va2_11&2_12(d).py

import random
def perf_measure(n,m,x,y,environ):
  score=0
  for i in range(1000):
    score=score+get_score(n,m,environ) ## increases the score for each time step
    if environ[x][y]==1:
      environ[x][y]=0
    x,y=bump_check(x,y,n,m,environ)
  return score

def bump_check(x,y,n,m,environ):
  if y<m-1:
    if environ[x][y+1]==2:
      x,y=bump_check(x,y+1,n,m,environ) ##checks column-wise for a bump
    else:
      return x,y+1
  elif y==m-1:
    if x<n-1:
      if environ[x+1][0]==2:
        x,y=bump_check(x+1,0,n,m,environ) ## moves to the next row
      else:
        return x+1,0
    elif x==n-1:
      return 0,0 ## restarts from the first square after reaching the end
  return x,y


def get_score(n,m,environ):
  score=0
  for r in range(n):
    for c in range(m):
      if environ[r][c]==0:
        score=score+1     ## increments score for each clean square
  return score


def environment():
  print("Enter the number of rows and columns")
  n=int(input())
  m=int(input())
  environ=[]

  #initializing the environment array to 0
  for i in range(n):
    row=[]
    for j in range(m):
      row.append(0)
    environ.append(row)

  #placing dirt in the environment- the probability of finding dirt for each square is 15%- the square containing dirt is represented by 1
  for i in range(n):
    for j in range(m):
      environ[i][j]=1 if random.random()<=0.15 else 0

  #placing obstacles in the environment- the probability of a square being an obstacle is 5%- the square containing an obstacle is represented by 2
  for i in range(n):
    for j in range(m):
      if environ[i][j]!=1:
        environ[i][j]=2 if random.random()<=0.05 else 0
  return n,m,environ


def print_env(n,m,environ):
  print("Environment--->")
  print()
  for i in range(n):
    for j in range(m):
      print(environ[i][j],end=' ')
    print()


def findAgentLoc(n,m,environ):
  for i in range(n):
    for j in range(m):
      if environ[i][j]==0 or environ[i][j]==1: ## agent's initial location can be a clean tile or a dirty tile
        agent_loc=[i,j]
        return agent_loc

n,m,environ=environment() ##creating the environment
print_env(n,m,environ)    ## printing the environment
agent_loc=findAgentLoc(n,m,environ)   ## making the first square without a bump as the agent location
x=agent_loc[0]
y=agent_loc[1]
point=perf_measure(n,m,x,y,environ) # calculating the score
ideal_score=(n*m)*1000  ## ideal score is the score we get if the squares are always clean
percentage=(point/ideal_score)*100
print("Performance score:",point)
print("Ideal score:",ideal_score)
print("Performance Percentage=",round(percentage,2),"%")