-
Notifications
You must be signed in to change notification settings - Fork 0
/
LetsgoObservation.py
151 lines (134 loc) · 6.98 KB
/
LetsgoObservation.py
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
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
import numpy as np
from Parameters import Factor
import LetsgoSerializer as ls
from SparseBayes import SparseBayes
from GlobalConfig import GetConfig
config = GetConfig()
sbr_models = {'I:bn':ls.load_model('_calibrated_confidence_score_sbr_bn.model'),\
'I:dp':ls.load_model('_calibrated_confidence_score_sbr_dp.model'),\
'I:ap':ls.load_model('_calibrated_confidence_score_sbr_ap.model'),\
'I:tt':ls.load_model('_calibrated_confidence_score_sbr_tt.model'),\
'yes':ls.load_model('_calibrated_confidence_score_sbr_yes.model'),\
'no':ls.load_model('_calibrated_confidence_score_sbr_no.model'),\
'multi2':ls.load_model('_calibrated_confidence_score_sbr_multi2.model'),\
'multi3':ls.load_model('_calibrated_confidence_score_sbr_multi3.model'),\
'multi4':ls.load_model('_calibrated_confidence_score_sbr_multi4.model')
}
def Calibrate(sbr_models,ua,cs):
def dist_squared(X,Y):
nx = X.shape[0]
ny = Y.shape[0]
return np.dot(np.atleast_2d(np.sum((X**2),1)).T,np.ones((1,ny))) + \
np.dot(np.ones((nx,1)),np.atleast_2d(np.sum((Y**2),1))) - 2*np.dot(X,Y.T);
def basis_vector(X,x,basisWidth):
BASIS = np.exp(-dist_squared(x,X)/(basisWidth**2))
return BASIS
sbr_model = None
if len(ua) == 1:
try:
sbr_model = sbr_models[ua[0]]
except:
# print 'No SBR model for %s'%ua[0]
pass
elif len(ua) == 2:
sbr_model = sbr_models['multi2']
elif len(ua) == 3:
sbr_model = sbr_models['multi3']
else:
sbr_model = sbr_models['multi4']
if sbr_model:
calibrated_cs = np.dot(basis_vector(sbr_model['data_points'],\
np.array([[cs]]),\
sbr_model['basis_width']),\
sbr_model['weights'])[0,0]
if calibrated_cs < 0:
calibrated_cs = 0
return float(calibrated_cs)
return cs
#def getObsFactor(turn,ceiling=1.0,p=1.5,use_cs=False,cs_weight=0.8):
def getObsFactor(turn,ceiling=1.0,p=1.0,use_cs=False,cs_weight=0.99999,domain=None):
# if turn['UA'] == ['non-understanding']:
# eps += 0.1
if domain:
fUAtt_Ott = Factor(('UA_tt',),domain=domain)
else:
fUAtt_Ott = Factor(('UA_tt',))
if config.getboolean('UserSimulation','extendedUserActionSet'):
ua_types = [['I:bn','I:dp','I:ap','I:tt'],\
['I:bn','I:dp','I:ap'],\
['I:dp','I:ap','I:tt'],\
['I:bn','I:dp','I:tt'],\
['I:dp','I:ap'],\
['I:dp','I:tt'],\
['I:ap','I:tt'],\
['I:bn','I:tt'],\
['I:bn'],\
['I:dp'],\
['I:ap'],\
['I:tt'],\
['yes'],\
['no'],\
['no','I:bn'],\
['no','I:dp'],\
['no','I:ap'],\
['no','I:tt'],\
['non-understanding']]
else:
ua_types = [['I:bn','I:dp','I:ap','I:tt'],\
['I:bn','I:dp','I:ap'],\
['I:dp','I:ap','I:tt'],\
['I:bn','I:dp','I:tt'],\
['I:dp','I:ap'],\
['I:bn','I:tt'],\
['I:bn'],\
['I:dp'],\
['I:ap'],\
['I:tt'],\
['yes'],\
['no'],\
['non-understanding']]
# print turn['UA']
# print turn['CS']
if turn['UA'] == ['non-understanding']:
for ua in ua_types:
