Given an array of size n and k, how do you find the maximum for every contiguous subarray of size k?
For example
arr = 1 5 2 6 3 1 24 7
k = 3
ans = 5 6 6 6 24 24
I was thinking of having an array of size k and each step evict the last element out and add the new element and find maximum among that. It leads to a running time of O(nk). Is there a better way to do this?
You have heard about doing it in O(n) using dequeue.
Well that is a well known algorithm for this question to do in O(n).
The method i am telling is quite simple and has time complexity O(n).
Your Sample Input:
n=10 , W = 3
10 3
1 -2 5 6 0 9 8 -1 2 0
Answer = 5 6 6 9 9 9 8 2
Concept: Dynamic Programming
Algorithm:
N is number of elements in an array and W is window size. So, Window number = N-W+1
Now divide array into blocks of W starting from index 1.
Here divide into blocks of size 'W'=3.
For your sample input:
We have divided into blocks because we will calculate maximum in 2 ways A.) by traversing from left to right B.) by traversing from right to left.
but how ??
Firstly, Traversing from Left to Right. For each element ai in block we will find maximum till that element ai starting from START of Block to END of that block.
So here,
Secondly, Traversing from Right to Left. For each element 'ai' in block we will find maximum till that element 'ai' starting from END of Block to START of that block.
So Here,
Now we have to find maximum for each subarray or window of size 'W'.
So, starting from index = 1 to index = N-W+1 .
max_val[index] = max(RL[index], LR[index+w-1]);
for index=1: max_val[1] = max(RL[1],LR[3]) = max(5,5)= 5
Simliarly, for all index i, (i<=(n-k+1)), value at RL[i] and LR[i+w-1]
are compared and maximum among those two is answer for that subarray.
So Final Answer : 5 6 6 9 9 9 8 2
Time Complexity: O(n)
Implementation code:
#include <iostream>
#include <cstdio>
#include <cstring>
#include <algorithm>
#define LIM 100001
using namespace std;
int arr[LIM]; // Input Array
int LR[LIM]; // maximum from Left to Right
int RL[LIM]; // maximum from Right to left
int max_val[LIM]; // number of subarrays(windows) will be n-k+1
int main(){
int n, w, i, k; // 'n' is number of elements in array
// 'w' is Window's Size
cin >> n >> w;
k = n - w + 1; // 'K' is number of Windows
for(i = 1; i <= n; i++)
cin >> arr[i];
for(i = 1; i <= n; i++){ // for maximum Left to Right
if(i % w == 1) // that means START of a block
LR[i] = arr[i];
else
LR[i] = max(LR[i - 1], arr[i]);
}
for(i = n; i >= 1; i--){ // for maximum Right to Left
if(i == n) // Maybe the last block is not of size 'W'.
RL[i] = arr[i];
else if(i % w == 0) // that means END of a block
RL[i] = arr[i];
else
RL[i] = max(RL[i+1], arr[i]);
}
for(i = 1; i <= k; i++) // maximum
max_val[i] = max(RL[i], LR[i + w - 1]);
for(i = 1; i <= k ; i++)
cout << max_val[i] << " ";
cout << endl;
return 0;
}
Running Code Link
I'll try to proof: (by #johnchen902)
If k % w != 1 (k is not the begin of a block)
Let k* = The begin of block containing k
ans[k] = max( arr[k], arr[k + 1], arr[k + 2], ..., arr[k + w - 1])
= max( max( arr[k], arr[k + 1], arr[k + 2], ..., arr[k*]),
max( arr[k*], arr[k* + 1], arr[k* + 2], ..., arr[k + w - 1]) )
= max( RL[k], LR[k+w-1] )
Otherwise (k is the begin of a block)
ans[k] = max( arr[k], arr[k + 1], arr[k + 2], ..., arr[k + w - 1])
= RL[k] = LR[k+w-1]
= max( RL[k], LR[k+w-1] )
Dynamic programming approach is very neatly explained by Shashank Jain. I would like to explain how to do the same using dequeue.
The key is to maintain the max element at the top of the queue(for a window ) and discarding the useless elements and we also need to discard the elements that are out of index of current window.
useless elements = If Current element is greater than the last element of queue than the last element of queue is useless .
