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$2\times 2$ Rubik’s Cube
Please solve the $2\times 2$ Rubik’s Cube by using FF planner. Here are 4 cases for you to verify the correctness of your programs. You should hand in 5 files, including a domain file (cube_domain.pddl) and 4 data files (cube1.pddl, cube2.pddl, cube3.pddl,cube4.pddl).
For more information about $2\times 2$ Rubik’s Cube, such as actions R, U and F, please refer to https://rubiks-cube-solver.com/2x2/.
做的时候遇到一些问题，网页版的PDDL Editor仅能跑出第一组数据，其他几组数据会超时。多次修改无效的情况下，我选择在 linux 系统下本地安装规划器完成这一项。依次运行下述指令，安装规划器。注意make的时候，如果提示缺少库文件，需要自己手动装。
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curl -O http://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz
tar zxvf FF-v2.3.tgz
cd FF-v2.3
make
将生成目录/FF-v2.3下的二进制文件ff拷贝到当前目录下，终端下依次执行如下四条指令运行规划并将结果写到对应文本文件内。
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./ff -o cube_domain.pddl -f cube1.pddl > cube1.txt
./ff -o cube_domain.pddl -f cube2.pddl > cube2.txt
./ff -o cube_domain.pddl -f cube3.pddl > cube3.txt
./ff -o cube_domain.pddl -f cube4.pddl > cube4.txt
以下是源代码和运行结果。其中，cube1 ?x ?y ?z指某一块方块在 x 轴、y 轴、z 轴三个方向上可视面的颜色，其它同。
cube_domain.pddl
(define
(domain cube)
(:predicates
(cube1 ?x ?y ?z)
(cube2 ?x ?y ?z)
(cube3 ?x ?y ?z)
(cube4 ?x ?y ?z)
(cube5 ?x ?y ?z)
(cube6 ?x ?y ?z)
(cube7 ?x ?y ?z)
(cube8 ?x ?y ?z)
)
(:action U:effect
(and
(forall
(?x ?y ?z)
(when
(cube5 ?x ?y ?z)
(and
(not
(cube5 ?x ?y ?z)
)
(cube7 ?y ?x ?z)
)
)
)
(forall
(?x ?y ?z)
(when
(cube7 ?x ?y ?z)
(and
(not
(cube7 ?x ?y ?z)
)
(cube8 ?y ?x ?z)
)
)
)
(forall
(?x ?y ?z)
(when
(cube8 ?x ?y ?z)
(and
(not
(cube8 ?x ?y ?z)
)
(cube6 ?y ?x ?z)
)
)
)
(forall
(?x ?y ?z)
(when
(cube6 ?x ?y ?z)
(and
(not
(cube6 ?x ?y ?z)
)
(cube5 ?y ?x ?z)
)
)
)
)
)
(:action Urev:effect
(and
(forall
(?x ?y ?z)
(when
(cube5 ?x ?y ?z)
(and
(not
(cube5 ?x ?y ?z)
)
(cube6 ?y ?x ?z)
)
)
)
(forall
(?x ?y ?z)
(when
(cube6 ?x ?y ?z)
(and
(not
(cube6 ?x ?y ?z)
)
(cube8 ?y ?x ?z)
)
)
)
(forall
(?x ?y ?z)
(when
(cube8 ?x ?y ?z)
(and
(not
(cube8 ?x ?y ?z)
)
(cube7 ?y ?x ?z)
)
)
)
(forall
(?x ?y ?z)
(when
(cube7 ?x ?y ?z)
(and
(not
(cube7 ?x ?y ?z)
)
(cube5 ?y ?x ?z)
