1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
|
colors = [['red', 'green', 'green', 'red' , 'red'],
['red', 'red', 'green', 'red', 'red'],
['red', 'red', 'green', 'green', 'red'],
['red', 'red', 'red', 'red', 'red']]
measurements = ['green', 'green', 'green' ,'green', 'green']
motions = [[0,0],[0,1],[1,0],[1,0],[0,1]]
sensor_right = 0.7
p_move = 0.8
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
equi_prob = 1./ (len(colors) * len(colors[0]))
mat_x = len(colors)
mat_y = len(colors[0])
p = [[equi_prob for x in range(mat_y)] for y in range(mat_x)]
def sense(p, Z):
q = p
s = 0
for i in range(mat_x):
for j in range(mat_y):
hit = (Z == colors[i][j])
q[i][j] = p[i][j] * (hit * sensor_right + (1-hit) * (1-sensor_right))
s += q[i][j]
for i in range(mat_x):
for j in range(mat_y):
q[i][j] = q[i][j] / s
return q
def moveRight(p):
q = [[equi_prob for x in range(mat_y)] for y in range(mat_x)]
for i in range(mat_x):
for j in range(mat_y):
k = i+1
if k >= mat_x:
k=0
q[k][j] = p[i][j]*(p_move) + p[k][j]*(1-p_move)
return q
def moveLeft(p):
q = [[equi_prob for x in range(mat_y)] for y in range(mat_x)]
for i in range(mat_x):
for j in range(mat_y):
k = i-1
if k < 0:
k=mat_x-1
q[k][j] = p[i][j]*(p_move) + p[k][j]*(1-p_move)
return q
def moveUp(p):
q = [[equi_prob for x in range(mat_y)] for y in range(mat_x)]
for i in range(mat_x):
for j in range(mat_y):
k = j-1
if k < 0:
k=mat_y-1
q[i][k] = p[i][j]*(p_move) + p[i][k]*(1-p_move)
return q
def moveDown(p):
q = [[equi_prob for x in range(mat_y)] for y in range(mat_x)]
for i in range(mat_x):
for j in range(mat_y):
k = j+1
if k >= mat_y:
k=0
q[i][k] = p[i][j]*(p_move) + p[i][k]*(1-p_move)
return q
def process(p):
for i in range(len(measurements)):
if motions[i][0] == 1:
p = moveRight(p)
if motions[i][0] == -1:
p = moveLeft(p)
if motions[i][1] == -1:
p = moveUp(p)
if motions[i][1] == 1:
p = moveDown(p)
p = sense(p, measurements[i])
return p
p = process(p)
#Your probability array must be printed
#with the following code.
#
show(p) |
AI-class : Algo pixel correspondence ( Unit 17-15 to 17-17 )
( Je n'ai pas trouvé de document clairs sur ce sujet )
Répondre avec citation
)
Envoyé par bertry
Il semble que le même type de cours interactifs soient programmés au MIT pour ce printemps : MITx 
Partager