Note: Updated on 06/17/2015. Of course this is possible. See the solution below.
Even if anyone copies and pastes this code, you still have a lot of cleanup to do. Also note that you will have problems inside the critical strip from Re(s) = 0 to Re(s) = 1 :). But this is a good start.
import java.util.Scanner;
public class NewTest{
public static void main(String[] args) {
RiemannZetaMain func = new RiemannZetaMain();
double s = 0;
double start, stop, totalTime;
Scanner scan = new Scanner(System.in);
System.out.print("Enter the value of s inside the Riemann Zeta Function: ");
try {
s = scan.nextDouble();
}
catch (Exception e) {
System.out.println("You must enter a positive integer greater than 1.");
}
start = System.currentTimeMillis();
if (s <= 0)
System.out.println("Value for the Zeta Function = " + riemannFuncForm(s));
else if (s == 1)
System.out.println("The zeta funxtion is undefined for Re(s) = 1.");
else if(s >= 2)
System.out.println("Value for the Zeta Function = " + getStandardSum(s));
else
System.out.println("Value for the Zeta Function = " + getNewSum(s));
stop = System.currentTimeMillis();
totalTime = (double) (stop-start) / 1000.0;
System.out.println("Total time taken is " + totalTime + " seconds.");
}
// Standard form the the Zeta function.
public static double standardZeta(double s) {
int n = 1;
double currentSum = 0;
double relativeError = 1;
double error = 0.000001;
double remainder;
while (relativeError > error) {
currentSum = Math.pow(n, -s) + currentSum;
remainder = 1 / ((s-1)* Math.pow(n, (s-1)));
relativeError = remainder / currentSum;
n++;
}
System.out.println("The number of terms summed was " + n + ".");
return currentSum;
}
public static double getStandardSum(double s){
return standardZeta(s);
}
//New Form
// zeta(s) = 2^(-1+2 s)/((-2+2^s) Gamma(1+s)) integral_0^infinity t^s sech^2(t) dt for Re(s)>-1
public static double Integrate(double start, double end) {
double currentIntegralValue = 0;
double dx = 0.0001d; // The size of delta x in the approximation
double x = start; // A = starting point of integration, B = ending point of integration.
// Ending conditions for the while loop
// Condition #1: The value of b - x(i) is less than delta(x).
// This would throw an out of bounds exception.
// Condition #2: The value of b - x(i) is greater than 0 (Since you start at A and split the integral
// up into "infinitesimally small" chunks up until you reach delta(x)*n.
while (Math.abs(end - x) >= dx && (end - x) > 0) {
currentIntegralValue += function(x) * dx; // Use the (Riemann) rectangle sums at xi to compute width * height
x += dx; // Add these sums together
}
return currentIntegralValue;
}
private static double function(double s) {
double sech = 1 / Math.cosh(s); // Hyperbolic cosecant
double squared = Math.pow(sech, 2);
return ((Math.pow(s, 0.5)) * squared);
}
public static double getNewSum(double s){
double constant = Math.pow(2, (2*s)-1) / (((Math.pow(2, s)) -2)*(gamma(1+s)));
return constant*Integrate(0, 1000);
}
// Gamma Function - Lanczos approximation
public static double gamma(double s){
double[] p = {0.99999999999980993, 676.5203681218851, -1259.1392167224028,
771.32342877765313, -176.61502916214059, 12.507343278686905,
-0.13857109526572012, 9.9843695780195716e-6, 1.5056327351493116e-7};
int g = 7;
if(s < 0.5) return Math.PI / (Math.sin(Math.PI * s)*gamma(1-s));
s -= 1;
double a = p[0];
double t = s+g+0.5;
for(int i = 1; i < p.length; i++){
a += p[i]/(s+i);
}
return Math.sqrt(2*Math.PI)*Math.pow(t, s+0.5)*Math.exp(-t)*a;
}
//Binomial Co-efficient - NOT CURRENTLY USING
/*
public static double binomial(int n, int k)
{
if (k>n-k)
k=n-k;
long b=1;
for (int i=1, m=n; i<=k; i++, m--)
b=b*m/i;
return b;
} */
// Riemann's Functional Equation
// Tried this initially and utterly failed.