)
)
)
)
)
(:action R:effect
(and
(forall
(?x ?y ?z)
(when
(cube2 ?x ?y ?z)
(and
(not
(cube2 ?x ?y ?z)
)
(cube6 ?x ?z ?y)
)
)
)
(forall
(?x ?y ?z)
(when
(cube6 ?x ?y ?z)
(and
(not
(cube6 ?x ?y ?z)
)
(cube8 ?x ?z ?y)
)
)
)
(forall
(?x ?y ?z)
(when
(cube8 ?x ?y ?z)
(and
(not
(cube8 ?x ?y ?z)
)
(cube4 ?x ?z ?y)
)
)
)
(forall
(?x ?y ?z)
(when
(cube4 ?x ?y ?z)
(and
(not
(cube4 ?x ?y ?z)
)
(cube2 ?x ?z ?y)
)
)
)
)
)
(:action Rrev:effect
(and
(forall
(?x ?y ?z)
(when
(cube2 ?x ?y ?z)
(and
(not
(cube2 ?x ?y ?z)
)
(cube4 ?x ?z ?y)
)
)
)
(forall
(?x ?y ?z)
(when
(cube4 ?x ?y ?z)
(and
(not
(cube4 ?x ?y ?z)
)
(cube8 ?x ?z ?y)
)
)
)
(forall
(?x ?y ?z)
(when
(cube8 ?x ?y ?z)
(and
(not
(cube8 ?x ?y ?z)
)
(cube6 ?x ?z ?y)
)
)
)
(forall
(?x ?y ?z)
(when
(cube6 ?x ?y ?z)
(and
(not
(cube6 ?x ?y ?z)
)
(cube2 ?x ?z ?y)
)
)
)
)
)
(:action F:effect
(and
(forall
(?x ?y ?z)
(when
(cube1 ?x ?y ?z)
(and
(not
(cube1 ?x ?y ?z)
)
(cube5 ?z ?y ?x)
)
)
)
(forall
(?x ?y ?z)
(when
(cube5 ?x ?y ?z)
(and
(not
(cube5 ?x ?y ?z)
)
(cube6 ?z ?y ?x)
)
)
)
(forall
(?x ?y ?z)
(when
(cube6 ?x ?y ?z)
(and
(not
(cube6 ?x ?y ?z)
)
(cube2 ?z ?y ?x)
)
)
)
(forall
(?x ?y ?z)
(when
(cube2 ?x ?y ?z)
(and
(not
(cube2 ?x ?y ?z)
)
(cube1 ?z ?y ?x)
)
)
)
)
)
(:action Frev:effect
(and
(forall
(?x ?y ?z)
(when
(cube1 ?x ?y ?z)
(and
(not
(cube1 ?x ?y ?z)
)
(cube2 ?z ?y ?x)
)
)
)
(forall
(?x ?y ?z)
(when
(cube2 ?x ?y ?z)
(and
(not
(cube2 ?x ?y ?z)
)
(cube6 ?z ?y ?x)
)
)
)
(forall
(?x ?y ?z)
(when
(cube6 ?x ?y ?z)
(and
(not
(cube6 ?x ?y ?z)
)
(cube5 ?z ?y ?x)
)
)
)
(forall
(?x ?y ?z)
(when
(cube5 ?x ?y ?z)
(and
(not
(cube5 ?x ?y ?z)
)
(cube1 ?z ?y ?x)
)
)
)
)
)
)
cube1.pddl
(define
(problem cube1)
(:domain cube)
(:objects Y W B G O R)
(:init
(cube1 B O Y)
(cube2 G R W)
(cube3 R Y B)
(cube4 O B W)
(cube5 O Y G)
(cube6 B R W)
(cube7 O W G)
(cube8 Y R G)
)
(:goal
(and
(cube1 R W B)
(cube2 O W B)
(cube3 R Y B)
(cube4 O Y B)
(cube5 R W G)
(cube6 O W G)
(cube7 R Y G)
(cube8 O Y G)
)
)
)
cube1.txt
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ff: parsing domain file
domain 'CUBE' defined
... done.
ff: parsing problem file
problem 'CUBE1' defined
... done.
