We Keep Coding

Symmetric 2D array of random numbers

public static int[][] Matrix(int n, int max, int min) {
int[][] grid = new int[3][3];
Random rand = new Random();
rand.setSeed(System.currentTimeMillis());
for (int i = 0; i < n; i++) {
for (int j = 0; j < i; j++) {
int value = Math.abs((min + rand.nextInt((max - min) + 1)));
grid[i][j] = value;
grid[j][i] = value;
}
}
return grid;
}
The following code prints a 2D symmetric array where the values are random numbers between a range (min and max) which prints the following result as example:
0 14 11
14 0 17
11 17 0
My problem with the code is it only prints 0 as the diagonal value. How can I change it to print the diagonal values where they are set as int min instead of 0? For example, in the code above int min is 8 hence it would give this result:
8 14 11
14 8 17
11 17 8

If you want to set the diagonal to the lower variable, you need to do two things.
One, because you set j < i, j will never equal i, meaning the diagonal will be set to 0 because Java initializes integers to 0 if they aren't given an explicit initialization value. I was able to access the diagonal by simply changing the < to an <=:
for(int i = 0; i < n; i++)
{
for(int j = 0; j <= i; j++)
{
...
}
}
Two, once i equals j, you need to add an if statement that checks for the case where they're equal. When they are, simply set the current grid cell to the lower variable. Don't forget to enclose the other half of the second for code block with an else block or you'll get unintended behavior:
for(int j = 0; j <= i; j++)
{
if(i == j)
{
grid[i][j] = lower;
}
else
{
...
}
}
Finally, your whole for loop block should look like this:
for(int i = 0; i < n; i++)
{
for( int j = 0; j <= i; j++)
{
if(i == j)
{
grid[i][j] = lower;
}
else
{
int value = Math.abs((lower + rand.nextInt((upper - lower) + 1)));
grid[i][j] = value;
grid[j][i] = value;
}
}
}

You are generating random value for all i and j except when i==j, which are the diagonal values. Also, all the values of the diagonals will be same. So before returning the grid, you can generate one last random value and put it to the diagonals. Something like this
int diagonalValue = Math.abs((min+ rand.nextInt((max- min) + 1)));
for( int k=0 ; k<n ; k++)
{
grid[k][k] = diagonalValue;
}

Code review:
Add invalid syntax checks to the beginning of the method;
Math.abs is redundant;
The inner loop should include the upper bound;
Your code might look something like this:
public static int[][] matrix(int n, int max, int min) {
// incorrect matrix size
if (n <= 0)
return new int[][]{{}};
int[][] grid = new int[n][n];
// incorrect random value bound
if (max - min <= 0)
return grid;
Random rand = new Random();
// interval excluding upper bound
IntStream.range(0, n).forEach(i ->
// interval including upper bound
IntStream.rangeClosed(0, i).forEach(j -> {
if (i == j) {
// main diagonal
grid[i][j] = min;
} else {
// peripheral elements
int value = min + rand.nextInt((max - min) + 1);
grid[i][j] = value;
grid[j][i] = value;
}
}));
return grid;
}
public static void main(String[] args) {
Arrays.stream(matrix(5, 9, 1))
.map(Arrays::toString)
.forEach(System.out::println);
}
Output:
[1, 6, 3, 3, 2]
[6, 1, 9, 4, 2]
[3, 9, 1, 6, 7]
[3, 4, 6, 1, 9]
[2, 2, 7, 9, 1]

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) }
}

Count and print Triplets in a sorted array

Given a sorted array of n integers, display triplets such that a[i] < a[j] < a[k].
My code is
public static void countTriplets(int arr[], int index, int arr1[], int position)
{
if (position == 3)
{
System.out.println(Arrays.toString(arr1));
return;
}
for (int i = index; i < arr.length; i++)
{
arr1[position] = arr[i];
countTriplets(arr, index + 1, arr1, position + 1);
}
}
However it prints all possible triplets.Where am i going wrong ?

