I need to write java methods to compute the Fibonacci series of ANY first two numbers imputed by the user, let's say that the user inputs 10 and 20, and wants the first 5 numbers of the series, the output would be 10 20 30 50 80. I have already implemented an iterative method that does this, but my trouble is with the RECURSIVE method to accomplish it.
public int fRec(int n)
{
//base case of recursion
if ((n == 0) || (n == 1))
return n;
else
//recursive step
return fRec(n-1) + fRec(n-2);
}
This is the typical recursive method to the fibonacci series, the n parameter represents up to what number the user wants the series to run, but how can i modify it to to make sure that the series uses the first two numbers that the user wants the series to begin with?
I would use memoization with a Map<Integer,Long> and pass the first and second terms to the constructor. For example,
public class Fibonacci {
public Fibonacci(long first, long second) {
memo.put(0, first);
memo.put(1, second);
}
Map<Integer, Long> memo = new HashMap<>();
public long fRec(int n) {
if (n < 0) {
return -1;
}
if (memo.containsKey(n)) {
return memo.get(n);
}
long r = fRec(n - 2) + fRec(n - 1);
memo.put(n, r);
return r;
}
public static void main(String[] args) {
Fibonacci f = new Fibonacci(10, 20);
for (int i = 0; i < 5; i++) {
System.out.println(f.fRec(i));
}
}
}
Which outputs (as requested)
10
20
30
50
80
To start with specific numbers in the series they will need to be returned for 0 and 1:
public int fib(int n, int start1, int start2) {
switch (n) {
case 0: return start1;
case 1: return start2;
default: return fib(n-1, start1, start2) + fib(n-2, start1, start2);
}
}
This is a pretty laborious way to calculate several members of the series as it's going all the way back to the start each time. Better would be to encapsulate in a class:
class Fib {
private int previous;
private int current;
public Fib(int start1, int start2) {
this.previous = start1;
this.current = start2;
}
public int next() {
int temp = previous + current;
previous = current;
current = successor;
return current;
}
}
This is another way of calculating Fibonacci series of any first two numbers.
public class StackOverflow {
public static void main(String[] args) {
int first = 10, second = 20;
System.out.println(first);
System.out.println(second);
recursive(first, second, 2);
}
public static void recursive(int first, int second, int count) {
if (count != 5){
int temp = first+second;
first= second;
second = temp;
System.out.println(second);
recursive(first, second, ++count);
}
}
}
Related
Is the following method tail-recursive?
I believe that it is not tail recursive because it relies on the previous results and so needs a stack frame, am I correct to state this?
public int[] fib(int n)
{
if(n <= 1){
return (new int[]{n,0});
}
else{
int[] F = fib(n-1);
return (new int[]{F[0]+ F[1], F[0]});
}
}
You are correct: It is not tail recursive because the last line is not of the form
return funcName(args);
Yes, you are correct, since it does not end with a call to itself of the form of
return fib(somevalue);
To convert it into a tail-recursive version you could do something like
// Tail Recursive
// Fibonacci implementation
class GFG
{
// A tail recursive function to
// calculate n th fibonacci number
static int fib(int n, int a, int b )
{
if (n == 0)
return a;
if (n == 1)
return b;
return fib(n - 1, b, a + b);
}
public static void main (String[] args)
{
int n = 9;
System.out.println("fib(" + n +") = "+
fib(n,0,1) );
}
}
Code taken from https://www.geeksforgeeks.org/tail-recursion-fibonacci/
I'm trying to implement a backtracking algorithm to balance weights on a scale. It's for university, so there are given weights I have to use (0, 2, 7, 20, 70, 200, 700). Weights can be placed on the scale multiple times to match the input. For example: input(80) -> result(20, 20, 20, 20) or input(16) -> result(7,7,2).
I have to use backtracking and recursion.
I have difficulties understanding how to do the backtracking if a proposal is wrong. I can only step back one step, but if the right solution requires two steps back my algorithm fails.
So my method isInvalid() is checking if the sum of all counterweights is higher than the input. If so, it will remove the last weight.
I guess this is my problem. For input(16) it produces (7,7,2) --> correct.
But for input(21) it never finishes, because it tries to add 20, and then tries to add 7. Then it will be over 21 and will remove 7, but it will never remove the 20.