Cueing down from goal distance: 10 into depth [1]
8 [1][2][3]
7 [1]
6 [1]
5 [1][2]
4 [1][2][3][4][5][6][7]
1 [1]
0
ff: found legal plan as follows
step 0: R
1: UREV
2: RREV
3: UREV
4: FREV
5: R
6: F
7: R
8: FREV
9: UREV
10: F
11: U
12: RREV
13: UREV
14: FREV
15: U
time spent: 0.02 seconds instantiating 6 easy, 0 hard action templates
0.00 seconds reachability analysis, yielding 1512 facts and 6 actions
0.00 seconds creating final representation with 1512 relevant facts
0.10 seconds building connectivity graph
0.04 seconds searching, evaluating 194 states, to a max depth of 7
0.16 seconds total time
cube2.pddl
(define
(problem cube2)
(:domain cube)
(:objects Y W B G O R)
(:init
(cube1 B R W)
(cube2 O B Y)
(cube3 W G R)
(cube4 R Y G)
(cube5 G O Y)
(cube6 B W O)
(cube7 R B Y)
(cube8 O G W)
)
(:goal
(and
(cube1 W B R)
(cube2 Y B R)
(cube3 W G R)
(cube4 Y G R)
(cube5 W B O)
(cube6 Y B O)
(cube7 W G O)
(cube8 Y G O)
)
)
)
cube2.txt
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ff: parsing domain file
domain 'CUBE' defined
... done.
ff: parsing problem file
problem 'CUBE2' defined
... done.
Cueing down from goal distance: 13 into depth [1][2]
12 [1]
11 [1][2]
10 [1]
8 [1][2]
6 [1][2][3][4][5]
5 [1][2][3][4][5][6]
4 [1][2][3][4][5]
3 [1][2][3][4][5][6][7][8][9]
2 [1][2][3]
Enforced Hill-climbing failed !
switching to Best-first Search now.
advancing to distance : 13
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2
1
0
ff: found legal plan as follows
step 0: RREV
1: FREV
2: U
3: FREV
4: U
5: FREV
6: R
7: FREV
8: U
9: RREV
10: FREV
11: R
12: UREV
13: F
14: U
15: RREV
16: UREV
17: FREV
18: U
19: F
20: UREV
21: RREV
22: F
23: R
24: FREV
25: R
26: F
27: R
28: FREV
29: RREV
30: UREV
31: R
32: U
33: RREV
34: U
35: F
36: UREV
37: FREV
38: UREV
39: RREV
40: F
41: R
42: U
43: R
44: UREV
45: RREV
time spent: 0.02 seconds instantiating 6 easy, 0 hard action templates
0.00 seconds reachability analysis, yielding 1512 facts and 6 actions
0.01 seconds creating final representation with 1512 relevant facts
0.09 seconds building connectivity graph
0.67 seconds searching, evaluating 8733 states, to a max depth of 9
0.79 seconds total time
cube3.pddl
(define
(problem cube3)
(:domain cube)
(:objects Y W B G O R)
(:init
(cube1 R Y G)
(cube2 O G W)
(cube3 Y O B)
(cube4 W B R)
(cube5 O W B)
(cube6 Y O G)
(cube7 W R G)
(cube8 R Y B)
)
(:goal
(and
(cube1 Y R B)
(cube2 W R B)
(cube3 Y O B)
(cube4 W O B)
(cube5 Y R G)
(cube6 W R G)
(cube7 Y O G)
(cube8 W O G)
)
)
)
cube3.txt
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ff: parsing domain file
domain 'CUBE' defined
... done.
ff: parsing problem file
problem 'CUBE3' defined
... done.
Cueing down from goal distance: 10 into depth [1]
9 [1][2]
8 [1]
7 [1][2][3][4]
5 [1][2][3][4]
4 [1][2][3][4][5][6][7][8][9]
3 [1]
2 [1][2]
Enforced Hill-climbing failed !
switching to Best-first Search now.
advancing to distance : 10
9
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5
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1
0
ff: found legal plan as follows
step 0: UREV
1: R
2: UREV
3: RREV
4: UREV
5: FREV
6: R
7: FREV
8: RREV
9: U
10: R
11: F
12: R
13: FREV
14: RREV
15: U
16: FREV
17: UREV
18: F
19: UREV
20: RREV
21: F
22: R
23: FREV
24: R
25: F
26: R
27: FREV
28: RREV
29: UREV
30: R
31: U
32: RREV
33: U
34: F
35: UREV
36: FREV
37: UREV
38: RREV
39: F
40: R
41: U
42: R
43: UREV
44: RREV
time spent: 0.00 seconds instantiating 6 easy, 0 hard action templates
0.00 seconds reachability analysis, yielding 1512 facts and 6 actions
0.02 seconds creating final representation with 1512 relevant facts
0.08 seconds building connectivity graph
0.13 seconds searching, evaluating 1710 states, to a max depth of 9
0.23 seconds total time
cube4.pddl
(define
(problem cube4)
(:domain cube)
(:objects Y W B G O R)
(:init
(cube1 B O Y)
(cube2 W O G)
(cube3 B O W)
(cube4 R G W)
(cube5 G Y R)
(cube6 Y O G)
(cube7 B Y R)
(cube8 R B W)
)
(:goal
(and
(cube1 B R W)
(cube2 G R W)
(cube3 B O W)
(cube4 G O W)
(cube5 B R Y)
(cube6 G R Y)
(cube7 B O Y)
(cube8 G O Y)
)
)
)
cube4.txt
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ff: parsing domain file
domain 'CUBE' defined
... done.
