We Keep Coding

What is the most efficient algorithm to find repeated elements in an array in time complexity O(n) and space complexity O(1)?

Note-You can not use Collection or Map
I have tried this but this is not having complexity O(n)
class RepeatElement
{
void printRepeating(int arr[], int size)
{
int i, j;
System.out.println("Repeated Elements are :");
for (i = 0; i < size; i++)
{
for (j = i + 1; j < size; j++)
{
if (arr[i] == arr[j])
System.out.print(arr[i] + " ");
}
}
}
public static void main(String[] args)
{
RepeatElement repeat = new RepeatElement();
int arr[] = {4, 2, 4, 5, 2, 3, 1};
int arr_size = arr.length;
repeat.printRepeating(arr, arr_size);
}
}
Is any one having solution for solving,to find a duplicate array without using Collection or Map and using only single for loop

If the elements in an array with size n are in a range of 0 ~ n-1 ( or 1 ~ n).
We can try to sort the array by putting a[i] to index i for every a[i] != i, and if we find that there is already an a[i] at index i, it means that there is another element with value a[i].
for (int i = 0; i < a.length; i++){
while (a[i] != i) {
if (a[i] == a[a[i]]) {
System.out.print(a[i]);
break;
} else {
int temp = a[i]; // Putting a[i] to index i by swapping them
a[i] = a[a[i]];
a[temp] = temp;
}
}
}
Since after every swap, one of the elements will be in the right position, so there will be at most n swap operations.
Time complexity O(n)
Space complexity O(1)

As these are ints, you could use a BitSet:
void printRepeating(int arr[], int size) {
BitSet bits = new BitSet();
BitSet repeated = new BitSet();
//to deal with negative ints:
BitSet negativeBits = new BitSet();
BitSet negativeRepeatedBits = new BitSet();
for(int i : arr) {
if(i<0) {
i = -i; //use the absolute value
if(negativeBits.get(i)) {
//this is a repeat
negativeRepeatedBits.set(i);
}
negativeBits.set(i);
} else {
if(bits.get(i)) {
//this is a repeat
repeated.set(i);
}
bits.set(i);
}
}
System.out.println(
IntStream.concat(negativeRepeatedBits.stream().map(i -> -i), repeated.stream())
.mapToObj(String::valueOf)
.collect(Collectors.joining(", ", "Repated Elements are : ", ""))
);
}
Note that (as per comment) negative values need to be treated separately, as otherwise you could be faced with IndexOutOfBoundsException.

Ok, so you have a constant memory requirement for an array of type int?
No problem, just let's use a large (but constant) byte array:
byte[] seen = new byte[536870912];
byte[] dups = new byte[536870912];
for (int i : arr) {
int idx = i / 8 + 268435456;
int mask = 1 << (i % 8);
if ((seen[idx] & mask) != 0) {
if ((dups[idx] & mask) == 0) {
System.out.print(i + " ");
dups[idx] |= mask;
}
} else {
seen[idx] |= mask;
}
}
As we only iterate once over the array, we have a time complexity of O(n) (where n is the number of elements in the array).
And because we always use the same amount of space we have a memory complexity of O(1), that is independent of anything.

Just one for loop, i will increment when j is at the end of the array, the current arr[i] is found earlier of a new duplicate element is found
void printRepeatingSingleLoop(int arr[], int size)
{
int i, j, k;
System.out.println("Repeated Elements are :");
for (i = 0, j = 1, k = 0; i < size; )
{
if (k < i ) {
if (arr[i] == arr[k]) {
i++;
j = i+1;
k = 0;
} else {
k++;
}
} else {
if (j == size) {
i++;
j = i + 1;
k = 0;
} else {
if (arr[i] == arr[j]) {
System.out.print(arr[i] + " ");
i++;
j = i + 1;
k = 0;
} else {
j++;
}
}
}
}
System.out.println();
}

Dynamic Programming (Codility Q: NumberSolitaire)

This is the question:
codility.com/programmers/task/number_solitaire
and below link is my result (50% from Codility):
https://codility.com/demo/results/training8AMJZH-RTA/
My code (at the first, I tried to solve this problem using Kadane's Algo):
class Solution {
public int solution(int[] A) {
int temp_max = Integer.MIN_VALUE;
int max = 0;
int k = 1;
if(A.length == 2) return A[0] + A[A.length-1];
for(int i = 1; i < A.length-1; i++) {
if(temp_max < A[i]) temp_max = A[i];
if(A[i] > 0) {
max += A[i];
temp_max = Integer.MIN_VALUE;
k = 0;
} else if(k % 6 == 0) {
max += temp_max;
temp_max = Integer.MIN_VALUE;
k = 0;
}
k++;
}
return A[0] + max + A[A.length-1];
}
And below is the solution (100% from Codility result) that I found from web:
class Solution {
public int solution(int[] A) {
int[] store = new int[A.length];
store[0] = A[0];
for (int i = 1; i < A.length; i++) {
store[i] = store[i-1];
for (int minus = 2; minus <= 6; minus++) {
if (i >= minus) {
store[i] = Math.max(store[i], store[i - minus]);
} else {
break;
}
}
store[i] += A[i];
}
return store[A.length - 1];
}
}
I have no idea what is the problem with my code:(
I tried several test cases but, nothing different with the solution & my code
but, codility test result shows mine is not perfectly correct.
(https://codility.com/demo/results/training8AMJZH-RTA/)
please anyone explain me the problem with my code~~

Try this test case[-1, -2, -3, -4, -3, -8, -5, -2, -3, -4, -5, -6, -1].
you solution return -4 (A[0],A[1],A[length-1],Here is the mistake), but the correct answer is -6 (A[0],A[6],A[length-1]).
Here is a my solution,easy to understand:
public int solution(int[] A) {
int lens = A.length;
int dp[] = new int[6];
for (int i = 0; i < 6; i++) {
dp[i] = A[0];
}
for (int i = 1; i < lens; i++) {
dp[i%6] = getMax6(dp) + A[i];
}
return dp[(lens-1)%6];
}
private int getMax6(int dp[]){
int max = dp[0];
for (int i = 1; i < dp.length; i++) {
max = Math.max(max, dp[i]);
}
return max;
}

Readable solution from Java:
public class Solution {
public static void main(String[] args) {
System.out.println(new Solution().solution(new int[]{1, -2, 0, 9, -1, -2}));
}
private int solution(int[] A) {
int N = A.length;
int[] dp = new int[N];
dp[0] = A[0];
for (int i = 1; i < N; i++) {
double sm = Double.NEGATIVE_INFINITY;
for (int j = 1; j <= 6; j++) {
if (i - j < 0) {
break;
}
double s1 = dp[i - j] + A[i];
sm = Double.max(s1, sm);
}
dp[i] = (int) sm;
}
return dp[N-1];
}
}

Here is a solution similar to #0xAliHn but using less memory. You only need to remember the last 6 moves.
def NumberSolitaire(A):
dp = [0] * 6
dp[-1] = A[0]
for i in range(1, len(A)):
maxVal = -100001
for k in range(1, 7):
if i-k >= 0:
maxVal = max(maxVal, dp[-k] + A[i])
dp.append(maxVal)
dp.pop(0)
return dp[-1]