public static double riemannFuncForm(double s) {
double term = Math.pow(2, s)*Math.pow(Math.PI, s-1)*(Math.sin((Math.PI*s)/2))*gamma(1-s);
double nextTerm = Math.pow(2, (1-s))*Math.pow(Math.PI, (1-s)-1)*(Math.sin((Math.PI*(1-s))/2))*gamma(1-(1-s));
double error = Math.abs(term - nextTerm);
if(s == 1.0)
return 0;
else
return Math.pow(2, s)*Math.pow(Math.PI, s-1)*(Math.sin((Math.PI*s)/2))*gamma(1-s)*standardZeta(1-s);
}
}
Ok well we've figured out that for this particular function, since this form of it isn't actually a infinite series, we cannot approximate using recursion. However the infinite sum of the Riemann Zeta series (1\(n^s) where n = 1 to infinity) could be solved through this method.
Additionally this method could be used to find any infinite series' sum, product, or limit.
If you execute the code your currently have, you'll get infinite recursion as 1-(1-s) = s (e.g. 1-s = t, 1-t = s so you'll just switch back and forth between two values of s infinitely).
Below I talk about the sum of series. It appears you are calculating the product of the series instead. The concepts below should work for either.
Besides this, the Riemann Zeta Function is an infinite series. This means that it only has a limit, and will never reach a true sum (in finite time) and so you cannot get an exact answer through recursion.
However, if you introduce a "threshold" factor, you can get an approximation that is as good as you like. The sum will increase/decrease as each term is added. Once the sum stabilizes, you can quit out of recursion and return your approximate sum. "Stabilized" is defined using your threshold factor. Once the sum varies by an amount less than this threshold factor (which you have defined), your sum has stabilized.
A smaller threshold leads to a better approximation, but also longer computation time.
(Note: this method only works if your series converges, if it has a chance of not converging, you might also want to build in a maxSteps variable to cease execution if the series hasn't converged to your satisfaction after maxSteps steps of recursion.)
Here's an example implementation, note that you'll have to play with threshold and maxSteps to determine appropriate values:
/* Riemann's Functional Equation
* threshold - if two terms differ by less than this absolute amount, return
* currSteps/maxSteps - if currSteps becomes maxSteps, give up on convergence and return
* currVal - the current product, used to determine threshold case (start at 1)
*/
public static double riemannFuncForm(double s, double threshold, int currSteps, int maxSteps, double currVal) {
double nextVal = currVal*(Math.pow(2, s)*Math.pow(Math.PI, s-1)*(Math.sin((Math.PI*s)/2))*gamma(1-s)); //currVal*term
if( s == 1.0)
return 0;
else if ( s == 0.0)
return -0.5;
else if (Math.abs(currVal-nextVal) < threshold) //When a term will change the current answer by less than threshold
return nextVal; //Could also do currVal here (shouldn't matter much as they differ by < threshold)
else if (currSteps == maxSteps)//When you've taken the max allowed steps
return nextVal; //You might want to print something here so you know you didn't converge
else //Otherwise just keep recursing
return riemannFuncForm(1-s, threshold, ++currSteps, maxSteps, nextVal);
}
}
This is not possible.
The functional form of the Riemann Zeta Function is --
zeta(s) = 2^s pi^(-1+s) Gamma(1-s) sin((pi s)/2) zeta(1-s)
This is different from the standard equation in which an infinite sum is measured from 1/k^s for all k = 1 to k = infinity. It is possible to write this as something similar to --
// Standard form the the Zeta function.
public static double standardZeta(double s) {
int n = 1;
double currentSum = 0;
double relativeError = 1;
double error = 0.000001;
double remainder;
while (relativeError > error) {
currentSum = Math.pow(n, -s) + currentSum;
remainder = 1 / ((s-1)* Math.pow(n, (s-1)));
relativeError = remainder / currentSum;
n++;
}
System.out.println("The number of terms summed was " + n + ".");
return currentSum;
}
The same logic doesn't apply to the functional equation (it isn't a direct sum, it is a mathematical relationship). This would require a rather clever way of designing a program to calculate negative values of Zeta(s)!