ff: parsing problem file
problem 'CUBE4' defined
... done.
Cueing down from goal distance: 14 into depth [1]
12 [1][2]
11 [1]
9 [1]
7 [1][2][3]
6 [1][2]
5 [1][2][3][4]
4 [1][2][3][4][5][6][7]
2 [1][2][3]
Enforced Hill-climbing failed !
switching to Best-first Search now.
advancing to distance : 14
12
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9
7
6
5
2
1
0
ff: found legal plan as follows
step 0: FREV
1: R
2: FREV
3: R
4: FREV
5: RREV
6: UREV
7: F
8: R
9: U
10: R
11: F
12: RREV
13: FREV
14: R
15: F
16: RREV
17: FREV
18: R
19: F
20: U
21: FREV
22: UREV
23: F
24: R
25: F
26: RREV
27: FREV
28: R
29: FREV
30: UREV
31: RREV
32: U
33: FREV
34: UREV
35: R
36: U
37: F
38: U
39: FREV
40: UREV
time spent: 0.00 seconds instantiating 6 easy, 0 hard action templates
0.01 seconds reachability analysis, yielding 1512 facts and 6 actions
0.00 seconds creating final representation with 1512 relevant facts
0.08 seconds building connectivity graph
0.15 seconds searching, evaluating 2462 states, to a max depth of 7
0.24 seconds total time
Diagnosing by Bayesian Networks
终端运行下述指令，将结果写到文本文件中。
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python Diagnosing.py > Diagnosing.txt
Variables and their domais
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# (1)PatientAge:['0-30','31-65','65+']
# (2)CTScanResult:['Ischemic Stroke','Hemmorraghic Stroke']
# (3)MRIScanResult: ['Ischemic Stroke','Hemmorraghic Stroke']
# (4)StrokeType: ['Ischemic Stroke','Hemmorraghic Stroke', 'Stroke Mimic']
# (5)Anticoagulants: ['Used','Not used']
# (6)Mortality:['True', 'False']
# (7)Disability: ['Negligible', 'Moderate', 'Severe']
CPTS
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# (1)
[PatientAge]
['0-30', 0.10],
['31-65', 0.30],
['65+', 0.60]
# (2)
[CTScanResult]
['Ischemic Stroke',0.7],
[ 'Hemmorraghic Stroke',0.3]
# (3)
[MRIScanResult]
['Ischemic Stroke',0.7],
[ 'Hemmorraghic Stroke',0.3]
# (4)
[Anticoagulants]
[Used',0.5],
['Not used',0.5]
# (5)
[CTScanResult, MRIScanResult,StrokeType])
['Ischemic Stroke','Ischemic Stroke','Ischemic Stroke',0.8],
['Ischemic Stroke','Hemmorraghic Stroke','Ischemic Stroke',0.5],
[ 'Hemmorraghic Stroke','Ischemic Stroke','Ischemic Stroke',0.5],
[ 'Hemmorraghic Stroke','Hemmorraghic Stroke','Ischemic Stroke',0],
['Ischemic Stroke','Ischemic Stroke','Hemmorraghic Stroke',0],
['Ischemic Stroke','Hemmorraghic Stroke','Hemmorraghic Stroke',0.4],
[ 'Hemmorraghic Stroke','Ischemic Stroke','Hemmorraghic Stroke',0.4],
[ 'Hemmorraghic Stroke','Hemmorraghic Stroke','Hemmorraghic Stroke',0.9],
['Ischemic Stroke','Ischemic Stroke','Stroke Mimic',0.2],
['Ischemic Stroke','Hemmorraghic Stroke','Stroke Mimic',0.1],
[ 'Hemmorraghic Stroke','Ischemic Stroke','Stroke Mimic',0.1],
[ 'Hemmorraghic Stroke','Hemmorraghic Stroke','Stroke Mimic',0.1],
# (6)
[StrokeType, Anticoagulants, Mortality]
['Ischemic Stroke', 'Used', 'False',0.28],
['Hemmorraghic Stroke', 'Used', 'False',0.99],
['Stroke Mimic', 'Used', 'False',0.1],
['Ischemic Stroke','Not used', 'False',0.56],