Count the number of unique elements in the array. Let it be 'N'. Then the answer is n * (n - 1) * (n - 2) / 6.
The reasoning is as follows: for any three distinct numbers a, b, c, we can form a tuple of sorted elements such that say b < c < a. Since we don't want repetitions, we have to count the number of unique elements.
For example, consider {1, 2, 3, 3, 4, 5, 5, 6}
Number of unique elements = 6. The answer is (6 * 5 * 4) / 6 = 20.
Some code in C++:
#include <stdio.h>
int count_triplets(int *a, int n)
{
int counter = 0;
if (n < 3) {
return 0;
}
for (int i = 0; i < n; i++) {
// jump to the last of the repeated values
if ((i < n - 1) && (a[i] == a[i + 1])) {
continue;
}
for (int j = i + 1; j < n; j++) {
// jump to the last of the repeated values
if ((j < n - 1) && (a[j] == a[j + 1])) {
continue;
}
for (int k = j + 1; k < n; k++) {
// jump to the last of the repeated values
if ((k < n - 1) && (a[k] == a[k + 1])) {
continue;
}
printf("[%d, %d, %d]\n", a[i], a[j], a[k]);
counter ++;
}
}
}
return counter;
}
int main(int argc, char *argv[])
{
printf("Enter the number of elements:");
int n = 0;
scanf("%d", &n);
printf("Enter the elements:\n");
int a[100] = {0};
for (int i = 0; i < n; i++) {
scanf("%d", &a[i]);
}
int triplets = count_triplets(a, n);
printf("Number of triplets = [%d]\n", triplets);
return 0;
}
This is not the most efficient but should lead you to more efficient solutions.

The simple way to do this is with nested loops:
for (int i = 0; i < arr.length-2; i++)
{
for (int j = i+1; j < arr.length-1; j++)
{
for (int k = j+1; k < arr.length; k++)
{
// output the triplet arr[i], arr[j], arr[k]
++numTriplets;
}
}
}
The code above will do what you're asking. It does not take into account the possibility of duplicates in the source array. Given the array [1, 2, 3, 4, 5], it outputs:
1,2,3
1,2,4
1,2,5
1,3,4
1,3,5
1,4,5
2,3,4
2,3,5
2,4,5
3,4,5
Update
The general solution to this problem is one of creating combinations. That is, selecting combinations of n items from a list of size m. The math tells us that the number of combinations is equal to:
m!
---------
(n!)(m-n)!
Substituting numbers for your example, we have:
c = 5!/((3!) * (5-3)!)
= 120/(6 * 2)
= 120/12
= 10
So you can compute the number of combinations in O(1) easily enough (if you use an approximation for the factorial function), but if you want to enumerate them your time complexity approaches O(m!) (for sufficiently large values of m).
You certainly can't enumerate all the combinations in O(n) or O(n log n). That would be kind of like asking for an algorithm that can enumerate all n-digit numbers in O(n) or O(n log n).

the answer can be reduced to selection of three numbers from n numbers which is nc3 i.e
return (n*(n-1)*(n-2))/3! where n>=3 else return 0

Programming Test - Codility - Dominator [closed]

Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 4 years ago.
Improve this question
I just had a codility problem give me a hard time and I'm still trying to figure out how the space and time complexity constraints could have been met.
The problem is as follows:
A dominant member in the array is one that occupies over half the positions in the array, for example:
{3, 67, 23, 67, 67}
67 is a dominant member because it appears in the array in 3/5 (>50%) positions.
Now, you are expected to provide a method that takes in an array and returns an index of the dominant member if one exists and -1 if there is none.
Easy, right? Well, I could have solved the problem handily if it were not for the following constraints:
Expected time complexity is O(n)
Expected space complexity is O(1)
I can see how you could solve this for O(n) time with O(n) space complexities as well as O(n^2) time with O(1) space complexities, but not one that meets both O(n) time and O(1) space.
I would really appreciate seeing a solution to this problem. Don't worry, the deadline has passed a few hours ago (I only had 30 minutes), so I'm not trying to cheat. Thanks.