/* This is my backtracking algorithm */
public Proposal calc(Proposal proposal) {
Proposal result;
if(proposal.isInvalid()) return null;
if(proposal.isSolution()) return proposal;
for (int i : proposal.possibleNextSteps()) {
Proposal newProposal = new Proposal(proposal.getWeight(), proposal.getCounterWeights());
newProposal.apply(i);
result = calc(newProposal);
if (result != null) return result;
}
return null;
}
/* this is the class Proposal (only required parts) */
public class Proposal {
private int weight;
private ArrayList<Integer> counterWeights;
private static Integer[] weights = {0, 2, 7, 20, 70, 200};
public Proposal(int weight, ArrayList<Integer> counterWeights) {
this.weight = weight;
this.counterWeights = counterWeights;
Arrays.sort(weights, Collections.reverseOrder());
}
public boolean isInvalid() {
if(counterWeights.stream().mapToInt(i -> i.intValue()).sum() > weight) {
counterWeights.remove(counterWeights.size()-1);
return true;
}
return false;
}
public boolean isSolution() {
return counterWeights.stream().mapToInt(value -> value).sum() == weight;
}
public Integer[] possibleNextSteps() {
return weights;
}
public void apply(int option) {
this.counterWeights.add(option);
}
}
What am I doing wrong?
And also, is this the right way to reverse my array of weights?
Thanks!
EDIT:
I tried something different.
I changed this:
Proposal newProposal = new Proposal(proposal.getWeight()- proposal.getSum(), new ArrayList<>());
And this:
public boolean isInvalid() {
return counterWeights.stream().mapToInt(value -> value).sum() > weight;
}
So now if I follow it step by step in debug mode, it is pretty much doing what I want it to do, but it does not pass the solutions from my recursion to my previous solution, so they do not add up to a final solution.
So basically I break down the problem in smaller problems (once i find a weight that fits, I'll call the method recursively with the difference between the total weight and the solution I've already found). But how do I pass the solutions to the calling method?
In the following implementation, a solution is an array of coefficients. a coefficient at index i is the number of times the weight at position i appears in the solution.
Note that you can have several solutions giving the same total weight, this implementation gives them all. It's easy to change it to return only the first solution found.
The recursive methode void solve(int weight, int n, int total) tries for index n all integers for which the total weight is no greater than the target weight.
public class Solver {
private final int[] weights;
private int[] current;
private final List<int[]> solutions = new ArrayList<>();
public Solver(int...weights) {
this.weights = weights;
}
public int[][] solve(int weight) {
current = new int[weights.length];
solutions.clear();
solve(weight, 0, 0);
return solutions.toArray(new int[solutions.size()][]);
}
public void printSolution(int[] solution) {
int total = 0;
for (int i = 0; i < solution.length; ++i) {
for (int j = 0; j < solution[i]; ++j) {
System.out.print(weights[i] + " ");
total += weights[i];
}
}
System.out.println(" total: " + total);
System.out.println();
}
private void solve(int weight, int n, int total) {
if (n >= current.length) {
if (total == weight) {
solutions.add(current.clone());
}
} else {
solve(weight, n+1, total);
while (total < weight) {
++current[n];
total += weights[n];
solve(weight, n+1, total);
}
current[n] = 0;
}
}
}
I need a task about finding Fibonacci Sequence for my independent project in Java. Here are methods for find.
private static long getFibonacci(int n) {
switch (n) {
case 0:
return 0;
case 1:
return 1;
default:
return (getFibonacci(n-1)+getFibonacci(n-2));
}
}
private static long getFibonacciSum(int n) {
long result = 0;
while(n >= 0) {
result += getFibonacci(n);
n--;
}
return result;
}
private static boolean isInFibonacci(long n) {
long a = 0, b = 1, c = 0;
while (c < n) {
c = a + b;
a = b;
b = c;
}
return c == n;
}
Here is main method:
long key = getFibonacciSum(n);
System.out.println("Sum of all Fibonacci Numbers until Fibonacci[n]: "+key);
System.out.println(getFibonacci(n)+" is Fibonacci[n]");
System.out.println("Is n2 in Fibonacci Sequence ?: "+isInFibonacci(n2));
Codes are completely done and working. But if the n or n2 will be more than normal (50th numbers in Fib. Seq.) ? Codes will be runout. Are there any suggestions ?
