Unpaired element in array

I started using codility after long, and turned to a very basic problem. It was easy and did not take me more than 10 minutes to solve. I worked out the testcases by hand, and then was browsing a bit on Java basics after I finished the code, then submitted it. Otherwise it was so easy that I doubt the possibility of this occurring on any interview.

Still I felt that it was beneficial to solve it because:

>> It helped me jargon bitwise operators in mind ensuring I wouldn't mistake the syntaxes due to rustiness (we bearly use bitwise in day-to-day job, do we?)

>> It gave me a confidence boost to crack the best solution with perfect testcases / edge cases in one go.

Problem:

A non-empty zero-indexed array A consisting of N integers is given. The array contains an odd number of elements, and each element of the array can be paired with another element that has the same value, except for one element that is left unpaired.

For example, in array A such that:

  A[0] = 9  A[1] = 3  A[2] = 9
  A[3] = 3  A[4] = 9  A[5] = 7
  A[6] = 9

• the elements at indexes 0 and 2 have value 9,
• the elements at indexes 1 and 3 have value 3,
• the elements at indexes 4 and 6 have value 9,
• the element at index 5 has value 7 and is unpaired.

Write a function:

class Solution { public int solution(int[] A); }

that, given an array A consisting of N integers fulfilling the above conditions, returns the value of the unpaired element.

For example, given array A such that:

  A[0] = 9  A[1] = 3  A[2] = 9
  A[3] = 3  A[4] = 9  A[5] = 7
  A[6] = 9

the function should return 7, as explained in the example above.

Assume that:

• N is an odd integer within the range [1..1,000,000];
• each element of array A is an integer within the range [1..1,000,000,000];
• all but one of the values in A occur an even number of times.

Complexity:

• expected worst-case time complexity is O(N);
• expected worst-case space complexity is O(1), beyond input storage (not counting the storage required for input arguments).

Elements of input arrays can be modified.

My solution that received 100%

class Solution { public int solution(int[] A) { int temp = 0; for(int i = 0; i < A.length; i++) { temp = temp ^ A[i]; } return temp; } }

Maximum Binary Gap : O(log n) solution

A binary gap within a positive integer N is any maximal sequence of consecutive zeros that is surrounded by ones at both ends in the binary representation of N.

For example, number 9 has binary representation 1001 and contains a binary gap of length 2. The number 529 has binary representation1000010001) and contains two binary gaps: one of length 4 and one of length 3. The number 20 has binary representation 10100 and contains one binary gap of length 1. The number 15 has binary representation 1111 and has no binary gaps.

Write a function:

class Solution { public int solution(int N); }

that, given a positive integer N, returns the length of its longest binary gap. The function should return 0 if N doesn't contain a binary gap.

For example, given N = 1041 the function should return 5, because N has binary representation 10000010001 and so its longest binary gap is of length 5.

Assume that:

·    N is an integer within the range [1..2,147,483,647].

Complexity:

expected worst-case time complexity is O(log(N));

expected worst-case space complexity is O(1).

// My perfect score solution

class Solution {

    public int solution(int N) {

        if(N < 1)

            return -1;    

        int res = 0;

        int gapLen = 0;

        boolean binGapStart = false;

        boolean gapZeroes = false;           

        while(N >= 1) {

            if(N%2 == 1) {

                binGapStart = binGapStart && gapZeroes;              

                if(binGapStart) {

                    res = (res > gapLen)? res:gapLen;

                    gapZeroes = false;

                    gapLen = 0;

                }

                else

                    binGapStart = true;

            }

            else{

                gapZeroes = binGapStart;

                if(gapZeroes)

                    gapLen++;

            }           

            N = N/2;

        }       

        return res;       

    }

}

Java Tidbidz 1: Access modifiers, Arrays

• Access Modifiers - All members of interfaces are implicitly public. It is, in fact, a compile-time error to specify any access specifier for an interface member other than public (although no access specifier at all defaults to public access).

• Array - Arrays are special objects in java with no "class definition" (no .class file).

• Array.length [public final int variable], but String.length()

Java tidbidz 2: Abstract class and Interface

• Interface variables static and final by default [interfaces cannot be instantiated in their own right; the value of the variable must be assigned in a static context in which no instance exists. The final modifier ensures the value assigned to the interface variable is a true constant that cannot be re-assigned by program code.]

• We can have abstract class without abstract method but not abstract method in non-abstract class, because declaring a class abstract only means that you don't allow it to be instantiated on its own, while an abstract method must be defined by subclasses.

• You can even have abstract classes with final methods but never final classes with abstract methods.

Detect and identify cycle in linked list

Tortoise-Hare Algorithm illustrated -

Detection:
The classic Tortoise-Hare algorithm (Floyd Cycle Detection) requires two pointers, one fast (Hare or H) and a slower (Tortoise or T). Start both of them from the same (head) node and run the Hare twice as fast as the Tortoise. Clearly, they will meet at some point if there is a cycle. So far so good, very clear and intuitive.

Identification:
Let's assume that the singly linked list is represented as x = {x(0), x(1), ..., x(z)}. It has a cycle of length n starting at node x(m). T and H meet at node x(k), which is i distance away from start of the cycle. [For simplicity, I am eliminating obvious assumptions like, i <=n, k>=m, etc).

As H is twice as fast as T, by the time T crosses k nodes, H will traverse 2k nodes. Hence, we can write the following equations,

For T: k = m + i
For H: 2k = m + n + i
=> 2(m + i) = m + n + i
=> m =n - i

Based on this observation, T is restarted from the head or x(0), and both T and H are moved at the same speed of 1 node per time.
You will see that they ALWAYS meet at start of the cycle. This is because, by the time T crosses m nodes to reach x(m), H will now also cross m nodes. But H is moving in the cycle. So, it will cross, remaining (n-i) nodes of cycle (as it was at node x(k), where k = m + i) and reach the beginning of the cycle, x(m). But hey! (n-i) is actually m! So, H has just crossed m nodes, and will stop at x(m), where T has just reached :)
Thus, we know where the cycle starts.
The reason I wrote this blog is because the usual descriptions of this algorithm just tells you to restart T from head and move them at the same speed without this little description (which can be intuitive for many). But I felt the need of explaining the reason.

The rest is simple. Keep either T or H fixed at x(m), move the other till it reaches the fixed pointer again. The number of nodes traversed is length of the cycle.