if ua == ['non-understanding']:
fUAtt_Ott[{'UA_tt':','.join(sorted(ua))}] = 1.0
else:
fUAtt_Ott[{'UA_tt':','.join(sorted(ua))}] = 0
return fUAtt_Ott
if use_cs:
cs = turn['CS']
# print turn['UA']
# print type(cs),cs
cs = Calibrate(sbr_models,turn['UA'],cs)
# print type(cs),cs
else: cs = 0.99
cs = cs*cs_weight
eps = (1.0 - cs)/len(ua_types)
for ua in ua_types:
if ua != ['non-understanding']:
fUAtt_Ott[{'UA_tt':','.join(sorted(ua))}] = \
min(\
cs*(float(len(set(ua).intersection(set(turn['UA']))))/len(set(ua).union(set(turn['UA']))))**p,\
ceiling)\
+ eps
else:
fUAtt_Ott[{'UA_tt':','.join(sorted(ua))}] = 0
# print set(ua).intersection(set(turn['UA']))
# print set(ua).union(set(turn['UA']))
# print float(len(set(ua).intersection(set(turn['UA']))))/len(set(ua).union(set(turn['UA'])))
# fUAtt_Ott[{'UA_tt':'I:bn,I:dp,I:ap,I:tt'}] = min(turn['CS']*(len(set(['I:bn','I:dp','I:ap','I:tt']).intersection(set(turn['UA'])))/len(set(['I:bn','I:dp','I:ap','I:tt']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'I:bn,I:dp,I:ap'}] = min(turn['CS']*(len(set(['I:bn','I:dp','I:ap']).intersection(set(turn['UA'])))/len(set(['I:bn','I:dp','I:ap']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'I:dp,I:ap,I:tt'}] = min(turn['CS']*(len(set(['I:dp','I:ap','I:tt']).intersection(set(turn['UA'])))/len(set(['I:dp','I:ap','I:tt']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'I:bn,I:dp,I:tt'}] = min(turn['CS']*(len(set(['I:bn','I:dp','I:tt']).intersection(set(turn['UA'])))/len(set(['I:bn','I:dp','I:tt']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'I:dp,I:ap'}] = min(turn['CS']*(len(set(['I:dp','I:ap']).intersection(set(turn['UA'])))/len(set(['I:dp','I:ap']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'I:bn,I:tt'}] = min(turn['CS']*(len(set(['I:bn','I:tt']).intersection(set(turn['UA'])))/len(set(['I:bn','I:tt']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'I:bn'}] = min(turn['CS']*(len(set(['I:bn']).intersection(set(turn['UA'])))/len(set(['I:bn']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'I:dp'}] = min(turn['CS']*(len(set(['I:dp']).intersection(set(turn['UA'])))/len(set(['I:dp']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'I:ap'}] = min(turn['CS']*(len(set(['I:ap']).intersection(set(turn['UA'])))/len(set(['I:ap']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'I:tt'}] = min(turn['CS']*(len(set(['I:tt']).intersection(set(turn['UA'])))/len(set(['I:tt']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'yes'}] = min(turn['CS']*(len(set(['yes']).intersection(set(turn['UA'])))/len(set(['yes']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'no'}] = min(turn['CS']*(len(set(['no']).intersection(set(turn['UA'])))/len(set(['no']).union(set(turn['UA']))))**p,ceiling) + eps
# fUAtt_Ott[{'UA_tt':'non-understanding'}] = min(turn['CS']*(len(set(['non-understanding']).intersection(set(turn['UA'])))/len(set(['non-understanding']).union(set(turn['UA']))))**p,ceiling) + eps
# print fUAtt_Ott
return fUAtt_Ott