I am new to Java and am I trying to implement a recursive method for finding "n choose k".
However, I've run into a problem.
public class App {
public static void main(String[] args) throws Exception {
int n = 3;
int k = 2;
int result = combRecursion(n, k);
System.out.println(result);
}
private static int combRecursion(int n, int k) {
if (k == 0) {
return 1;
} else {
return (combRecursion(n - 1, k - 1) + combRecursion(n - 1, k));
}
}
Output:
many repetitions of this line:
at App.combRecursion(App.java:14)
It's possible to pick k items from the set of n items only if n is greater or equal to k.
You need to cut off fruitless branches of recursion spawn by the call combRecursion(n - 1, k) which doesn't reduce the number of item in the sample.
When you need to create a recursive method, it should always contain two parts:
Base case - that represents a set of edge-cases, trivial scenarios for which the result is known in advance. If the recursive method hits the base case (parameters passed to the method match one of the conditions of the base case), recursion terminates. In for this task, the base case will represent a situation when the source list was discovered completely and position is equal to its size (invalid index).
Recursive case - a part of a solution where recursive calls are made and where the main logic resides.
Your recursive case is correct: it spawns two recursive branches of execution (one will "pick" the current item, the second will "reject" it).
But in the base case, you've missed the scenario mentioned above, we need to address these situations:
n isn't large enough (k > n), so that is not possible to fetch k item. And the return value will be 0 (or instead of returning a value, you might throw an exception).
k == 0 result should be 1 (it's always possible to take 0 items, and there's only one way to do it - don't pick anything).
When k == n - there's only one way to construct a combination, as #akuzminykh has pointed out. And the return value will be 1
Note that because your goal is to get familiar with the recursion (I'm pretty sure that you're doing it as an exercise) there's no need to mimic the mathematical formula in your solution, use pure logic.
Here is how you can implement it:
private static int combRecursion(int n, int k) {
if (k > n) return 0; // base case - impossible to construct a combination
if (n == k || k == 0) return 1; // base case - a combination was found
// recursive case
return combRecursion(n - 1, k - 1) + combRecursion(n - 1, k);
}
main() - demo
public static void main(String[] args) {
System.out.println(combRecursion(3, 2));
System.out.println(combRecursion(5, 2));
}
Output
3 // pick 2 item from the set of 3 items
10 // pick 2 item from the set of 5 items
Your base case ought to include both n == k || k == 0 for "n choose k" to be implemented correctly. That way, both calls will eventually terminate (even though your current implementation is rather inefficient as it has exponential runtime). A better implementation would use the formula n!/k!/(n-k)! or the multiplicative formula to run in linear time:
int factorial(int n) {
int res = 1;
for (; n > 1; n--) {
res *= n;
}
return res
}
int choose(int n, int k) {
return factorial(n)/factorial(k)/factorial(n-k);
}
further optimizing this is left as an exercise to the reader (hint: a single for loop suffices).
Related
After about 7 hours in a row I really need some help , I need to return from recursion the amount of options that can be by setting numbers from 1 to chosen number(maximum number) , it's forbidden to use loops/arrays , only recursion , the numbers are all positive(more than 0) and goes only more positively , example : good one : {1,2} , bad one : {2,1}.
example :
n = 3 , max = 2
n : The numbers that should be in the row
, max : The maximum number that can be in the row.
{1,1,1}
{1,1,2}
{1,2,2}
{2,2,2}
from that example that should return 4 because there are 4 options of 3 numbers that their value is maximum 2.
another one:
n=2
max=3
{1,1}
{1,2}
{1,3}
{2,2}
{2,3}
{3,3}
from that example it should return 6 because there are 6 options.
Without prior knowledge, this would probably be a challenging question even for an experienced mathematician. It is the count of multisets, one of the fundamental building blocks in combinatorics. I'll explain my understanding of the idea for the recurrence relation in Wikipedia.
Typically k is used for the multiset cardinality (what your question refers to as n), while n is used as the cardinality of the set (not multiset) to choose from (the max in your question).