Note : We are storing the index in queue not the element itself. It will be more clear from the code itself.
1. If Current element is greater than the last element of queue than the last element of queue is useless . We need to delete that last element.
(and keep deleting until the last element of queue is smaller than current element).
2. If if current_index - k >= q.front() that means we are going out of window so we need to delete the element from front of queue.
vector<int> max_sub_deque(vector<int> &A,int k)
{
deque<int> q;
for(int i=0;i<k;i++)
{
while(!q.empty() && A[i] >= A[q.back()])
q.pop_back();
q.push_back(i);
}
vector<int> res;
for(int i=k;i<A.size();i++)
{
res.push_back(A[q.front()]);
while(!q.empty() && A[i] >= A[q.back()] )
q.pop_back();
while(!q.empty() && q.front() <= i-k)
q.pop_front();
q.push_back(i);
}
res.push_back(A[q.front()]);
return res;
}
Since each element is enqueued and dequeued atmost 1 time to time complexity is O(n+n) = O(2n) = O(n).
And the size of queue can not exceed the limit k . so space complexity = O(k).
An O(n) time solution is possible by combining the two classic interview questions:
Make a stack data-structure (called MaxStack) which supports push, pop and max in O(1) time.
This can be done using two stacks, the second one contains the minimum seen so far.
Model a queue with a stack.
This can done using two stacks. Enqueues go into one stack, and dequeues come from the other.
For this problem, we basically need a queue, which supports enqueue, dequeue and max in O(1) (amortized) time.
We combine the above two, by modelling a queue with two MaxStacks.
To solve the question, we queue k elements, query the max, dequeue, enqueue k+1 th element, query the max etc. This will give you the max for every k sized sub-array.
I believe there are other solutions too.
1)
I believe the queue idea can be simplified. We maintain a queue and a max for every k. We enqueue a new element, and dequeu all elements which are not greater than the new element.
2) Maintain two new arrays which maintain the running max for each block of k, one array for one direction (left to right/right to left).
3) Use a hammer: Preprocess in O(n) time for range maximum queries.
The 1) solution above might be the most optimal.
You need a fast data structure that can add, remove and query for the max element in less than O(n) time (you can just use an array if O(n) or O(nlogn) is acceptable). You can use a heap, a balanced binary search tree, a skip list, or any other sorted data structure that performs these operations in O(log(n)).
The good news is that most popular languages have a sorted data structure implemented that supports these operations for you. C++ has std::set and std::multiset (you probably need the latter) and Java has PriorityQueue and TreeSet.
Here is the java implementation
public static Integer[] maxsInEveryWindows(int[] arr, int k) {
Deque<Integer> deque = new ArrayDeque<Integer>();
/* Process first k (or first window) elements of array */
for (int i = 0; i < k; i++) {
// For very element, the previous smaller elements are useless so
// remove them from deque
while (!deque.isEmpty() && arr[i] >= arr[deque.peekLast()]) {
deque.removeLast(); // Remove from rear
}
// Add new element at rear of queue
deque.addLast(i);
}
List<Integer> result = new ArrayList<Integer>();
// Process rest of the elements, i.e., from arr[k] to arr[n-1]
for (int i = k; i < arr.length; i++) {
// The element at the front of the queue is the largest element of
// previous window, so add to result.
result.add(arr[deque.getFirst()]);
// Remove all elements smaller than the currently
// being added element (remove useless elements)
while (!deque.isEmpty() && arr[i] >= arr[deque.peekLast()]) {
deque.removeLast();
}
// Remove the elements which are out of this window
while (!deque.isEmpty() && deque.getFirst() <= i - k) {
deque.removeFirst();
}
// Add current element at the rear of deque
deque.addLast(i);
}
// Print the maximum element of last window
result.add(arr[deque.getFirst()]);
return result.toArray(new Integer[0]);
}
Here is the corresponding test case
#Test
public void maxsInWindowsOfSizeKTest() {
Integer[] result = ArrayUtils.maxsInEveryWindows(new int[]{1, 2, 3, 1, 4, 5, 2, 3, 6}, 3);
assertThat(result, equalTo(new Integer[]{3, 3, 4, 5, 5, 5, 6}));
result = ArrayUtils.maxsInEveryWindows(new int[]{8, 5, 10, 7, 9, 4, 15, 12, 90, 13}, 4);
assertThat(result, equalTo(new Integer[]{10, 10, 10, 15, 15, 90, 90}));
}
Using a heap (or tree), you should be able to do it in O(n * log(k)). I'm not sure if this would be indeed better.