Googled "computing dominant member of array", it was the first result. See the algorithm described on page 3.
element x;
int count ← 0;
For(i = 0 to n − 1) {
if(count == 0) { x ← A[i]; count++; }
else if (A[i] == x) count++;
else count−−;
}
Check if x is dominant element by scanning array A
Basically observe that if you find two different elements in the array, you can remove them both without changing the dominant element on the remainder. This code just keeps tossing out pairs of different elements, keeping track of the number of times it has seen the single remaining unpaired element.

Find the median with BFPRT, aka median of medians (O(N) time, O(1) space). Then scan through the array -- if one number dominates, the median will be equal to that number. Walk through the array and count the number of instances of that number. If it's over half the array, it's the dominator. Otherwise, there is no dominator.

Adding a Java 100/100 O(N) time with O(1) space:
https://codility.com/demo/results/demoPNG8BT-KEH/
class Solution {
public int solution(int[] A) {
int indexOfCandidate = -1;
int stackCounter = 0, candidate=-1, value=-1, i =0;
for(int element: A ) {
if (stackCounter == 0) {
value = element;
++stackCounter;
indexOfCandidate = i;
} else {
if (value == element) {
++stackCounter;
} else {
--stackCounter;
}
}
++i;
}
if (stackCounter > 0 ) {
candidate = value;
} else {
return -1;
}
int countRepetitions = 0;
for (int element: A) {
if( element == candidate) {
++countRepetitions;
}
if(countRepetitions > (A.length / 2)) {
return indexOfCandidate;
}
}
return -1;
}
}
If you want to see the Java source code it's here, I added some test cases as comments as the beginning of the file.

Java solution with score 100%
public int solution(int[] array) {
int candidate=0;
int counter = 0;
// Find candidate for leader
for(int i=0; i<array.length; i++){
if(counter == 0) candidate = i;
if(array[i] == array[candidate]){
counter++;
}else {
counter--;
}
}
// Count candidate occurrences in array
counter = 0;
for(int i=0; i<array.length; i++){
if(array[i] == array[candidate]) counter++;
}
// Check that candidate occurs more than array.lenght/2
return counter>array.length/2 ? candidate : -1;
}

In python, we are lucky some smart people have bothered to implement efficient helpers using C and shipped it in the standard library. The collections.Counter is useful here.
>>> data = [3, 67, 23, 67, 67]
>>> from collections import Counter
>>> counter = Counter(data) # counter accepts any sequence/iterable
>>> counter # dict like object, where values are the occurrence
Counter({67: 3, 3: 1, 23: 1})
>>> common = counter.most_common()[0]
>>> common
(67, 3)
>>> common[0] if common[1] > len(data) / 2.0 + 1 else -1
67
>>>
If you prefer a function here is one ...
>>> def dominator(seq):
counter = Counter(seq)
common = counter.most_common()[0]
return common[0] if common[1] > len(seq) / 2.0 + 1 else -1
...
>>> dominator([1, 3, 6, 7, 6, 8, 6])
-1
>>> dominator([1, 3, 6, 7, 6, 8, 6, 6])
6

This question looks hard if a small trick does not come to the mind :). I found this trick in this document of codility : https://codility.com/media/train/6-Leader.pdf.
The linear solution is explained at the bottom of this document.
I implemented the following java program which gave me a score of 100 on the same lines.
public int solution(int[] A) {
Stack<Integer> stack = new Stack<Integer>();
for (int i =0; i < A.length; i++)
{
if (stack.empty())
stack.push(new Integer(A[i]));
else
{
int topElem = stack.peek().intValue();
if (topElem == A[i])
{
stack.push(new Integer(A[i]));
}
else
{
stack.pop();
}
}
}
if (stack.empty())
return -1;
int elem = stack.peek().intValue();
int count = 0;
int index = 0;
for (int i = 0; i < A.length; i++)
{
if (elem == A[i])
{
count++;
index = i;
}
}
if (count > ((double)A.length/2.0))
return index;
else
return -1;
}