There is a way to calculate Fibonacci numbers instantaneously by using Binet's Formula
Algorithm:
function fib(n):
root5 = squareroot(5)
gr = (1 + root5) / 2
igr = 1 - gr
value = (power(gr, n) - power(igr, n)) / root5
// round it to the closest integer since floating
// point arithmetic cannot be trusted to give
// perfect integer answers.
return floor(value + 0.5)
Once you do this, you need to be aware of the programming language you're using and how it behaves. This will probably return a floating point decimal type, whereas integers are probably desired.
The complexity of this solution is O(1).
Yes, one improvement you can do is to getFibonacciSum(): instead of calling again and again to isInFibonacci which re-calculates everything from scratch, you can do the exact same thing that isInFibonacci is doing and get the sum in one pass, something like:
private static int getFibonacciSum(int n) {
int a = 0, b = 1, c = 0, sum = 0;
while (c < n) {
c = a + b;
a = b;
sum += b;
b = c;
}
sum += c;
return sum;
}
Well, here goes my solution using a Map and some math formulas. (source:https://www.nayuki.io/page/fast-fibonacci-algorithms)
F(2k) = F(k)[2F(k+1)−F(k)]
F(2k+1) = F(k+1)^2+F(k)^2
It is also possible implement it using lists instead of a map but it is just reinventing the wheel.
When using Iteration solution, we don't worry about running out of memory, but it takes a lot of time to get fib(1000000), for example. In this solution we may be running out of memory for very very very very big inputs (like 10000 billion, idk) but it is much much much faster.
public BigInteger fib(BigInteger n) {
if (n.equals(BigInteger.ZERO))
return BigInteger.ZERO;
if (n.equals(BigInteger.ONE) || n.equals(BigInteger.valueOf(2)))
return BigInteger.ONE;
BigInteger index = n;
//we could have 2 Lists instead of a map
Map<BigInteger,BigInteger> termsToCalculate = new TreeMap<BigInteger,BigInteger>();
//add every index needed to calculate index n
populateMapWhitTerms(termsToCalculate, index);
termsToCalculate.put(n,null); //finally add n to map
Iterator<Map.Entry<BigInteger, BigInteger>> it = termsToCalculate.entrySet().iterator();//it
it.next(); //it = key number 1, contains fib(1);
it.next(); //it = key number 2, contains fib(2);
//map is ordered
while (it.hasNext()) {
Map.Entry<BigInteger, BigInteger> pair = (Entry<BigInteger, BigInteger>)it.next();//first it = key number 3
index = (BigInteger) pair.getKey();
if(index.remainder(BigInteger.valueOf(2)).equals(BigInteger.ZERO)) {
//index is divisible by 2
//F(2k) = F(k)[2F(k+1)−F(k)]
pair.setValue(termsToCalculate.get(index.divide(BigInteger.valueOf(2))).multiply(
(((BigInteger.valueOf(2)).multiply(
termsToCalculate.get(index.divide(BigInteger.valueOf(2)).add(BigInteger.ONE)))).subtract(
termsToCalculate.get(index.divide(BigInteger.valueOf(2)))))));
}
else {
//index is odd
//F(2k+1) = F(k+1)^2+F(k)^2
pair.setValue((termsToCalculate.get(index.divide(BigInteger.valueOf(2)).add(BigInteger.ONE)).multiply(
termsToCalculate.get(index.divide(BigInteger.valueOf(2)).add(BigInteger.ONE)))).add(
(termsToCalculate.get(index.divide(BigInteger.valueOf(2))).multiply(
termsToCalculate.get(index.divide(BigInteger.valueOf(2))))))
);
}
}
// fib(n) was calculated in the while loop
return termsToCalculate.get(n);
}
private void populateMapWhitTerms(Map<BigInteger, BigInteger> termsToCalculate, BigInteger index) {
if (index.equals(BigInteger.ONE)) { //stop
termsToCalculate.put(BigInteger.ONE, BigInteger.ONE);
return;
} else if(index.equals(BigInteger.valueOf(2))){
termsToCalculate.put(BigInteger.valueOf(2), BigInteger.ONE);
return;
} else if(index.remainder(BigInteger.valueOf(2)).equals(BigInteger.ZERO)) {
