模拟多键字典
multi_key_dict https://pypi.python.org/pypi/multi_key_dict不允许__getitem__() https://docs.python.org/3/reference/datamodel.html#object.__getitem__同时使用多个键...
(e.g. d["red", "green"])
可以用以下方式模拟多键tuple https://docs.python.org/3.5/library/stdtypes.html#tuples or set https://docs.python.org/3.5/library/stdtypes.html#set键。如果顺序不重要的话set https://docs.python.org/3.5/library/stdtypes.html#set似乎是最好的（实际上是可散列的frozen set https://docs.python.org/3.5/library/stdtypes.html#frozenset， 以便 ["red", "blue"]是同一个["blue", "red"].
模拟多值字典
多值是通过使用某些数据类型而固有的，它可以是any storage element https://docs.python.org/3.5/library/stdtypes.html#mapping-types-dict可以方便地建立索引。一个标准dict https://docs.python.org/3.5/library/stdtypes.html#dict应该提供这一点。
非决定论
Using a probability distribution defined by the rules and assumptions1, non-deterministic selection is performed using this recipe https://docs.python.org/3/library/random.html#examples-and-recipes from the python docs.
MultiKeyMultiValNonDeterministicDict Class
What a name. \o/-nice!
This class takes multiple keys that define a probabilistic rule set of multiple values. During item creation (__setitem__() https://docs.python.org/3/reference/datamodel.html#object.__setitem__) all value probabilities are precomputed for all combinations of keys1. During item access (__getitem__() https://docs.python.org/3/reference/datamodel.html#object.__getitem__) the precomputed probability distribution is selected and the result is evaluated based on a random weighted selection.
定义
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
Usage
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}
}
Testing
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%
概率符合规则！
脚注
-
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。这是为了all键的组合，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。
