is zero. I would suggest (for a start) changing constants like 2 into 2.0 to ensure they're treated as doubles.
You may also want to (though it may not be necessary) cast all your i variables to floating point values as well, just for completeness, such as:
r = 1.0 / 2.0 / (double)i / ((double)i - 1.0);
The fact that r is a double in no way affects the calculations done on the right of the =. It only affects the final bit (the actual assignment).
1/2, 1/4 and -1/2 will always be zero because of the integer division.So try with 1.0/2.0, 1.0/4.0 and -1.0/2.0 to get it sorted out quickly. But follow the basics and do not use many magic numbers inside a code. Consider creating constants for them and use .
I am using a Softmax activation function in the last layer of a neural network. But I have problems with a safe implementation of this function.
A naive implementation would be this one:
Vector y = mlp(x); // output of the neural network without softmax activation function
for(int f = 0; f < y.rows(); f++)
y(f) = exp(y(f));
y /= y.sum();
This does not work very well for > 100 hidden nodes because the y will be NaN in many cases (if y(f) > 709, exp(y(f)) will return inf). I came up with this version:
Vector y = mlp(x); // output of the neural network without softmax activation function
for(int f = 0; f < y.rows(); f++)
y(f) = safeExp(y(f), y.rows());
y /= y.sum();
where safeExp is defined as
double safeExp(double x, int div)
{
static const double maxX = std::log(std::numeric_limits<double>::max());
const double max = maxX / (double) div;
if(x > max)
x = max;
return std::exp(x);
}
This function limits the input of exp. In most of the cases this works but not in all cases and I did not really manage to find out in which cases it does not work. When I have 800 hidden neurons in the previous layer it does not work at all.
However, even if this worked I somehow "distort" the result of the ANN. Can you think of any other way to calculate the correct solution? Are there any C++ libraries or tricks that I can use to calculate the exact output of this ANN?
edit: The solution provided by Itamar Katz is:
Vector y = mlp(x); // output of the neural network without softmax activation function
double ymax = maximal component of y
for(int f = 0; f < y.rows(); f++)
y(f) = exp(y(f) - ymax);
y /= y.sum();
And it really is mathematically the same. In practice however, some small values become 0 because of the floating point precision. I wonder why nobody ever writes these implementation details down in textbooks.
First go to log scale, i.e calculate log(y) instead of y. The log of the numerator is trivial. In order to calculate the log of the denominator, you can use the following 'trick': http://lingpipe-blog.com/2009/06/25/log-sum-of-exponentials/
I know it's already answered but I'll post here a step-by-step anyway.
put on log:
zj = wj . x + bj
oj = exp(zj)/sum_i{ exp(zi) }
log oj = zj - log sum_i{ exp(zi) }
Let m be the max_i { zi } use the log-sum-exp trick:
log oj = zj - log {sum_i { exp(zi + m - m)}}
= zj - log {sum_i { exp(m) exp(zi - m) }},
= zj - log {exp(m) sum_i {exp(zi - m)}}
= zj - m - log {sum_i { exp(zi - m)}}
the term exp(zi-m) can suffer underflow if m is much greater than other z_i, but that's ok since this means z_i is irrelevant on the softmax output after normalization. final results is:
oj = exp (zj - m - log{sum_i{exp(zi-m)}})
I have problem with precision. I have to make my c++ code to have same precision as matlab. In matlab i have script which do some stuff with numbers etc. I got code in c++ which do the same as that script. Output on the same input is diffrent :( I found that in my script when i try 104 >= 104 it returns false. I tried to use format long but it did not help me to find out why its false. Both numbers are type of double. i thought that maybe matlab stores somewhere the real value of 104 and its for real like 103.9999... So i leveled up my precision in c++. It also didnt help because when matlab returns me value of 50.000 in c++ i got value of 50.050 with high precision. Those 2 values are from few calculations like + or *. Is there any way to make my c++ and matlab scrips have same precision?
for i = 1:neighbors
y = spoints(i,1)+origy;
x = spoints(i,2)+origx;
% Calculate floors, ceils and rounds for the x and y.
fy = floor(y); cy = ceil(y); ry = round(y);
fx = floor(x); cx = ceil(x); rx = round(x);
% Check if interpolation is needed.
if (abs(x - rx) < 1e-6) && (abs(y - ry) < 1e-6)
% Interpolation is not needed, use original datatypes
N = image(ry:ry+dy,rx:rx+dx);
D = N >= C;
else
% Interpolation needed, use double type images
ty = y - fy;
tx = x - fx;
% Calculate the interpolation weights.
