I have the following input:

 R = 'r' _ = 'g'  grid = [     [_,_,_],     [_,R,_],     [_,_,_] ]  p = 1.0 / 9 initial_beliefs = [     [p,p,p],     [p,p,p],     [p,p,p] ]  observation = R  #result expected_beliefs_after = [     [1/11, 1/11, 1/11],     [1/11, 3/11, 1/11],     [1/11, 1/11, 1/11] ]  p_hit  = 3.0 p_miss = 1.0 

Out of this I wrote for a course the following code:

def sense(color, grid, beliefs, p_hit, p_miss):  #Calculate height & width height = len(beliefs) width = len(beliefs[0])  #Generate new grid new_beliefs = [[0.0 for i in range(width)] for j in range(height)]  #Variable for sum s = 0  #New beliefs for i, row in enumerate(beliefs):     for j, cell in enumerate(row):         hit = (color == grid[i][j])         new_cell = beliefs[i][j] * (hit * p_hit + (1-hit) * p_miss)         new_beliefs[i][j] = new_cell      #Sum new beliefs for normalization     s = s + sum(new_beliefs[i])  #Update new beliefs after normalization for i, row in enumerate(new_beliefs):     for j, cell in enumerate(row):         new_beliefs[i][j] = new_beliefs[i][j] / s  return new_beliefs 

My code works perfectly fine, but I am a bit unsecure about the DRY principle. Would you consider this as good coded, or do you have any suggestions how to improve my code? Note: the input is given from my course, and this I just posted that you can see what’s given to the function.

Thanks a lot, Marc