['Hemmorraghic Stroke', 'Not used', 'False',0.58],
['Stroke Mimic', 'Not used', 'False',0.05],
['Ischemic Stroke', 'Used' ,'True',0.72],
['Hemmorraghic Stroke', 'Used', 'True',0.01],
['Stroke Mimic', 'Used', 'True',0.9],
['Ischemic Stroke', 'Not used' ,'True',0.44],
['Hemmorraghic Stroke', 'Not used', 'True',0.42 ],
['Stroke Mimic', 'Not used', 'True',0.95]
# (7)
[StrokeType, PatientAge, Disability]
['Ischemic Stroke', '0-30','Negligible', 0.80],
['Hemmorraghic Stroke', '0-30','Negligible', 0.70],
['Stroke Mimic', '0-30', 'Negligible',0.9],
['Ischemic Stroke', '31-65','Negligible', 0.60],
['Hemmorraghic Stroke', '31-65','Negligible', 0.50],
['Stroke Mimic', '31-65', 'Negligible',0.4],
['Ischemic Stroke', '65+' , 'Negligible',0.30],
['Hemmorraghic Stroke', '65+' , 'Negligible',0.20],
['Stroke Mimic', '65+' , 'Negligible',0.1],
['Ischemic Stroke', '0-30' ,'Moderate',0.1],
['Hemmorraghic Stroke', '0-30' ,'Moderate',0.2],
['Stroke Mimic', '0-30' ,'Moderate',0.05],
['Ischemic Stroke', '31-65','Moderate',0.3],
['Hemmorraghic Stroke', '31-65','Moderate',0.4],
['Stroke Mimic', '31-65','Moderate',0.3],
['Ischemic Stroke', '65+' ,'Moderate',0.4],
['Hemmorraghic Stroke', '65+' ,'Moderate',0.2],
['Stroke Mimic', '65+' ,'Moderate',0.1],
['Ischemic Stroke', '0-30' ,'Severe',0.1],
['Hemmorraghic Stroke', '0-30' ,'Severe',0.1],
['Stroke Mimic', '0-30' ,'Severe',0.05],
['Ischemic Stroke', '31-65','Severe',0.1],
['Hemmorraghic Stroke', '31-65','Severe',0.1],
['Stroke Mimic', '31-65','Severe',0.3],
['Ischemic Stroke', '65+' ,'Severe',0.3],
['Hemmorraghic Stroke', '65+' ,'Severe',0.6],
['Stroke Mimic', '65+' ,'Severe',0.8]
Calculation
Please implement the VE algorithm (C++ or Python) to calculate the following probability value:
- p1 = P(Mortality=’True’ $\land$ CTScanResult=’Ischemic Stroke’ $\mid$ PatientAge=’31-65’ )
- p2 = P(Disability=’Moderate’ $\land$ CTScanResult=’Hemmorraghic Stroke’ $\mid$ PatientAge=’65+’ $\land$ MRIScanResult=’Hemmorraghic Stroke’)
- p3 = P(StrokeType=’Hemmorraghic Stroke’ $\mid$ PatientAge=’65+’ $\land$ CTScanResult=’Hemmorraghic Stroke’ $\land$ MRIScanResult=’Ischemic Stroke’)
- p4 = P(Anticoagulants=’Used’ $\mid$ PatientAge=’31-65’)
- p5 = P(Disability=’Negligible’)
CodeDiagnosing.py
为了简化代码实现，下面变量取值的0、1、2代表该变量取值在对应 domain 中的下标。在之前做过的实验上改改就成功了。
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class VariableElimination:
@staticmethod
def inference(factorList, queryVariables,
orderedListOfHiddenVariables, evidenceList):
for ev in evidenceList:
# Your code here
for factor in factorList:
if ev in factor.varList:
if len(factor.varList) > 1:
factorList.append(
factor.restrict(ev, evidenceList[ev]))
factorList.remove(factor)
for var in orderedListOfHiddenVariables:
# Your code here
new_var_list = []
for e in factorList:
if var in e.varList:
new_var_list.append(e)
first = True
for e in new_var_list:
for i in factorList:
if i.name == e.name:
factorList.remove(i)
if first:
new_var = e
first = False
else:
new_var = new_var.multiply(e)