Here's my C solution which scores 100%
int solution(int A[], int N) {
int candidate;
int count = 0;
int i;
// 1. Find most likely candidate for the leader
for(i = 0; i < N; i++){
// change candidate when count reaches 0
if(count == 0) candidate = i;
// count occurrences of candidate
if(A[i] == A[candidate]) count++;
else count--;
}
// 2. Verify that candidate occurs more than N/2 times
count = 0;
for(i = 0; i < N; i++) if(A[i] == A[candidate]) count++;
if (count <= N/2) return -1;
return candidate; // return index of leader
}

100%
import java.util.HashMap;
import java.util.Map;
class Solution {
public static int solution(int[] A) {
final int N = A.length;
Map<Integer, Integer> mapOfOccur = new HashMap((N/2)+1);
for(int i=0; i<N; i++){
Integer count = mapOfOccur.get(A[i]);
if(count == null){
count = 1;
mapOfOccur.put(A[i],count);
}else{
mapOfOccur.replace(A[i], count, ++count);
}
if(count > N/2)
return i;
}
return -1;
}
}

Does it have to be a particularly good algorithm? ;-)
static int dominant(final int... set) {
final int[] freqs = new int[Integer.MAX_VALUE];
for (int n : set) {
++freqs[n];
}
int dom_freq = Integer.MIN_VALUE;
int dom_idx = -1;
int dom_n = -1;
for (int i = set.length - 1; i >= 0; --i) {
final int n = set[i];
if (dom_n != n) {
final int freq = freqs[n];
if (freq > dom_freq) {
dom_freq = freq;
dom_n = n;
dom_idx = i;
} else if (freq == dom_freq) {
dom_idx = -1;
}
}
}
return dom_idx;
}
(this was primarily meant to poke fun at the requirements)

Consider this 100/100 solution in Ruby:
# Algorithm, as described in https://codility.com/media/train/6-Leader.pdf:
#
# * Iterate once to find a candidate for dominator.
# * Count number of candidate occurences for the final conclusion.
def solution(ar)
n_occu = 0
candidate = index = nil
ar.each_with_index do |elem, i|
if n_occu < 1
# Here comes a new dominator candidate.
candidate = elem
index = i
n_occu += 1
else
if candidate == elem
n_occu += 1
else
n_occu -= 1
end
end # if n_occu < 1
end
# Method result. -1 if no dominator.
# Count number of occurences to check if candidate is really a dominator.
if n_occu > 0 and ar.count {|_| _ == candidate} > ar.size/2
index
else
-1
end
end
#--------------------------------------- Tests
def test
sets = []
sets << ["4666688", [1, 2, 3, 4], [4, 6, 6, 6, 6, 8, 8]]
sets << ["333311", [0, 1, 2, 3], [3, 3, 3, 3, 1, 1]]
sets << ["313131", [-1], [3, 1, 3, 1, 3, 1]]
sets << ["113333", [2, 3, 4, 5], [1, 1, 3, 3, 3, 3]]
sets.each do |name, one_of_expected, ar|
out = solution(ar)
raise "FAILURE at test #{name.inspect}: #{out.inspect} not in #{expected.inspect}" if not one_of_expected.include? out
end
puts "SUCCESS: All tests passed"
end