w1 = (1 - tx) * (1 - ty);
w2 = tx * (1 - ty);
w3 = (1 - tx) * ty ;
w4 = tx * ty ;
%Compute interpolated pixel values
N = w1*d_image(fy:fy+dy,fx:fx+dx) + w2*d_image(fy:fy+dy,cx:cx+dx) + ...
w3*d_image(cy:cy+dy,fx:fx+dx) + w4*d_image(cy:cy+dy,cx:cx+dx);
D = N >= d_C;
end
I got problems in else which is in line 12. tx and ty eqauls 0.707106781186547 or 1 - 0.707106781186547. Values from d_image are in range 0 and 255. N is value 0..255 of interpolating 4 pixels from image. d_C is value 0.255. Still dunno why matlab shows that when i have in N vlaues like: x x x 140.0000 140.0000 and in d_C: x x x 140 x. D gives me 0 on 4th position so 140.0000 != 140. I Debugged it trying more precision but it still says that its 140.00000000000000 and it is still not 140.
int Codes::Interpolation( Point_<int> point, Point_<int> center , Mat *mat)
{
int x = center.x-point.x;
int y = center.y-point.y;
Point_<double> my;
if(x<0)
{
if(y<0)
{
my.x=center.x+LEN;
my.y=center.y+LEN;
}
else
{
my.x=center.x+LEN;
my.y=center.y-LEN;
}
}
else
{
if(y<0)
{
my.x=center.x-LEN;
my.y=center.y+LEN;
}
else
{
my.x=center.x-LEN;
my.y=center.y-LEN;
}
}
int a=my.x;
int b=my.y;
double tx = my.x - a;
double ty = my.y - b;
double wage[4];
wage[0] = (1 - tx) * (1 - ty);
wage[1] = tx * (1 - ty);
wage[2] = (1 - tx) * ty ;
wage[3] = tx * ty ;
int values[4];
//wpisanie do tablicy 4 pixeli ktore wchodza do interpolacji
for(int i=0;i<4;i++)
{
int val = mat->at<uchar>(Point_<int>(a+help[i].x,a+help[i].y));
values[i]=val;
}
double moze = (wage[0]) * (values[0]) + (wage[1]) * (values[1]) + (wage[2]) * (values[2]) + (wage[3]) * (values[3]);
return moze;
}
LEN = 0.707106781186547 Values in array values are 100% same as matlab values.
Matlab uses double precision. You can use C++'s double type. That should make most things similar, but not 100%.
As someone else noted, this is probably not the source of your problem. Either there is a difference in the algorithms, or it might be something like a library function defined differently in Matlab and in C++. For example, Matlab's std() divides by (n-1) and your code may divide by n.
First, as a rule of thumb, it is never a good idea to compare floating point variables directly. Instead of, for example instead of if (nr >= 104) you should use if (nr >= 104-e), where e is a small number, like 0.00001.
However, there must be some serious undersampling or rounding error somewhere in your script, because getting 50050 instead of 50000 is not in the limit of common floating point imprecision. For example, Matlab can have a step of as small as 15 digits!
I guess there are some casting problems in your code, for example
int i;
double d;
// ...
d = i/3 * d;
will will give a very inaccurate result, because you have an integer division. d = (double)i/3 * d or d = i/3. * d would give a much more accurate result.
The above example would NOT cause any problems in Matlab, because there everything is already a floating-point number by default, so a similar problem might be behind the differences in the results of the c++ and Matlab code.
Seeing your calculations would help a lot in finding what went wrong.
EDIT:
In c and c++, if you compare a double with an integer of the same value, you have a very high chance that they will not be equal. It's the same with two doubles, but you might get lucky if you perform the exact same computations on them. Even in Matlab it's dangerous, and maybe you were just lucky that as both are doubles, both got truncated the same way.
By you recent edit it seems, that the problem is where you evaluate your array. You should never use == or != when comparing floats or doubles in c++ (or in any languages when you use floating-point variables). The proper way to do a comparison is to check whether they are within a small distance of each other.
An example: using == or != to compare two doubles is like comparing the weight of two objects by counting the number of atoms in them, and deciding that they are not equal even if there is one single atom difference between them.
MATLAB uses double precision unless you say otherwise. Any differences you see with an identical implementation in C++ will be due to floating-point errors.
Write a program that determines how far and for how long a time a rock will travel when you throw it off a cliff. Click here to copy the file toss.txt to your desktop (right click the file name and choose Save as). The file contains the height of the cliff in meters.