For f(n, k), the base cases are:
f(n, 0) = 1
one way to fill the empty multiset
And,
f(0, k) = 0
no ways to choose from an empty set
For the regular case, we consider the nth element (from the set of choices). We'd like to count all the combinations that include it and all those where it's missing. Counting all combinations without the nth element is easy: we have the same multiset counting function applied to k with one less choice:
f(n - 1, k)
Now to count the combinations that include at least one nth element, we imagine taking all the ways of choosing from n items (some of which will not include an nth element) but saving one place in each combination where we will place an nth element, so we end up with:
f(n, k - 1)
Putting it all together:
function f(n, k){
if (n == 0)
return 0;
if (k == 0)
return 1;
return f(n - 1, k) + f(n, k - 1);
}
console.log(f(2, 3));
console.log(f(3, 2));
Recursion can be hard to comprehend at first, but it is very clear to read once you get to know it. The downside is that recursion requires way more space than the basic for-loop (Space complexity of recursive function). For some problems it can be easier to first write the recursive version and afterwards write it as for-loop. Also, if space is not a problem, it helps to make your code clean (no for-loops!)
I made some basic recursion that gives the correct answer for at least the two examples you wrote down. It may be possible that I missed an edge case: maybe a good practise to write every function call and some (edgy) test cases.
public int recursiveWrapper(int n, int max) {
return recursive(n, max, 1, 1);
}
public int recursive(int n, int max, int lower, int current) {
// // for your convenience
// System.out.println("n:" + n + " max:" + max + " lowerbound:" + lower + " current:" + current);
// Base case
if (n <= 1 && lower == max) {
return 1;
}
// Recursive step
// Sequence complete, move to next column
if (current == max) {
// Make sure the lower bound does not go beyond the max. number
int updatedLower = (lower + 1 > max) ? lower : lower + 1;
return 1 + recursive(n - 1, max, updatedLower, updatedLower);
}
return 1 + recursive(n, max, lower, current + 1);
}
In short:
In the second example:
n=2
max=3
{1,1}
{1,2}
{1,3}
{2,2}
{2,3}
{3,3}
Note the pattern of the numbers that appears due to the rule that the numbers from left to right have to be equal or larger:
Second column: 1>2>3 > 2>3 > 3
First column: 1>1>1 > 2>2 > 3
The 'lower bound' parameter in the recursion is basically the lowest possible number the new 'sequence' can take (where each sequence is lower bound -> max number). The base case is then when the lower bound equals the upper bound and each column has done all it 'sequences'. Possibly not a very clear explanation - maybe it helps when you see what is printed out by the commented line in the code I copy pasted.
Note: Maybe it is possible to do the recursion with less parameters. Make sure to read a lot about recursion (for example wikipedia or your studybook?). Recursions makes it easier to find solutions and understand complex and abstract problems.
I have write some less efficient code due to time, try look at this, it will give you dir, i hope,
package com.exercise;
import java.util.Arrays;
public class Permutation {
public static void permutation(String str) {
permutation("", str);
}
private static void permutation(String prefix, String str) {
int n = str.length();
if (n == 0)
System.out.println(prefix);
else {
for (int i = 0; i < n; i++)
permutation(prefix + str.charAt(i), str.substring(0, i) + str.substring(i + 1, n));
}
}
private static void permutationOnInt(String prefix, String str, int max) {
int n = str.length();
if (n == 0)
System.out.println(prefix);
else {
for (int i = 0; i <= n; i++)
permutation(prefix + str.charAt(i), str.substring(0, i) + str.substring(i + 1, n));
}
}
public static int[] return_Array(int length) {
int[] a = new int[length];
for (int i = 0; i < length; i++) {
a[i] = i + 1;
}
return a;
}
public static void main(String[] args) {
String fn = Arrays.toString(return_Array(3));
String apple = String.join(",", fn.replace("[", "").replace("]", "").replace(",", "").replaceAll("\\s+", ""));
permutationOnInt("", apple, 3);
}
}
After you get the result you can convert it back to the array.