Here is an easy to read, 100% score version in Objective-c
if (A.count > 100000)
return -1;
NSInteger occur = 0;
NSNumber *candidate = nil;
for (NSNumber *element in A){
if (!candidate){
candidate = element;
occur = 1;
continue;
}
if ([candidate isEqualToNumber:element]){
occur++;
}else{
if (occur == 1){
candidate = element;
continue;
}else{
occur--;
}
}
}
if (candidate){
occur = 0;
for (NSNumber *element in A){
if ([candidate isEqualToNumber:element])
occur++;
}
if (occur > A.count / 2)
return [A indexOfObject:candidate];
}
return -1;

100% score JavaScript solution. Technically it's O(nlogn) but still passed.
function solution(A) {
if (A.length == 0)
return -1;
var S = A.slice(0).sort(function(a, b) {
return a - b;
});
var domThresh = A.length/2;
var c = S[Math.floor(domThresh)];
var domCount = 0;
for (var i = 0; i < A.length; i++) {
if (A[i] == c)
domCount++;
if (domCount > domThresh)
return i;
}
return -1;
}

This is the solution in VB.NET with 100% performance.
Dim result As Integer = 0
Dim i, ladderVal, LadderCount, size, valCount As Integer
ladderVal = 0
LadderCount = 0
size = A.Length
If size > 0 Then
For i = 1 To size - 1
If LadderCount = 0 Then
LadderCount += 1
ladderVal = A(i)
Else
If A(i) = ladderVal Then
LadderCount += 1
Else
LadderCount -= 1
End If
End If
Next
valCount = 0
For i = 0 To size - 1
If A(i) = ladderVal Then
valCount += 1
End If
Next
If valCount <= size / 2 Then
result = 0
Else
LadderCount = 0
For i = 0 To size - 1
If A(i) = ladderVal Then
valCount -= 1
LadderCount += 1
End If
If LadderCount > (LadderCount + 1) / 2 And (valCount > (size - (i + 1)) / 2) Then
result += 1
End If
Next
End If
End If
Return result
See the correctness and performance of the code

Below solution resolves in complexity O(N).
public int solution(int A[]){
int dominatorValue=-1;
if(A != null && A.length>0){
Hashtable<Integer, Integer> count=new Hashtable<>();
dominatorValue=A[0];
int big=0;
for (int i = 0; i < A.length; i++) {
int value=0;
try{
value=count.get(A[i]);
value++;
}catch(Exception e){
}
count.put(A[i], value);
if(value>big){
big=value;
dominatorValue=A[i];
}
}
}
return dominatorValue;
}

100% in PHP https://codility.com/demo/results/trainingVRQGQ9-NJP/
function solution($A){
if (empty($A)) return -1;
$copy = array_count_values($A); // 3 => 7, value => number of repetition
$max_repetition = max($copy); // at least 1 because the array is not empty
$dominator = array_search($max_repetition, $copy);
if ($max_repetition > count($A) / 2) return array_search($dominator, $A); else return -1;
}

i test my code its work fine in arrays lengths between 2 to 9
public static int sol (int []a)
{
int count = 0 ;
int candidateIndex = -1;
for (int i = 0; i <a.length ; i++)
{
int nextIndex = 0;
int nextOfNextIndex = 0;
if(i<a.length-2)
{
nextIndex = i+1;
nextOfNextIndex = i+2;
}
if(count==0)
{
candidateIndex = i;
}
if(a[candidateIndex]== a[nextIndex])
{
count++;
}
if (a[candidateIndex]==a[nextOfNextIndex])
{
count++;
}
}
count -- ;
return count>a.length/2?candidateIndex:-1;
}

Adding a Java 100/100 O(N) time with O(1) space:
// you can also use imports, for example:
import java.util.Stack;
// you can write to stdout for debugging purposes, e.g.
// System.out.println("this is a debug message");
class Solution {
public int solution(int[] A) {
// write your code in Java SE 8
int count = 0;
Stack<Integer> integerStack = new Stack<Integer>();
for (int i = 0; i < A.length; i++) {
if (integerStack.isEmpty()) {
integerStack.push(A[i]);
} else if (integerStack.size() > 0) {
if (integerStack.peek() == A[i])
integerStack.push(A[i]);
else
integerStack.pop();
}
}
if (!integerStack.isEmpty()) {
for (int i = 0; i < integerStack.size(); i++) {
for (int j = 0; j < A.length; j++) {
if (integerStack.get(i) == A[j])
count++;
if (count > A.length / 2)
return j;
}
count = 0;
}
}
return -1;
}
}
Here is test result from codility.

