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I am trying to implement mergesort in Java. I understand the algorithm, but am having trouble with the implementation. Here is the code I have written:
package sort;
import java.util.Arrays;
public class MergeSort {
public MergeSort() {
}
public static void main(String[] args) {
int[] d = {26,14,72,34,622,483};
MergeSort ms = new MergeSort();
int[] results = ms.mergesort(d);
for (int i = 0; i < results.length; i++) {
System.out.println(results[i]);
}
}
public int[] mergesort(int[] a) {
System.out.println("Another call to merge sort");
if (a.length <= 1) { return a;}
int mid = a.length / 2;
int[] left = Arrays.copyOfRange(a,0,mid);
int[] right = Arrays.copyOfRange(a,mid + 1,a.length);
left = mergesort(left);
right = mergesort(right);
int[] result = merge(left,right);
return result;
}
private int[] merge(int[] b, int[] c) {
System.out.println("Another call to merge");
int[] result = new int[b.length+c.length];
if (b.length == 0) {
return c;
} else if (c.length == 0) {
return b;
} else if (b[0] < c[0] && b.length == 1) {
result[0] = b[0];
for (int i = 1; i < result.length; i++) {
result[i] = c[i -1];
}
return result;
}else if (b[0] > c[0] && c.length == 1) {
result[0] = c[0];
for (int i = 0; i < result.length; i++) {
result[i] = b[i-1];
}
return result;
}
else if (b[0] < c[0]) {
result[0] = b[0];
result = merge(result,merge(Arrays.copyOfRange(b,1,b.length),c));
return result;
} else if (b[0] > c[0]) {
result[0] = c[0];
result = merge(result,merge(b,Arrays.copyOfRange(c,1,c.length)));
return result;
} else {
System.out.println("Fell to the bottom.");
return result;
}
}
}
The problem is my merge function. I tried to follow the algorithm here: http://discrete.gr/complexity/ but I kept getting IndexOutOfBoundsExceptions because if the array was only of size 1, there isn't an index 1 for Arrays.copyOfRange to grab. I tried to fix this in my code, but it's getting really messy. With the way it is now, it will make a lot of calls down into the functions, and then throw a IndexOutOfBoundsException. I would really appreciate some help with this. And I'm just doing this on my own to try to figure it out, not as a homework assignment.
I would recommend implementing merge iteratively instead of recursively. Having merge also be a bad concat function, which is used in your example, is confusing (at least move it to a separate function).
In your case, the error is here:
for (int i = 0; i < result.length; i++) {
result[i] = b[i-1];
}
i needs to start from 1, otherwise i - 1 == -1 which is never a valid index.
In future, note where the exception occurs in your question, and run your program through a debugger, in this case it shows the problem immediately.
EDIT: ok, that still doesn't result in a correct program. Additional errors:
int[] right = Arrays.copyOfRange(a,mid + 1,a.length);
// needs to be "mid", not "mid + 1", read the doc on the function
EDIT2: Final lines need to be:
else if (b[0] < c[0]) {
result = merge(new int[]{b[0]},merge(Arrays.copyOfRange(b,1,b.length),c));
return result;
} else if (b[0] > c[0]) {
result = merge(new int[]{c[0]},merge(b,Arrays.copyOfRange(c,1,c.length)));
return result;
} else {
otherwise, the result gets very long.
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 + " ");
}
}
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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);
}
If an array contains 1,7,7,3,6 and if user asks what 2nd largest element is the output should be 7(not 6) since duplicate values are treated as distinct.
This is my code.
I am using Deterministic search to find the suitable pivot .
Its complexity is O(n).
I am stuck at the error generated by my code .
Please do help me.