// index is divisible by 2
// FORMUMA: F(2k) = F(k)[2F(k+1)−F(k)]
// add F(k) key to termsToCalculate (the key is replaced if it is already there, we are working with a map here)
termsToCalculate.put(index.divide(BigInteger.valueOf(2)), null);
populateMapWhitTerms(termsToCalculate, index.divide(BigInteger.valueOf(2)));
// add F(k+1) to termsToCalculate
termsToCalculate.put(index.divide(BigInteger.valueOf(2)).add(BigInteger.ONE), null);
populateMapWhitTerms(termsToCalculate, index.divide(BigInteger.valueOf(2)).add(BigInteger.ONE));
} else {
// index is odd
// FORMULA: F(2k+1) = F(k+1)^2+F(k)^2
// add F(k+1) to termsToCalculate
termsToCalculate.put(((index.subtract(BigInteger.ONE)).divide(BigInteger.valueOf(2)).add(BigInteger.ONE)),null);
populateMapWhitTerms(termsToCalculate,((index.subtract(BigInteger.ONE)).divide(BigInteger.valueOf(2)).add(BigInteger.ONE)));
// add F(k) to termsToCalculate
termsToCalculate.put((index.subtract(BigInteger.ONE)).divide(BigInteger.valueOf(2)), null);
populateMapWhitTerms(termsToCalculate, (index.subtract(BigInteger.ONE)).divide(BigInteger.valueOf(2)));
}
}
This method of solution is called dynamic programming
In this method we are remembering the previous results
so when recursion happens then the cpu doesn't have to do any work to recompute the same value again and again
class fibonacci
{
static int fib(int n)
{
/* Declare an array to store Fibonacci numbers. */
int f[] = new int[n+1];
int i;
/* 0th and 1st number of the series are 0 and 1*/
f[0] = 0;
f[1] = 1;
for (i = 2; i <= n; i++)
{
/* Add the previous 2 numbers in the series
and store it */
f[i] = f[i-1] + f[i-2];
}
return f[n];
}
public static void main (String args[])
{
int n = 9;
System.out.println(fib(n));
}
}
public static long getFib(final int index) {
long a=0,b=0,total=0;
for(int i=0;i<= index;i++) {
if(i==0) {
a=0;
total=a+b;
}else if(i==1) {
b=1;
total=a+b;
}
else if(i%2==0) {
total = a+b;
a=total;
}else {
total = a+b;
b=total;
}
}
return total;
}
I have checked all solutions and for me, the quickest one is to use streams and this code could be easily modified to collect all Fibonacci numbers.
public static Long fibonaciN(long n){
return Stream.iterate(new long[]{0, 1}, a -> new long[]{a[1], a[0] + a[1]})
.limit(n)
.map(a->a[0])
.max(Long::compareTo)
.orElseThrow();
}
50 or just below 50 is as far as you can go with straight recursive implementation. You can switch to iterative or dynamic programming (DP) approaches if you want to go much higher than that. I suggest learning about those from this: https://www.javacodegeeks.com/2014/02/dynamic-programming-introduction.html. And don't forget to look the a solution in the comment by David therein, real efficient. The links shows how even n = 500000 can be computed instantaneously using the DP method. The link also explains the concept of "memoization" to speed up computation by storing intermediate (but later on re-callable) results.
The Problem
Given a set of integers, find a subset of those integers which sum to 100,000,000.
Solution
I am attempting to build a tree containing all the combinations of the given set along with the sum. For example, if the given set looked like 0,1,2, I would build the following tree, checking the sum at each node:
{}
{} {0}
{} {1} {0} {0,1}
{} {2} {1} {1,2} {0} {2} {0,1} {0,1,2}
Since I keep both the array of integers at each node and the sum, I should only need the bottom (current) level of the tree in memory.
Issues
My current implementation will maintain the entire tree in memory and therefore uses way too much heap space.
How can I change my current implementation so that the GC will take care of my upper tree levels?
