sta*_*kit 11 python dictionary recommendation-engine fuzzy-logic data-structures
python中已有一个多键词典,也是一个多值词典.我需要一个python字典,它是:
例:
# probabilistically fetch any one of baloon, toy or car
d['red','blue','green']== "baloon" or "car" or "toy"
d ['red'] == d ['green']的概率很高,d ['red']!= d ['red']的概率很低但可能
单个输出值应根据来自键的规则进行概率确定(模糊),例如:在上述情况下规则可以是如果键具有"红色"和"蓝色"则返回"气球"80%的时间如果只有蓝色然后返回"玩具"15%的时间其他"汽车"5%的时间.
应该设计setitem方法,以便可以遵循:
d["red", "blue"] =[
("baloon",haseither('red','green'),0.8),
("toy",.....)
,....
]
上面使用谓词函数和相应的概率为字典分配多个值.而不是上面的赋值列表,甚至字典作为赋值将是更可取的:
d["red", "blue"] ={
"baloon": haseither('red','green',0.8),
"toy": hasonly("blue",0.15),
"car": default(0.05)
}
如果"红色"或绿色存在,上述气球将返回80%的时间,如果蓝色存在则返回玩具15%的时间,并且在没有任何条件的情况下返回5%的时间.
是否有任何现有的数据结构已经满足python中的上述要求？如果没有那么如何修改multikeydict代码以满足python中的上述要求？
如果使用字典,那么可以有配置文件或使用适当的嵌套装饰器来配置上述概率谓词逻辑,而不必硬编码if\else语句.
注意:上面是一个基于规则的自动响应程序应用程序的有用自动机,因此,如果python中有任何类似的基于规则的框架,即使它不使用字典结构,也要告诉我吗？
multi_key_dict不允许__getitem__()同时使用多个密钥...
（例如d["red", "green"]）
tuple可以用或键模拟多键set。如果顺序不重要，set似乎是最好的（实际上是 hashable frozen set，因此 ["red", "blue"]与 a 相同["blue", "red"]。
多值是使用某些数据类型所固有的，它可以是任何可以方便索引的存储元素。标准dict应该提供这一点。
使用由规则和假设1定义的概率分布，使用Python 文档中的此配方执行非确定性选择。
MultiKeyMultiValNonDeterministicDict班级多好的名字啊。\在冰上！
此类采用多个键来定义多个值的概率规则集。在项目创建 () 过程中，将针对所有键1__setitem__()组合预先计算所有值概率。在项目访问 ( ) 期间，选择预先计算的概率分布，并根据随机加权选择评估结果。__getitem__()
import random
import operator
import bisect
import itertools
# or use itertools.accumulate in python 3
def accumulate(iterable, func=operator.add):
'Return running totals'
# accumulate([1,2,3,4,5]) --> 1 3 6 10 15
# accumulate([1,2,3,4,5], operator.mul) --> 1 2 6 24 120
it = iter(iterable)
try:
total = next(it)
except StopIteration:
return
yield total
for element in it:
total = func(total, element)
yield total
class MultiKeyMultiValNonDeterministicDict(dict):
def key_combinations(self, keys):
"""get all combinations of keys"""
return [frozenset(subset) for L in range(0, len(keys)+1) for subset in itertools.combinations(keys, L)]
def multi_val_rule_prob(self, rules, rule):
"""
assign probabilities for each value,
spreading undefined result probabilities
uniformly over the leftover results not defined by rule.