Importent : This code is totally not optimized. I will post optimized later
(This may seem like this was already answered, but I am looking something more specific.) For schoolwork I need to write a method that calculates the different ways a rectangle can be tiled by a domino tile of 2*1. From what I can see, it would be the fibonacci numbers of the area. I wrote code that compiled in the compiler, but not sure it really makes sense and am clueless where to go from here. How would I be able to implement this better?
public static int domino(int n, int m) // the method signature is what I must use according the hw instructions
{
int area = n*m; // calculating the area of the passed in rectangle
int dominoes = area/2; // calculating how many dominos will be needed to cover the area
if (dominoes<=2) { // because fib 1 equals 1 and fib 2 equals 1
return 1;
} //also the stopping point
else {return domino(dominoes-1, 0) + domino(dominoes-2, 0);}
}
I do not need to worry about efficiency for this homework.
You are not correctly computing the Fibonacci numbers using your recursive calls. You are executing:
else {return domino(dominoes-1, 0) + domino(dominoes-2, 0);}
So essentially, in the first recursive call n == (dominoes - 1) and m == 0. This means that calculating the area always results in 0, as multiplying anything by 0 equals 0.
My advice would be to use an extra Fibonacci function like so:
public static int domino(int n, int m) {
// return the fibonacci number of the number of dominoes in the given rectangle
return fib((n * m) / 2);
}
public static int fib(int n) {
if(n <= 2)
// seed values of the fibonacci sequence
return 1;
else
return fib(n - 1) + fib(n - 2);
}
I'm trying to implement a code that returns the sum of all prime numbers under 2 million. I have an isPrime(int x) method that returns true if the the number is prime. Here it is:
public static boolean isPrime(int x) {
for (int i = 2; i < x; i++) {
if (x % i == 0) {
return false;
}
}
return true;
}
And the other method, which I'm trying to implement recursively, only works until a certain number, over that number and I get a stack overflow error. The highest I got the code to work was for 10,000.
Here it is:
public static int sumOfPrimes(int a) {
if (a < 2000000) { //this is the limit
if (isPrime(a)) {
return a + sumOfPrimes(a + 1);
} else {
return sumOfPrimes(a + 1);
}
}
return -1;
}
So why do I get a stack overflow error when the number gets bigger and how can I deal with this?
Also, how do you normally deal with writing code for such big numbers? IE: normal number operations like this but for larger numbers? I wrote this recursively because I thought it would be more efficient but it still wont work.
Your isPrime function is inefficient, it doesn't have to go to x, it's enough to go to the square root of x.
But that is not the reason why your solution doesn't work. You cannot have a recursion depth of 1 million.
I would solve this problem iteratively, using the sieve of eratosthenes and for loop over the resulting boolean array.
In general if you would still like to use recursion, you can use tail recursion.
In recursion each function call will push some data to the stack, which is limited, thus generating a stackoverflow error. In tail recursion you won't be pushing anything to the stack, thus not throwing the exception.
Basically all you need is sending the data of the previous computation as parameter instead of having it on the stack.
So:
function(int x) {
// end condition
return function(x - 1) + x;
}
with tail recursion would be
function (int max, int curr, int prev, int sum) {
if (curr > max)
return sum;
return function (max, curr + 1, curr, sum + curr)
}
Keep in mind this is just pseudo code not real java code, but is close enough to the java code.
For more info check
What is tail recursion?
Use Sieve of Eratosthenes:-
Following is the algorithm to find all the prime numbers less than or equal to a given integer n by Eratosthenes’ method:
1) Create a list of consecutive integers from 2 to n: (2, 3, 4, …, n).
2) Initially, let p equal 2, the first prime number.
3) Starting from p, count up in increments of p and mark each of these numbers greater than p itself in the list. These numbers will be 2p, 3p, 4p, etc.; note that some of them may have already been marked.
4) Find the first number greater than p in the list that is not marked. If there was no such number, stop. Otherwise, let p now equal this number (which is the next prime), and repeat from step 3.
public static void main(String[] args) {
int n = 30;
System.out.printf("Following are the prime numbers below %d\n", n);
SieveOfEratosthenes(n);
}
static void markMultiples(boolean arr[], int a, int n)
{
int i = 2, num;
while ( (num = i*a) <= n )
{
arr[ num-1 ] = true; // minus 1 because index starts from 0.