here is the Python implementation in O(1)...Thanks to #Shahshank Jain in advance..
from sys import stdin,stdout
from operator import *
n,w=map(int , stdin.readline().strip().split())
Arr=list(map(int , stdin.readline().strip().split()))
k=n-w+1 # window size = k
leftA=[0]*n
rightA=[0]*n
result=[0]*k
for i in range(n):
if i%w==0:
leftA[i]=Arr[i]
else:
leftA[i]=max(Arr[i],leftA[i-1])
for i in range(n-1,-1,-1):
if i%w==(w-1) or i==n-1:
rightA[i]=Arr[i]
else:
rightA[i]=max(Arr[i],rightA[i+1])
for i in range(k):
result[i]=max(rightA[i],leftA[i+w-1])
print(*result,sep=' ')
Method 1: O(n) time, O(k) space
We use a deque (it is like a list but with constant-time insertion and deletion from both ends) to store the index of useful elements.
The index of the current max is kept at the leftmost element of deque. The rightmost element of deque is the smallest.
In the following, for easier explanation we say an element from the array is in the deque, while in fact the index of that element is in the deque.
Let's say {5, 3, 2} are already in the deque (again, if fact their indexes are).
If the next element we read from the array is bigger than 5 (remember, the leftmost element of deque holds the max), say 7: We delete the deque and create a new one with only 7 in it (we do this because the current elements are useless, we have found a new max).
If the next element is less than 2 (which is the smallest element of deque), say 1: We add it to the right ({5, 3, 2, 1})
If the next element is bigger than 2 but less than 5, say 4: We remove elements from right that are smaller than the element and then add the element from right ({5, 4}).
Also we keep elements of the current window only (we can do this in constant time because we are storing the indexes instead of elements).
from collections import deque
def max_subarray(array, k):
deq = deque()
for index, item in enumerate(array):
if len(deq) == 0:
deq.append(index)
elif index - deq[0] >= k: # the max element is out of the window
deq.popleft()
elif item > array[deq[0]]: # found a new max
deq = deque()
deq.append(index)
elif item < array[deq[-1]]: # the array item is smaller than all the deque elements
deq.append(index)
elif item > array[deq[-1]] and item < array[deq[0]]:
while item > array[deq[-1]]:
deq.pop()
deq.append(index)
if index >= k - 1: # start printing when the first window is filled
print(array[deq[0]])
Proof of O(n) time: The only part we need to check is the while loop. In the whole runtime of the code, the while loop can perform at most O(n) operations in total. The reason is that the while loop pops elements from the deque, and since in other parts of the code, we do at most O(n) insertions into the deque, the while loop cannot exceed O(n) operations in total. So the total runtime is O(n) + O(n) = O(n)
Method 2: O(n) time, O(n) space
This is the explanation of the method suggested by S Jain (as mentioned in the comments of his post, this method doesn't work with data streams, which most sliding window questions are designed for).
The reason that method works is explained using the following example:
array = [5, 6, 2, 3, 1, 4, 2, 3]
k = 4
[5, 6, 2, 3 1, 4, 2, 3 ]
LR: 5 6 6 6 1 4 4 4
RL: 6 6 3 3 4 4 3 3
6 6 4 4 4
To get the max for the window [2, 3, 1, 4],
we can get the max of [2, 3] and max of [1, 4], and return the bigger of the two.
Max of [2, 3] is calculated in the RL pass and max of [1, 4] is calculated in LR pass.
Using Fibonacci heap, you can do it in O(n + (n-k) log k), which is equal to O(n log k) for small k, for k close to n this becomes O(n).
The algorithm: in fact, you need:
n inserts to the heap
n-k deletions
n-k findmax's
How much these operations cost in Fibonacci heaps? Insert and findmax is O(1) amortized, deletion is O(log n) amortized. So, we have
O(n + (n-k) log k + (n-k)) = O(n + (n-k) log k)
Sorry, this should have been a comment but I am not allowed to comment for now.
