Using Binary Search with sorted Array with duplicates [duplicate]

为君一笑 提交于 2019-11-28 04:46:06

Well, if you actually do have a sorted array, you can do a binary search until you find one of the indexes you're looking for, and from there, the rest should be easy to find since they're all next to each-other.

once you've found your first one, than you go find all the instances before it, and then all the instances after it.

Using that method you should get roughly O(lg(n)+k) where k is the number of occurrences of the value that you're searching for.

EDIT:

And, No, you will never be able to access all k values in anything less than O(k) time.


Second edit: so that I can feel as though I'm actually contributing something useful:

Instead of just searching for the first and last occurrences of X than you can do a binary search for the first occurence and a binary search for the last occurrence. which will result in O(lg(n)) total. once you've done that, you'll know that all the between indexes also contain X(assuming that it's sorted)

You can do this by searching checking if the value is equal to x , AND checking if the value to the left(or right depending on whether you're looking for the first occurrence or the last occurrence) is equal to x.

You will get the result in O(lg n)

public static void PrintIndicesForValue(int[] numbers, int target) {
    if (numbers == null)
        return;

    int low = 0, high = numbers.length - 1;
    // get the start index of target number
    int startIndex = -1;
    while (low <= high) {
        int mid = (high - low) / 2 + low;
        if (numbers[mid] > target) {
            high = mid - 1;
        } else if (numbers[mid] == target) {
            startIndex = mid;
            high = mid - 1;
        } else
            low = mid + 1;
    }

    // get the end index of target number
    int endIndex = -1;
    low = 0;
    high = numbers.length - 1;
    while (low <= high) {
        int mid = (high - low) / 2 + low;
        if (numbers[mid] > target) {
            high = mid - 1;
        } else if (numbers[mid] == target) {
            endIndex = mid;
            low = mid + 1;
        } else
            low = mid + 1;
    }

    if (startIndex != -1 && endIndex != -1){
        for(int i=0; i+startIndex<=endIndex;i++){
            if(i>0)
                System.out.print(',');
            System.out.print(i+startIndex);
        }
    }
}
public void PrintIndicesForValue42(int[] sortedArrayOfInts) {
    int index_occurrence_of_42 = left = right = binarySearch(sortedArrayOfInts, 42);
    while (left - 1 >= 0) {
        if (sortedArrayOfInts[left-1] == 42)
            left--;
    }
    while (right + 1 < sortedArrayOfInts.length) {
        if (sortedArrayOfInts[right+1] == 42)
            right++;
    }
    System.out.println("Indices are from: " + left + " to " + right);
}

This would run in O(log(n) + #occurrences) Read and understand the code. It's simple enough.

A Hashmap might work, if you're not required to use a binary search.

Create a HashMap where the Key is the value itself, and then value is an array of indices where that value is in the array. Loop through your array, updating each array in the HashMap for each value.

Lookup time for the indices for each value will be ~ O(1), and creating the map itself will be ~ O(n).

Find_Key(int arr[], int size, int key){
int begin = 0;
int end = size - 1;
int mid = end / 2;
int res = INT_MIN;

while (begin != mid)
{
    if (arr[mid] < key)
        begin = mid;
    else
    {
        end = mid;
        if(arr[mid] == key)
            res = mid;
    }
    mid = (end + begin )/2;
}
return res;
}

Assuming the array of ints is in ascending sorted order; Returns the index of the first index of key occurrence or INT_MIN. Runs in O(lg n).

Below is the java code which returns the range for which the search-key is spread in the given sorted array:

public static int doBinarySearchRec(int[] array, int start, int end, int n) {
    if (start > end) {
        return -1;
    }
    int mid = start + (end - start) / 2;

    if (n == array[mid]) {
        return mid;
    } else if (n < array[mid]) {
        return doBinarySearchRec(array, start, mid - 1, n);
    } else {
        return doBinarySearchRec(array, mid + 1, end, n);
    }
}

/**
 * Given a sorted array with duplicates and a number, find the range in the
 * form of (startIndex, endIndex) of that number. For example,
 * 
 * find_range({0 2 3 3 3 10 10}, 3) should return (2,4). find_range({0 2 3 3
 * 3 10 10}, 6) should return (-1,-1). The array and the number of
 * duplicates can be large.
 * 
 */
public static int[] binarySearchArrayWithDup(int[] array, int n) {

    if (null == array) {
        return null;
    }
    int firstMatch = doBinarySearchRec(array, 0, array.length - 1, n);
    int[] resultArray = { -1, -1 };
    if (firstMatch == -1) {
        return resultArray;
    }
    int leftMost = firstMatch;
    int rightMost = firstMatch;

    for (int result = doBinarySearchRec(array, 0, leftMost - 1, n); result != -1;) {
        leftMost = result;
        result = doBinarySearchRec(array, 0, leftMost - 1, n);
    }

    for (int result = doBinarySearchRec(array, rightMost + 1, array.length - 1, n); result != -1;) {
        rightMost = result;
        result = doBinarySearchRec(array, rightMost + 1, array.length - 1, n);
    }

    resultArray[0] = leftMost;
    resultArray[1] = rightMost;

    return resultArray;
}

It is using Modified Binary Search. It will be O(LogN). Space complexity will be O(1). We are calling BinarySearchModified two times. One for finding start index of element and another for finding end index of element.

