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
Related
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).
I am trying to solve a problem on leetcode.com Ugly Number II.
problem: An ugly number is a positive integer whose prime factors are limited to 2, 3, and 5.
Given an integer n, return the nth ugly number.
example:
Input: n = 10
Output: 12
Explanation: [1, 2, 3, 4, 5, 6, 8, 9, 10, 12] is the sequence of the first 10 ugly numbers.
This is my solution
class Solution {
public int nthUglyNumber(int n) {
int outputNumber = 6;
int temp = 1;
if (n < 7) {
return n;
}
int i = 7;
while (i != (n + 1)) {
outputNumber = outputNumber + 1;
temp = outputNumber;
while (temp % 5 == 0) {
temp = temp / 5;
}
while (temp % 2 == 0) {
temp = temp / 2;
}
while (temp % 3 == 0) {
temp = temp / 3;
}
if (temp == 1) {
i = i + 1;
}
}
return outputNumber;
}
}
this works for small numbers, but when the input is a big number, then I have Time Limit Exceeded
The question is how to optimize this code?
Thank you!
Hint: You're looking for numbers of the form 2a×3b×5c for non-negative integers a, b, c. Instead of looking for ugly numbers, wouldn't it be easier to just generate them?
I used two tricks to make it about twice as fast, but it's still far too slow. I suspect the check-all-integers-for-ugliness approach is hopeless, and you'll find faster approaches in the discussions on LeetCode.
class Solution {
public int nthUglyNumber(int n) {
for (int i = 1; true; i++)
if (1418776204833984375L % (i / (i & -i)) == 0)
if (--n == 0)
return i;
}
}
The two tricks:
i & -i extracts the lowest 1-bit, so dividing by that takes out every factor 2.
1418776204833984375 is 319×513. Every positive int with only factors 3 and 5 divides that, and every other positive int doesn't.
I think the easiest way is to just maintain a collection of ugly numbers that we will need to visit. We start with a collection containing just 1, and then at each step, we remove the lowest value, and add the values found by multiplying our lowest value by 2, by 3, and by 5. Since these may be duplicates (24 = 8 * 3 and 24 = 12 * 2) but we only want them once apiece, our collection should be a Set.
My Java is far too rusty, but here's a JavaScript implementation that you could use as pseudocode:
const ugly = (count) => {
const upcoming = new Set ([1])
const found = []
while (found .length < count) {
const next = Math .min (...upcoming .values ())
found .push (next)
upcoming .delete (next)
upcoming .add (2 * next)
upcoming .add (3 * next)
upcoming .add (5 * next)
}
return found
}
const uglies = ugly (1690) // This was the upper bound specified in the problem
const nthUglyNumber = (n) => uglies [n - 1]
console .log (nthUglyNumber (10))
console .log (nthUglyNumber (1690))
ugly finds the first count ugly numbers, returning them as an Array. Internally, it keeps two collections. upcoming is the ugly numbers we know we would eventually hit. found is an array of numbers we have actually reached, always taking the smallest of the upcoming values. When we select the next one, we remove it from upcoming and add to upcoming its 2-, 3-, and 5-multiples.
This could be modified to be our only function. But it's nice to call it once for our top value, and store the resulting array, as uglies. Then nthUglyNumber is a simple function that extracts the value at the correct index.
I am trying to prepare for a contest but my program speed is always dreadfully slow as I use O(n). First of all, I don't even know how to make it O(log n), or I've never heard about this paradigm. Where can I learn about this?