#leo and #Clay Goddard
You can save yourselves from re-computing the maximum by storing both maximum and 2nd maximum of the window in the beginning
(2nd maximum will be the maximum only if there are two maximums in the initial window). If the maximum slides out of the window you still have the next best candidate to compare with the new entry. So you get O(n) , otherwise if you allowed the whole re-computation again the worst case order would be O(nk), k is the window size.
class MaxFinder
{
// finds the max and its index
static int[] findMaxByIteration(int arr[], int start, int end)
{
int max, max_ndx;
max = arr[start];
max_ndx = start;
for (int i=start; i<end; i++)
{
if (arr[i] > max)
{
max = arr[i];
max_ndx = i;
}
}
int result[] = {max, max_ndx};
return result;
}
// optimized to skip iteration, when previous windows max element
// is present in current window
static void optimizedPrintKMax(int arr[], int n, int k)
{
int i, j, max, max_ndx;
// for first window - find by iteration.
int result[] = findMaxByIteration(arr, 0, k);
System.out.printf("%d ", result[0]);
max = result[0];
max_ndx = result[1];
for (j=1; j <= (n-k); j++)
{
// if previous max has fallen out of current window, iterate and find
if (max_ndx < j)
{
result = findMaxByIteration(arr, j, j+k);
max = result[0];
max_ndx = result[1];
}
// optimized path, just compare max with new_elem that has come into the window
else
{
int new_elem_ndx = j + (k-1);
if (arr[new_elem_ndx] > max)
{
max = arr[new_elem_ndx];
max_ndx = new_elem_ndx;
}
}
System.out.printf("%d ", max);
}
}
public static void main(String[] args)
{
int arr[] = {10, 9, 8, 7, 6, 5, 4, 3, 2, 1};
//int arr[] = {1,5,2,6,3,1,24,7};
int n = arr.length;
int k = 3;
optimizedPrintKMax(arr, n, k);
}
}
package com;
public class SlidingWindow {
public static void main(String[] args) {
int[] array = { 1, 5, 2, 6, 3, 1, 24, 7 };
int slide = 3;//say
List<Integer> result = new ArrayList<Integer>();
for (int i = 0; i < array.length - (slide-1); i++) {
result.add(getMax(array, i, slide));
}
System.out.println("MaxList->>>>" + result.toString());
}
private static Integer getMax(int[] array, int i, int slide) {
List<Integer> intermediate = new ArrayList<Integer>();
System.out.println("Initial::" + intermediate.size());
while (intermediate.size() < slide) {
intermediate.add(array[i]);
i++;
}
Collections.sort(intermediate);
return intermediate.get(slide - 1);
}
}
Here is the solution in O(n) time complexity with auxiliary deque
public class TestSlidingWindow {
public static void main(String[] args) {
int[] arr = { 1, 5, 7, 2, 1, 3, 4 };
int k = 3;
printMaxInSlidingWindow(arr, k);
}
public static void printMaxInSlidingWindow(int[] arr, int k) {
Deque<Integer> queue = new ArrayDeque<Integer>();
Deque<Integer> auxQueue = new ArrayDeque<Integer>();
int[] resultArr = new int[(arr.length - k) + 1];
int maxElement = 0;
int j = 0;
for (int i = 0; i < arr.length; i++) {
queue.add(arr[i]);
if (arr[i] > maxElement) {
maxElement = arr[i];
}
/** we need to maintain the auxiliary deque to maintain max element in case max element is removed.
We add the element to deque straight away if subsequent element is less than the last element
(as there is a probability if last element is removed this element can be max element) otherwise
remove all lesser element then insert current element **/
if (auxQueue.size() > 0) {
if (arr[i] < auxQueue.peek()) {
auxQueue.push(arr[i]);
} else {
while (auxQueue.size() > 0 && (arr[i] > auxQueue.peek())) {
auxQueue.pollLast();
}
auxQueue.push(arr[i]);
}
}else {
auxQueue.push(arr[i]);
}
if (queue.size() > 3) {
int removedEl = queue.removeFirst();
if (maxElement == removedEl) {
maxElement = auxQueue.pollFirst();
}
}
if (queue.size() == 3) {
resultArr[j++] = maxElement;
}
}
for (int i = 0; i < resultArr.length; i++) {
System.out.println(resultArr[i]);
}
}
}
static void countDistinct(int arr[], int n, int k)
{
System.out.print("\nMaximum integer in the window : ");
// Traverse through every window
for (int i = 0; i <= n - k; i++) {
System.out.print(findMaximuminAllWindow(Arrays.copyOfRange(arr, i, arr.length), k)+ " ");
}
}
private static int findMaximuminAllWindow(int[] win, int k) {
// TODO Auto-generated method stub
int max= Integer.MIN_VALUE;
for(int i=0; i<k;i++) {
if(win[i]>max)
max=win[i];
}
return max;
}
arr = 1 5 2 6 3 1 24 7
We have to find the maximum of subarray, Right?
