def sonar_pass(self, loc):
(r,c) = loc
# self.grid[r][c] = True
d = dist.DDist({1: self.grid[r][c], 0: (1-self.grid[r][c])})
def prob_hit_given_wall(x):
if x == 1:
return dist.DDist({1.: 0.8, 0.: 0.2})
return dist.DDist({1.: 0.1, 0.: 0.9})
self.grid[r][c] = dist.bayes_rule(d, prob_hit_given_wall, 0).prob(1.)
def sonar_hit(self, loc):
(r,c) = loc
# self.grid[r][c] = False
d = dist.DDist({1:(self.grid[r][c]), 0:(1-self.grid[r][c])})
def prob_hit_given_wall(x): #x is 0 or 1
if x == 1:
return dist.DDist({1.: 0.8, 0.: 0.2})
return dist.DDist({1.: 0.1, 0.: 0.9})
self.grid[r][c] = dist.bayes_rule(d, prob_hit_given_wall, 1).prob(1.)
def sonar_hit(self, loc):
(r, c) = loc
# self.grid[r][c] = False
d = dist.DDist({1: (self.grid[r][c]), 0: (1 - self.grid[r][c])})
def prob_hit_given_wall(x): #x is 0 or 1
if x == 1:
return dist.DDist({1.: 0.8, 0.: 0.2})
return dist.DDist({1.: 0.1, 0.: 0.9})
self.grid[r][c] = dist.bayes_rule(d, prob_hit_given_wall, 1).prob(1.)
def sonar_pass(self, loc):
(r, c) = loc
# self.grid[r][c] = True
d = dist.DDist({1: self.grid[r][c], 0: (1 - self.grid[r][c])})
def prob_hit_given_wall(x):
if x == 1:
return dist.DDist({1.: 0.8, 0.: 0.2})
return dist.DDist({1.: 0.1, 0.: 0.9})
self.grid[r][c] = dist.bayes_rule(d, prob_hit_given_wall, 0).prob(1.)