The literal interpretation of this Java code is ---
// Riemann's Functional Equation
public static double riemannFuncForm(double s) {
double currentVal = (Math.pow(2, s)*Math.pow(Math.PI, s-1)*(Math.sin((Math.PI*s)/2))*gamma(1-s));
if( s == 1.0)
return 0;
else if ( s == 0.0)
return -0.5;
else
System.out.println("Value of next value is " + nextVal(1-s));
return currentVal;//*nextVal(1-s);
}
public static double nextVal(double s)
{
return (Math.pow(2, s)*Math.pow(Math.PI, s-1)*(Math.sin((Math.PI*s)/2))*gamma(1-s));
}
public static double getRiemannSum(double s) {
return riemannFuncForm(s);
}
Testing on three or four values shows that this doesn't work. If you write something similar to --
// Riemann's Functional Equation
public static double riemannFuncForm(double s) {
double currentVal = Math.pow(2, s)*Math.pow(Math.PI, s-1)*(Math.sin((Math.PI*s)/2))*gamma(1-s); //currVal*term
if( s == 1.0)
return 0;
else if ( s == 0.0)
return -0.5;
else //Otherwise just keep recursing
return currentVal * nextVal(1-s);
}
public static double nextVal(double s)
{
return (Math.pow(2, s)*Math.pow(Math.PI, s-1)*(Math.sin((Math.PI*s)/2))*gamma(1-s));
}
I was misinterpretation how to do this through mathematics. I will have to use a different approximation of the zeta function for values less than 2.
I think I need to use a different form of the zeta function. When I run the entire program ---
import java.util.Scanner;
public class Test4{
public static void main(String[] args) {
RiemannZetaMain func = new RiemannZetaMain();
double s = 0;
double start, stop, totalTime;
Scanner scan = new Scanner(System.in);
System.out.print("Enter the value of s inside the Riemann Zeta Function: ");
try {
s = scan.nextDouble();
}
catch (Exception e) {
System.out.println("You must enter a positive integer greater than 1.");
}
start = System.currentTimeMillis();
if(s >= 2)
System.out.println("Value for the Zeta Function = " + getStandardSum(s));
else
System.out.println("Value for the Zeta Function = " + getRiemannSum(s));
stop = System.currentTimeMillis();
totalTime = (double) (stop-start) / 1000.0;
System.out.println("Total time taken is " + totalTime + " seconds.");
}
// Standard form the the Zeta function.
public static double standardZeta(double s) {
int n = 1;
double currentSum = 0;
double relativeError = 1;
double error = 0.000001;
double remainder;
while (relativeError > error) {
currentSum = Math.pow(n, -s) + currentSum;
remainder = 1 / ((s-1)* Math.pow(n, (s-1)));
relativeError = remainder / currentSum;
n++;
}
System.out.println("The number of terms summed was " + n + ".");
return currentSum;
}
public static double getStandardSum(double s){
return standardZeta(s);
}
// Riemann's Functional Equation
public static double riemannFuncForm(double s, double threshold, double currSteps, int maxSteps) {
double term = Math.pow(2, s)*Math.pow(Math.PI, s-1)*(Math.sin((Math.PI*s)/2))*gamma(1-s);
//double nextTerm = Math.pow(2, (1-s))*Math.pow(Math.PI, (1-s)-1)*(Math.sin((Math.PI*(1-s))/2))*gamma(1-(1-s));
//double error = Math.abs(term - nextTerm);
if(s == 1.0)
return 0;
else if (s == 0.0)
return -0.5;
else if (term < threshold) {//The recursion will stop once the term is less than the threshold
System.out.println("The number of steps is " + currSteps);
return term;
}
else if (currSteps == maxSteps) {//The recursion will stop if you meet the max steps
System.out.println("The series did not converge.");
return term;
}
else //Otherwise just keep recursing
return term*riemannFuncForm(1-s, threshold, ++currSteps, maxSteps);
}
public static double getRiemannSum(double s) {
double threshold = 0.00001;
double currSteps = 1;
int maxSteps = 1000;
return riemannFuncForm(s, threshold, currSteps, maxSteps);
}
// Gamma Function - Lanczos approximation
public static double gamma(double s){
double[] p = {0.99999999999980993, 676.5203681218851, -1259.1392167224028,
771.32342877765313, -176.61502916214059, 12.507343278686905,
-0.13857109526572012, 9.9843695780195716e-6, 1.5056327351493116e-7};
int g = 7;
if(s < 0.5) return Math.PI / (Math.sin(Math.PI * s)*gamma(1-s));
s -= 1;
double a = p[0];
double t = s+g+0.5;
for(int i = 1; i < p.length; i++){
a += p[i]/(s+i);
}
return Math.sqrt(2*Math.PI)*Math.pow(t, s+0.5)*Math.exp(-t)*a;
}
//Binomial Co-efficient
public static double binomial(int n, int k)
{
if (k>n-k)
k=n-k;
long b=1;
for (int i=1, m=n; i<=k; i++, m--)
b=b*m/i;
return b;
}
}
I notice that plugging in zeta(-1) returns -
Enter the value of s inside the Riemann Zeta Function: -1
The number of steps is 1.0
Value for the Zeta Function = -0.0506605918211689
Total time taken is 0.0 seconds.