private static int BinarySearchModified(int[] input, double toSearch)
    {
        int start = 0;
        int end = input.Length - 1;

        while (start <= end)
        {
            int mid = start + (end - start)/2;
            if (toSearch < input[mid]) end = mid - 1;
            else start = mid + 1;
        }

        return start;
    }


    public static Result GetRange(int[] input, int toSearch)
    {
        if (input == null) return new Result(-1, -1);

        int low = BinarySearchModified(input, toSearch - 0.5);

        if ((low >= input.Length) || (input[low] != toSearch)) return new Result(-1, -1);

        int high = BinarySearchModified(input, toSearch + 0.5);

        return new Result(low, high - 1);
    } 

 public struct Result
    {
        public int LowIndex;
        public int HighIndex;

        public Result(int low, int high)
        {
            LowIndex = low;
            HighIndex = high;
        }
    }

I came up with the solution using binary search, only thing is to do the binary search on both the sides if the match is found.

public static void main(String[] args) {
    int a[] ={1,2,2,5,5,6,8,9,10};
    System.out.println(2+" IS AVAILABLE  AT = "+findDuplicateOfN(a, 0, a.length-1, 2));
    System.out.println(5+" IS AVAILABLE  AT = "+findDuplicateOfN(a, 0, a.length-1, 5));
    int a1[] ={2,2,2,2,2,2,2,2,2};
    System.out.println(2+" IS AVAILABLE  AT = "+findDuplicateOfN(a1, 0, a1.length-1, 2));

    int a2[] ={1,2,3,4,5,6,7,8,9};
    System.out.println(10+" IS AVAILABLE  AT = "+findDuplicateOfN(a2, 0, a2.length-1, 10));
}

public static String findDuplicateOfN(int[] a, int l, int h, int x){
    if(l>h){
        return "";
    }
    int m = (h-l)/2+l;
    if(a[m] == x){
        String matchedIndexs = ""+m;
        matchedIndexs = matchedIndexs+findDuplicateOfN(a, l, m-1, x);
        matchedIndexs = matchedIndexs+findDuplicateOfN(a, m+1, h, x);
        return matchedIndexs;
    }else if(a[m]>x){
        return findDuplicateOfN(a, l, m-1, x);
    }else{
        return findDuplicateOfN(a, m+1, h, x);
    }
}


2 IS AVAILABLE  AT = 12 
5 IS AVAILABLE  AT = 43 
2 IS AVAILABLE  AT = 410236578 
10 IS AVAILABLE  AT =

I think this is still providing the results in O(logn) complexity.

public void printCopies(int[] array)
{
    HashMap<Integer, Integer> memberMap = new HashMap<Integer, Integer>();
    for(int i = 0; i < array.size; i++)
       if(!memberMap.contains(array[i]))
           memberMap.put(array[i], 1);
       else
       {
           int temp = memberMap.get(array[i]); //get the number of occurances
           memberMap.put(array[i], ++temp); //increment his occurance
       }

    //check keys which occured more than once
    //dump them in a ArrayList
    //return this ArrayList
 }

Alternatevely, instead of counting the number of occurances, you can put their indices in a arraylist and put that in the map instead of the count.

   HashMap<Integer, ArrayList<Integer>> 
   //the integer is the value, the arraylist a list of their indices

public void printCopies(int[] array)
{
    HashMap<Integer, ArrayList<Integer>> memberMap = new HashMap<Integer, ArrayList<Integer>>();
    for(int i = 0; i < array.size; i++)
       if(!memberMap.contains(array[i]))
       {
           ArrayList temp = new ArrayList();
           temp.add(i);
           memberMap.put(array[i], temp);
       }
       else
       {
           ArrayList temp = memberMap.get(array[i]); //get the lsit of indices
           temp.add(i);
           memberMap.put(array[i], temp); //update the index list
       }

    //check keys which return lists with length > 1
    //handle the result any way you want
 }

heh, i guess this will have to be posted.

 int predefinedDuplicate = //value here;
 int index = Arrays.binarySearch(array, predefinedDuplicate);
 int leftIndex, rightIndex;
 //search left
 for(leftIndex = index; array[leftIndex] == array[index]; leftIndex--); //let it run thru it
 //leftIndex is now the first different element to the left of this duplicate number string
 for(rightIndex = index; array[rightIndex] == array[index]; rightIndex++); //let it run thru it

 //right index contains the first different element to the right of the string
 //you can arraycopy this [leftIndex+1, rightIndex-1] string or just print it
 for(int i = leftIndex+1; i<rightIndex; i++)
 System.out.println(array[i] + "\t");

Another result for log(n) binary search for leftmost target and rightmost target. This is in C++, but I think it is quite readable.

The idea is that we always end up when left = right + 1. So, to find leftmost target, if we can move right to rightmost number which is less than target, left will be at the leftmost target.

For leftmost target:

int binary_search(vector<int>& nums, int target){
    int n = nums.size();
    int left = 0, right = n - 1;

    // carry right to the greatest number which is less than target.
    while(left <= right){
        int mid = (left + right) / 2;
        if(nums[mid] < target)
            left = mid + 1;
        else
            right = mid - 1;
    }
    // when we are here, right is at the index of greatest number
    // which is less than target and since left is at the next, 
    // it is at the first target's index
    return left;
}

For the rightmost target, the idea is very similar:

int binary_search(vector<int>& nums, int target){
    while(left <= right){
        int mid = (left + right) / 2;
        // carry left to the smallest number which is greater than target.
        if(nums[mid] <= target)
            left = mid + 1;
        else
            right = mid - 1;
    }
    // when we are here, left is at the index of smallest number
    // which is greater than target and since right is at the next, 
    // it is at the first target's index
    return right;
}
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