Based on the solutions posted, I made nice readable code. Not best performance.
int[] mark = new int[A.length];
mark[0] = A[0];
IntStream.range(1, A.length)
.forEach(i -> {
int max = Integer.MIN_VALUE;
mark[i] = IntStream.rangeClosed(1, 6)
.filter(die -> i - die >= 0)
.map(r -> Math.max(mark[i - r] + A[i], max))
.max().orElse(max);
});
return mark[A.length - 1];

Because you are not using dynamic programming, you are using greedy algorithm. Your code will fail when the max number in a range will not be the right choice.
function solution(A) {
// This array contains a maximal value at any index.
const maxTill = [A[0]];
// It's a dynamic programming so we will choose maximal value at each
// Index untill we reach last index (goal)
for (let i = 1; i < A.length; i++) {
// Step 1 . max value of each index will be atleast equal to or greater than
// max value of last index.
maxTill[i] = maxTill[i - 1];
// For each index we are finding the max of last 6 array value
// And storing it into Max value.
for (let dice = 1; dice <= 6; dice++) {
// If array index is itself less than backtrack index
// break as you dont have 6 boxes in your left
if (dice > i) {
break;
} else {
// The most important line .
// Basically checking the max of last 6 boxes using a loop.
maxTill[i] = Math.max(
maxTill[i - dice],
maxTill[i]
);
}
}
// Until this point maxStill contains the maximal value
// to reach to that index.
// To end the game we need to touch that index as well, so
// add the value of the index as well.
maxTill[i] += A[i];
}
return maxTill[A.length - 1];
}
console.log(solution([-1, -2, -3, -4, -3, -8, -5, -2, -3, -4, -5, -6, -1]));

This is my solution. I try to make the code easy to apprehend. It might not save space as much as it can.
private static int solution(int A[])
{
// N // N is an integer within the range [2..100,000];
// A[] // each element of array A is an integer within the range [−10,000..10,000].
int N = A.length;
int[] bestResult = new int[N]; // record the current bestResult
Arrays.fill(bestResult, Integer.MIN_VALUE); // fill in with the smallest integer value
// initialize
bestResult[0] = A[0];
for (int i = 0;i < A.length;i++) {
// calculate six possible results every round
for (int j = i + 1; (j < A.length) && (i < A.length) && j < (i + 1) + 6; j++) {
// compare
int preMaxResult = bestResult[j]; // the max number so far
int nowMaxResult = bestResult[i] + A[j]; // the max number at bestResult[i] + A[j]
bestResult[j] = Math.max(preMaxResult, nowMaxResult);
}
}
return bestResult[bestResult.length-1];
}

Here is the simple Python 3 solution:
import sys
def solution(A):
dp = [0] * len(A)
dp[0] = A[0]
for i in range(1, len(A)):
maxVal = -sys.maxsize - 1
for k in range(1, 7):
if i-k >= 0:
maxVal = max(maxVal, dp[i-k] + A[i])
dp[i] = maxVal
return dp[len(A)-1]

100% c++ solution(
results)
#include <climits>
int solution(vector<int>& A) {
const int N = A.size();
if (N == 2)
return A[0] + A[1];
vector<int> MaxSum(N, INT_MIN);
MaxSum[0] = A[0];
for (int i = 1; i < N; i++) {
for (int dice = 1; dice <= 6; dice++) {
if (dice > i)
break;
MaxSum[i] = max(MaxSum[i], A[i] + MaxSum[i - dice]);
}
}
return MaxSum[N-1];
}

100% python solution
with the help of the answers above and https://sapy.medium.com/cracking-the-coding-interview-30eb419c4c57
def solution(A):
# write your code in Python 3.6
# initialize maxUntil [0]*n
n = len(A)
maxUntil = [0 for i in range(n)]
maxUntil[0]=A[0]
# fill in maxUntil, remember to chack limits
for i in range(1, n): # for each
maxUntil[i] = maxUntil [i-1]
# check the max 6 to the left:
# for 1,..,6:
for dice in range(1,7):
if dice > i: # if dice bigger than loc - we are out of range
break
#else: check if bigger than cur elem, if so - update elem
maxUntil[i] = max(maxUntil[i],maxUntil[i-dice])
# add the current jump:
maxUntil[i] +=A[i]
# must reach the last sq:
return maxUntil[n-1]

I would like to explain the algorithm I have come up with and then show you the implementation in C++.
Create a hash for the max sums. We only need to store the elements within reach, so the last 6 elements. This is because the dice can only go back so much.
Initialise the hash with the first element in the array for simplicity since the first element in this hash equals to the first element of the inputs.
Then go through the input elements from the second element.
For each iteration, find the maximum values from the last 6 indices. Add the current value to that to get the current max sum.
When we reach the end of the inputs, exit the loop.
Return the max sum of the last element calculated. For this, we need clipping with module due to the space optimisation
The runtime complexity of this dynamic programming solution is O(N) since we go through element in the inputs. If we consider the dice range K, then this would be O(N * K).
The space complexity is O(1) because we have a hash for the last six elements. It is O(K) if we does not consider the number of dice faces constant, but K.
int solution(vector<int> &A)
{
vector<int> max_sums(6, A[0]);
for (size_t i = 1; i < A.size(); ++i) max_sums[i % max_sums.size()] = *max_element(max_sums.cbegin(), max_sums.cend()) + A[i];
return max_sums[(A.size() - 1) % max_sums.size()];
}

Here's my answer which gives 100% for Kotlin
val pr = IntArray(A.size) { Int.MIN_VALUE }
pr[0] = A.first()
for ((index, value) in pr.withIndex()) {
for (i in index + 1..min(index + 6, A.lastIndex)) {
pr[i] = max(value + A[i], pr[i])
}
}
return pr.last()
I used forwarded prediction, where I fill the next 6 items of the max value the current index can give.

