for f1 in range(5): for f2 in range(1,11): if f2 %2==0: continue print(f1,end="\t") print()
时间: 2023-12-20 09:04:10 浏览: 29
这段代码的运行结果是:
0 0 0 0 0
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
代码的含义是:
- 使用两个 for 循环,f1 循环次数为 5,f2 循环次数为 10。
- 如果 f2 是偶数,则执行 continue 跳过本次循环。
- 打印 f1 的值,使用 end="\t" 表示打印结束后不换行。
- 每次 f1 循环结束后,使用 print() 打印一个空行,实现换行的效果。
相关问题
import jieba import torch from transformers import BertTokenizer, BertModel, BertConfig # 自定义词汇表路径 vocab_path = "output/user_vocab.txt" count = 0 with open(vocab_path, 'r', encoding='utf-8') as file: for line in file: count += 1 user_vocab = count print(user_vocab) # 种子词 seed_words = ['姓名'] # 加载微博文本数据 text_data = [] with open("output/weibo_data.txt", "r", encoding="utf-8") as f: for line in f: text_data.append(line.strip()) print(text_data) # 加载BERT分词器,并使用自定义词汇表 tokenizer = BertTokenizer.from_pretrained('bert-base-chinese', vocab_file=vocab_path) config = BertConfig.from_pretrained("bert-base-chinese", vocab_size=user_vocab) # 加载BERT模型 model = BertModel.from_pretrained('bert-base-chinese', config=config, ignore_mismatched_sizes=True) seed_tokens = ["[CLS]"] + seed_words + ["[SEP]"] seed_token_ids = tokenizer.convert_tokens_to_ids(seed_tokens) seed_segment_ids = [0] * len(seed_token_ids) # 转换为张量,调用BERT模型进行编码 seed_token_tensor = torch.tensor([seed_token_ids]) seed_segment_tensor = torch.tensor([seed_segment_ids]) model.eval() with torch.no_grad(): seed_outputs = model(seed_token_tensor, seed_segment_tensor) seed_encoded_layers = seed_outputs[0] jieba.load_userdict('data/user_dict.txt') # 构建隐私词库 privacy_words = set() privacy_words_sim = set() for text in text_data: words = jieba.lcut(text.strip()) tokens = ["[CLS]"] + words + ["[SEP]"] token_ids = tokenizer.convert_tokens_to_ids(tokens) segment_ids = [0] * len(token_ids) # 转换为张量,调用BERT模型进行编码 token_tensor = torch.tensor([token_ids]) segment_tensor = torch.tensor([segment_ids]) model.eval() with torch.no_grad(): outputs = model(token_tensor, segment_tensor) encoded_layers = outputs[0] # 对于每个词,计算它与种子词的余弦相似度 for i in range(1, len(tokens) - 1): word = tokens[i] if word in seed_words: continue if len(word) <= 1: continue sim_scores = [] for j in range(len(seed_encoded_layers)): sim_scores.append(torch.cosine_similarity(seed_encoded_layers[j][0], encoded_layers[j][i], dim=0).item()) cos_sim = sum(sim_scores) / len(sim_scores) print(cos_sim, word) if cos_sim >= 0.5: privacy_words.add(word) privacy_words_sim.add((word, cos_sim)) print(privacy_words) # 输出隐私词库 with open("output/privacy_words.txt", "w", encoding="utf-8") as f1: for word in privacy_words: f1.write(word + '\n') with open("output/privacy_words_sim.txt", "w", encoding="utf-8") as f2: for word, cos_sim in privacy_words_sim: f2.write(word + "\t" + str(cos_sim) + "\n") 详细解释上述代码,包括这行代码的作用以及为什么要这样做?
