OPEN COURSE : HORA 6 OPEN COURSE

# The function localize takes the following arguments:

# colors:

# 2D list, each entry either 'R' (for red cell) or 'G' (for green cell)

# measurements:

# list of measurements taken by the robot, each entry either 'R' or 'G'

# motions:

# list of actions taken by the robot, each entry of the form [dy,dx],

# where dx refers to the change in the x-direction (positive meaning

# movement to the right) and dy refers to the change in the y-direction

# (positive meaning movement downward)

# NOTE: the *first* coordinate is change in y; the *second* coordinate is

# change in x

# sensor_right:

# float between 0 and 1, giving the probability that any given

# measurement is correct; the probability that the measurement is

# incorrect is 1-sensor_right

# p_move:

# float between 0 and 1, giving the probability that any given movement

# command takes place; the probability that the movement command fails

# (and the robot remains still) is 1-p_move; the robot will NOT overshoot

# its destination in this exercise

# The function should RETURN (not just show or print) a 2D list (of the same

# dimensions as colors) that gives the probabilities that the robot occupies

# each cell in the world.

# Compute the probabilities by assuming the robot initially has a uniform

# probability of being in any cell.

# Also assume that at each step, the robot:

# 1) first makes a movement,

# 2) then takes a measurement.

# Motion:

# [0,0] - stay

# [0,1] - right

# [0,-1] - left

# [1,0] - down

# [-1,0] - up

def localize(colors,measurements,motions,sensor_right,p_move):

# initializes p to a uniform distribution over a grid of the same dimensions as colors

pinit = 1.0 / float(len(colors)) / float(len(colors[0]))

p = [[pinit for row in range(len(colors[0]))] for col in range(len(colors))]

# >>> Insert your code here <<<

for k in range (0, (len(measurements))):

s=0

q=[]

for i in range (0, (len(p))):

q.append([])