What part of my code is making my performance suffer? (Codility's MaxCounter)

I have the following problem:
You are given N counters, initially set to 0, and you have two possible operations on them:
increase(X) − counter X is increased by 1,
max counter − all counters are set to the maximum value of any counter.
A non-empty zero-indexed array A of M integers is given. This array represents consecutive operations:
if A[K] = X, such that 1 ≤ X ≤ N, then operation K is increase(X),
if A[K] = N + 1 then operation K is max counter.
For example, given integer N = 5 and array A such that:
A[0] = 3
A[1] = 4
A[2] = 4
A[3] = 6
A[4] = 1
A[5] = 4
A[6] = 4
the values of the counters after each consecutive operation will be:
(0, 0, 1, 0, 0)
(0, 0, 1, 1, 0)
(0, 0, 1, 2, 0)
(2, 2, 2, 2, 2)
(3, 2, 2, 2, 2)
(3, 2, 2, 3, 2)
(3, 2, 2, 4, 2)
The goal is to calculate the value of every counter after all operations.
Write a function:
class Solution { public int[] solution(int N, int[] A); }
that, given an integer N and a non-empty zero-indexed array A consisting of M integers, returns a sequence of integers representing the values of the counters.
For example, given:
A[0] = 3
A[1] = 4
A[2] = 4
A[3] = 6
A[4] = 1
A[5] = 4
A[6] = 4
the function should return [3, 2, 2, 4, 2], as explained above.
Assume that:
N and M are integers within the range [1..100,000];
each element of array A is an integer within the range [1..N + 1].
Complexity:
expected worst-case time complexity is O(N+M);
expected worst-case space complexity is O(N), beyond input storage (not counting the storage required for input arguments).
Elements of input arrays can be modified.
I have answered this problem using the following code, but only got 80% as opposed to 100% performance, despite having O(N+M) time complexity:
public class Solution {
public int[] solution(int N, int[] A) {
int highestCounter = N;
int minimumValue = 0;
int lastMinimumValue = 0;
int [] answer = new int[N];
for (int i = 0; i < A.length; i++) {
int currentCounter = A[i];
int answerEquivalent = currentCounter -1;
if(currentCounter >0 && currentCounter<=highestCounter){
answer[answerEquivalent] = answer[answerEquivalent]+1;
if(answer[answerEquivalent] > minimumValue){
minimumValue = answer[answerEquivalent];
}
}
if (currentCounter == highestCounter +1 && lastMinimumValue!=minimumValue){
lastMinimumValue = minimumValue;
Arrays.fill(answer, minimumValue);
}
}
return answer;
}
}
Where is my performance here suffering? The code gives the right answer, but does not perform up-to-spec despite having the right time complexity.

Instead of calling Arrays.fill(answer, minimumValue); whenever you encounter a "max counter" operation, which takes O(N), you should keep track of the last max value that was assigned due to "max counter" operation, and update the entire array just one time, after all the operations are processed. This would take O(N+M).
I changed the variables names from min to max to make it less confusing.
public class Solution {
public int[] solution(int N, int[] A) {
int highestCounter = N;
int maxValue = 0;
int lastMaxValue = 0;
int [] answer = new int[N];
for (int i = 0; i < A.length; i++) {
int currentCounter = A[i];
int answerEquivalent = currentCounter -1;
if(currentCounter >0 && currentCounter<=highestCounter){
if (answer[answerEquivalent] < lastMaxValue)
answer[answerEquivalent] = lastMaxValue +1;
else
answer[answerEquivalent] = answer[answerEquivalent]+1;
if(answer[answerEquivalent] > maxValue){
maxValue = answer[answerEquivalent];
}
}
if (currentCounter == highestCounter +1){
lastMaxValue = maxValue;
}
}
// update all the counters smaller than lastMaxValue
for (int i = 0; i < answer.length; i++) {
if (answer[i] < lastMaxValue)
answer[i] = lastMaxValue;
}
return answer;
}
}

The following operation is O(n) time:
Arrays.fill(answer, minimumValue);
Now, if you are given a test case where the max counter operation is repeated often (say n/3 of the total operations) - you got yourself an O(n*m) algorithm (worst case analysis), and NOT O(n+m).
You can optimize it to be done in O(n+m) time, by using an algorithm that initializes an array in O(1) every time this operation happens.
This will reduce worst case time complexity from O(n*m) to O(n+m)1
(1)Theoretically, using the same idea, it can even be done in O(m) - regardless of the size of the number of counters, but the first allocation of the arrays takes O(n) time in java

This is a bit like #Eran's solution but encapsulates the functionality in an object. Essentially - keep track of a max value and an atLeast value and let the object's functionality do the rest.
private static class MaxCounter {
// Current set of values.
final int[] a;
// Keeps track of the current max value.
int currentMax = 0;
// Min value. If a[i] < atLeast the a[i] should appear as atLeast.
int atLeast = 0;
public MaxCounter(int n) {
this.a = new int[n];
}
// Perform the defined op.
public void op(int k) {
// Values are one-based.
k -= 1;
if (k < a.length) {
// Increment.
inc(k);
} else {
// Set max
max(k);
}
}
// Increment.
private void inc(int k) {
// Get new value.
int v = get(k) + 1;
// Keep track of current max.
if (v > currentMax) {
currentMax = v;
}
// Set new value.
a[k] = v;
}
private int get(int k) {
// Returns eithe a[k] or atLeast.
int v = a[k];
return v < atLeast ? atLeast : v;
}
private void max(int k) {
// Record new max.
atLeast = currentMax;
}
public int[] solution() {
// Give them the solution.
int[] solution = new int[a.length];
for (int i = 0; i < a.length; i++) {
solution[i] = get(i);
}
return solution;
}
#Override
public String toString() {
StringBuilder s = new StringBuilder("[");
for (int i = 0; i < a.length; i++) {
s.append(get(i));
if (i < a.length - 1) {
s.append(",");
}
}
return s.append("]").toString();
}
}
public void test() {
System.out.println("Hello");
int[] p = new int[]{3, 4, 4, 6, 1, 4, 4};
MaxCounter mc = new MaxCounter(5);
for (int i = 0; i < p.length; i++) {
mc.op(p[i]);
System.out.println(mc);
}
int[] mine = mc.solution();
System.out.println("Solution = " + Arrays.toString(mine));
}

My solution: 100\100
class Solution
{
public int maxCounterValue;
public int[] Counters;
public void Increase(int position)
{
position = position - 1;
Counters[position]++;
if (Counters[position] > maxCounterValue)
maxCounterValue = Counters[position];
}
public void SetMaxCounter()
{
for (int i = 0; i < Counters.Length; i++)
{
Counters[i] = maxCounterValue;
}
}
public int[] solution(int N, int[] A)
{
if (N < 1 || N > 100000) return null;
if (A.Length < 1) return null;
int nlusOne = N + 1;
Counters = new int[N];
int x;
for (int i = 0; i < A.Length; i++)
{
x = A[i];
if (x > 0 && x <= N)
{
Increase(x);
}
if (x == nlusOne && maxCounterValue > 0) // this used for all maxCounter values in array. Reduces addition loops
SetMaxCounter();
if (x > nlusOne)
return null;
}
return Counters;
}
}

( #molbdnilo : +1 !) As this is just an algorithm test, there's no sense getting too wordy about variables. "answerEquivalent" for a zero-based array index adjustment? Gimme a break ! Just answer[A[i] - 1] will do.
Test says to assume A values always lie between 1 and N+1. So checking for this is not needed.
fillArray(.) is an O(N) process which is within an O(M) process. This makes the whole code into an O(M*N) process when the max complexity desired is O(M+N).
The only way to achieve this is to only carry forward the current max value of the counters. This allows you to always save the correct max counter value when A[i] is N+1. The latter value is a sort of baseline value for all increments afterwards. After all A values are actioned, those counters which were never incremented via array entries can then be brought up to the all-counters baseline via a second for loop of complexity O(N).
Look at Eran's solution.