I think this question has already been resolved somewhere. The "official" solution should be :
public int dominator(int[] A) {
int N = A.length;
for(int i = 0; i< N/2+1; i++)
{
int count=1;
for(int j = i+1; j < N; j++)
{
if (A[i]==A[j]) {count++; if (count > (N/2)) return i;}
}
}
return -1;
}

How about sorting the array first? You then compare middle and first and last elements of the sorted array to find the dominant element.
public Integer findDominator(int[] arr) {
int[] arrCopy = arr.clone();
Arrays.sort(arrCopy);
int length = arrCopy.length;
int middleIndx = (length - 1) /2;
int middleIdxRight;
int middleIdxLeft = middleIndx;
if (length % 2 == 0) {
middleIdxRight = middleIndx+1;
} else {
middleIdxRight = middleIndx;
}
if (arrCopy[0] == arrCopy[middleIdxRight]) {
return arrCopy[0];
}
if (arrCopy[middleIdxLeft] == arrCopy[length -1]) {
return arrCopy[middleIdxLeft];
}
return null;
}

C#
int dominant = 0;
int repeat = 0;
int? repeatedNr = null;
int maxLenght = A.Length;
int halfLenght = A.Length / 2;
int[] repeations = new int[A.Length];
for (int i = 0; i < A.Length; i++)
{
repeatedNr = A[i];
for (int j = 0; j < A.Length; j++)
{
if (repeatedNr == A[j])
{
repeations[i]++;
}
}
}
repeatedNr = null;
for (int i = 0; i < repeations.Length; i++)
{
if (repeations[i] > repeat)
{
repeat = repeations[i];
repeatedNr = A[i];
}
}
if (repeat > halfLenght)
dominant = int.Parse(repeatedNr.ToString());

class Program
{
static void Main(string[] args)
{
int []A= new int[] {3,6,2,6};
int[] B = new int[A.Length];
Program obj = new Program();
obj.ABC(A,B);
}
public int ABC(int []A, int []B)
{
int i,j;
int n= A.Length;
for (j=0; j<n ;j++)
{
int count = 1;
for (i = 0; i < n; i++)
{
if ((A[j]== A[i] && i!=j))
{
count++;
}
}
int finalCount = count;
B[j] = finalCount;// to store the no of times a number is repeated
}
// int finalCount = count / 2;
int finalCount1 = B.Max();// see which number occurred max times
if (finalCount1 > (n / 2))
{ Console.WriteLine(finalCount1); Console.ReadLine(); }
else
{ Console.WriteLine("no number found"); Console.ReadLine(); }
return -1;
}
}

In Ruby you can do something like
def dominant(a)
hash = {}
0.upto(a.length) do |index|
element = a[index]
hash[element] = (hash[element] ? hash[element] + 1 : 1)
end
res = hash.find{|k,v| v > a.length / 2}.first rescue nil
res ||= -1
return res
end

#Keith Randall solution is not working for {1,1,2,2,3,2,2}
his solution was:
element x;
int count ← 0;
For(i = 0 to n − 1) {
if(count == 0) { x ← A[i]; count++; }
else if (A[i] == x) count++;
else count−−;
}
Check if x is dominant element by scanning array A
I converted it into java as below:
int x = 0;
int count = 0;
for(int i = 0; i < (arr.length - 1); i++) {
if(count == 0) {
x = arr[i];
count++;
}
else if (arr[i] == x)
count++;
else count--;
}
return x;
Out put : 3
Expected: 2

