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 #17 : [Find whether path exist]

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Given a N X N matrix (M) filled with 1 , 0 , 2 , 3 . Your task is to find whether there is a path possible from source to destination, while traversing through blank cells only. You can traverse up, down, right and left.
  • A value of cell 1 means Source.
  • A value of cell 2 means Destination.
  • A value of cell 3 means Blank cell.
  • A value of cell 0 means Blank Wall.
Note : there is only single source and single destination.

Examples:
Input : M[3][3] = {{ 0 , 3 , 2 },
                   { 3 , 3 , 0 },
                   { 1 , 3 , 0 }};
Output : Yes

Input : M[4][4] = {{ 0 , 3 , 1 , 0 },
                   { 3 , 0 , 3 , 3 },
                   { 2 , 3 , 0 , 3 },
                   { 0 , 3 , 3 , 3 }};
Output : Yes

Input:
The first line of input is an integer T denoting the no of test cases. Then T test cases follow. Each test case consists of 2 lines . The first line of each test case contains an integer N denoting the size of the square matrix . Then in the next line are N*N space separated values of the matrix (M) .

Output:
For each test case in a new line print 1 if the path exist from source to destination else print 0.

Constraints:
1<=T<=20
1<=N<=20

Example:
Input:
2
4
3 0 0 0 0 3 3 0 0 1 0 3 0 2 3 3
3
0 3 2 3 0 0 1 0 0
Output:
1
0
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Solution:
Hoho, Yeah, maybe it looks hard , but the problem is marked as MEDIUM ... so...
This is graph problem, we gonna use BFS  and here is the code: