def _computeNoisyPositions(self, state):
"""
Compute a noisy position from true ghosts positions.
XXX: DO NOT MODIFY THAT FUNCTION !!!
Doing so will result in a 0 grade.
"""
positions = state.getGhostPositions()
w = self.args.w
w2 = 2 * w + 1
div = float(w2 * w2)
new_positions = []
for p in positions:
(x, y) = p
dist = util.Counter()
for i in range(x - w, x + w + 1):
for j in range(y - w, y + w + 1):
dist[(i, j)] = 1.0 / div
dist.normalize()
new_positions.append(util.chooseFromDistribution(dist))
return new_positions
def update_belief_state(self, evidences, pacman_position):
"""
Given a list of (noised) distances from pacman to ghosts,
returns a list of belief states about ghosts positions
Arguments:
----------
- `evidences`: list of distances between
pacman and ghosts at state x_{t}
where 't' is the current time step
- `pacman_position`: 2D coordinates position
of pacman at state x_{t}
where 't' is the current time step
Return:
-------
- A list of Z belief states at state x_{t}
as N*M numpy mass probability matrices
where N and M are respectively width and height
of the maze layout and Z is the number of ghosts.
N.B. : [0,0] is the bottom left corner of the maze
"""
beliefStates = self.beliefGhostStates
# XXX: Your code here
height = self.walls.height
width = self.walls.width
z = 0
while z < len(beliefStates):
for m in range(0, width):
for n in range(0, height):
if (not self.walls[m][n]) and (not pacman_position
== (m, n)):
currDistance = util.manhattanDistance(
pacman_position, (m, n))
sigma = np.sqrt(self.sensor_variance)
mu = currDistance
sModel = (1 / (np.sqrt(2 * np.pi) * sigma)) * np.exp(
-np.power(evidences[z] - mu, 2.) /
(2 * np.power(sigma, 2.)))
tModel = util.Counter()
if self.ghost_type == "scared": k = 3
if self.ghost_type == "afraid": k = 1
if self.ghost_type == "confused": k = 0
for i in range(0, width):
for j in range(0, height):
succDistance = util.manhattanDistance(
pacman_position, (i, j))
if ((m + 1 == i and n == j) or
(m - 1 == i and n == j) or
(m == i and n + 1 == j) or
(m == i and n - 1 == j)) and (
not self.walls[i][j]):
if succDistance > currDistance:
tModel[(i, j)] = np.power(2, k)
else:
tModel[(i, j)] = 1
else:
tModel[(i, j)] = 0
tModel.normalize()
epsilon = 0
for i in range(0, width):
for j in range(0, height):
epsilon = epsilon + tModel[
(i, j)] * beliefStates[z][i][j]
beliefStates[z][m][n] = sModel * epsilon
else:
beliefStates[z][m][n] = 0
#On peut mtn calculer alpha.
alpha = np.sum(beliefStates[z])
beliefStates[z] = np.divide(beliefStates[z], alpha)
z = z + 1
# XXX: End of your code
self.beliefGhostStates = beliefStates
return beliefStates