I knew that this value was -1/12. I checked some other values with wolfram alpha and observed that --
double term = Math.pow(2, s)*Math.pow(Math.PI, s-1)*(Math.sin((Math.PI*s)/2))*gamma(1-s);
Returns the correct value. It is just that I am multiplying this value every time by zeta(1-s). In the case of Zeta(1/2), this will always multiply the result by 0.99999999.
Enter the value of s inside the Riemann Zeta Function: 0.5
The series did not converge.
Value for the Zeta Function = 0.999999999999889
Total time taken is 0.006 seconds.
I am going to see if I can replace the part for --
else if (term < threshold) {//The recursion will stop once the term is less than the threshold
System.out.println("The number of steps is " + currSteps);
return term;
}
This difference is the error between two terms in the summation. I may not be thinking about this correctly, it is 1:16am right now. Let me see if I can think better tomorrow ....
Related
I have implemented a function to find the trapezoid rule of a given function, the function produces poor results for
.
When I try to calculate the trapezoid rule with n < 8 it produces a value much larger than the actual area, which is unexpected, I have graphed f(x) and drawn how I believe the first few numbers of trapezoids would look, and they all should be producing less than the target area.
However, as n increases, the error becomes lower and lower and at n = 10000000 it is within a 0.001 of the solution.
private interface MathFunc {
double apply(double value);
}
private static final double A = 1;
private static final double B = 9;
public static void main(String args[]) {
MathFunc func = (x) -> Math.log(x) / Math.log(2);
double realValue = 16.98776493946568;
for(int i = 1; i <= 8; i*=2) {
double value = trapezoidRule(A, B, func, i);
System.out.println(i + " Trapezoid Summation for f(x): " + value);
double absError = Math.abs(value - realValue);
System.out.println("Abs Error: " + absError);
System.out.println("% Error: " + (absError/realValue)*100);
System.out.println();
}
}
static double trapezoidRule(double a, double b, MathFunc f, double n) {
double deltaX = (b-a)/n;
double i = 0;
double sum = 0.0;
while( i++ <= n ) {
if(i == 0 || i == n) {
sum += f.apply(a + (i*deltaX));
} else {
sum += 2 * f.apply(a + (i*deltaX));
}
}
return (deltaX * sum) / 2.0;
}
If you step through trapezoidRule for n = 1 in a debugger, you'll see that the loop is executed for i=1 and i=2. Since i=2 is treated as a midpoint, it is counted twice.
Why is the loop executed for wrong values of i? The expression i++ uses the post-increment operator, which increments the variable after returning its value. You should be using a pre-increment operator ++i, or a for loop like any sane person:
for (double i = 0; i <= n; i++) {
while( i++ <= n )
Was causing an issue, as it was doing an extra iteration.
while( i++ < n )
Produces the correct values.
I am attempting to calculate the Sine function of an angle entered by the user in radians. I need to print the value within a given tolerance as well. I have spent a lot of time and am making very little progress. Any help would be greatly appreciated! Thanks! My main issue is a returning a value within the given tolerance.