So, What is meant by subarray?
SubArray = Partial set and it should be in order and contiguous.
From the above array
{1,5,2} {6,3,1} {1,24,7} all are the subarray examples
n = 8 // Array length
k = 3 // window size
For finding the maximum, we have to iterate through the array, and find the maximum.
From the window size k,
{1,5,2} = 5 is the maximum
{5,2,6} = 6 is the maximum
{2,6,3} = 6 is the maximum
and so on..
ans = 5 6 6 6 24 24
It can be evaluated as the n-k+1
Hence, 8-3+1 = 6
And the length of an answer is 6 as we seen.
How can we solve this now?
When the data is moving from the pipe, the first thought for the data structure came in mind is the Queue
But, rather we are not discussing much here, we directly jump on the deque
Thinking Would be:
Window is fixed and data is in and out
Data is fixed and window is sliding
EX: Time series database
While (Queue is not empty and arr[Queue.back() < arr[i]] {
Queue.pop_back();
Queue.push_back();
For the rest:
Print the front of queue
// purged expired element
While (queue not empty and queue.front() <= I-k) {
Queue.pop_front();
While (Queue is not empty and arr[Queue.back() < arr[i]] {
Queue.pop_back();
Queue.push_back();
}
}
arr = [1, 2, 3, 1, 4, 5, 2, 3, 6]
k = 3
for i in range(len(arr)-k):
k=k+1
print (max(arr[i:k]),end=' ') #3 3 4 5 5 5 6
Two approaches.
Segment Tree O(nlog(n-k))
Build a maximum segment-tree.
Query between [i, i+k)
Something like..
public static void printMaximums(int[] a, int k) {
int n = a.length;
SegmentTree tree = new SegmentTree(a);
for (int i=0; i<=n-k; i++) System.out.print(tree.query(i, i+k));
}
Deque O(n)
If the next element is greater than the rear element, remove the rear element.
If the element in the front of the deque is out of the window, remove the front element.
public static void printMaximums(int[] a, int k) {
int n = a.length;
Deque<int[]> deck = new ArrayDeque<>();
List<Integer> result = new ArrayList<>();
for (int i=0; i<n; i++) {
while (!deck.isEmpty() && a[i] >= deck.peekLast()[0]) deck.pollLast();
deck.offer(new int[] {a[i], i});
while (!deck.isEmpty() && deck.peekFirst()[1] <= i - k) deck.pollFirst();
if (i >= k - 1) result.add(deck.peekFirst()[0]);
}
System.out.println(result);
}
Here is an optimized version of the naive (conditional) nested loop approach I came up with which is much faster and doesn't require any auxiliary storage or data structure.
As the program moves from window to window, the start index and end index moves forward by 1. In other words, two consecutive windows have adjacent start and end indices.
For the first window of size W , the inner loop finds the maximum of elements with index (0 to W-1). (Hence i == 0 in the if in 4th line of the code).
Now instead of computing for the second window which only has one new element, since we have already computed the maximum for elements of indices 0 to W-1, we only need to compare this maximum to the only new element in the new window with the index W.
But if the element at 0 was the maximum which is the only element not part of the new window, we need to compute the maximum using the inner loop from 1 to W again using the inner loop (hence the second condition maxm == arr[i-1] in the if in line 4), otherwise just compare the maximum of the previous window and the only new element in the new window.
void print_max_for_each_subarray(int arr[], int n, int k)
{
int maxm;
for(int i = 0; i < n - k + 1 ; i++)
{
if(i == 0 || maxm == arr[i-1]) {
maxm = arr[i];
for(int j = i+1; j < i+k; j++)
if(maxm < arr[j]) maxm = arr[j];
}
else {
maxm = maxm < arr[i+k-1] ? arr[i+k-1] : maxm;
}
cout << maxm << ' ';
}
cout << '\n';
}
You can use Deque data structure to implement this. Deque has an unique facility that you can insert and remove elements from both the ends of the queue unlike the traditional queue where you can only insert from one end and remove from other.