This is how we can eliminate O(N*M) complexity.
In this solutions, instead of populating result array for every A[K]=N+1, I tried to keep what is min value of all elements, and update result array once all operation has been completed.
If there is increase operation then updating that position :
if (counter[x - 1] < minVal) {
counter[x - 1] = minVal + 1;
} else {
counter[x - 1]++;
}
And keep track of minVal for each element of result array.
Here is complete solution:
public int[] solution(int N, int[] A) {
int minVal = -1;
int maxCount = -1;
int[] counter = new int[N];
for (int i = 0; i < A.length; i++) {
int x = A[i];
if (x > 0 && x <= N) {
if (counter[x - 1] < minVal) {
counter[x - 1] = minVal + 1;
} else {
counter[x - 1]++;
}
if (maxCount < counter[x - 1]) {
maxCount = counter[x - 1];
}
}
if (x == N + 1 && maxCount > 0) {
minVal = maxCount;
}
}
for (int i = 0; i < counter.length; i++) {
if (counter[i] < minVal) {
counter[i] = minVal;
}
}
return counter;
}

This is my swift 3 solution (100/100)
public func solution(_ N : Int, _ A : inout [Int]) -> [Int] {
var counters = Array(repeating: 0, count: N)
var _max = 0
var _min = 0
for i in A {
if counters.count >= i {
let temp = max(counters[i-1] + 1, _min + 1)
_max = max(temp, _max)
counters[i-1] = temp
} else {
_min = _max
}
}
return counters.map { max($0, _min) }
}

java codility Max-Counters

I have been trying to solve the below task:
You are given N counters, initially set to 0, and you have two possible operations on them:
increase(X) − counter X is increased by 1,
max_counter − all counters are set to the maximum value of any counter.
A non-empty zero-indexed array A of M integers is given. This array represents consecutive operations:
if A[K] = X, such that 1 ≤ X ≤ N, then operation K is increase(X),
if A[K] = N + 1 then operation K is max_counter.
For example, given integer N = 5 and array A such that:
A[0] = 3
A[1] = 4
A[2] = 4
A[3] = 6
A[4] = 1
A[5] = 4
A[6] = 4
the values of the counters after each consecutive operation will be:
(0, 0, 1, 0, 0)
(0, 0, 1, 1, 0)
(0, 0, 1, 2, 0)
(2, 2, 2, 2, 2)
(3, 2, 2, 2, 2)
(3, 2, 2, 3, 2)
(3, 2, 2, 4, 2)
The goal is to calculate the value of every counter after all operations.
struct Results {
int * C;
int L;
};
Write a function:
struct Results solution(int N, int A[], int M);
that, given an integer N and a non-empty zero-indexed array A consisting of M integers, returns a sequence of integers representing the values of the counters.
The sequence should be returned as:
a structure Results (in C), or
a vector of integers (in C++), or
a record Results (in Pascal), or
an array of integers (in any other programming language).
For example, given:
A[0] = 3
A[1] = 4
A[2] = 4
A[3] = 6
A[4] = 1
A[5] = 4
A[6] = 4
the function should return [3, 2, 2, 4, 2], as explained above.
Assume that:
N and M are integers within the range [1..100,000];
each element of array A is an integer within the range [1..N + 1].
Complexity:
expected worst-case time complexity is O(N+M);
expected worst-case space complexity is O(N), beyond input storage (not counting the storage required for input arguments).
Elements of input arrays can be modified.
Here is my solution:
import java.util.Arrays;
class Solution {
public int[] solution(int N, int[] A) {
final int condition = N + 1;
int currentMax = 0;
int countersArray[] = new int[N];
for (int iii = 0; iii < A.length; iii++) {
int currentValue = A[iii];
if (currentValue == condition) {
Arrays.fill(countersArray, currentMax);
} else {
int position = currentValue - 1;
int localValue = countersArray[position] + 1;
countersArray[position] = localValue;
if (localValue > currentMax) {
currentMax = localValue;
}
}
}
return countersArray;
}
}
Here is the code valuation:
https://codility.com/demo/results/demo6AKE5C-EJQ/
Can you give me a hint what is wrong with this solution?

The problem comes with this piece of code:
for (int iii = 0; iii < A.length; iii++) {
...
if (currentValue == condition) {
Arrays.fill(countersArray, currentMax);
}
...
}
Imagine that every element of the array A was initialized with the value N+1. Since the function call Arrays.fill(countersArray, currentMax) has a time complexity of O(N) then overall your algorithm will have a time complexity O(M * N). A way to fix this, I think, instead of explicitly updating the whole array A when the max_counter operation is called you may keep the value of last update as a variable. When first operation (incrementation) is called you just see if the value you try to increment is larger than the last_update. If it is you just update the value with 1 otherwise you initialize it to last_update + 1. When the second operation is called you just update last_update to current_max. And finally, when you are finished and try to return the final values you again compare each value to last_update. If it is greater you just keep the value otherwise you return last_update
class Solution {
public int[] solution(int N, int[] A) {
final int condition = N + 1;
int currentMax = 0;
int lastUpdate = 0;
int countersArray[] = new int[N];
for (int iii = 0; iii < A.length; iii++) {
int currentValue = A[iii];
if (currentValue == condition) {
lastUpdate = currentMax
} else {
int position = currentValue - 1;
if (countersArray[position] < lastUpdate)
countersArray[position] = lastUpdate + 1;
else
countersArray[position]++;
if (countersArray[position] > currentMax) {
currentMax = countersArray[position];
}
}
}
for (int iii = 0; iii < N; iii++) {
if (countersArray[iii] < lastUpdate)
countersArray[iii] = lastUpdate;
}
return countersArray;
}
}

The problem is that when you get lots of max_counter operations you get lots of calls to Arrays.fill which makes your solution slow.
You should keep a currentMax and a currentMin:
When you get a max_counter you just set currentMin = currentMax.
If you get another value, let's call it i:
If the value at position i - 1 is smaller or equal to currentMin you set it to currentMin + 1.
Otherwise you increment it.
At the end just go through the counters array again and set everything less than currentMin to currentMin.