++i;
}
}
// A function to print all prime numbers smaller than n
static void SieveOfEratosthenes(int n)
{
// There are no prime numbers smaller than 2
if (n >= 2)
{
// Create an array of size n and initialize all elements as 0
boolean[] arr=new boolean[n];
for(int index=0;index<arr.length-1;index++){
arr[index]=false;
}
for (int i=1; i<n; ++i)
{
if ( arr[i] == false )
{
//(i+1) is prime, print it and mark its multiples
System.out.printf("%d ", i+1);
markMultiples(arr, i+1, n);
}
}
}
}
Output:-
Following are the prime numbers below 30
2 3 5 7 11 13 17 19 23 29
Given an Integer set, {x | 1 <= x <= n}. Consider a combination, something like 50C6 (select 6 from 50). Calculating the number of combinations and iterating over them (in sorted order) is easy.
For example, this does the trick:
public static void combo(int[] combo, int index, int f, int t) {
if (index >= combo.length) {
// display combination
// ...
return;
}
for (int i = f; i <= t - (combo.length - index) + 1; i++) {
combo[index] = i;
combo(combo, index + 1, i + 1, t);
}
}
For the above, calling combo(new int[]{0, 0, 0, 0}, 0, 1, 9) will list all the 9C4 combinations in sorted order, all 126 of them.
What I would like is the following. Given k, I'd like the algorithm to give the combination.
// Select r from c and return combination k.
public static int[] combo(int c, int r, int k) {
}
For example, combo(3,2,1) should return {1,2} and combo(3,2,3) should return {2,3} (assuming the first combination is 1 and not 0 - but that's trivial).
Doing this in O(nCr) is easy and takes little memory... Doing it in O(1) is also easy, but is requires lots of memory for larger combinations and requires pre-calculation. I don't know whether it's possible to do this in better time than O(nCr) without using a lookup table. Any confirmation/guidance would be appreciated.
Okay, I've worked it out and I am quite happy with the final result. The basic idea is as follows:
Let's say we want the k'th entry of nCr. Then, the number of combinations where we start with a 1 is (n-1)C(r-1) and a 2 is (n-2)C(r-2), etc. So, all you have to do is find out which digit needs to go at the first spot and then repeat the process for every one of the r spots.
For example, let's say we want the 30'th entry of 9C3. For 1, we have 8C2 = 28. That's not enough. For 2, 7C2 = 21. So, the first digit must be a 2 and the first entry that started with a 2 was entry 29. So now you simply repeat this process for the second and third entry.
The non-recursive solution is as follows:
public static int[] getCombo(int n, int r, int k) {
int[] result = new int[r];
int cur = 1;
int sum =0;
while (r > 0) {
int tot = c(n - cur, r - 1);
if (sum + tot < k) {
sum += tot;
cur++;
} else {
result[result.length - r] = cur++;
r--;
}
}
return result;
}
The function c() above, simply calculates "n select r".
I particularly like this as it is O(r).
So you can find the value of nCp by the equation n!/(p!*(n-p)!). So say you're solving 4C3 and you're looking for the kth combo. If the first value is a 1 then that means that you have 3C2 left which calculates to 3. So if k < 3 the first value is a 1. If not you go to 3C2 + 3C1 for the second value. And you recuse down the line. No sure if it's actually faster (the calculation of nCp) but it's an interesting way to think about the problem.
Suppose, I have an unsorted array of overlapped ranges. Each range is just a pair of integers begin and end. Now I want to find if a given key belongs to at least one of the ranges. Probably, I have to know the ranges it belongs as well.
We can assume the ranges array takes ~1M and fits the memory. I am looking for an easy algorithm, which uses only standard JDK collections without any 3d-party libraries and special data structures, but works reasonably fast.
What would you suggest?
Sort the ranges numerically by a custom Comparator, then for each key k build a one-element range [k, k] and do a binary search for this range with a different Comparator.