For example,
If you had an integer array with zeroes and ones, such as [ 0, 0, 0, 1, 0, 1 ], and now you wanted to replace every 0 with 1 only if one of it's neighbors has the value of 1, what is the most efficient way to go about doing if this must occur t number of times? (The program must do this for a number of t times)
EDIT:
Here's my inefficient solution:
import java.util.Scanner;
public class Main {
static Scanner input = new Scanner(System.in);
public static void main(String[] args) {
int n;
long t;
n = input.nextInt();
t = input.nextLong();
input.nextLine();
int[] units = new int[n + 2];
String inputted = input.nextLine();
input.close();
for(int i = 1; i <= n; i++) {
units[i] = Integer.parseInt((""+inputted.charAt(i - 1)));
}
int[] original;
for(int j = 0; j <= t -1; j++) {
units[0] = units[n];
units[n + 1] = units[1];
original = units.clone();
for(int i = 1; i <= n; i++) {
if(((original[i - 1] == 0) && (original[i + 1] == 1)) || ((original[i - 1] == 1) && (original[i + 1] == 0))) {
units[i] = 1;
} else {
units[i] = 0;
}
}
}
for(int i = 1; i <= n; i++) {
System.out.print(units[i]);
}
}
}
This is an elementary cellular automaton. Such a dynamical system has properties that you can use for your advantages. In your case, for example, you can set to value 1 every cell at distance at most t from any initial value 1 (cone of light property). Then you may do something like:
get a 1 in the original sequence, say it is located at position p.
set to 1 every position from p-t to p+t.
You may then take as your advantage in the next step that you've already set position p-t to p+t... This can let you compute the final step t without computing intermediary steps (good factor of acceleration isn't it?).
You can also use some tricks as HashLife, see 1.
As I was saying in the comments, I'm fairly sure you can keep out the array and clone operations.
You can modify a StringBuilder in-place, so no need to convert back and forth between int[] and String.
For example, (note: This is on the order of an O(n) operation for all T <= N)
public static void main(String[] args) {
System.out.println(conway1d("0000001", 7, 1));
System.out.println(conway1d("01011", 5, 3));
}
private static String conway1d(CharSequence input, int N, long T) {
System.out.println("Generation 0: " + input);
StringBuilder sb = new StringBuilder(input); // Will update this for all generations
StringBuilder copy = new StringBuilder(); // store a copy to reference current generation
for (int gen = 1; gen <= T; gen++) {
// Copy over next generation string
copy.setLength(0);
copy.append(input);
for (int i = 0; i < N; i++) {
conwayUpdate(sb, copy, i, N);
}
input = sb.toString(); // next generation string
System.out.printf("Generation %d: %s\n", gen, input);
}
return input.toString();
}
private static void conwayUpdate(StringBuilder nextGen, final StringBuilder currentGen, int charPos, int N) {
int prev = (N + (charPos - 1)) % N;
int next = (charPos + 1) % N;
// **Exactly one** adjacent '1'
boolean adjacent = currentGen.charAt(prev) == '1' ^ currentGen.charAt(next) == '1';
nextGen.setCharAt(charPos, adjacent ? '1' : '0'); // set cell as alive or dead
}
For the two samples in the problem you posted in the comments, this code generates this output.
Generation 0: 0000001
Generation 1: 1000010
1000010
Generation 0: 01011
Generation 1: 00011
Generation 2: 10111
Generation 3: 10100
10100
The BigO notation is a simplification to understand the complexity of the Algorithm. Basically, two algorithms O(n) can have very different execution times. Why? Let's unroll your example:
You have two nested loops. The outer loop will run t times.
The inner loop will run n times
For each time the loop executes, it will take a constant k time.
So, in essence your algorithm is O(k * t * n). If t is in the same order of magnitude of n, then you can consider the complexity as O(k * n^2).
There is two approaches to optimize this algorithm:
Reduce the constant time k. For example, do not clone the whole array on each loop, because it is very time consuming (clone needs to do a full array loop to clone).
The second optimization in this case is to use Dynamic Programing (https://en.wikipedia.org/wiki/Dynamic_programming) that can cache information between two loops and optimize the execution, that can lower k or even lower the complexity from O(nˆ2) to O(n * log n).
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.
I need to write an algorithm that takes an integer and returns all possible format of addition
e.g.