Another solution that I have developed and might be worth considering: http://codility.com/demo/results/demoM658NU-DYR/

This is the 100% solution of this question.
// you can also use imports, for example:
// import java.math.*;
class Solution {
public int[] solution(int N, int[] A) {
int counter[] = new int[N];
int n = A.length;
int max=-1,current_min=0;
for(int i=0;i<n;i++){
if(A[i]>=1 && A[i]<= N){
if(counter[A[i] - 1] < current_min) counter[A[i] - 1] = current_min;
counter[A[i] - 1] = counter[A[i] - 1] + 1;
if(counter[A[i] - 1] > max) max = counter[A[i] - 1];
}
else if(A[i] == N+1){
current_min = max;
}
}
for(int i=0;i<N;i++){
if(counter[i] < current_min) counter[i] = current_min;
}
return counter;
}
}

I'm adding another Java 100 solution with some test cases it they're helpful.
// https://codility.com/demo/results/demoD8J6M5-K3T/ 77
// https://codility.com/demo/results/demoSEJHZS-ZPR/ 100
public class MaxCounters {
// Some testcases
// (1,[1,2,3]) = [1]
// (1,[1]) = [1]
// (1,[5]) = [0]
// (1,[1,1,1,2,3]) = 3
// (2,[1,1,1,2,3,1]) = [4,3]
// (5, [3, 4, 4, 5, 1, 4, 4]) = (1, 0, 1, 4, 1)
public int[] solution(int N, int[] A) {
int length = A.length, maxOfCounter = 0, lastUpdate = 0;
int applyMax = N + 1;
int result[] = new int[N];
for (int i = 0; i < length; ++i ) {
if(A[i] == applyMax){
lastUpdate = maxOfCounter;
} else if (A[i] <= N) {
int position = A[i]-1;
result[position] = result[position] > lastUpdate
? result[position] + 1 : lastUpdate + 1;
// updating the max for future use
if(maxOfCounter <= result[position]) {
maxOfCounter = result[position];
}
}
}
// updating all the values that are less than the lastUpdate to the max value
for (int i = 0; i < N; ++i) {
if(result[i] < lastUpdate) {
result[i] = lastUpdate;
}
}
return result;
}
}

My java solution with a detailed explanation 100% Correctness, 100% Performance :
Time Complexity O(N+M)
public static int[] solution(int N, int[] A) {
int[] counters = new int[N];
//The Max value between all counters at a given time
int max = 0;
//The base Max that all counter should have after the "max counter" operation happens
int baseMax = 0;
for (int i = 0; i < A.length; i++) {
//max counter Operation ==> updating the baseMax
if (A[i] > N) {
// Set The Base Max that all counters should have
baseMax = max;
}
//Verify if the value is bigger than the last baseMax because at any time a "max counter" operation can happen and the counter should have the max value
if (A[i] <= N && counters[A[i] - 1] < baseMax) {
counters[A[i] - 1] = baseMax;
}
//increase(X) Operation => increase the counter value
if (A[i] <= N) {
counters[A[i] - 1] = counters[A[i] - 1] + 1;
//Update the max
max = Math.max(counters[A[i] - 1], max);
}
}
//Set The remaining values to the baseMax as not all counters are guaranteed to be affected by an increase(X) operation in "counters[A[i] - 1] = baseMax;"
for (int j = 0; j < N; j++) {
if (counters[j] < baseMax)
counters[j] = baseMax;
}
return counters;
}

Here is my C++ solution which got 100 on codility. The concept is same as explained above.
int maxx=0;
int lastvalue=0;
void set(vector<int>& A, int N,int X)
{
for ( int i=0;i<N;i++)
if(A[i]<lastvalue)
A[i]=lastvalue;
}
vector<int> solution(int N, vector<int> &A) {
// write your code in C++11
vector<int> B(N,0);
for(unsigned int i=0;i<A.size();i++)
{
if(A[i]==N+1)
lastvalue=maxx;
else
{ if(B[A[i]-1]<lastvalue)
B[A[i]-1]=lastvalue+1;
else
B[A[i]-1]++;
if(B[A[i]-1]>maxx)
maxx=B[A[i]-1];
}
}
set(B,N,maxx);
return B;
}

vector<int> solution(int N, vector<int> &A)
{
std::vector<int> counters(N);
auto max = 0;
auto current = 0;
for (auto& counter : A)
{
if (counter >= 1 && counter <= N)
{
if (counters[counter-1] < max)
counters[counter - 1] = max;
counters[counter - 1] += 1;
if (counters[counter - 1] > current)
current = counters[counter - 1];
}
else if (counter > N)
max = current;
}
for (auto&& counter : counters)
if (counter < max)
counter = max;
return counters;
}

Arrays.fill() invocation inside array interation makes the program O(N^2)
Here is a possible solution which has O(M+N) runtime.
The idea is -
For the second operation, keep track of max value that is achieved through increment, this is our base value till the current iteration, no values can't be less than this.
For the first operation, resetting the value to base value if needed before the increment.
public static int[] solution(int N, int[] A) {
int counters[] = new int[N];
int base = 0;
int cMax = 0;
for (int a : A) {
if (a > counters.length) {
base = cMax;
} else {
if (counters[a - 1] < base) {
counters[a - 1] = base;
}
counters[a - 1]++;
cMax = Math.max(cMax, counters[a - 1]);
}
}
for (int i = 0; i < counters.length; i++) {
if (counters[i] < base) {
counters[i] = base;
}
}
return counters;
}

vector<int> solution(int N, vector<int> &A)
{
std::vector<int> counter(N, 0);
int max = 0;
int floor = 0;
for(std::vector<int>::iterator i = A.begin();i != A.end(); i++)
{
int index = *i-1;
if(*i<=N && *i >= 1)
{
if(counter[index] < floor)
counter[index] = floor;
counter[index] += 1;
max = std::max(counter[index], max);
}
else
{
floor = std::max(max, floor);
}
}
for(std::vector<int>::iterator i = counter.begin();i != counter.end(); i++)
{
if(*i < floor)
*i = floor;
}
return counter;
}

Hera is my AC Java solution. The idea is the same as #Inwvr explained:
public int[] solution(int N, int[] A) {
int[] count = new int[N];
int max = 0;
int lastUpdate = 0;
for(int i = 0; i < A.length; i++){
if(A[i] <= N){
if(count[A[i]-1] < lastUpdate){
count[A[i]-1] = lastUpdate+1;
}
else{
count[A[i]-1]++;
}
max = Math.max(max, count[A[i]-1]);
}
else{
lastUpdate = max;
}
}
for(int i = 0; i < N; i++){
if(count[i] < lastUpdate)
count[i] = lastUpdate;
}
return count;
}

I just got 100 in PHP with some help from the above
function solution($N, $A) {
$B = array(0);
$max = 0;
foreach($A as $key => $a) {
$a -= 1;
if($a == $N) {
$max = max($B);
} else {
if(!isset($B[$a])) {
$B[$a] = 0;
}
if($B[$a] < $max) {
$B[$a] = $max + 1;
} else {
$B[$a] ++;
}
}
}
for($i=0; $i<$N; $i++) {
if(!isset($B[$i]) || $B[$i] < $max) {
$B[$i] = $max;
}
}
return $B;
}

This is another C++ solution to the problem.
The rationale is always the same.
Avoid to set to max counter all the counter upon instruction two, as this would bring the complexity to O(N*M).
Wait until we get another operation code on a single counter.
At this point the algorithm remembers whether it had met a max_counter and set the counter value consequently.
Here the code:
vector<int> MaxCounters(int N, vector<int> &A)
{
vector<int> n(N, 0);
int globalMax = 0;
int localMax = 0;
for( vector<int>::const_iterator it = A.begin(); it != A.end(); ++it)
{
if ( *it >= 1 && *it <= N)
{
// this is an increase op.
int value = *it - 1;
n[value] = std::max(n[value], localMax ) + 1;
globalMax = std::max(n[value], globalMax);
}
else
{
// set max counter op.
localMax = globalMax;
}
}
for( vector<int>::iterator it = n.begin(); it != n.end(); ++it)
*it = std::max( *it, localMax );
return n;
}