The Comparator for searching's compare(x,y) should return
<0 if x.max < y.min
>0 if x.min > y.max
0 otherwise (its two range arguments overlap).
As noted by #Per, you need a different, stricter Comparator for sorting, but the first two clauses still hold.
This should work even if the ranges overlap, though you may want to merge overlapping ranges after sorting to speed up the search. The merging can be done in O(N) time.
This is in effect a static interval tree, i.e. one without O(lg N) insertion or deletion, in the same way that a sorted array can be considered a static binary search tree.
If you don't need to know which interval contains your point (EDIT: I guess you probably do, but I'll leave this answer for others with this question who don't), then
Preprocess the intervals by computing two arrays B and E. B is the values of begin in sorted order. E is the values of end in sorted order.
To query a point x, use binary search to find the least index i such that B[i] > x and the least index j such that E[j] ≥ x. The number of intervals [begin, end] containing x is i - j.
class Interval {
double begin, end;
}
class BeginComparator implements java.util.Comparator<Interval> {
public int compare(Interval o1, Interval o2) {
return Double.compare(o1.begin, o2.begin);
}
};
public class IntervalTree {
IntervalTree(Interval[] intervals_) {
intervals = intervals_.clone();
java.util.Arrays.sort(intervals, new BeginComparator());
maxEnd = new double[intervals.length];
initializeMaxEnd(0, intervals.length);
}
double initializeMaxEnd(int a, int b) {
if (a >= b) {
return Double.NEGATIVE_INFINITY;
}
int m = (a + b) >>> 1;
maxEnd[m] = initializeMaxEnd(a, m);
return Math.max(Math.max(maxEnd[m], intervals[m].end), initializeMaxEnd(m + 1, b));
}
void findContainingIntervals(double x, int a, int b, java.util.Collection<Interval> result) {
if (a >= b) {
return;
}
int m = (a + b) >>> 1;
Interval i = intervals[m];
if (x < i.begin) {
findContainingIntervals(x, a, m, result);
} else {
if (x <= i.end) {
result.add(i);
}
if (maxEnd[m] >= x) {
findContainingIntervals(x, a, m, result);
}
findContainingIntervals(x, m + 1, b, result);
}
}
java.util.Collection<Interval> findContainingIntervals(double x) {
java.util.Collection<Interval> result = new java.util.ArrayList<Interval>();
findContainingIntervals(x, 0, intervals.length, result);
return result;
}
Interval[] intervals;
double[] maxEnd;
public static void main(String[] args) {
java.util.Random r = new java.util.Random();
Interval[] intervals = new Interval[10000];
for (int j = 0; j < intervals.length; j++) {
Interval i = new Interval();
do {
i.begin = r.nextDouble();
i.end = r.nextDouble();
} while (i.begin >= i.end);
intervals[j] = i;
}
IntervalTree it = new IntervalTree(intervals);
double x = r.nextDouble();
java.util.Collection<Interval> result = it.findContainingIntervals(x);
int count = 0;
for (Interval i : intervals) {
if (i.begin <= x && x <= i.end) {
count++;
}
}
System.out.println(result.size());
System.out.println(count);
}
}
I believe this is what you are looking for: http://en.wikipedia.org/wiki/Interval_tree
But check this simpler solution first to see if it fits your needs: Using java map for range searches
simple solution with O(n) complexity:
for(Range range: ranges){
if (key >= range.start && key <= range.end)
return range;
}
More clever algorithm can be applied if we know more information about ranges.
Is they sorted? Is they overlapped? and so on
Given just your specification, I would be inclined to order the ranges by size, with the widest ranges first (use a custom Comparator to facilitate this). Then simply iterate through them and return true as soon as you find a range that contains the key. Because we know nothing else about the data, of course the widest ranges are the most likely to contain a given key; searching them first could be a (small) optimization.
You could preprocess the list in other ways. For instance, you could exclude any ranges that are completely enclosed by other ranges. You could order by begin and early-exit as soon as you encounter a begin value greater than your key.