我有一个很长的字典列表:
river 4
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longitude: 1
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river: 1
branch: 1
world--four: 1
considering: 1
wales 1
area: 1
germany: 1
austria: 1
mississippi: 1
france: 1
proper: 1
exceptionally: 1
turkey: 1
england: 1
areas: 1
combined: 1
scotland: 1
italy: 1
spain: 1
almost: 1
fertile: 1
region: 1
wide: 1
drainage-basin: 1
portugal: 1
ireland: 1
valley: 1
其中p和q是字典的关键我猜。而且我还想添加一个函数,当你输入单词时,它会找到字典并按降序返回其他字典的相似性。期望的相似性输出是:
Enter conceptword (or blank line to end): river
river
water 0.489
mississippi 0.052
spain 0.033
cairo 0.000
Synonym for river is water
任何人都可以提供帮助或提供解决方案,或者告诉他们如何提取共享的单词'计算的价值?感谢。
答案 0 :(得分:0)
我对您的问题设置并不完全确定,但我想这可以帮助您入门。假设您的词典集合是这样的:
import math
dict_list = {
"water": {
"ground": 1,
"journey": 1,
"longitude": 1,
"main": 1,
"contrary": 1,
"cover": 1,
"delaware": 1,
"remarkable": 1
},
"mississippi": {
"main": 1,
"contrary": 1,
"cover": 1,
"delaware": 1,
"remarkable": 1,
"steamboats": 1,
"germany": 1
}
}
如果要将余弦规则应用于它们,则需要设置向量,以显示两个词典中出现的每个单词的出现(即,如果它出现在一个词汇中,而另一个词汇出现在另一个词汇表中则需要为零。 ):
def setup_vec(dict1, dict2):
dict1_missing = list(set(dict2.keys() - set(dict1.keys())))
dict2_missing = list(set(dict1.keys() - set(dict2.keys())))
for i in dict1_missing:
dict1[i] = 0
for i in dict2_missing:
dict2[i] = 0
vec1 = []
vec2 = []
for i in dict1.keys():
vec1.append(dict1[i])
vec2.append(dict2[i])
return([vec1, vec2])
请注意,矢量元素的顺序很重要。如果你用" water"和"密西西比"你得到:
[[1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1]]
之后,您只需要应用规则:
def dot_prod(p, q):
return sum([p[x]*q[x] for x in range(0, len(p))])
def norm(p):
return math.sqrt(dot_prod(p, p))
def cosine_metric(p, q):
return dot_prod(p, q)/(norm(p) * norm(q))
和针对所有其他字典计算给定字典的函数:
def find_dict(word):
if word in dict_list.keys():
for other_dicts in dict_list.keys():
if other_dicts != word:
vecs = setup_vec(dict_list[word], dict_list[other_dicts])
print(other_dicts + " " + str(cosine_metric(vecs[0], vecs[1])))
这里的结果是:
find_dict('water')
0.6681531047810609
剩下的任务是订购输出,这不应该很难。