import java.util.Random;
import java.util.Scanner;
public class deven {
public static void main(String args[]){
Scanner in=new Scanner(System.in);
int len=in.nextInt();
int n=in.nextInt();
int array[]=new int[len];
for (int i = 0; i < len; i++) {
array[i]=in.nextInt();
}
System.out.println(select(array,len,n));
}
static int below[];
static int above[];
static int pivot;
static int i;
static int j;
static int x;
static int y;
static int index;
static Random rand=new Random();
static int select(int array[],int len,int n){
if(len==1)
return array[0];
pivot=pivot(array, len);
below=new int[len];
above=new int[len];
//System.out.println("Block");
x=0;
y=0;
int temp=0;
for(i=0;i<len;i++){
if(array[i]>pivot){
below[x++]=array[i];
}
else if(array[i]<pivot){
above[y++]=array[i];
}
else {
if(temp!=0){
below[x++]=array[i];
}
temp=1;
}
}
i = x;
j = len - y;
if(n<i) return select(below,x,n);
else if(n>=j) return(select(above,y,n-j));
else return(pivot);
}
static int pivot(int array[],int len){
if(len==1){
return array[0];
}
int numOfGroups=len/5;
if(len%5!=0){
numOfGroups++;
}
int setOfMedians[]=new int[numOfGroups];
for (int i = 0 ; i < numOfGroups ; i++)
{
int[] subset;
if(array.length % 5 > 0)
{
if (i == numOfGroups - 1)
{
subset = new int[array.length % 5];
}
else
{
subset = new int[5];
}
}
else
{
subset = new int[5];
}
for (int j = 0; j < subset.length ; j++)
{
subset[j] = array[5*i+j];
}
setOfMedians[i] = median(subset);
}
int goodpivot=select(setOfMedians, numOfGroups,numOfGroups/2 );
return goodpivot;
}
static int median(int[] array)
{
if (array.length == 1)
{
return array[0];
}
int smallerCount = 0;
for (int i = 0 ; i < array.length ; i++)
{
for (int j = 0 ; j < array.length ; j++)
{
if (array[i] < array[j])
{
smallerCount++;
}
}
if (smallerCount == (array.length - 1)/2)
{
return array[i];
}
smallerCount = 0;
}
return -1;
}
}
Input
6
3
1 2 3 1 2 3
Output
Exception in thread "main" java.lang.StackOverflowError
at deven.pivot(deven.java:99)
at deven.select(deven.java:34)
at deven.pivot(deven.java:102)
at deven.select(deven.java:34)
at deven.select(deven.java:59)
at deven.select(deven.java:59)
at deven.select(deven.java:59)
If you maintained an equalsCount in addition to your smallerCount, you should be able to detect whether your candidate value is the median when it is also a duplicate.
(Explanation)
You appear to be deliberately returning -1 as an invalid value when your median method fails unexpectedly. It would be more proper to throw an exception of some sort, but what you really want is for it to never reach that point.
Your algorithm fails when the median is a duplicate. In the set { 1, 2, 2, 2, 3 }, for example, 2 is the obvious median, but there's never a point where there are exactly 2 values "smaller than" any of the values being verified.
If you count both smaller and equal values, then you can know your candidate to be a median if either your current test passes, or if the smaller count is less than the midpoint AND the smaller + equal count is greater than the midpoint.
The problem is your median method. It should not return -1. In the last line of the median method, instead of
return -1;
change it to
return array[rand.nextInt(array.length)];
Please note that this fix is just an attempt to fix the error you have. It's not a good fix in the sense that median method does not return the median. I think the application should be refactored. The idea of the fix is actually in the pivot method. A good pivot is the median. But if you cannot find the median efficiently, then a pivot can be a random choice among the array.
Update:
Let's fix the median method:
static int median(int[] array) {
if (array.length == 0) {
throw new IllegalArgumentException("array cannot be empty.");
}
int mid = array.length / 2;
for (int candidate : array) {
int lower = 0;
int higher = 0;
for (int value : array) {
if (value < candidate) {
lower++;
}
else if (value > candidate) {
higher++;
}
}
if (lower <= mid && higher <= mid) {
return candidate;
}
}
throw new IllegalStateException();
}
I'm trying to implement multi-threading using merge sort. I have it making new threads at the point where it cuts an array in half.