Following is the code for the above problem.
public int[] maxSlidingWindow(int[] nums, int k) {
int n = nums.length;
int[] maxInWindow = new int[n - k + 1];
Deque<Integer> dq = new LinkedList<Integer>();
int i = 0;
for(; i<k; i++){
while(!dq.isEmpty() && nums[dq.peekLast()] <= nums[i]){
dq.removeLast();
}
dq.addLast(i);
}
for(; i <n; i++){
maxInWindow[i - k] = nums[dq.peekFirst()];
while(!dq.isEmpty() && dq.peekFirst() <= i - k){
dq.removeFirst();
}
while(!dq.isEmpty() && nums[dq.peekLast()] <= nums[i]){
dq.removeLast();
}
dq.addLast(i);
}
maxInWindow[i - k] = nums[dq.peekFirst()];
return maxInWindow;
}
the resultant array will have n - k + 1 elements where n is length of the given array, k is the given window size.
We can solve it using the Python , applying the slicing.
def sliding_window(a,k,n):
max_val =[]
val =[]
val1=[]
for i in range(n-k-1):
if i==0:
val = a[0:k+1]
print("The value in val variable",val)
val1 = max(val)
max_val.append(val1)
else:
val = a[i:i*k+1]
val1 =max(val)
max_val.append(val1)
return max_val
Driver Code
a = [15,2,3,4,5,6,2,4,9,1,5]
n = len(a)
k = 3
sl=s liding_window(a,k,n)
print(sl)
Create a TreeMap of size k. Put first k elements as keys in it and assign any value like 1(doesn't matter). TreeMap has the property to sort the elements based on key so now, first element in map will be min and last element will be max element. Then remove 1 element from the map whose index in the arr is i-k. Here, I have considered that Input elements are taken in array arr and from that array we are filling the map of size k. Since, we can't do anything with sorting happening inside TreeMap, therefore this approach will also take O(n) time.
100% working Tested (Swift)
func maxOfSubArray(arr:[Int],n:Int,k:Int)->[Int]{
var lenght = arr.count
var resultArray = [Int]()
for i in 0..<arr.count{
if lenght+1 > k{
let tempArray = Array(arr[i..<k+i])
resultArray.append(tempArray.max()!)
}
lenght = lenght - 1
}
print(resultArray)
return resultArray
}
This way we can use:
maxOfSubArray(arr: [1,2,3,1,4,5,2,3,6], n: 9, k: 3)
Result:
[3, 3, 4, 5, 5, 5, 6]
Just notice that you only have to find in the new window if:
* The new element in the window is smaller than the previous one (if it's bigger, it's for sure this one).
OR
* The element that just popped out of the window was the current bigger.
In this case, re-scan the window.
for how big k? for reasonable-sized k. you can create k k-sized buffers and just iterate over the array keeping track of max element pointers in the buffers - needs no data structures and is O(n) k^2 pre-allocation.
A complete working solution in Amortised Constant O(1) Complexity.
https://github.com/varoonverma/code-challenge.git
Compare the first k elements and find the max, this is your first number
then compare the next element to the previous max. If the next element is bigger, that is your max of the next subarray, if its equal or smaller, the max for that sub array is the same
then move on to the next number
max(1 5 2) = 5
max(5 6) = 6
max(6 6) = 6
... and so on
max(3 24) = 24
max(24 7) = 24
It's only slightly better than your answer
I'm looking for a way to calculate the the start and end index for of each sub array when dividing a larger array into n pieces.
For example, let's say I have an array of length 421 and I want to divide it up into 5 (relatively) equal parts. Then beginning and ending indices of the five sub arrays would be something like this: [0, 83], [84, 167], [168, 251], [252, 335], [336, 420]. Note that this isn't a homework question. Just worded the problem in more general terms.