The program will then:
Open the file toss.txt and read the cliff height into a double-precision variable, then echo print the value of the cliff height to the screen with an appropriate label.
Ask the user for the angle at which the rock is thrown (90 degrees is straight up, and 0 degrees is straight forward), and the velocity at which the rock is thrown (in miles per hour).
Check to make sure the angle is greater than or equal to 0 and less than or equal to 90. If it is not, the program terminates and prints an appropriate error message to the screen.
Check to make sure the velocity is less than or equal to 100 mph and greater than or equal to 0 mph. If it is not, the program terminates and prints an appropriate error message to the screen.
If the angle and velocity are valid, the program completes the calculations as follows:
Converts miles per hour to meters per second.
Converts the angle to radians.
Calculates the time traveled using the following equations:
where
Calculates the distance traveled in the horizontal direction using:
Outputs the time and distance traveled in the horizontal direction to the screen with appropriate labels.
Prints an appropriate message telling the user if the distance traveled in the horizontal direction was greater than, less than, or equal to the height of the cliff.
/* This program */
using namespace std;
#include<iostream>
#include<cmath>
#include<iomanip>
#include<fstream>
int readit ();
int calcit (double, double, double);
int main()
{
readit ();
system ("pause");
return 0;
}
int readit ()
{
double hite, angl, v;
ifstream datain ( "toss.txt" );
datain >> hite;
cout << "The cliff height is " << hite << " meters"<< endl;
cout << "Enter the angle in degrees (from horizontal) the rock is thrown: "
<< endl;
cin >> angl;
if (angl>=0 && angl<=90)
{
cout << endl << "The angle you have entered is "<<angl<< endl <<endl;
}
else
{
cout << "The angle you have entered is not acceptable" << endl;
return 0;
}
cout << "Enter the velocity in mph the rock is thrown: " << endl;
cin >> v;
if (v>=0 && v<=100)
{
cout << endl << "The velocity at which the rock is thrown is "<<v<<
" mph" << endl << endl;
}
else
{
cout << "The velocity you have entered is not acceptable" << endl;
return 0;
}
calcit (hite, angl, v);
}
int calcit (double hite, double angl, double v)
{
double tyme, dist;
v = v * (1609.344/3600);
angl = angl*(M_PI/180);
tyme = -v*sin(angl) + (sqrt((v*sin(angl)*v*sin(angl)) + 2*9.8*hite)/9.8) + (2*(v*sin(angl))/9.8);
dist = (tyme * v) * cos(angl);
cout << tyme << " " << dist <<endl;
}
I am trying to get the correct time the rock is traveling before it hits the ground but i keep getting incorrect answers. I am not sure if i am turning the equation to figure out the time the rock will be in the air until impact into c++ language right. any have any ideas??? i really need to finish this damn project.
Starting from the equation for the y (height above 0) for the rock we have
y = h + v*sin(a)*t - g/2*t^2
which transforms into
g/2 T^2 - v*sin(a)*T - h == 0
when we solve for the final condition y(T)=0.
This yields
T = v*sin(a)/g + sqrt(v*sin(a)*v*sin(a) + 2*g*h)/g
I just can't figure out where the first part -v*sin(angl) in your equation comes from. Everything else looks just fine. So it seems not to be with your code but with the equation you started.
The equation you want is:
s =ut + 1/2 at^2
s = Total distance traveled. (Height of the cliff)
u = Starting velocity (In your case negative as you are throwing
away from the target. And take into account
that not all the starting velocity is away
from the target (eg angle 0 mean u = 0))
a = acceleration (9.81 m/s2)
t = time (The value you want to calculate).
Rearrange the formula to solve for t
To find the solution for t where s = 0...
This formula is you basic quadratic:
y = a.x^2 + b.x + c
Where:
x/y are variables.
a/b/c are constants.
The solution for a quadratic equation where y is 0 is:
x = [ -b ± sqrt(b^2 - 4ac) ] / 2a
Notice the ± symbol. There are actually two solutions to the problem.
You should be able to deduce which one is correct for you as the other
is probably negative.
In your particular case the map is:
x ==> t
y ==> 0
a ==> 1/2.g
b ==> u
c ==> -s
I would suggest a few things to "clean up" the code a bit:
If functions return int ensure that they do really return something. (main doesn't have to but other functions do).
Calculate v * sin(ang1) once then use it in your formula thereafter. Not only more efficient but will make your code clearer.