The array is sorted depending on the:
[size of the array] vs [how many times I create new threads]
For instance: the array will be sorted if I let it create merely two threads on an array of size 70, but if I let it create 6, it will come back unsorted. One thing I thought it might be is that the threads weren't sync'd, but I used threadName.join()
here is some code: merge.java
import java.util.Random;
public class merge implements Runnable {
int[] list;
int length;
int countdown;
public merge(int size, int[] newList, int numberOfThreadReps, int firstMerge) {
length = size;
countdown = numberOfThreadReps;
list = newList;
if (firstMerge == 1)
threadMerge(0, length - 1);
}
public void run() {
threadMerge(0, length - 1);
}
public void printList(int[] list, int size) {
for (int i = 0; i < size; i++) {
System.out.println(list[i]);
}
}
public void regMerge(int low, int high) {
if (low < high) {
int middle = (low + high) / 2;
regMerge(low, middle);
regMerge(middle + 1, high);
mergeJoin(low, middle, high);
}
}
public void mergeJoin(int low, int middle, int high) {
int[] helper = new int[length];
for (int i = low; i <= high; i++) {
helper[i] = list[i];
}
int i = low;
int j = middle + 1;
int k = low;
while (i <= middle && j <= high) {
if (helper[i] <= helper[j]) {
list[k] = helper[i];
i++;
} else {
list[k] = helper[j];
j++;
}
k++;
}
while (i <= middle) {
list[k] = helper[i];
k++;
i++;
}
helper = null;
}
public void threadMerge(int low, int high) {
if (countdown > 0) {
if (low < high) {
countdown--;
int middle = (low + high) / 2;
int[] first = new int[length / 2];
int[] last = new int[length / 2 + ((length % 2 == 1) ? 1 : 0)];
for (int i = 0; i < length / 2; i++)
first[i] = list[i];
for (int i = 0; i < length / 2 + ((length % 2 == 1) ? 1 : 0); i++)
last[i] = list[i + length / 2];
merge thread1 = new merge(length / 2, first, countdown, 0);// 0
// is
// so
// that
// it
// doesn't
// call
// threadMerge
// twice
merge thread2 = new merge(length / 2
+ ((length % 2 == 1) ? 1 : 0), last, countdown, 0);
Thread merge1 = new Thread(thread1);
Thread merge2 = new Thread(thread2);
merge1.start();
merge2.start();
try {
merge1.join();
merge2.join();
} catch (InterruptedException ex) {
System.out.println("ERROR");
}
for (int i = 0; i < length / 2; i++)
list[i] = thread1.list[i];
for (int i = 0; i < length / 2 + ((length % 2 == 1) ? 1 : 0); i++)
list[i + length / 2] = thread2.list[i];
mergeJoin(low, middle, high);
} else {
System.out.println("elsd)");
}
} else {
regMerge(low, high);
}
}
}
proj4.java
import java.util.Random;
public class proj4 {
public static void main(String[] args) {
int size = 70000;
int threadRepeat = 6;
int[] list = new int[size];
list = fillList(list, size);
list = perm(list, size);
merge mergy = new merge(size, list, threadRepeat, 1);
// mergy.printList(mergy.list,mergy.length);
for (int i = 0; i < mergy.length; i++) {
if (mergy.list[i] != i) {
System.out.println("error)");
}
}
}
public static int[] fillList(int[] list, int size) {
for (int i = 0; i < size; i++)
list[i] = i;
return list;
}
public static int[] perm(int[] list, int size) {
Random generator = new Random();
int rand = generator.nextInt(size);
int temp;
for (int i = 0; i < size; i++) {
rand = generator.nextInt(size);
temp = list[i];
list[i] = list[rand];
list[rand] = temp;
}
return list;
}
}
so TL;DR my array isn't getting sorted by a multithreaded merge sort based on the size of the array and the number of times I split the array by using threads...why is that?
Wow. This was an interesting exercise in masochism. I'm sure you've moved on but I thought for posterity...
The bug in the code is in mergeJoin with the middle argument. This is fine for regMerge but in threadMerge the middle passed in is (low + high) / 2 instead of (length / 2) - 1. Since in threadMerge low is always 0 and high is length - 1 and the first array has (length / 2) size. This means that for lists with an odd number of entries, it will often fail depending on randomization.
There are also a number of style issues which makes this program significantly more complicated and error prone:
The code passes around a size of the arrays when Java has a convenient list.length call which would be more straightforward and safer.
The code duplicates calculations (see length/2) in a number of places.
The code should be able to sort inside the array without creating sub-arrays.
Classes should start with an uppercase letter (Merge instead of merge)
firstMerge should be a boolean
The code names the Thread variable merge1 and the merge variable thread1. Gulp.
The merge constructor calling threadMerge(0,length -1) is strange. I would just put that call after the new call back in proj4. Then firstMerge can be removed.
I would consider switching to having high be one past the maximum value instead of the maximum. We tend to think like for (int i = 0; i < 10; i++) more than i <= 9. Then the code can have j go from low to < middle and k from middle to < high. Better symmetry.
Best of luck.