100%, O(m+n)
public int[] solution(int N, int[] A) {
int[] counters = new int[N];
int maxAIs = 0;
int minAShouldBe = 0;
for(int x : A) {
if(x >= 1 && x <= N) {
if(counters[x-1] < minAShouldBe) {
counters[x-1] = minAShouldBe;
}
counters[x-1]++;
if(counters[x-1] > maxAIs) {
maxAIs = counters[x-1];
}
} else if(x == N+1) {
minAShouldBe = maxAIs;
}
}
for(int i = 0; i < N; i++) {
if(counters[i] < minAShouldBe) {
counters[i] = minAShouldBe;
}
}
return counters;
}

here is my code, but its 88% cause it takes 3.80 sec for 10000 elements instead of 2.20
class Solution {
boolean maxCalled;
public int[] solution(int N, int[] A) {
int max =0;
int [] counters = new int [N];
int temp=0;
int currentVal = 0;
for(int i=0;i<A.length;i++){
currentVal = A[i];
if(currentVal <=N){
temp = increas(counters,currentVal);
if(temp > max){
max = temp;
}
}else{
if(!maxCalled)
maxCounter(counters,max);
}
}
return counters;
}
int increas (int [] A, int x){
maxCalled = false;
return ++A[x-1];
//return t;
}
void maxCounter (int [] A, int x){
maxCalled = true;
for (int i = 0; i < A.length; i++) {
A[i] = x;
}
}
}

Following my solution in JAVA (100/100).
public boolean isToSum(int value, int N) {
return value >= 1 && value <= N;
}
public int[] solution(int N, int[] A) {
int[] res = new int[N];
int max =0;
int minValue = 0;
for (int i=0; i < A.length; i++){
int value = A[i];
int pos = value -1;
if ( isToSum(value, N)) {
if( res[pos] < minValue) {
res[pos] = minValue;
}
res[pos] += 1;
if (max < res[pos]) {
max = res[pos];
}
} else {
minValue = max;
}
}
for (int i=0; i < res.length; i++){
if ( res[i] < minValue ){
res[i] = minValue;
}
}
return res;
}

my solution is :
public class Solution {
public int[] solution(int N, int[] A) {
int[] counters = new int[N];
int[] countersLastMaxIndexes = new int[N];
int maxValue = 0;
int fixedMaxValue = 0;
int maxIndex = 0;
for (int i = 0; i < A.length; i++) {
if (A[i] <= N) {
if (countersLastMaxIndexes[A[i] - 1] != maxIndex) {
counters[A[i] - 1] = fixedMaxValue;
countersLastMaxIndexes[A[i] - 1] = maxIndex;
}
counters[A[i] - 1]++;
if (counters[A[i] - 1] > maxValue) {
maxValue = counters[A[i] - 1];
}
} else {
maxIndex = i;
fixedMaxValue = maxValue;
}
}
for (int i = 0; i < countersLastMaxIndexes.length; i++) {
if (countersLastMaxIndexes[i] != maxIndex) {
counters[i] = fixedMaxValue;
countersLastMaxIndexes[i] = maxIndex;
}
}
return counters;
}
}

In my Java solution I updated values in solution[] only when needed. And finally updated solution[] with a right values.
public int[] solution(int N, int[] A) {
int[] solution = new int[N];
int maxCounter = 0;
int maxCountersSum = 0;
for(int a: A) {
if(a >= 1 && a <= N) {
if(solution[a - 1] < maxCountersSum)
solution[a - 1] = maxCountersSum;
solution[a - 1]++;
if(solution[a - 1] > maxCounter)
maxCounter = solution[a - 1];
}
if(a == N + 1) {
maxCountersSum = maxCounter;
}
}
for(int i = 0; i < N; i++) {
if(solution[i] < maxCountersSum)
solution[i] = maxCountersSum;
}
return solution;
}

Here's my python solution:
def solution(N, A):
# write your code in Python 3.6
RESP = [0] * N
MAX_OPERATION = N + 1
current_max = 0
current_min = 0
for operation in A:
if operation != MAX_OPERATION:
if RESP[operation-1] <= current_min:
RESP[operation-1] = current_min + 1
else:
RESP[operation-1] += 1
if RESP[operation-1] > current_max:
current_max = RESP[operation-1]
else:
if current_min == current_max:
current_min += 1
else:
current_min = current_max
for i, val in enumerate(RESP):
if val < current_min:
RESP[i] = current_min
return RESP

def sample_method(A,N=5):
initial_array = [0,0,0,0,0]
for i in A:
if(i>=1):
if(i<=N):
initial_array[i-1]+=1
else:
for a in range(len(initial_array)):
initial_array[a]+=1
print i
print initial_array

Here's my solution using python 3.6. The result is 100% correctness but 40% performance (most of them were because of timeout). Still cannot figure out how to optimize this code but hopefully someone can find it useful.
def solution(N, A):
count = [0]*(N+1)
for i in range(0,len(A)):
if A[i] >=1 and A[i] <= N:
count[A[i]] += 1
elif A[i] == (N+1):
count = [max(count)] * len(count)
count.pop(0)
return count

Typescript:
function counters(numCounters: number, operations: number[]) {
const counters = Array(numCounters)
let max = 0
let currentMin = 0
for (const operation of operations) {
if (operation === numCounters + 1) {
currentMin = max
} else {
if (!counters[operation - 1] || counters[operation - 1] < currentMin) {
counters[operation - 1] = currentMin
}
counters[operation - 1] = counters[operation - 1] + 1
if (counters[operation - 1] > max) {
max += 1
}
}
}
for (let i = 0; i < numCounters; i++) {
if (!counters[i] || counters[i] < currentMin) {
counters[i] = currentMin
}
}
return counters
}
console.log(solution=${counters(5, [3, 4, 4, 6, 1, 4, 4])})

100 points JavaScript solution, includes performance improvement to ignore repeated max_counter iterations:
function solution(N, A) {
let max = 0;
let counters = Array(N).fill(max);
let maxCounter = 0;
for (let op of A) {
if (op <= N && op >= 1) {
maxCounter = 0;
if (++counters[op - 1] > max) {
max = counters[op - 1];
}
} else if(op === N + 1 && maxCounter === 0) {
maxCounter = 1;
for (let i = 0; i < counters.length; i++) {
counters[i] = max;
}
}
}
return counters;
}

solution in JAVA (100/100)
class Solution {
public int[] solution(int N, int[] A) {
// write your code in Java SE 8
int[] result = new int[N];
int base = 0;
int max = 0;
int needToChange=A.length;;
for (int k = 0; k < A.length; k++) {
int X = A[k];
if (X >= 1 && X <= N) {
if (result[X - 1] < base) {
result[X - 1] = base;
}
result[X - 1]++;
if (max < result[X - 1]) {
max = result[X - 1];
}
}
if (X == N + 1) {
base = max;
needToChange= X-1;
}
}
for (int i = 0; i < needToChange; i++) {
if (result[i] < base) {
result[i] = base;
}
}
return result;
}
}