This is my answer in Java: I store a count in seperate array which counts duplicates of each of the entries of the input array and then keeps a pointer to the array position that has the most duplicates. This is the dominator.
private static void dom(int[] a) {
int position = 0;
int max = 0;
int score = 0;
int counter = 0;
int[]result = new int[a.length];
for(int i = 0; i < a.length; i++){
score = 0;
for(int c = 0; c < a.length;c++){
if(a[i] == a[c] && c != i ){
score = score + 1;
result[i] = score;
if(result[i] > position){
position = i;
}
}
}
}
//This is just to facilitate the print function and MAX = the number of times that dominator number was found in the list.
for(int x = 0 ; x < result.length-1; x++){
if(result[x] > max){
max = result[x] + 1;
}
}
System.out.println(" The following number is the dominator " + a[position] + " it appears a total of " + max);
}

Algorithm to find the largest integer in array

I am trying to create a method which returns an int - the value of the largest integer in the sent array.
The way I want this method to work, is to check the first and the last element of the array in a for-loop, and work their way to the middle. So i = first integer, k = last integer. When i = 0, k = n-1 (indexes), when i = 1, k = n-2 if you catch my drift. In every loop it needs to check if a[i]>a[k]. Then they switch places. Then I know that the largest number is in the leading half of the array, and then I want it to check that half, so ultimately the largest int is at index 0.
I tried like this:
public static int maxOfArray(int[] a)
{
int length = a.length;
if(length<1)
throw new NoSuchElementException("Not at least one integer in array");
while (length > 1)
{
int k = length;
for(int i = 0; i < length/2; i++)
{
k--;
if(a[i]<a[k])
{
int j = a[i];
a[i] = a[k];
a[k] = j;
}
}
length /=2;
}
return a[0];
}
..but I don't really get it.. I'm having a hard time "picturing" what's happening here.. But it's not always working.. (though sometimes).
EDIT
Also: The array {6,15,2,5,8,14,10,16,11,17,13,7,1,18,3,4,9,12}; will spit out 17 as the largest number. I realize I have to fix the odd-length bug, but I would like to solve this even-length array first..

A bug is when encountering length is odd.
In these cases, you "miss" the middle element.
Example: for input int[] arr = { 8, 1, 5, 4, 9, 4, 3, 7, 2 }; - the element 9 will be compared and checked against itself, but then you reduce the size of length, you exclude 9 from the array you are going to iterate next.
I believe it can be solved by reducing the problem to ceil(length/2) instead of length/2 (and handling special case of length==1)
The other issue as was mentioned in comments is: you need to iterate up to length/2 rather then up to length, otherwise you are overriding yourself.
Lastly - the sign is wrong.
if(a[i]>a[k])
should be
if(a[i]<a[k])
Remember - you are trying to swap the elements if the first is smaller the the second in order to push the larger elements to the head of your array.

but I don't really get it.. I'm having a hard time "picturing" what's happening here.. But it's not always working.. (though sometimes).
In that case you should use a debugger to step through the code to get a picture of what each line of code does.
What I would do is:
public static int maxOfArray(int[] a) {
int max = a[0];
for (int i : a)
if (max < i)
max = i;
return max;
}
public static int findMaxTheHardWay(int[] array) {
for (int length = array.length; length > 1; length = (length + 1) / 2) {
for (int i = 0; i < length / 2; i++) {
if (array[i] < array[length - i - 1])
array[i] = array[length - i - 1]; // don't need to swap.
}
}
return array[0];
}
public static void main(String... args) {
Random rand = new Random(1);
for (int i = 1; i <= 1000; i++) {
int[] a = new int[i];
for (int j = 0; j < i; j++) a[j] = rand.nextInt();
int max = maxOfArray(a);
int max2 = findMaxTheHardWay(a);
if (max != max2)
throw new AssertionError(i + ": " + max + " != " + max2);
}
}

