forked from faif/python-patterns
-
Notifications
You must be signed in to change notification settings - Fork 0
/
blackboard.py
130 lines (101 loc) · 3.79 KB
/
blackboard.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
"""
@author: Eugene Duboviy <[email protected]> | github.com/duboviy
In Blackboard pattern several specialised sub-systems (knowledge sources)
assemble their knowledge to build a possibly partial or approximate solution.
In this way, the sub-systems work together to solve the problem,
where the solution is the sum of its parts.
https://en.wikipedia.org/wiki/Blackboard_system
"""
import abc
import random
class Blackboard:
def __init__(self):
self.experts = []
self.common_state = {
"problems": 0,
"suggestions": 0,
"contributions": [],
"progress": 0, # percentage, if 100 -> task is finished
}
def add_expert(self, expert):
self.experts.append(expert)
class Controller:
def __init__(self, blackboard):
self.blackboard = blackboard
def run_loop(self):
"""
This function is a loop that runs until the progress reaches 100.
It checks if an expert is eager to contribute and then calls its contribute method.
"""
while self.blackboard.common_state["progress"] < 100:
for expert in self.blackboard.experts:
if expert.is_eager_to_contribute:
expert.contribute()
return self.blackboard.common_state["contributions"]
class AbstractExpert(metaclass=abc.ABCMeta):
def __init__(self, blackboard):
self.blackboard = blackboard
@property
@abc.abstractmethod
def is_eager_to_contribute(self):
raise NotImplementedError("Must provide implementation in subclass.")
@abc.abstractmethod
def contribute(self):
raise NotImplementedError("Must provide implementation in subclass.")
class Student(AbstractExpert):
@property
def is_eager_to_contribute(self):
return True
def contribute(self):
self.blackboard.common_state["problems"] += random.randint(1, 10)
self.blackboard.common_state["suggestions"] += random.randint(1, 10)
self.blackboard.common_state["contributions"] += [self.__class__.__name__]
self.blackboard.common_state["progress"] += random.randint(1, 2)
class Scientist(AbstractExpert):
@property
def is_eager_to_contribute(self):
return random.randint(0, 1)
def contribute(self):
self.blackboard.common_state["problems"] += random.randint(10, 20)
self.blackboard.common_state["suggestions"] += random.randint(10, 20)
self.blackboard.common_state["contributions"] += [self.__class__.__name__]
self.blackboard.common_state["progress"] += random.randint(10, 30)
class Professor(AbstractExpert):
@property
def is_eager_to_contribute(self):
return True if self.blackboard.common_state["problems"] > 100 else False
def contribute(self):
self.blackboard.common_state["problems"] += random.randint(1, 2)
self.blackboard.common_state["suggestions"] += random.randint(10, 20)
self.blackboard.common_state["contributions"] += [self.__class__.__name__]
self.blackboard.common_state["progress"] += random.randint(10, 100)
def main():
"""
>>> blackboard = Blackboard()
>>> blackboard.add_expert(Student(blackboard))
>>> blackboard.add_expert(Scientist(blackboard))
>>> blackboard.add_expert(Professor(blackboard))
>>> c = Controller(blackboard)
>>> contributions = c.run_loop()
>>> from pprint import pprint
>>> pprint(contributions)
['Student',
'Student',
'Student',
'Student',
'Scientist',
'Student',
'Student',
'Student',
'Scientist',
'Student',
'Scientist',
'Student',
'Student',
'Scientist',
'Professor']
"""
if __name__ == "__main__":
random.seed(1234) # for deterministic doctest outputs
import doctest
doctest.testmod()