(At the moment I am just throwing a RuntimeException when I have found the target sum but this is obviously just for playing around)
public class RecursiveSolver {
static final int target = 100000000;
static final int[] set = new int[]{98374328, 234234123, 2341234, 123412344, etc...};
Tree initTree() {
return nextLevel(new Tree(null), 0);
}
Tree nextLevel(Tree currentLocation, int current) {
if (current == set.length) { return null; }
else if (currentLocation.sum == target) throw new RuntimeException(currentLocation.getText());
else {
currentLocation.left = nextLevel(currentLocation.copy(), current + 1);
Tree right = currentLocation.copy();
right.value = add(currentLocation.value, set[current]);
right.sum = currentLocation.sum + set[current];
currentLocation.right = nextLevel(right, current + 1);
return currentLocation;
}
}
int[] add(int[] array, int digit) {
if (array == null) {
return new int[]{digit};
}
int[] newValue = new int[array.length + 1];
for (int i = 0; i < array.length; i++) {
newValue[i] = array[i];
}
newValue[array.length] = digit;
return newValue;
}
public static void main(String[] args) {
RecursiveSolver rs = new RecursiveSolver();
Tree subsetTree = rs.initTree();
}
}
class Tree {
Tree left;
Tree right;
int[] value;
int sum;
Tree(int[] value) {
left = null;
right = null;
sum = 0;
this.value = value;
if (value != null) {
for (int i = 0; i < value.length; i++) sum += value[i];
}
}
Tree copy() {
return new Tree(this.value);
}
}
The time and space you need for building the tree here is absolutely nothing at all.
The reason is because, if you're given
A node of the tree
The depth of the node
The ordered array of input elements
you can simply compute its parent, left, and right children nodes using O(1) operations. And you have access to each of those things while you're traversing the tree, so you don't need anything else.
The problem is NP-complete.
If you really want to improve performance, then you have to forget about your tree implementation. You either have to just generate all the subsets and sum them up or to use dynamic programming.
The choice depends on the number of elements to sum and the sum you want to achieve. You know the sum it is 100,000,000, bruteforce exponential algorithm runs in O(2^n * n) time, so for number below 22 it makes sense.
In python you can achieve this with a simple:
def powerset(iterable):
"powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
You can significantly improve this complexity (sacrificing the memory) by using meet in the middle technique (read the wiki article). This will decrease it to O(2^(n/2)), which means that it will perform better than DP solution for n <~ 53
After thinking more about erip's comments, I realized he is correct - I shouldn't be using a tree to implement this algorithm.
Brute force usually is O(n*2^n) because there are n additions for 2^n subsets. Because I only do one addition per node, the solution I came up with is O(2^n) where n is the size of the given set. Also, this algorithm is only O(n) space complexity. Since the number of elements in the original set in my particular problem is small (around 25) O(2^n) complexity is not too much of a problem.
The dynamic solution to this problem is O(t*n) where t is the target sum and n is the number of elements. Because t is very large in my problem, the dynamic solution ends up with a very long runtime and a high memory usage.
This completes my particular solution in around 311 ms on my machine, which is a tremendous improvement over the dynamic programming solutions I have seen for this particular class of problem.
public class TailRecursiveSolver {
public static void main(String[] args) {
final long starttime = System.currentTimeMillis();
try {
step(new Subset(null, 0), 0);
}
catch (RuntimeException ex) {
System.out.println(ex.getMessage());
final long endtime = System.currentTimeMillis();
System.out.println(endtime - starttime);
}
}
static final int target = 100000000;
static final int[] set = new int[]{ . . . };
static void step(Subset current, int counter) {
if (current.sum == target) throw new RuntimeException(current.getText());
else if (counter == set.length) {}
else {
step(new Subset(add(current.subset, set[counter]), current.sum + set[counter]), counter + 1);
step(current, counter + 1);
}
}
static int[] add(int[] array, int digit) {
if (array == null) {
return new int[]{digit};
}
int[] newValue = new int[array.length + 1];
for (int i = 0; i < array.length; i++) {
newValue[i] = array[i];
}
newValue[array.length] = digit;
return newValue;
}
}
class Subset {
int[] subset;
int sum;
Subset(int[] subset, int sum) {
this.subset = subset;
this.sum = sum;
}
public String getText() {
String ret = "";
for (int i = 0; i < (subset == null ? 0 : subset.length); i++) {
ret += " + " + subset[i];
}
if (ret.startsWith(" ")) {
ret = ret.substring(3);
ret = ret + " = " + sum;
} else ret = "null";
return ret;
}
}
EDIT -
The above code still runs in O(n*2^n) time - since the add method runs in O(n) time. This following code will run in true O(2^n) time, and is MUCH more performant, completing in around 20 ms on my machine.