Like you have given Pi a "constant", do that with other numbers you are using like 9.8 (gravitational force?)
If you have a confusing formula in the code, just introduce more variable names until the meaning becomes obvious. So long as you don't reassign different values to the same variables, this will not make the program confusing.
int calcit (double hite_meters, double angl_deg, double v_mph)
{
double const gravity = 9.8;
double v_ms = v_mph * (1609.344/3600);
double angl_rad = angl_deg * (M_PI/180);
double v_vertical = v_ms * sin( angl_rad );
double time_up = v_vertical / gravity; // [m/s] / [m/s^2] = [s]
double time_down_over_cliff = time_up;
// use quadratic formula t = ( -v - ( v^2 - 4gd )^1/2 ) / 2g:
double time_under_cliff = ( - v_vertical
- sqrt( ( v_vertical * v_vertical )
- ( 4 * - gravity * hite_meters ) ) // negative gravity = down
) / ( 2 * - gravity ); // ( [m/s] + ([m/s]^2 - [m/s^2]*[m])^1/2 ) / [m/s^2]
// = [m/s] / [m/s^2] = [s]
double time_total = time_up + time_down_over_cliff + time_under_cliff;
double v_horizontal = v_ms * cos( angl_rad );
double dist_horizontal = v_ms * time_total;
cout << time_total << " " << dist_horizontal <<endl;
}
Every line of code produces a new, relevant piece of information. When converting to a new unit, I introduce a new variable with a new name. Formulas involving more than one unit get the unit types explained in a comment. This should help turn up unit conversion errors which otherwise I can't help you catch.
Writing this kind of code involves more typing, but the time saved on head-scratching and asking for help more than makes up for it.
The program itself is not any less efficient. More importantly, it may be easily modified, so it won't turn into an inefficient mess after a few revisions.
Strange things happen when i try to find the cube root of a number.
The following code returns me undefined. In cmd : -1.#IND
cout<<pow(( double )(20.0*(-3.2) + 30.0),( double )1/3)
While this one works perfectly fine. In cmd : 4.93242414866094
cout<<pow(( double )(20.0*4.5 + 30.0),( double )1/3)
From mathematical way it must work since we can have the cube root from a negative number.
Pow is from Visual C++ 2010 math.h library. Any ideas?
pow(x, y) from <cmath> does NOT work if x is negative and y is non-integral.
This is a limitation of std::pow, as documented in the C standard and on cppreference:
Error handling
Errors are reported as specified in math_errhandling
If base is finite and negative and exp is finite and non-integer, a domain error occurs and a range error may occur.
If base is zero and exp is zero, a domain error may occur.
If base is zero and exp is negative, a domain error or a pole error may occur.
There are a couple ways around this limitation:
Cube-rooting is the same as taking something to the 1/3 power, so you could do std::pow(x, 1/3.).
In C++11, you can use std::cbrt. C++11 introduced both square-root and cube-root functions, but no generic n-th root function that overcomes the limitations of std::pow.
The power 1/3 is a special case. In general, non-integral powers of negative numbers are complex. It wouldn't be practical for pow to check for special cases like integer roots, and besides, 1/3 as a double is not exactly 1/3!
I don't know about the visual C++ pow, but my man page says under errors:
EDOM The argument x is negative and y is not an integral value. This would result in a complex number.
You'll have to use a more specialized cube root function if you want cube roots of negative numbers - or cut corners and take absolute value, then take cube root, then multiply the sign back on.
Note that depending on context, a negative number x to the 1/3 power is not necessarily the negative cube root you're expecting. It could just as easily be the first complex root, x^(1/3) * e^(pi*i/3). This is the convention mathematica uses; it's also reasonable to just say it's undefined.
While (-1)^3 = -1, you can't simply take a rational power of a negative number and expect a real response. This is because there are other solutions to this rational exponent that are imaginary in nature.
http://www.wolframalpha.com/input/?i=x^(1/3),+x+from+-5+to+0
Similarily, plot x^x. For x = -1/3, this should have a solution. However, this function is deemed undefined in R for x < 0.
Therefore, don't expect math.h to do magic that would make it inefficient, just change the signs yourself.
Guess you gotta take the negative out and put it in afterwards. You can have a wrapper do this for you if you really want to.
function yourPow(double x, double y)
{
if (x < 0)
return -1.0 * pow(-1.0*x, y);
else
return pow(x, y);
}
Don't cast to double by using (double), use a double numeric constant instead:
double thingToCubeRoot = -20.*3.2+30;
cout<< thingToCubeRoot/fabs(thingToCubeRoot) * pow( fabs(thingToCubeRoot), 1./3. );
Should do the trick!