factorList.append(new_var.sumout(var))
print("RESULT:")
res = factorList[0]
for factor in factorList[1:]:
res = res.multiply(factor)
total = sum(res.cpt.values())
res.cpt = {k: v/total for k, v in res.cpt.items()}
res.printInf()
@staticmethod
def printFactors(factorList):
for factor in factorList:
factor.printInf()
class Util:
@staticmethod
def to_binary(num, len):
return format(num, '0' + str(len) + 'b')
class Node:
def __init__(self, name, var_list):
self.name = name
self.varList = var_list
self.cpt = {}
def setCpt(self, cpt):
self.cpt = cpt
def printInf(self):
print("Name = " + self.name)
print(" vars " + str(self.varList))
for key in self.cpt:
print(" key: " + key + " val : " + str(self.cpt[key]))
print()
def multiply(self, factor):
"""function that multiplies with another factor"""
# Your code here
new_cpt = {}
new_var_list = list(self.varList)
idx1 = []
idx2 = []
for var in factor.varList:
if var in new_var_list:
idx1.append(self.varList.index(var))
idx2.append(factor.varList.index(var))
else:
new_var_list.append(var)
for k1, v1 in self.cpt.items():
for k2, v2 in factor.cpt.items():
flag = True
for i in range(len(idx1)):
if k1[idx1[i]] != k2[idx2[i]]:
flag = False
break
if flag:
new_key = k1
for i in range(len(k2)):
if i not in idx2:
new_key += k2[i]
new_cpt[new_key] = v1 * v2
new_node = Node("f" + str(new_var_list), new_var_list)
new_node.setCpt(new_cpt)
return new_node
def sumout(self, variable):
"""function that sums out a variable given a factor"""
# Your code here
new_cpt = {}
new_var_list = list(self.varList)
new_var_list.remove(variable)
idx = self.varList.index(variable)
for k, v in self.cpt.items():
tmp = k[:idx] + k[idx+1:]
if tmp not in new_cpt.keys():
new_cpt[tmp] = v
else:
new_cpt[tmp] += v
new_node = Node("f" + str(new_var_list), new_var_list)
new_node.setCpt(new_cpt)
return new_node
def restrict(self, variable, value):
"""function that restricts a variable to some value
in a given factor"""
# Your code here
new_cpt = {}
new_var_list = list(self.varList)
new_var_list.remove(variable)
idx = self.varList.index(variable)
value = str(value)
for k, v in self.cpt.items():
if k[idx] == value:
new_cpt[k[:idx] + k[idx+1:]] = v
new_node = Node("f" + str(new_var_list), new_var_list)
new_node.setCpt(new_cpt)
return new_node
PatientAge = Node("PatientAge", ["PatientAge"])
MRIScanResult = Node("MRIScanResult", ["MRIScanResult"])
CTScanResult = Node("CTScanResult", ["CTScanResult"])
Anticoagulants = Node("Anticoagulants", ["Anticoagulants"])
StrokeType = Node("StrokeType", ["StrokeType",
"CTScanResult", "MRIScanResult"])
Disability = Node("Disability", ["Disability", "StrokeType", "PatientAge"])
Mortality = Node("Mortality", ["Mortality", "StrokeType", "Anticoagulants"])
PatientAge.setCpt({'0': 0.10, '1': 0.30, '2': 0.60})
CTScanResult.setCpt({'0': 0.7, '1': 0.3})
MRIScanResult.setCpt({'0': 0.7, '1': 0.3})
Anticoagulants.setCpt({'0': 0.5, '1': 0.5})
StrokeType.setCpt({'000': 0.8, '001': 0.5,
'010': 0.5, '011': 0.0,
'100': 0.0, '101': 0.4,
'110': 0.4, '111': 0.9,