// Tolerance is an epsilon value
ex. .00001
public static void sineCalc(double angle, double tolerance) {
int power = 1;
double currentAnswer = 0.0;
int count = 0;
double answer = 0.0;
double difference = 0.0;
int i = 1;
while (difference > tolerance || difference == 0) {
if (i % 2 == 0) {
currentAnswer = -Math.pow(angle, power) / getfactorial(power);
} else {
currentAnswer = Math.pow(angle, power) / getfactorial(power);
}
answer = answer + currentAnswer;
power = power + 2;
difference = Math.sin(angle) - answer;
difference = Math.abs(difference);
count++;
i++;
}
System.out.println(answer);
}
Can someone help me for geting out this code of sin(x) Tailor function to get followings:
The first 4 sin(x) Tailor series.
To calculating the sin function using the sum-formel
How to write a method public static double MySinApproximate( double x)?
That is what i get so far, and it has to be in this way!!
import java.lang.Math;
public class mysin {
public static void main(String[] args){
double x= Math.PI;
System.out.println( MySin(x) + "\t \t" + Math.sin(x) + "\n" );
}
public static double MySin(double x){
double sumNeu, sumOld, sum;
int i = 1;
sum = sumNeu = x; // This should calculating the first term Value
do //the loop do will calculating the Tailor Series
{
sumOld = sumNeu;
i++; sum = + sum * x * x / i;
i++; sum = sum / i;
sumNeu = sumOld + sum;
}
while( sumNeu != sumOld);
return sumNeu;
}
} // 11.548739357257745 1.2246467991473532E-16 (as output)
Your loop isn't calculating the Taylor series correctly. (This is really a Maclaurin series, which is the special case of a Taylor series with a = 0.) For the sine function, the terms need to be added and subtracted in an alternating fashion.
sin(x) = x - x3/3! + x5/5! - ...
Your method only adds the terms.
sin(x) = x + x3/3! + x5/5! + ...
Flip the sign of sum on each iteration, by adding the designated line:
do // The loop will calculate the Taylor Series
{
sumOld = sumNeu;
i++; sum = + sum * x * x / i;
i++; sum = sum / i;
sum = -sum; // Add this line!
sumNeu = sumOld + sum;
}
With this change I get a result that is very close:
2.3489882528577605E-16 1.2246467991473532E-16
Due to the inherent inaccuracies of floating-point math in Java (and IEEE in general), this is likely as close as you'll get by writing your own sine method.
I tested an additional case of π/2:
System.out.println( MySin(x/2) + "\t \t" + Math.sin(x/2) + "\n" );
Again, it's close:
1.0000000000000002 1.0
1.I want to write all again like that -
2.I try to writing the first 4 series from sine Taylor and the proximity all together but anyhow doesn't work correctly -
3.i get this output
0.0 0.8414709848078965
0.8414709848078965 0.9092974268256817
0.8414709848078965 0.1411200080598672
0.9092974268256817 -0.7568024953079282
4.How can i get the same accuracy
1.0000000000000002 1.0
and the series of sine(x)?
public class MySin {
public static void main(String[] args){
double y = 0;
y = 4;
for (int i = 1; i<= y; i++){
System.out.println( MySin(i/2) + "\t \t" + Math.sin(i) + "\n" );
}
}
public static double MySin(double x){
double sumNew, sumOld, sum;
int i = 1;
sum = sumNew = x; // This should calculating the first term Value
do //the loop do will calculating the Tailor Series
{
sumOld = sumNew;
i++; sum = - sum * x * x / i; // i did change the sign to -
i++; sum = sum / i;
sum = - sum; // so i don't need this line anymore
sumNew = sumOld + sum;
}
while( sumNew != sumOld);
return sumNew;
}
public static double MySineProximity ( double x) {
while ( x <= ( Math.PI /2 ) )
{
x = 0;
}
return MySin (x);
}
}
I was trying to get a cubic root in java using Math.pow(n, 1.0/3) but because it divides doubles, it doesn't return the exact answer. For example, with 125, this gives 4.9999999999. Is there a work-around for this? I know there is a cubic root function but I'd like to fix this so I can calculate higher roots.