Let's say we have n elements in the array. We want to divide it into k parts. Then if n%k == 0 it's easy enough - every subarray will contain n/k elements. If n%k != 0 then we must uniformly distribute n%k among some subarrays, for example the first ones.
To find a start and end index(inclusive) of each consecutive subarray do as follows:
Calculate n % k as remainder to keep track of whether a subarray should be 1 position longer or not.
Introduce 2 variables for keeping start and end positions left and right. For the first subarray, left = 0.
Calculate right as left + n/k + remainder > 0 : 1 : 0. Store or print left and right.
Update left to a new position left = right + 1. Decrement remainder as it has just been used.
Repeat steps 3 and 4 until all k intervals are created.
Now let's see some sample code:
public static void main(String[] args) {
int n = 421;
int k = 5;
int length = n / k;
int remaining = n % k;
int left = 0;
for (int i = 0; i < k; i++) {
int right = left + (length - 1) + (remaining > 0 ? 1 : 0);
System.out.println("[" + left + "," + right + "]");
remaining--;
left = right + 1;
}
}
Output
[0,84]
[85,168]
[169,252]
[253,336]
[337,420]
The required integer math makes it a bit tricky. Integer division always truncates but you need to round so the error is distributed evenly. Integer rounding X / Y is done by adding half of Y, so (X + Y/2) / Y. The last interval is special, it needs to end at exactly the array length with no regard for rounding.
Encoding this approach in a method:
public static int[] Partition(Array arr, int divisions) {
if (arr.Length < divisions || divisions < 1) throw new ArgumentException();
var parts = new int[divisions + 1];
for (int ix = 0; ix < divisions; ++ix) {
parts[ix] = (ix * arr.Length + divisions / 2) / divisions;
}
parts[divisions] = arr.Length;
return parts;
}
Test it like:
static void Main(string[] args) {
var arr = new int[421];
var parts = Partition(arr, 1);
for (int ix = 0; ix < parts.Length-1; ++ix) {
Console.WriteLine("{0,3}..{1,-3}", parts[ix], parts[ix + 1]);
}
Console.ReadLine();
}
Get confident that it works well by checking the edge cases, like Partition(new int[6], 5). In which case you want one division that is 2 long and the rest is 1. That works, try some other ones.
I like to think of it this way:
If you want to divide M elements into N parts, then all together the first x parts should should have Math.round(x*M/N) elements.
If you want to find the start and end of segment x, then, you can calculate those directly, because it starts after the first Math.round((x-1)*M/N) elements and includes up to the Math.round(x *M/N)th element.
Note that I'm not providing formulas for the actual indexes because there are a lot of ways to represent those -- 0 or 1-based, inclusive or exclusive ranges -- and it can be confusing to try to remember the right formulas for different schemes. Figure it out in terms of the number of elements before the start and to the end, which always applies.
P.S. You can do that rounding multiplication and division in integer arithmetic like this: Math.round(x*M / N) = (x * M + (N/2)) / N
You can create a formula for both start and end indexes for any sub array.
let say x is a size of sub array, then start index of nth array will be (n-1)*x and end index will be (n*x-1)
int arrLength = 424; // your input
int sections = 5;// your input
int minSize = arrLength / sections; // minimum size of array
int reminder = arrLength % sections; // we need to distribute the reminder to sub arrays
int maxSize = minSize + 1; // maximum size of array
int subArrIndex = 1;
// lets print sub arrays with maximum size which will be equal to reminders
while (reminder > 0 && subArrIndex <= sections )
{
Console.WriteLine(string.Format("SubArray #{0}, Start - {1}, End - {2}", subArrIndex, ((subArrIndex-1)*maxSize), (subArrIndex*maxSize-1)));
reminder--;
subArrIndex++;
}
// lets print remaining arrays
while (subArrIndex <= sections)
{
Console.WriteLine(string.Format("SubArray #{0}, Start - {1}, End - {2}", subArrIndex, ((subArrIndex - 1) * minSize), (subArrIndex * minSize - 1)));
subArrIndex++;
}
Output :
SubArray #1, Start - 0, End - 84
SubArray #2, Start - 85, End - 169
SubArray #3, Start - 170, End - 254
SubArray #4, Start - 255, End - 339
SubArray #5, Start - 336, End - 419