Also: don't include <math.h> in C++ projects, but use <cmath> instead.
Alternatively, use pow from the <complex> header for the reasons stated by buddhabrot
pow( x, y ) is the same as (i.e. equivalent to) exp( y * log( x ) )
if log(x) is invalid then pow(x,y) is also.
Similarly you cannot perform 0 to the power of anything, although mathematically it should be 0.
C++11 has the cbrt function (see for example http://en.cppreference.com/w/cpp/numeric/math/cbrt) so you can write something like
#include <iostream>
#include <cmath>
int main(int argc, char* argv[])
{
const double arg = 20.0*(-3.2) + 30.0;
std::cout << cbrt(arg) << "\n";
std::cout << cbrt(-arg) << "\n";
return 0;
}
I do not have access to the C++ standard so I do not know how the negative argument is handled... a test on ideone http://ideone.com/bFlXYs seems to confirm that C++ (gcc-4.8.1) extends the cube root with this rule cbrt(x)=-cbrt(-x) when x<0; for this extension you can see http://mathworld.wolfram.com/CubeRoot.html
I was looking for cubit root and found this thread and it occurs to me that the following code might work:
#include <cmath>
using namespace std;
function double nth-root(double x, double n){
if (!(n%2) || x<0){
throw FAILEXCEPTION(); // even root from negative is fail
}
bool sign = (x >= 0);
x = exp(log(abs(x))/n);
return sign ? x : -x;
}
I think you should not confuse exponentiation with the nth-root of a number. See the good old Wikipedia
because the 1/3 will always return 0 as it will be considered as integer...
try with 1.0/3.0...
it is what i think but try and implement...
and do not forget to declare variables containing 1.0 and 3.0 as double...
Here's a little function I knocked up.
#define uniform() (rand()/(1.0 + RAND_MAX))
double CBRT(double Z)
{
double guess = Z;
double x, dx;
int loopbreaker;
retry:
x = guess * guess * guess;
loopbreaker = 0;
while (fabs(x - Z) > FLT_EPSILON)
{
dx = 3 * guess*guess;
loopbreaker++;
if (fabs(dx) < DBL_EPSILON || loopbreaker > 53)
{
guess += uniform() * 2 - 1.0;
goto retry;
}
guess -= (x - Z) / dx;
x = guess*guess*guess;
}
return guess;
}
It uses Newton-Raphson to find a cube root.
Sometime Newton -Raphson gets stuck, if the root is very close to 0 then the derivative can
get large and it can oscillate. So I've clamped and forced it to restart if that happens.
If you need more accuracy you can change the FLT_EPSILONs.
If you ever have no math library you can use this way to compute the cubic root:
cubic root
double curt(double x) {
if (x == 0) {
// would otherwise return something like 4.257959840008151e-109
return 0;
}
double b = 1; // use any value except 0
double last_b_1 = 0;
double last_b_2 = 0;
while (last_b_1 != b && last_b_2 != b) {
last_b_1 = b;
// use (2 * b + x / b / b) / 3 for small numbers, as suggested by willywonka_dailyblah
b = (b + x / b / b) / 2;
last_b_2 = b;
// use (2 * b + x / b / b) / 3 for small numbers, as suggested by willywonka_dailyblah
b = (b + x / b / b) / 2;
}
return b;
}
It is derives from the sqrt algorithm below. The idea is that b and x / b / b bigger and smaller from the cubic root of x. So, the average of both lies closer to the cubic root of x.
Square Root And Cubic Root (in Python)
def sqrt_2(a):
if a == 0:
return 0
b = 1
last_b = 0
while last_b != b:
last_b = b
b = (b + a / b) / 2
return b
def curt_2(a):
if a == 0:
return 0
b = a
last_b_1 = 0;
last_b_2 = 0;
while (last_b_1 != b and last_b_2 != b):
last_b_1 = b;
b = (b + a / b / b) / 2;
last_b_2 = b;
b = (b + a / b / b) / 2;
return b
In contrast to the square root, last_b_1 and last_b_2 are required in the cubic root because b flickers. You can modify these algorithms to compute the fourth root, fifth root and so on.
Thanks to my math teacher Herr Brenner in 11th grade who told me this algorithm for sqrt.
Performance
I tested it on an Arduino with 16mhz clock frequency:
0.3525ms for yourPow
0.3853ms for nth-root
2.3426ms for curt