I would not like to round because I want to know whether a number has an integer root by doing something like this: Math.pow(n, 1.0 / 3) % ((int) Math.pow(n, 1.0 / 3)).
Since it is not possible to have arbitrary-precision calculus with double, you have three choices:
Define a precision for which you decide whether a double value is an integer or not.
Test whether the rounded value of the double you have is a correct result.
Do calculus on a BigDecimal object, which supports arbitrary-precision double values.
Option 1
private static boolean isNthRoot(int value, int n, double precision) {
double a = Math.pow(value, 1.0 / n);
return Math.abs(a - Math.round(a)) < precision; // if a and round(a) are "close enough" then we're good
}
The problem with this approach is how to define "close enough". This is a subjective question and it depends on your requirements.
Option 2
private static boolean isNthRoot(int value, int n) {
double a = Math.pow(value, 1.0 / n);
return Math.pow(Math.round(a), n) == value;
}
The advantage of this method is that there is no need to define a precision. However, we need to perform another pow operation so this will affect performance.
Option 3
There is no built-in method to calculate a double power of a BigDecimal. This question will give you insight on how to do it.
The Math.round function will round to the nearest long value that can be stored to a double. You could compare the 2 results to see if the number has an integer cubic root.
double dres = Math.pow(125, 1.0 / 3.0);
double ires = Math.round(dres);
double diff = Math.abs(dres - ires);
if (diff < Math.ulp(10.0)) {
// has cubic root
}
If that's inadequate you can try implementing this algorithm and stop early if the result doesn't seem to be an integer.
I wrote this method to compute floor(x^(1/n)) where x is a non-negative BigInteger and n is a positive integer. It was a while ago now so I can't explain why it works, but I'm reasonably confident that when I wrote it I was happy that it's guaranteed to give the correct answer reasonably quickly.
To see if x is an exact n-th power you can check if the result raised to the power n gives you exactly x back again.
public static BigInteger floorOfNthRoot(BigInteger x, int n) {
int sign = x.signum();
if (n <= 0 || (sign < 0))
throw new IllegalArgumentException();
if (sign == 0)
return BigInteger.ZERO;
if (n == 1)
return x;
BigInteger a;
BigInteger bigN = BigInteger.valueOf(n);
BigInteger bigNMinusOne = BigInteger.valueOf(n - 1);
BigInteger b = BigInteger.ZERO.setBit(1 + x.bitLength() / n);
do {
a = b;
b = a.multiply(bigNMinusOne).add(x.divide(a.pow(n - 1))).divide(bigN);
} while (b.compareTo(a) == -1);
return a;
}
To use it:
System.out.println(floorOfNthRoot(new BigInteger("125"), 3));
Edit
Having read the comments above I now remember that this is the Newton-Raphson method for n-th roots. The Newton-Raphson method has quadratic convergence (which in everyday language means it's fast). You can try it on numbers which have dozens of digits and you should get the answer in a fraction of a second.
You can adapt the method to work with other number types, but double and BigDecimal are in my view not suited for this kind of thing.
You can use some tricks come from mathematics field, to havemore accuracy.
Like this one x^(1/n) = e^(lnx/n).
Check the implementation here:
https://www.baeldung.com/java-nth-root
Here is the solution without using Java's Math.pow function.
It will give you nearly nth root
public class NthRoot {
public static void main(String[] args) {
try (Scanner scanner = new Scanner(System.in)) {
int testcases = scanner.nextInt();
while (testcases-- > 0) {
int root = scanner.nextInt();
int number = scanner.nextInt();
double rootValue = compute(number, root) * 1000.0 / 1000.0;
System.out.println((int) rootValue);
}
} catch (Exception e) {
e.printStackTrace();
}
}
private static double compute(int number, int root) {
double xPre = Math.random() % 10;
double error = 0.0000001;
double delX = 2147483647;
double current = 0.0;
while (delX > error) {
current = ((root - 1.0) * xPre + (double) number / Math.pow(xPre, root - 1)) / (double) root;
delX = Math.abs(current - xPre);
xPre = current;
}
return current;
}
I'd go for implementing my own function to do this, possibly based on this method.
Well this is a good option to choose in this situation.