It is limited to sets less than 64 elements due to storing the current subset as the bits in a long.
public class SubsetSumSolver {
static boolean found = false;
static final int target = 100000000;
static final int[] set = new int[]{ . . . };
public static void main(String[] args) {
step(0,0,0);
}
static void step(long subset, int sum, int counter) {
if (sum == target) {
found = true;
System.out.println(getText(subset, sum));
}
else if (!found && counter != set.length) {
step(subset + (1 << counter), sum + set[counter], counter + 1);
step(subset, sum, counter + 1);
}
}
static String getText(long subset, int sum) {
String ret = "";
for (int i = 0; i < 64; i++) if((1 & (subset >> i)) == 1) ret += " + " + set[i];
if (ret.startsWith(" ")) ret = ret.substring(3) + " = " + sum;
else ret = "null";
return ret;
}
}
EDIT 2 -
Here is another version uses a meet in the middle attack, along with a little bit shifting in order to reduce the complexity from O(2^n) to O(2^(n/2)).
If you want to use this for sets with between 32 and 64 elements, you should change the int which represents the current subset in the step function to a long although performance will obviously drastically decrease as the set size increases. If you want to use this for a set with odd number of elements, you should add a 0 to the set to make it even numbered.
import java.util.ArrayList;
import java.util.List;
public class SubsetSumMiddleAttack {
static final int target = 100000000;
static final int[] set = new int[]{ ... };
static List<Subset> evens = new ArrayList<>();
static List<Subset> odds = new ArrayList<>();
static int[][] split(int[] superSet) {
int[][] ret = new int[2][superSet.length / 2];
for (int i = 0; i < superSet.length; i++) ret[i % 2][i / 2] = superSet[i];
return ret;
}
static void step(int[] superSet, List<Subset> accumulator, int subset, int sum, int counter) {
accumulator.add(new Subset(subset, sum));
if (counter != superSet.length) {
step(superSet, accumulator, subset + (1 << counter), sum + superSet[counter], counter + 1);
step(superSet, accumulator, subset, sum, counter + 1);
}
}
static void printSubset(Subset e, Subset o) {
String ret = "";
for (int i = 0; i < 32; i++) {
if (i % 2 == 0) {
if ((1 & (e.subset >> (i / 2))) == 1) ret += " + " + set[i];
}
else {
if ((1 & (o.subset >> (i / 2))) == 1) ret += " + " + set[i];
}
}
if (ret.startsWith(" ")) ret = ret.substring(3) + " = " + (e.sum + o.sum);
System.out.println(ret);
}
public static void main(String[] args) {
int[][] superSets = split(set);
step(superSets[0], evens, 0,0,0);
step(superSets[1], odds, 0,0,0);
for (Subset e : evens) {
for (Subset o : odds) {
if (e.sum + o.sum == target) printSubset(e, o);
}
}
}
}
class Subset {
int subset;
int sum;
Subset(int subset, int sum) {
this.subset = subset;
this.sum = sum;
}
}
I know my code has a lot of issues right now, but I just want to get the ideas correct before trying anything. I need to have a method which accepts an integer n that returns the nth number in the Fibonacci sequence. While solving it normally with recursion, I have to minimize runtime so when it gets something like the 45th integer, it will still run fairly quickly. Also, I can't use class constants and globals.
The normal way w/ recursion.
public static int fibonacci(int n) {
if (n <= 2) { // to indicate the first two elems in the sequence
return 1;
} else { // goes back to very first integer to calculate (n-1) and (n+1) for (n)
return fibonacci(n-1) + fibonacci(n-2);
}
}
I believe the issue is that there is a lot of redundancy in this process. I figure that I can create a List to calculate up to nth elements so it only run through once before i return the nth element. However, I am having trouble seeing how to use recursion in that case though.
If I am understanding it correctly, the standard recursive method is slow because there are a lot of repeats:
fib(6) = fib(5) + fib(4)
fib(5) = fib(4) + fib(3)
fib(4) = fib(3) + 1
fib(3) = 1 + 1
Is this the correct way of approaching this? Is it needed to have some form of container to have a faster output while still being recursive? Should I use a helper method? I just recently got into recursive programming and I am having a hard time wrapping my head around this since I've been so used to iterative approaches. Thanks.