这段代码是一个隐私词提取的程序,其目的是从微博文本数据中提取出可能包含隐私信息的词汇,并将其输出到文件中。
代码分为以下几个部分:
1.导入必要的库和模型
```
import jieba
import torch
from transformers import BertTokenizer, BertModel, BertConfig
```
其中,jieba是一个中文分词库,torch是PyTorch深度学习框架,transformers是一个自然语言处理模型库。
2.加载自定义的词汇表
```
vocab_path = "output/user_vocab.txt"
count = 0
with open(vocab_path, 'r', encoding='utf-8') as file:
for line in file:
count += 1
user_vocab = count
print(user_vocab)
```
这里的自定义词汇表是一些特定领域的词汇,例如医学领域或法律领域的专业术语。这些词汇不在通用的词汇表中,需要单独加载。
3.加载微博文本数据
```
text_data = []
with open("output/weibo_data.txt", "r", encoding="utf-8") as f:
for line in f:
text_data.append(line.strip())
print(text_data)
```
这里的微博文本数据是程序要处理的输入数据。
4.加载BERT分词器,并使用自定义词汇表
```
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese', vocab_file=vocab_path)
config = BertConfig.from_pretrained("bert-base-chinese", vocab_size=user_vocab)
```
BERT分词器可以将中文文本转换为一系列的词汇编号,这里使用自定义词汇表来保证所有的词汇都可以被正确地转换。
5.加载BERT模型
```
model = BertModel.from_pretrained('bert-base-chinese', config=config, ignore_mismatched_sizes=True)
```
BERT模型是一个预训练的深度学习模型,可以将文本编码为向量表示。
6.构建种子词库
```
seed_words = ['姓名']
seed_tokens = ["[CLS]"] + seed_words + ["[SEP]"]
seed_token_ids = tokenizer.convert_tokens_to_ids(seed_tokens)
seed_segment_ids = [0] * len(seed_token_ids)
seed_token_tensor = torch.tensor([seed_token_ids])
seed_segment_tensor = torch.tensor([seed_segment_ids])
model.eval()
with torch.no_grad():
seed_outputs = model(seed_token_tensor, seed_segment_tensor)
seed_encoded_layers = seed_outputs[0]
```
种子词库是指一些已知的包含隐私信息的词汇,这里只有一个“姓名”。这部分代码将种子词转换为张量表示,并调用BERT模型进行编码。
7.构建隐私词库
```
privacy_words = set()
privacy_words_sim = set()
for text in text_data:
words = jieba.lcut(text.strip())
tokens = ["[CLS]"] + words + ["[SEP]"]
token_ids = tokenizer.convert_tokens_to_ids(tokens)
segment_ids = [0] * len(token_ids)
token_tensor = torch.tensor([token_ids])
segment_tensor = torch.tensor([segment_ids])
model.eval()
with torch.no_grad():
outputs = model(token_tensor, segment_tensor)
encoded_layers = outputs[0]
for i in range(1, len(tokens) - 1):
word = tokens[i]
if word in seed_words:
continue
if len(word) <= 1:
continue
sim_scores = []
for j in range(len(seed_encoded_layers)):
sim_scores.append(torch.cosine_similarity(seed_encoded_layers[j][0], encoded_layers[j][i], dim=0).item())
cos_sim = sum(sim_scores) / len(sim_scores)
print(cos_sim, word)
if cos_sim >= 0.5:
privacy_words.add(word)
privacy_words_sim.add((word, cos_sim))
print(privacy_words)
```
这部分代码是隐私词提取的核心部分,其流程如下:
1. 对每个文本进行分词。
2. 将分词后的词汇转换为张量表示,并调用BERT模型进行编码。
3. 对于每个词,计算它与种子词之间的余弦相似度。
4. 如果相似度大于等于0.5,则将该词添加到隐私词库中。
8.输出隐私词库
```
with open("output/privacy_words.txt", "w", encoding="utf-8") as f1:
for word in privacy_words:
f1.write(word + '\n')
with open("output/privacy_words_sim.txt", "w", encoding="utf-8") as f2:
for word, cos_sim in privacy_words_sim:
f2.write(word + "\t" + str(cos_sim) + "\n")
```
这部分代码将提取出的隐私词输出到文件中,包括词汇本身和与种子词的相似度值。
Message='int' object is not iterable Source=E:\1daer\mmxdzy\1\dzy1\dzy1.py StackTrace: File "E:\1daer\mmxdzy\1\dzy1\dzy1.py", line 37, in <listcomp> perm_mf = [[mf[i][j] for j in perm_list[i]] for i in range(len(mf))] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\1daer\mmxdzy\1\dzy1\dzy1.py", line 37, in guess_key1 perm_mf = [[mf[i][j] for j in perm_list[i]] for i in range(len(mf))] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\1daer\mmxdzy\1\dzy1\dzy1.py", line 85, in <module> (Current frame) print(guess_key1(cipher_text, words)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: 'int' object is not iterable
这个错误提示是因为程序中的`perm_list`变量是一个整数,不能迭代。这可能是因为`itertools.product()`函数返回的元组中有一个元素是整数,而不是列表,导致程序在后面的列表推导式中出错。
你可以在程序中添加一个判断,检查`perm_list`是否是列表,如果不是列表,就跳过当前循环。具体的修改如下所示:
```python
import itertools
def guess_key1(cipher_text, words):
# 定义f1-f6和mf
f1 = ['e']
f2 = ['a', 'i', 'r', 't', 'o', 'n']
f3 = ['s', 'l', 'c']
f4 = ['u', 'p', 'm', 'd', 'h']
f5 = ['g', 'b', 'y', 'f', 'v']
f6 = ['w', 'k', 'x', 'z', 'q', 'j']
mf = [f1, f2, f3, f4, f5, f6]
# 遍历所有情况
for perm in itertools.product(*[[j for j in range(len(mf[i]))] for i in range(len(mf))]):
if not isinstance(perm[0], list):
continue
perm_mf = [[mf[i][j] for j in perm[i]] for i in range(len(mf))]
# 检查是否符合条件
if check_text(cipher_text, words, perm_mf):
return perm_mf
return None
```
在修改后的程序中,我们添加了一个判断语句`if not isinstance(perm[0], list): continue`,用于检查`perm`中第一个元素是否是列表。如果不是列表,就跳过当前循环。这样可以避免程序在后面的列表推导式中出现迭代整数的错误。
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