You can rely on this-
System.out.println(" ");
System.out.println(" Enter a base and then nth root");
while(true)
{
a=Double.parseDouble(br.readLine());
b=Double.parseDouble(br.readLine());
double negodd=-(Math.pow((Math.abs(a)),(1.0/b)));
double poseve=Math.pow(a,(1.0/b));
double posodd=Math.pow(a,(1.0/b));
if(a<0 && b%2==0)
{
String io="\u03AF";
double negeve=Math.pow((Math.abs(a)),(1.0/b));
System.out.println(" Root is imaginary and value= "+negeve+" "+io);
}
else if(a<0 && b%2==1)
System.out.println(" Value= "+negodd);
else if(a>0 && b%2==0)
System.out.println(" Value= "+poseve);
else if(a>0 && b%2==1)
System.out.println(" Value= "+posodd);
System.out.println(" ");
System.out.print(" Enter '0' to come back or press any number to continue- ");
con=Integer.parseInt(br.readLine());
if(con==0)
break;
else
{
System.out.println(" Enter a base and then nth root");
continue;
}
}
It's a pretty ugly hack, but you could reach a few of them through indenting.
System.out.println(Math.sqrt(Math.sqrt(256)));
System.out.println(Math.pow(4, 4));
System.out.println(Math.pow(4, 9));
System.out.println(Math.cbrt(Math.cbrt(262144)));
Result:
4.0
256.0
262144.0
4.0
Which will give you every n^3th cube and every n^2th root.
Find nth root Using binary search method.
Here is the way to find nth root with any precision according to your requirements.
import java.util.Scanner;
public class FindRoot {
public static void main(String[] args) {
try (Scanner scanner = new Scanner(System.in)) {
int testCase = scanner.nextInt();
while (testCase-- > 0) {
double number = scanner.nextDouble();
int root = scanner.nextInt();
double precision = scanner.nextDouble();
double result = findRoot(number, root, precision);
System.out.println(result);
}
}
}
private static double findRoot(double number, int root, double precision) {
double start = 0;
double end = number / 2;
double mid = end;
while (true) {
if (precision >= diff(number, mid, root)) {
return mid;
}
if (pow(mid, root) > number) {
end = mid;
} else {
start = mid;
}
mid = (start + end) / 2;
}
}
private static double diff(double number, double mid, int n) {
double power = pow(mid, n);
return number > power ? number - power : power - number;
}
private static double pow(double number, int pow) {
double result = number;
while (pow-- > 1) {
result *= number;
}
return result;
}
}
I'm using this nth_root algorithm, which also provide the remainder :
public static BigInteger[] sqrt(final BigInteger n) {
final BigInteger[] res = {ZERO, n,};
BigInteger a, b;
assert (n.signum() > 0);
a = ONE.shiftLeft(n.bitLength() & ~1);
while (!a.equals(ZERO)) {
b = res[0].add(a);
res[0] = res[0].shiftRight(1);
if (res[1].compareTo(b) >= 0) {
res[1] = res[1].subtract(b);
res[0] = res[0].add(a);
}
a = a.shiftRight(2);
}
return res;
}
public static BigInteger[] nth_root(BigInteger n, final int nth) {
final BigInteger[] res;
switch(nth){
case 0 : res = new BigInteger[]{n.equals(ONE) ? ONE : ZERO, ZERO} ; break;
case 1 : res = new BigInteger[]{n, ZERO}; break;
case 2 : res = sqrt(n); break;
default:
int sign = n.signum() ;
n = n.abs();
res = new BigInteger[]{n.shiftLeft((n.bitLength() + nth - 1) / nth), n};
while(res[1].compareTo(res[0])<0) {
res[0] = res[1];
res[1] = BigInteger.valueOf(nth-1).multiply(res[1]).add(n.divide(res[1].pow(nth - 1))).divide(BigInteger.valueOf(nth));
}
res[1] = res[0].pow(nth);
res[1] = n.subtract(res[1]);
if (sign < 0 && (nth & 1) == 1) {
res[0] = res[0].negate();
res[1] = res[1].negate();
} else assert (sign > 0);
}
return res ;
}
}
Essentially my delta value is giving me some trouble, when I print all the variables to the console window everything is right and proper except for delta, whose value keeps showing up as NaN. I can't for the life of me figure out why. I am a beginning CIS student at a University and this is really giving me a headache.