This is rather a crazy way to solve the problem, but I'll play along.
The problem is in the inner loop.
You start out with i = 0 and k = length - 1.
If a[i] > a[k] you swap them.
...
You end up with k = 0 and i = length - 1
If a[i] > a[k] you swap them.
If you look at that carefully you will notice that if we swapped the elements in the first swap, we will also swap them in the last swap; i.e. we will UNDO the effects of the first swap. And the same applies pair-wise through the entire array slice.
See?
What you need to do is to stop the inner loop half way ... and then take account of the case where length is odd.
By the way, the reason I called this "rather crazy", because the obvious and simple way is much faster: O(N) versus O(NlogN)

int a[] = {1,7,3};
List<Integer> list = Arrays.asList(a);
Integer largest = Collections.max(list);
This will give you Largest number in Array.

Here is a solution that fits the specifications that you want (unlike many other here, humm, humm):
final Integer[] input = {1, 2, 6, 32, 4, 44 ,12, 42, 3, 7, 17, 22, 57, 23, 102, 103 };
int half = (input.length / 2);
int mod = input.length % 2;
while (half >= 0) {
for (int i = 0, j = (half * 2) + mod - 1; i <= half && j >= half; i++, j--) {
if (input[i] < input[j]) {
final int tmp = input[i];
input[i] = input[j];
input[j] = tmp;
}
}
if (half == 0) break;
half = half / 2;
mod = half % 2;
}
//Here, input[0] = the biggest number in the original input.
Edit: Added mod, so it works if the last element is the largest..

I think your code is working, you just have to ceil the length / 2 in case of odd array but my tests return proper result:
package org.devince.largestinteger;
import java.util.NoSuchElementException;
public class LargestInteger {
final static int[] input = {1, 2, 6, 32, 4, 44 ,12, 42, 3, 7, 17, 22, 57, 23, 102, 103 };
// final static int[] input = { 8, 1, 5, 4, 9, 4, 3, 7, 2 };
// final static int[] input = {1,3,7};
/**
* #param args
*/
public static void main(String[] args) {
System.out.println(String.valueOf(maxOfArray(input)));
}
public static int maxOfArray(int[] a)
{
int length = a.length;
if(length<1)
throw new NoSuchElementException("Not at least one integer in array");
while (length > 1)
{
int k = length;
for(int i = 0; i < length; i++)
{
k--;
if(a[i]>a[k])
{
int j = a[i];
a[i] = a[k];
a[k] = j;
}
}
length = (int) Math.ceil(length / 2f);
}
return a[0];
}
}

Why not just store the first value of the array to a variable max.
After that just loop through the array starting from second position till the last ,
in the loop just check if the current value is greater than max or not.If it is greater just assign max that value.
Return max and you have the largest number.
public int FindLargest()
{
int[] num = { 1, 2, 5, 12, 13, 56, 16, 4 };
int max = num[0];
for (int i = 1; i <num.length; i++)
{
if (num[i] > max)
{
max = num[i];
}
}
return max;
}

As the same u can approach like also,
int length = a.length;
while (length > 1)
{
int k = length;
for(int i = 0; i < length; i++)
{
for(int y = k-1; y >= i; y--)
{
if(a[i]<a[y])
{
int j = a[i];
a[i] = a[y];
a[y] = j;
}
}
}
length /=2;
}

final int validSampleRates[] = new int[]{
5644800, 2822400, 352800, 192000, 176400, 96000,
88200, 50400, 50000, 4800,47250, 44100, 44056, 37800, 32000, 22050, 16000, 11025, 4800, 8000};
ArrayList <Integer> YourArray = new ArrayList <Integer> ():
for (int smaple : validSampleRates){
YourArray.add(smaple);
}
Integer largest = Collections.max(YourArray);
System.out.println("Largest " + String.valueOf(largest));
The best way is to use Array that extends List Collection as ArrayList