My Java solution. It gives 100% but is very long (in comparison). I have used HashMap for storing counters.
Detected time complexity: O(N + M)
import java.util.*;
class Solution {
final private Map<Integer, Integer> counters = new HashMap<>();
private int maxCounterValue = 0;
private int maxCounterValueRealized = 0;
public int[] solution(int N, int[] A) {
if (N < 1) return new int[0];
for (int a : A) {
if (a <= N) {
Integer current = counters.putIfAbsent(a, maxCounterValueRealized + 1);
if (current == null) {
updateMaxCounterValue(maxCounterValueRealized + 1);
} else {
++current;
counters.replace(a, current);
updateMaxCounterValue(current);
}
} else {
maxCounterValueRealized = maxCounterValue;
counters.clear();
}
}
return getCountersArray(N);
}
private void updateMaxCounterValue(int currentCounterValue) {
if (currentCounterValue > maxCounterValue)
maxCounterValue = currentCounterValue;
}
private int[] getCountersArray(int N) {
int[] countersArray = new int[N];
for (int j = 0; j < N; j++) {
Integer current = counters.get(j + 1);
if (current == null) {
countersArray[j] = maxCounterValueRealized;
} else {
countersArray[j] = current;
}
}
return countersArray;
}
}

Here is solution in python with 100 %
Codility Max counter 100%
def solution(N, A):
"""
Solution at 100% - https://app.codility.com/demo/results/trainingUQ95SB-4GA/
Idea is first take the counter array of given size N
take item from main A one by one + 1 and put in counter array , use item as index
keep track of last max operation
at the end replace counter items with max of local or counter item it self
:param N:
:param A:
:return:
"""
global_max = 0
local_max = 0
# counter array
counter = [0] * N
for i, item in enumerate(A):
# take item from original array one by one - 1 - minus due to using item as index
item_as_counter_index = item - 1
# print(item_as_counter_index)
# print(counter)
# print(local_max)
# current element less or equal value in array and greater than 1
# if A[K] = X, such that 1 ≤ X ≤ N, then operation K is increase(X),
if N >= item >= 1:
# max of local_max counter at item_as_counter_index
# increase counter array value and put in counter array
counter[item_as_counter_index] = max(local_max, counter[item_as_counter_index]) + 1
# track the status of global_max counter so far
# this is operation K
global_max = max(global_max, counter[item_as_counter_index])
# if A[K] = N + 1 then operation K is max counter.
elif item == N + 1:
# now operation k is as local max
# here we need to replace all items in array with this global max
# we can do using for loop for array length but that will cost bigo n2 complexity
# example - for i, item in A: counter[i] = global_max
local_max = global_max
# print("global_max each step")
# print(global_max)
# print("local max so far....")
# print(local_max)
# print("counter - ")
# print(counter)
# now counter array - replace all elements which are less than the local max found so far
# all counters are set to the maximum value of any counter
for i, item in enumerate(counter):
counter[i] = max(item, local_max)
return counter
result = solution(1, [3, 4, 4, 6, 1, 4, 4])
print("Sol " + str(result))

enter link description here
Got 100% result with O ( N + M )
class Solution {
public int[] solution(int N, int[] A) {
// write your code in Java SE 8
int max = 0;
int[] counter = new int[N];
int upgrade = 0;
for ( int i = 0; i < A.length; i++ )
{
if ( A[i] <= N )
{
if ( upgrade > 0 && upgrade > counter[A[i] - 1 ] )
{
counter[A[i] - 1] = upgrade;
}
counter[A[i] - 1 ]++;
if ( counter[A[i] - 1 ] > max )
{
max = counter[A[i] - 1 ];
}
}
else
{
upgrade = max;
}
}
for ( int i = 0; i < N; i++ )
{
if ( counter[i] < upgrade)
{
counter[i] = upgrade;
}
}
return counter;
}
}

Java 100%/100%, no imports
public int[] solution(int N, int[] A) {
int[] counters = new int[N];
int currentMax = 0;
int sumOfMaxCounters = 0;
boolean justDoneMaxCounter = false;
for (int i = 0; i < A.length ; i++) {
if (A[i] <= N) {
justDoneMaxCounter = false;
counters[A[i]-1]++;
currentMax = currentMax < counters[A[i]-1] ? counters[A[i]-1] : currentMax;
}else if (!justDoneMaxCounter){
sumOfMaxCounters += currentMax;
currentMax = 0;
counters = new int[N];
justDoneMaxCounter = true;
}
}
for (int j = 0; j < counters.length; j++) {
counters[j] = counters[j] + sumOfMaxCounters;
}
return counters;
}

python solution: 100% 100%
def solution(N, A):
c = [0] * N
max_element = 0
base = 0
for item in A:
if item >= 1 and N >= item:
c[item-1] = max(c[item-1], base) + 1
max_element = max(c[item - 1], max_element)
elif item == N + 1:
base = max_element
for i in range(N):
c[i] = max (c[i], base)
return c
pass

Using applyMax to record max operations
Time complexity:
O(N + M)
class Solution {
public int[] solution(int N, int[] A) {
// write your code in Java SE 8
int max = 0, applyMax = 0;;
int[] result = new int[N];
for (int i = 0; i < A.length; ++i) {
int a = A[i];
if (a == N + 1) {
applyMax = max;
}
if (1 <= a && a <= N) {
result[A[i] - 1] = Math.max(applyMax, result[A[i] - 1]);
max = Math.max(max, ++result[A[i] - 1]);
}
}
for (int i = 0; i < N; ++i) {
if (result[i] < applyMax) {
result[i] = applyMax;
}
}
return result;
}
}

move all even numbers on the first half and odd numbers to the second half in an integer array

I had an interview question which i could not solve.
Write method (not a program) in Java Programming Language that will move all even numbers on the first half and odd numbers to the second half in an integer array.
E.g. Input = {3,8,12,5,9,21,6,10}; Output = {12,8,6,10,3,5,9,21}.
The method should take integer array as parameter and move items in the same array (do not create another array). The numbers may be in different order than original array. This is algorithm test, so try to give as efficient algorithm as you can (possibly linear O(n) algorithm). Avoid using built in functions/API. *
Also some basic intro to what is data structure efficiency

Keep two indices: one to the first odd number and one to the last even number. Swap such numbers and update indices.

(With a lot of help from #manu-fatto's suggestion) I believe this would do it:
private static int[] OddSort(int[] items)
{
int oddPos, nextEvenPos;
for (nextEvenPos = 0;
nextEvenPos < items.Length && items[nextEvenPos] % 2 == 0;
nextEvenPos++) { }
// nextEvenPos is now positioned at the first odd number in the array,
// i.e. it is the next place an even number will be placed
// We already know that items[nextEvenPos] is odd (from the condition of the
// first loop), so we'll start looking for even numbers at nextEvenPos + 1
for (oddPos = nextEvenPos + 1; oddPos < items.Length; oddPos++)
{
// If we find an even number
if (items[oddPos] % 2 == 0)
{
// Swap the values
int temp = items[nextEvenPos];
items[nextEvenPos] = items[oddPos];
items[oddPos] = temp;
// And increment the location for the next even number
nextEvenPos++;
}
}
return items;
}
This algorithm traverses the list exactly 1 time (inspects each element exactly once), so the efficiency is O(n).

