Update README.md

README.md

@@ -211,11 +211,7 @@ def run(np_array_A_matrix):
 
 ### Response
 ```txt
-The Python code provided is used to generate an analytical model for a Markov chain with a given adjacency matrix.
-The model is then used to compute the Perron-Frobenius eigenvector and the corresponding matrix. The resulting matrices are then used to compute the Z-matrix and
-the H-matrix. The H-matrix is then returned as the output of the function. The code is designed to handle large matrices and perform computations efficiently.
-The matrices are manipulated using numpy's powerful and efficient numerical computation library.
-The code also includes comments to explain the functionality of each part of the code. 
+The given python code is a function that calculates the transition probability matrix, P, for a given adjacency matrix A, and then uses these matrices to calculate the Perron-Frobenius eigenvector and its inverse matrix Z, and finally, the H matrix which is the inverse of the Z matrix. The H matrix is then returned as the output of the function. The adjacency matrix A is a square matrix where each element at position (i, j) represents the probability of transitioning from state i to state j. The function first perturbs the adjacency matrix to avoid singularities, then calculates the transition probability matrix P, the Perron-Frobenius eigenvector and its inverse matrix Z, and finally, the H matrix. The H matrix is then returned as the output of the function.
 ```
 
 ### Team