If I eneter: 6
it would return the following String:
0+6=6
1+1+1+1+1+1=6
1+1+1+1+2=6
1+1+1+3=6
1+1+4=6
1+5=6
2+1+1+1+1=6
2+1+1+2=6
2+1+3=6
2+4=6
3+1+1+1=6
3+1+2=6
3+3=6
4+1+1=6
4+2=6
5+1=6
6+0=6
Here is my try:
import java.util.*;
public class Test
{
public static void main(String[] args)
{
Scanner in = new Scanner(System.in);
System.out.print("Enter an integer? ");
int num = in.nextInt();
System.out.println();
calculate(num);
}
private static void calculate(int n)
{
int[] arInt = new int[n];
for(int i = 0; i <= n; i++)
{
for(int j = 0; j <= n; j++)
{
arInt[j] = i;
}
// ...
}
}
}
I agree with Brad. The best way to complete this would probably be through recursion. In fact, I was working on something related to this last night. I solved my problem using a recursive backtracking algorithm. Check out the Wikipedia page: Backtracking
Now, I make no guarantees that there aren't better, less complex ways to solve this. However, with recursive backtracking you will find all the solutions.
One thing to watch out for though, that 0. You can throw any amount of zeros into an addition/subtraction and it will come out the same.
If you asked the question, you are probably stuck... so i give you a hint :
Usually, in this kind of problem, you do not consider the same combinations with different permutations as different counts, and you do not consider addtion by 0: see Partition.
However, in your example, you seem to be distinguishing different permutations and counting 0. I am pertty much sure that you are not supposed to include 0 because that will give you infinitely many examples to any n. (By the way the answer you gave does not include all counts.) So I assume that you distinguish different permutations but not allow segment into 0. That actually makes the problem much easier.
Suppose you have n = 6.
O O O O O O
^ ^ ^ ^ ^
Think about the n - 1 = 5 positions between the six objects above. For each position, you can decide to either segment at the point or not. For example,
O|O O O|O O
^ ^ ^ ^ ^
is one possible segmentation. Interpret this as: 1+3+2, taking the consecutive objects not segmented by '|'. You should be able to get all possible ways in this way. Namely, for n-1 positions, either segment it or not. For any n, your list should be of 2^(n-1) examples.
E.g. for n = 3:
1+1+1, 2+1, 1+2, 3 => 4 different ways = 2^(3-1)
for n = 6, you should have 2^(6-1) = 32 examples, but you only have 17, which immediately tells that your list is not complete.
Finally note that, as I wrote at the beginning, your question is different from the partion question which is much more standard.
It looks like a homework, so I won't try to write it for you. But I will give you a hint about the solution. You have fixed quantity, imagine e.g. marbles. You are trying to find all possible numbers that add up to that quantity. This means you have to divide the marbles into groups somehow. If you know basic combinatorics, you can easily count the possibilities and enumerate them using an algorithm. Good luck!
Possible solution in Java using recursion:
public void run(int n)
{
List<StringBuilder> combos = showAdditionsFor(n);
for (StringBuilder s : combos)
{
if (s.indexOf("+") < 0)
{
System.out.println(s + " + 0 = " + n);
System.out.println("0 + " + s + " = " + n);
}
else
{
System.out.println(s + " = " + n);
}
}
}
List<StringBuilder> showAdditionsFor(int n)
{
List<StringBuilder> list = new ArrayList<StringBuilder>();
if (n == 0)
list.add(new StringBuilder(""));
else if (n == 1)
list.add(new StringBuilder(String.valueOf(1)));
else
{
for (int i = 1; i <=n; i++)
{
//get n-i list
List<StringBuilder> tempList = showAdditionsFor(n-i);
appendToEachListElement(String.valueOf(i),tempList);
list.addAll(tempList);
}
}
return list;
}
private void appendToEachListElement(String x, List<StringBuilder>l)
{
for (StringBuilder s : l)
{
if (s.length() == 0)
s.append(x);
else
s.append("+" + x);
}
}