Here's my flawed and unfinished code:
public static int fasterFib(int n) {
ArrayList<Integer> results = new ArrayList<Integer>();
if (n <= 2) { // if
return 1;
} else if (results.size() <= n){ // If the list has fewer elems than
results.add(0, 1);
results.add(0, 1);
results.add(results.get(results.size() - 1 + results.get(results.size() - 2)));
return fasterFib(n); // not sure what to do with this yet
} else if (results.size() == n) { // base case if reached elems
return results.get(n);
}
return 0;
}
I think you want to use a Map<Integer, Integer> instead of a List. You should probably move that collection outside of your method (so it can cache the results) -
private static Map<Integer, Integer> results = new HashMap<>();
public static int fasterFib(int n) {
if (n == 0) {
return 0;
} else if (n <= 2) { // if
return 1;
}
if (results.get(n) != null) {
return results.get(n);
} else {
int v = fasterFib(n - 1) + fasterFib(n - 2);
results.put(n, v);
return v;
}
}
This optimization is called memoization, from the Wikipedia article -
In computing, memoization is an optimization technique used primarily to speed up computer programs by keeping the results of expensive function calls and returning the cached result when the same inputs occur again.
You can use Map::computeIfAbsent method (since 1.8) to re-use the already calculated numbers.
import java.util.HashMap;
import java.util.Map;
public class Fibonacci {
private final Map<Integer, Integer> cache = new HashMap<>();
public int fib(int n) {
if (n <= 2) {
return n;
} else {
return cache.computeIfAbsent(n, (key) -> fib(n - 1) + fib(n - 2));
}
}
}
The other way to do this is to use a helper method.
static private int fibonacci(int a, int b, int n) {
if(n == 0) return a;
else return fibonacci(b, a+b, n-1);
}
static public int fibonacci(int n) {
return fibonacci(0, 1, n);
}
How about a class and a private static HashMap?
import java.util.HashMap;
public class Fibonacci {
private static HashMap<Integer,Long> cache = new HashMap<Integer,Long>();
public Long get(Integer n) {
if ( n <= 2 ) {
return 1L;
} else if (cache.containsKey(n)) {
return cache.get(n);
} else {
Long result = get(n-1) + get(n-2);
cache.put(n, result);
System.err.println("Calculate once for " + n);
return result;
}
}
/**
* #param args
*/
public static void main(String[] args) {
Fibonacci f = new Fibonacci();
System.out.println(f.get(10));
System.out.println(f.get(15));
}
}
public class Fibonacci {
private Map<Integer, Integer> cache = new HashMap<>();
private void addToCache(int index, int value) {
cache.put(index, value);
}
private int getFromCache(int index) {
return cache.computeIfAbsent(index, this::fibonacci);
}
public int fibonacci(int i) {
if (i == 1)
addToCache(i, 0);
else if (i == 2)
addToCache(i, 1);
else
addToCache(i, getFromCache(i - 1) + getFromCache(i - 2));
return getFromCache(i);
}
}
You can use memoization (store the values you already have in an array, if the value at a given index of this array is not a specific value you have given to ignore --> return that).
Code:
public static void main(String[] args) {
Scanner s = new Scanner(System.in);
int n = Integer.parseInt(s.nextLine());
int[] memo = new int[n+1];
for (int i = 0; i < n+1 ; i++) {
memo[i] = -1;
}
System.out.println(fib(n,memo));
}
static int fib(int n, int[] memo){
if (n<=1){
return n;
}
if(memo[n] != -1){
return memo[n];
}
memo[n] = fib(n-1,memo) + fib(n-2,memo);
return memo[n];
}
Explaination:
memo :
-> int array (all values -1)
-> length (n+1) // easier for working on index
You assign a value to a given index of memo ex: memo[2]
memo will look like [-1,-1, 1, ..... ]
Every time you need to know the fib of 2 it will return memo[2] -> 1
Which saves a lot of computing time on bigger numbers.
private static Map<Integer, Integer> cache = new HashMap<Integer, Integer(){
{
put(0, 1);
put(1, 1);
}
};
/**
* Smallest fibonacci sequence program using dynamic programming.
* #param n
* #return
*/
public static int fibonacci(int n){
return n < 2 ? n : cache.computeIfAbsent(n, (key) -> fibonacci( n - 1) + fibonacci(n - 2));
}
public static long Fib(int n, Dictionary<int, long> dict)
{
if (n <= 1)
return n;
if (dict.ContainsKey(n))
return dict[n];
var value = Fib(n - 1,dict) + Fib(n - 2,dict);
dict[n] = value;
return value;
}