MeanNearestNeighbors (MNN) - algorithm for balancing dataset - In progress #1

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One of the challenges in classification problems are unbalanced datasets. I was Data Science Intern when the company that I worked for, assigned me such an interesting challenge where the dataset was unbalanced.  However, I realized this type of problem like unbalanced dataset is а common thing in real life. I tried most of the algorithms (undersampling, oversampling) like SMOTE, NearMiss, CondensedNearestNeighbors, RandomUnderSampler, RandomOverSampler,  KMeansSMOTЕ and rest of them. Anyway, they didn't help me in that case, on the contrary, they worsened my model.  I was like: "but, but, you should have been helpful in creating the predictive model" So, I'm trying to create another algorithm based on undersampling concept when it comes to balancing datasets. I called it Mean Nearest Neighbors (MNN). What's the initial idea: It's simple. Actually, the algorithm is just a modification of the other undersampling algorithms. In the data where target labe...

Competitive Programming #15 : [Count unset bits in a given Range]

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Given a non-negative number n and two values l and r. The problem is to count the number of unset bits in the range l to r in the binary representation of n, i.e. to count unset bits from the rightmost lth bit to the rightmost rth bit.

Examples:
Input : n = 42, l = 2, r = 5
Output : 2
(42)10 = (101010)2
There are '2' unset bits in the range 2 to 5.

Input : n = 80, l = 1, r = 4
Output : 4
 
Input: The first line of the input contains a single integer T, denoting the number of test cases. ThenT test cases follow. Each test-case has one line of the input, the line contains three integers n, l and r.
Output: Print the required output.
Constraints:
1<=T<=100
1<=n<=100000

Examples:
Input:
2
64 4 7
80 1 4
Output:
3
4

Solution: 
 
The most important thing is RANGE in that number !!!