udacity-ai-1.py

colors = [['red', 'green', 'green', 'red' , 'red'],
          ['red', 'red', 'green', 'red', 'red'],
          ['red', 'red', 'green', 'green', 'red'],
          ['red', 'red', 'red', 'red', 'red']]
# for testing
colors =  [['red',   'green']
         , ['green', 'green']]

measurements = ['red', 'green', 'green' ,'green', 'green']


motions = [[0,0],[0,1],[1,0],[1,0],[0,1]]

sensor_right = 0.7
# for testing

p_move = 0.8
p_undershoot = 0.1
p_overshoot = 0.1
# for testing

def show(p):
  for i in range(len(p)):
    print p[i]

#DO NOT USE IMPORT
#ENTER CODE BELOW HERE
#ANY CODE ABOVE WILL CAUSE
#HOMEWORK TO BE GRADED
#INCORRECT

rows = len(colors)
cols = len(colors[0])

# normalized, uniform distribution
p = [0] * rows
for i in range(rows):
  p[i] = [1. / ( rows * cols )] * cols

def sum2d(p):
  s = 0
  for row in range(len(p)):
    for col in range(len(p[0])):
      s = s + p[row][col]
  return s

def normalize(p):
  s = sum2d(p)
  q = []
  s = s if s != 0 else 1
  for i in range(len(p)):
    row = []
    for j in range(len(p[0])):
      row.append(p[i][j] / s)
    q.append(row)
  return q

def move(p, m):
  # move the robot
  for row in range(rows):
    for col in range(cols):
      pass
      # todo: write move code
  return p

# p is the world position probability distribution
# Z is the senser reading
# does not normalize
def sense(p, Z):
  q = []
  for i in range(rows):
    row = []
    for j in range(cols):
      row.append( p[i][j] * ( p_move if colors[i][j] is Z else 1 - p_move ) )
    q.append(row)
  return q

# p = move(p, motions[0])
print "show p"
show(p)
p = normalize(sense(p, measurements[0]))
print "show p"
show(p)

#Your probability array must be printed 
#with the following code.

print "show p"
show(p)