把这段代码import math from collections import defaultdict corpus =["二价 二价 二价 四价 预约", "四价 四价 四价 九价 预约", "九价 九价 九价 九价 预约"] words = [] for sentence in corpus: words.append(sentence.strip().split()) # 进行词频统计 def Counter(words): word_count = [] for sentence in words: word_dict = defaultdict(int) for word in sentence: word_dict[word] += 1 word_count.append(word_dict) return word_count word_count = Counter(words) # 计算TF(word代表被计算的单词，word_dict是被计算单词所在句子分词统计词频后的字典) def tf(word, word_dict): return word_dict[word] / sum(word_dict.values()) # 统计含有该单词的句子数 def count_sentence(word, word_count): return sum([1 for i in word_count if i.get(word)]) # i[word] >= 1 # 计算IDF def idf(word, word_count): return math.log((len(word_count) / (count_sentence(word, word_count) + 1)),10) # 计算TF-IDF def tfidf(word, word_dict, word_count): return tf(word, word_dict) * idf(word, word_count) p = 1 for word_dict in word_count: print("part:{}".format(p)) p += 1 for word, cnt in word_dict.items(): print("word: {} ---- TF-IDF:{}".format(word, tfidf(word, word_dict, word_count))) print("word: {} ---- TF:{}".format(word, tf(word, word_dict))) print("word: {} ---- IDF:{}".format(word, idf(word, word_count))) print("word: {} ---- count_sentence:{}".format(word, count_sentence(word, word_count)))中计算idf的def idf(word, word_count)部分改成自定义输入权重[1，2，3]得到最终的改进后的TF-IDF值，请帮我改进一下代码

好的，根据您的要求，我修改了代码，将idf的计算方式修改为自定义输入权重[1,2,3]，代码如下：

import math
from collections import defaultdict

corpus =["二价 二价 二价 四价 预约", "四价 四价 四价 九价 预约", "九价 九价 九价 九价 预约"]
words = []
for sentence in corpus:
    words.append(sentence.strip().split())

# 进行词频统计
def Counter(words):
    word_count = []
    for sentence in words:
        word_dict = defaultdict(int)
        for word in sentence:
            word_dict[word] += 1
        word_count.append(word_dict)
    return word_count

word_count = Counter(words)

# 计算TF(word代表被计算的单词，word_dict是被计算单词所在句子分词统计词频后的字典)
def tf(word, word_dict):
    return word_dict[word] / sum(word_dict.values())

# 统计含有该单词的句子数
def count_sentence(word, word_count):
    return sum([1 for i in word_count if i.get(word)])  # i[word] >= 1

# 计算IDF
def idf(word, word_count, weights):
    return math.log((len(word_count) / (count_sentence(word, word_count) + 1)),10) * weights[word]

# 计算TF-IDF
def tfidf(word, word_dict, word_count, weights):
    return tf(word, word_dict) * idf(word, word_count, weights)

# 自定义权重
weights = {
    "二价": 1,
    "四价": 2,
    "九价": 3,
}

p = 1
for word_dict in word_count:
    print("part:{}".format(p))
    p += 1
    for word, cnt in word_dict.items():
        print("word: {} ---- TF-IDF:{}".format(word, tfidf(word, word_dict, word_count, weights)))
        print("word: {} ---- TF:{}".format(word, tf(word, word_dict)))
        print("word: {} ---- IDF:{}".format(word, idf(word, word_count, weights)))
        print("word: {} ---- count_sentence:{}".format(word, count_sentence(word, word_count)))

在这个代码中，我们增加了一个新的参数weights，用来存储自定义的权重，然后在idf计算时，乘以相应的权重。最后在计算tfidf时，将权重传递给idf函数即可。