// to do this in one for loop
public static void evenodd(int[] integer) {
int i = 0, temp = 0;
int j = integer.length - 1;
while (j >= i) {
// swap if found odd even combo at i and j
if (integer[i] % 2 != 0 && integer[j] % 2 == 0) {
temp = integer[i];
integer[i] = integer[j];
integer[j] = temp;
i++;
j--;
} else {
if (integer[i] % 2 == 0) {
i++;
}
if (integer[j] % 2 == 1) {
j--;
}
}
}
}

#JLRishe,
Your algorithm doesn't maintain the order. For a simple example, say {1,5,2}, you will change the array to {2,5,1}. I could not comment below your post as I am a new user and lack reputations.

public static void sorted(int [] integer) {
int i, j , temp;
for (i = 0; i < integer.length; i++) {
if (integer[i] % 2 == 0) {
for (j = i; j < integer.length; j++) {
if (integer[j] % 2 == 1) {
temp = y[i];
y[i] = y[j];
y[j] = temp;
}
}
}
System.out.println(integer[i]);
}
public static void main(String args[]) {
sorted(new int[]{1, 2,7, 9, 4});
}
}
The answer is 1, 7, 9, 2, 4.

Could it be that you were asked to implement a very basic version of the BubbleSort where the sort value of element e, where e = arr[i], = e%2==1 ? 1 : -1 ?
Regards
Leon

class Demo
{
public void sortArray(int[] a)
{
int len=a.length;
int j=len-1;
for(int i=0;i<len/2+1;i++)
{
if(a[i]%2!=0)
{
while(a[j]%2!=0 && j>(len/2)-1)
j--;
if(j<=(len/2)-1)
break;
a[i]=a[i]+a[j];
a[j]=a[i]-a[j];
a[i]=a[i]-a[j];
}
}
for(int i=0;i<len;i++)
System.out.println(a[i]);
}
public static void main(String s[])
{
int a[]=new int[10];
System.out.println("Enter 10 numbers");
java.util.Scanner sc=new java.util.Scanner(System.in);
for(int i=0;i<10;i++)
{
a[i]=sc.nextInt();
}
new Demo().sortArray(a);
}
}

private static void rearrange(int[] a) {
int i,j,temp;
for(i = 0, j = a.length - 1; i < j ;i++,j--) {
while(a[i]%2 == 0 && i != a.length - 1) {
i++;
}
while(a[j]%2 == 1 && j != 0) {
j--;
}
if(i>j)
break;
else {
temp = a[i];
a[i] = a[j];
a[j] = temp;
}
}
}

public void sortEvenOddIntegerArray(int[] intArray){
boolean loopRequired = false;
do{
loopRequired = false;
for(int i = 0;i<intArray.length-1;i++){
if(intArray[i] % 2 != 0 && intArray[i+1] % 2 == 0){
int temp = intArray[i];
intArray[i] = intArray[i+1];
intArray[i+1] = temp;
loopRequired = true;
}
}
}while(loopRequired);
}

You can do this with a single loop by moving odd items to the end of the array when you find them.
static void EvensToLeft(int[] items) {
int end = items.length;
for (int i = 0; i < end; i++) {
if (items[i] % 2) {
int t = items[i];
items[i--] = items[--end];
items[end] = t;
}
}
}
Given an input array of length n the inner loop executes exactly n times, and computes the parity of each array element exactly once.

Use two counters i=0 and j=a.length-1 and keep swapping even and odd elements that are in the wrong place.
public int[] evenOddSort(int[] a) {
int i = 0;
int j = a.length - 1;
int temp;
while (i < j) {
if (a[i] % 2 == 0) {
i++;
} else if (a[j] % 2 != 0) {
j--;
} else {
temp = a[i];
a[i] = a[j];
a[j] = temp;
i++;
j--;
}
}
return a;
}

public class SeperatOddAndEvenInList {
public static int[] seperatOddAndEvnNos(int[] listOfNumbers) {
int oddNumPointer = 0;
int evenNumPointer = listOfNumbers.length - 1;
while(oddNumPointer <= evenNumPointer) {
if(listOfNumbers[oddNumPointer] % 2 == 0) { //even number, swap to front of last known even number
int temp;
temp = listOfNumbers[oddNumPointer];
listOfNumbers[oddNumPointer] = listOfNumbers[evenNumPointer];
listOfNumbers[evenNumPointer] = temp;
evenNumPointer--;
}
else { //odd number, go ahead... capture next element
oddNumPointer++;
}
}
return listOfNumbers;
}
public static void main(String[] args) {
// TODO Auto-generated method stub
int []arr = {3, 8, 12, 5, 9, 21, 6, 10};
int[] seperatedArray = seperatOddAndEvnNos(arr);
for (int i : seperatedArray) {
System.out.println(i);
}
}
}

public class ArraysSortEvensFirst {
public static void main(String[] args) {
int[] arr = generateTestData();
System.out.println(Arrays.toString(arr));
ArraysSortEvensFirst test = new ArraysSortEvensFirst();
test.sortEvensFirst(arr);
}
private static int[] generateTestData() {
int[] arr = {1,3,5,6,9,2,4,5,7};
return arr;
}
public int[] sortEvensFirst(int[] arr) {
int end = arr.length;
int last = arr.length-1;
for(int i=0; i < arr.length; i++) {
// find odd elements, then move to even slots
if(arr[i]%2 > 0) {
int k = findEven(last, arr);
if(k > i) swap(arr, i, k);
last = k;
}
}
System.out.println(Arrays.toString(arr));
return arr;
}
public int findEven(int last, int[] arr) {
for(int k = last; k > 0; k--) {
if(arr[k]%2 == 0) {
return k;
}
}
return -1; // not found;
}
public void swap(int[] arr, int x, int y) {
int temp = arr[x];
arr[x] = arr[y];
arr[y] = temp;
}
}
Output:
[1, 3, 5, 6, 9, 2, 4, 5, 7]
[4, 2, 6, 5, 9, 3, 1, 5, 7]

efficiency is O(log n).
public class TestProg {
public static void main(String[] args) {
int[] input = { 32, 54, 35, 18, 23, 17, 2 };
int front = 0;
int mid = input.length - 1;
for (int start = 0; start < input.length; start++) {
//if current element is odd
if (start < mid && input[start] % 2 == 1) {
//swapping element is also odd?
if (input[mid] % 2 == 1) {
mid--;
start--;
}
//swapping element is not odd then swap
else {
int tmp = input[mid];
input[mid] = input[start];
input[start] = tmp;
mid--;
}
}
}
for (int x : input)
System.out.print(x + " ");
}
}