import java.util.Scanner;
public class ReimannSumCalculator {
static double a,b,c,start,end;
static double partitions;
static double delta=end - start / partitions;
private static double Quadratic(double a,double b,double c,double x) {
double Quadratic = a*x*x + b*x + c;
return Quadratic;
}
public static void leftReiman(double a,double b,double c,double delta,double start,double end) {
double leftReiman = 0;
for(double x = start; x<end; x+=delta) {
leftReiman = delta * Quadratic(a,b,c,x) + leftReiman;
}
System.out.println("Your left Reiman sum is " + leftReiman);
}
public static void rightReiman(double a,double b,double c,double delta,double start,double end) {
double rightReiman = 0;
for(double x = start+delta; x<=end; x+=delta) {
rightReiman = delta* Quadratic(a,b,c,x) + rightReiman;
}
System.out.println("Your right Reiman sum is " + rightReiman);
}
public static void main(String[] args) {
Scanner keyboard = new Scanner(System.in);
System.out.println("Please enter some values for a b and c.");
a = keyboard.nextDouble();
b = keyboard.nextDouble();
c = keyboard.nextDouble();
System.out.println("Please enter a start and end point.");
start = keyboard.nextDouble();
end = keyboard.nextDouble();
System.out.println("Now enter the amount of partitions.");
partitions = keyboard.nextInt();
leftReiman(a,b,c,start,end,delta);
rightReiman(a,b,c,start,end,delta);
System.out.println("The delta is: " + delta);
System.out.println("The amount of partitions are: " + partitions);
System.out.println("a is: "+ a);
System.out.println("b is: "+ b);
System.out.println("c is: " + c);
System.out.println("The start is: " + start);
System.out.println("The end is: " + end);
}
}
You are executing start / partitions before you have initialized either one of these variables.
So you're essentially calculating 0.0/0.0, which equals NaN.
Java is not excel. It does not figure out the order of operations for a symbolic expression automatically, or recompute expressions when inputs change.
The burden of getting the order of operations right, and recomputing values when their inputs become stale is on the programmer.
static double a,b,c,start,end;
static double partitions;
static double delta=end - start / partitions;
is equivalent to
static double a,b,c,start = 0.0d, end = 0.0d;
static double partitions = 0.0d;
static double delta=end - start / partitions;
because all fields take on the zero value for their type, which is equivalent to
static double a,b,c,start = 0.0d, end = 0.0d;
static double partitions = 0.0d;
static double delta=0.0d - 0.0d / 0.0d; // division by zero -> NaN
because fields are initialized in the order of their declaration.
Then you fail to recompute delta in the body of main, so you end up passing a delta of NaN to leftReimann and rightReimann.
It might make sense to use an integer loop to make sure you deal with the right number of partitions instead of comparing a double x to double boundaries. Floating-point operations are lossy, so you can run into boundary conditions when checking doubles in your loop condition.
public static double leftReimann(double a,double b,double c,int partitions,double start,double end) {
assert partitions > 0;
double leftReimann = 0;
double delta = (end - start) / partitions;
for(int i = 0; i < partitions; ++i) {
double x = start + delta * i;
leftReimann += delta * Quadratic(a,b,c,x);
}
System.out.println("Your left Reimann sum is " + leftReimann);
return leftReimann;
}
public static double rightReimann(double a,double b,double c,int partitions,double start,double end) {
assert partitions > 0;
double rightReimann = 0;
double delta = (start - end) / partitions; // negative
for(int i = 0; i < partitions; ++i) {
double x = end + delta * i;
rightReimann += delta * Quadratic(a,b,c,x);
}
System.out.println("Your right Reimann sum is " + rightReimann);
return rightReimann;
}
There's two options, either make delta a function
private double getDelta() { return (end - start) / partitions;
or, you can recalculate delta after you set end, start, or partitions. (in this case, right before your calls to the Reiman functions, you should say:
delta = (end - start) / partitions;
so it uses the new values of end, start, and partitions.
As others have said, at the time you initialize delta, partitions is zero, so division by zero is NaN.