'200': 0.2, '201': 0.1,
'210': 0.1, '211': 0.1})
Mortality.setCpt({'000': 0.56, '001': 0.28,
'010': 0.58, '011': 0.99,
'020': 0.05, '021': 0.10,
'100': 0.44, '101': 0.72,
'110': 0.42, '111': 0.01,
'120': 0.95, '121': 0.90})
Disability.setCpt({'000': 0.80, '010': 0.70, '020': 0.90,
'001': 0.60, '011': 0.50, '021': 0.40,
'002': 0.30, '012': 0.20, '022': 0.10,
'100': 0.10, '110': 0.20, '120': 0.05,
'101': 0.30, '111': 0.40, '121': 0.30,
'102': 0.40, '112': 0.20, '122': 0.10,
'200': 0.10, '210': 0.10, '220': 0.05,
'201': 0.10, '211': 0.10, '221': 0.30,
'202': 0.30, '212': 0.60, '222': 0.80})
print("P1 **********************")
VariableElimination.inference(
[PatientAge, MRIScanResult, CTScanResult, Anticoagulants, StrokeType,
Disability, Mortality], ['Mortality', 'CTScanResult'],
['MRIScanResult', 'Anticoagulants', 'StrokeType', 'Disability'],
{'PatientAge': 1}
)
print("P2 **********************")
VariableElimination.inference(
[PatientAge, MRIScanResult, CTScanResult, Anticoagulants, StrokeType,
Disability, Mortality], ['Disability', 'CTScanResult'],
['Anticoagulants', 'StrokeType', 'Mortality'],
{'PatientAge': 2, 'MRIScanResult': 1}
)
print("P3 **********************")
VariableElimination.inference(
[PatientAge, MRIScanResult, CTScanResult, Anticoagulants,
StrokeType, Disability, Mortality], ['StrokeType'],
['Disability', 'Anticoagulants', 'Mortality'],
{'PatientAge': 2, 'CTScanResult': 1, 'MRIScanResult': 0}
)
print("P4 **********************")
VariableElimination.inference(
[PatientAge, MRIScanResult, CTScanResult, Anticoagulants,
StrokeType, Disability, Mortality], ['Anticoagulants'],
['MRIScanResult', 'CTScanResult', 'StrokeType', 'Disability', 'Mortality'],
{'PatientAge': 1}
)
print("P5 **********************")
VariableElimination.inference(
[PatientAge, MRIScanResult, CTScanResult, Anticoagulants,
StrokeType, Disability, Mortality], ['Disability'],
['PatientAge', 'MRIScanResult', 'CTScanResult',
'Anticoagulants', 'StrokeType', 'Mortality'],
{}
)
ResultDiagnosing.txt
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P1 **********************
RESULT:
Name = f['CTScanResult', 'Mortality']
vars ['CTScanResult', 'Mortality']
key: 00 val : 0.283605
key: 01 val : 0.41639499999999996
key: 10 val : 0.17587499999999998
key: 11 val : 0.12412500000000001
P2 **********************
RESULT:
Name = f['CTScanResult', 'Disability']
vars ['CTScanResult', 'Disability']
key: 00 val : 0.16800000000000004
key: 01 val : 0.20300000000000004
key: 02 val : 0.329
key: 10 val : 0.057
key: 11 val : 0.057
key: 12 val : 0.18600000000000005
P3 **********************
RESULT:
Name = f['StrokeType']
vars ['StrokeType']
key: 0 val : 0.5000000000000001
key: 1 val : 0.4
key: 2 val : 0.10000000000000002
P4 **********************
RESULT:
Name = f['Anticoagulants']
vars ['Anticoagulants']
key: 0 val : 0.5000000000000001
key: 1 val : 0.49999999999999994
P5 **********************
RESULT:
Name = f['Disability']
vars ['Disability']
key: 0 val : 0.38977
key: 1 val : 0.292515
key: 2 val : 0.317715