n = len(corpus) idf = {} for doc in corpus: for word in set(doc): if word not in idf: idf[word] = 0 idf[word] += 1 for word in idf: idf[word] = math.log(n / idf[word]) 在不引入其他包的前提下,将这部分代码改为:log(语料库中所有词语的频数之和/给定词ti在语料库中的总频数)
时间: 2024-03-08 14:48:30 浏览: 44
可以按照如下方式改写代码:
```
total_freq = 0
idf = {}
# 统计语料库中所有词语的频数之和
for doc in corpus:
total_freq += len(doc)
# 计算每个词语的idf值
for doc in corpus:
for word in set(doc):
if word not in idf:
idf[word] = 0
idf[word] += 1
for word in idf:
idf[word] = math.log(total_freq / idf[word])
```
其中,`total_freq`变量用来统计语料库中所有词语的频数之和,然后在计算每个词语的idf值时,直接使用该变量即可。最后,对每个词语的idf值进行计算。
相关问题
优化这段代码,如果word不在doc中则不计算idf值,直接print输出语句error:该词不在语料库中。 for word in tf_dict: # 计算 idf doc_count = 0 for doc in corpus: if word in doc: doc_count += 1 idf = math.log(len(corpus) / doc_count)
可以使用Python的set(集合)数据结构来优化代码,将语料库中的每个文档转换为单词的set,然后检查单词是否在set中出现过,而不是在文本中搜索单词。这样可以大大提高速度,因为在set中搜索单词的时间复杂度为O(1)。以下是代码示例:
```
import math
# 将每个文档转换为单词的set
corpus_sets = [set(doc) for doc in corpus]
for word in tf_dict:
# 检查单词是否在语料库中
if word in set.union(*corpus_sets):
doc_count = sum(1 for doc in corpus_sets if word in doc)
idf = math.log(len(corpus) / doc_count)
print(word, idf)
else:
print("error: 该词不在语料库中")
```
这样,如果单词不在语料库中,就会输出错误消息,否则将计算idf值并进行打印输出。
def Stop_words(): stopword = [] data = [] f = open('C:/Users/Administrator/Desktop/data/stopword.txt',encoding='utf8') for line in f.readlines(): data.append(line) for i in data: output = str(i).replace('\n','')#replace用法和sub函数很接近 stopword.append(output) return stopword # 采用jieba进行词性标注,对当前文档过滤词性和停用词 def Filter_word(text): filter_word = [] stopword = Stop_words() text = jieba.posseg.cut(text) for word, flag in text: if flag.startswith('n') is False:#用于检测字符串是否以指定的子字符串开始 continue if not word in stopword and len(word) > 1: filter_word.append(word) return filter_word # 对文档集过滤词性和停用词 def Filter_words(data_path =r'C:/Users/Administrator/Desktop/data//corpus.txt'): document = [] for line in open(data_path, 'r',encoding= 'utf8') : segment = jieba.posseg.cut(line.strip()) filter_words = [] stopword = Stop_words() for word, flag in segment: if flag.startswith('n') is False: continue if not word in stopword and len(word) > 1: filter_words.append(word) document.append(filter_words) return document def tf_idf(): tf_dict = {} idf_dict = {} filter_word = Filter_word(text) for word in filter_word: if word not in tf_dict: tf_dict[word] = 1 else: tf_dict[word] += 1 for word in tf_dict: tf_dict[word] = tf_dict[word] / len(text) document = Filter_words() doc_total = len(document) for doc in document: for word in set(doc): if word not in idf_dict: idf_dict[word] = 1 else: idf_dict[word] += 1 for word in idf_dict: idf_dict[word] = math.log(doc_total / (idf_dict[word] + 1)) tf_idf_dict = {} for word in filter_word: if word not in idf_dict: idf_dict[word] = 0 tf_idf_dict[word] = tf_dict[word] * idf_dict[word] return tf_idf_dict tf_idf_dict = tf_idf() keyword = 6 print('TF-IDF模型结果:') for key, value in sorted(tf_idf_dict.items(), key=operator.itemgetter(1),reverse=True)[:keyword]: print(key, end=' ') print('\n')
这段代码实现了一个 TF-IDF 模型,用于计算文本中关键词的权重。其中,Stop_words 函数用于读取停用词表,Filter_word 函数用于对单个文档进行过滤,Filter_words 函数用于对整个文档集进行过滤。tf_idf 函数用于计算 TF-IDF 值,最后输出了权重最高的前 keyword 个关键词。