"""
all_results = set([result for result_probs in rules.values() for result in result_probs])
prob = rules[rule]
leftover_prob = 1.0 - sum([x for x in prob.values()])
leftover_results = len(all_results) - len(prob)
for result in all_results:
if result not in prob:
# spread undefined prob uniformly over leftover results
prob[result] = leftover_prob/leftover_results
return prob
def multi_key_rule_prob(self, key, val):
"""
assign probability distributions for every combination of keys,
using the default for combinations not defined in rule set
"""
combo_probs = {}
for combo in self.key_combinations(key):
if combo in val:
result_probs = self.multi_val_rule_prob(val, combo).items()
else:
result_probs = self.multi_val_rule_prob(val, frozenset([])).items()
combo_probs[combo] = result_probs
return combo_probs
def weighted_random_choice(self, weighted_choices):
"""make choice from weighted distribution"""
choices, weights = zip(*weighted_choices)
cumdist = list(accumulate(weights))
return choices[bisect.bisect(cumdist, random.random() * cumdist[-1])]
def __setitem__(self, key, val):
"""
set item in dictionary,
assigns values to keys with precomputed probability distributions
"""
precompute_val_probs = self.multi_key_rule_prob(key, val)
# use to show ALL precomputed probabilities for key's rule set
# print precompute_val_probs
dict.__setitem__(self, frozenset(key), precompute_val_probs)
def __getitem__(self, key):
"""
get item from dictionary,
randomly select value based on rule probability
"""
key = frozenset([key]) if isinstance(key, str) else frozenset(key)
val = None
weighted_val = None
if key in self.keys():
val = dict.__getitem__(self, key)
weighted_val = val[key]
else:
for k in self.keys():
if key.issubset(k):
val = dict.__getitem__(self, k)
weighted_val = val[key]
# used to show probabality for key
# print weighted_val
if weighted_val:
prob_results = self.weighted_random_choice(weighted_val)
else:
prob_results = None
return prob_results
d = MultiKeyMultiValNonDeterministicDict()
d["red","blue","green"] = {
# {rule_set} : {result: probability}
frozenset(["red", "green"]): {"ballon": 0.8},
frozenset(["blue"]): {"toy": 0.15},
frozenset([]): {"car": 0.05}
}
检查概率
N = 10000
red_green_test = {'car':0.0, 'toy':0.0, 'ballon':0.0}
red_blue_test = {'car':0.0, 'toy':0.0, 'ballon':0.0}
blue_test = {'car':0.0, 'toy':0.0, 'ballon':0.0}
red_blue_green_test = {'car':0.0, 'toy':0.0, 'ballon':0.0}
default_test = {'car':0.0, 'toy':0.0, 'ballon':0.0}
for _ in xrange(N):
red_green_test[d["red","green"]] += 1.0
red_blue_test[d["red","blue"]] += 1.0
blue_test[d["blue"]] += 1.0
default_test[d["green"]] += 1.0
red_blue_green_test[d["red","blue","green"]] += 1.0
print 'red,green test =', ' '.join('{0}: {1:05.2f}%'.format(key, 100.0*val/N) for key, val in red_green_test.items())
print 'red,blue test =', ' '.join('{0}: {1:05.2f}%'.format(key, 100.0*val/N) for key, val in red_blue_test.items())
print 'blue test =', ' '.join('{0}: {1:05.2f}%'.format(key, 100.0*val/N) for key, val in blue_test.items())
print 'default test =', ' '.join('{0}: {1:05.2f}%'.format(key, 100.0*val/N) for key, val in default_test.items())
print 'red,blue,green test =', ' '.join('{0}: {1:05.2f}%'.format(key, 100.0*val/N) for key, val in red_blue_green_test.items())
red,green test = car: 09.89% toy: 10.06% ballon: 80.05%
red,blue test = car: 05.30% toy: 47.71% ballon: 46.99%
blue test = car: 41.69% toy: 15.02% ballon: 43.29%
default test = car: 05.03% toy: 47.16% ballon: 47.81%
red,blue,green test = car: 04.85% toy: 49.20% ballon: 45.95%
分布假设
由于规则集尚未完全定义，因此对概率分布进行了假设，其中大部分是在 中完成的multi_val_rule_prob()。基本上任何未定义的概率都会均匀分布在其余值上。这是针对所有键组合完成的，并为随机加权选择创建通用键接口。
给定示例规则集
d["red","blue","green"] = {
# {rule_set} : {result: probability}
frozenset(["red", "green"]): {"ballon": 0.8},
frozenset(["blue"]): {"toy": 0.15},
frozenset([]): {"car": 0.05}
}
这将创建以下发行版
'red' = [('car', 0.050), ('toy', 0.475), ('ballon', 0.475)]
'green' = [('car', 0.050), ('toy', 0.475), ('ballon', 0.475)]
'blue' = [('car', 0.425), ('toy', 0.150), ('ballon', 0.425)]
'blue,red' = [('car', 0.050), ('toy', 0.475), ('ballon', 0.475)]
'green,red' = [('car', 0.098), ('toy', 0.098), ('ballon', 0.800)]
'blue,green' = [('car', 0.050), ('toy', 0.475), ('ballon', 0.475)]
'blue,green,red'= [('car', 0.050), ('toy', 0.475), ('ballon', 0.475)]
default = [('car', 0.050), ('toy', 0.475), ('ballon', 0.475)]
如果这不正确，请指教。