scoreslist = [] with open("scores.txt","r",encoding = "utf-8") as fi: #以中文编码方式打开文本文件 for line in fi: scoreslist.append(_______) #1读入文件中每行数据，构造二维列表 no = ________ #2科目数 num = ________ #3专业数 for i in range(0,num+1): print("{0:>20}".format(str(scoreslist[i]))) #输出二维列表中所有元素 ___________ #4换行 for i in range(1,num+1): scoreslist[i].append(max(map(int,scoreslist[i][1:]))) #计算最高分 scoreslist[i].append(min(map(int,scoreslist[i][1:]))) #计算最低分 for i in range(__________): #5遍历专业 for j in range(__________): #6遍历科目 if int(scoreslist[i][j]) == scoreslist[i][-2]: scoreslist[i][-2] =(scoreslist[0][j],scoreslist[i][-2]) #找最高分科目和分数 if int(scoreslist[i][j]) == scoreslist[i][-1]: scoreslist[i][-1] =(scoreslist[0][j],scoreslist[i][-1]) #找最低分科目和分数 print("专业:{0:<10}\t最高分{1:>}\t最低分{2:>}".format(_________________,str(scoreslist[i][-2]),str(scoreslist[i][-1]))) #7格式化输出结果

# Prediction settings -------------------------------------------------------------------------------------------------- source: # source directory for images or videos show: False # show results if possible save_txt: False # save results as .txt file save_conf: False # save results with confidence scores save_crop: False # save cropped images with results hide_labels: False # hide labels hide_conf: False # hide confidence scores vid_stride: 1 # video frame-rate stride line_thickness: 3 # bounding box thickness (pixels) visualize: False # visualize model features augment: False # apply image augmentation to prediction sources agnostic_nms: False # class-agnostic NMS classes: # filter results by class, i.e. class=0, or class=[0,2,3] retina_masks: False # use high-resolution segmentation masks boxes: True # Show boxes in segmentation predictions解释

- save_txt：是否将结果保存为.txt文件。 - save_conf：是否将结果保存为包括置信度得分的结果。 - save_crop：是否保存裁剪后的图像及其结果。 - hide_labels：是否隐藏结果中的标签。 - hide_conf：是否隐藏结果中...

from pypinyin import pinyin, Style import json # 加载数据集 with open('data.json', 'r', encoding='utf-8') as f: data = json.load(f) # 获取用户输入 input_pinyin = input('请输入拼音串：') input_pinyin_list = input_pinyin.split() # 计算概率 scores = {} for word, pinyin_list in data.items(): score = 0 for pinyin in pinyin_list: if pinyin in input_pinyin_list: score += 1 scores[word] = score / len(pinyin_list) # 按概率值排序 result = max(scores, key=scores.get) print(result) 请更改以下代码输入的拼音对应输出的字

with open('data.json', 'r', encoding='utf-8') as f: data = json.load(f) # 获取用户输入 input_pinyin = input('请输入拼音串：') input_pinyin_list = input_pinyin.split() # 计算概率 scores = {} for ...

idx = np.argsort(self.scores).astype(int)[::-1]

这段代码使用了 np.argsort 函数来获取 self.scores 数组中按升序排列的索引，然后通过 astype(int) 将浮点数索引转换为整数索引，最后使用 [::-1] 将数组反转，以便得到按降序排列的索引。具体来说，假设...

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？

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...

补全代码def scoresToFile(scores): file = open("scores.txt","at") scores = [ ] #补全 file.writelines(scores) file.close()

这段代码是定义一个名...函数中打开了scores.txt文件，并且设定了打开模式为"at"，这表示是以追加模式打开文件。此外，函数中定义了一个空的列表scores=[]，这可能用于预留一个可传入的scores值，以便将其写入文件中。

with open('./score.txt','r') as f: lines=f.readlines() scores=[] for line in lines: scorelist=scores.append(list(line.strip().split(',')[1:])) scoreint=[int(score) for score in scorelist] scores.append(scoreint)

with open('./score.txt', 'r') as f: lines = f.readlines() scores = [] for line in lines: scorelist = line.strip().split(',')[1:] scoreint = [int(score) for score in scorelist] scores.append...

已知数据文件scores.txt中保存有学生信息，格式例如：张三 82 78 91

with open('scores.txt', 'r') as f: # 接下来的操作都在with语句块中进行，这样可以自动关闭文件 2. 逐行读取文件内容，并解析出学生信息和成绩 python with open('scores.txt', 'r') as f: for line in...

将下面代码中的numpy操作转换为纯pytorch函数：landms = landms * 640 landms = landms.cpu().numpy() # ignore low scores inds = np.where(scores > args.confidence_threshold)[:] # ) boxes = boxes[inds] landms = landms[inds] scores = scores[inds] # keep top-K before NMS order = scores.argsort()[::-1][:args.top_k] boxes = boxes[order] landms = landms[order] scores = scores[order] # do NMS dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)

landms = landms * 640 landms = landms.cpu().numpy() 转换为纯pytorch函数: landms = landms * 640 landms = landms.detach().numpy()

p_attn = F.softmax(scores, dim = -1)

2. scores：这是另一个变量，通常在注意力机制中，它是一个二维张量（或数组），包含了不同注意力头（或元素）之间的得分或相似度分数。 3. F.softmax(scores, dim = -1)：这是PyTorch中的softmax函数调用。...

import csv with open('./score.txt', 'r') as f: lines = f.readlines() scores = {} for line in lines: for i in range(1:4): player=[i] scorelist = line.strip().split(',')[1:] scoreint = [int(score) for score in scorelist] scores.get(player,scoreint)

with open('./score.txt', 'r') as f: lines = f.readlines() scores = {} for line in lines: for i in range(1, 4): player = [i] scorelist = line.strip().split(',')[1:] scoreint = [int(score) for ...

将下面代码中的numpy操作转换为纯pytorch函数：boxes = boxes.cpu().numpy() scores = conf.squeeze(0).data.cpu().numpy()[:, 1] landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance']) landms = landms * 640 landms = landms.cpu().numpy() # ignore low scores inds = np.where(scores > args.confidence_threshold)[:] # ) boxes = boxes[inds] landms = landms[inds] scores = scores[inds] # keep top-K before NMS order = scores.argsort()[::-1][:args.top_k] boxes = boxes[order] landms = landms[order] scores = scores[order] # do NMS dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)

boxes = boxes.cpu() scores = conf.squeeze(0).data.cpu()[:, 1] landms = decode_landm(landms.data.squeeze(0), prior_data, cfg[variance]) landms = landms * 640 landms = landms.cpu()

scores. txt 文件存放着某班学生的成绩，包含学号、平时成绩、期末成绩三列。” 请根据平时成绩占 40%，期末成绩占 60%的比例计算总评成绩，写入另一个文件 scored. txt 中 scores. txt 内容如下。学号平时期末 00165754 0028085+ 0037580 scored. txt 内容如下：学号总评。 001 714 002834 003 78-

with open('scores.txt', 'r') as f: lines = f.readlines() # 计算每个学生的总评成绩，并写入 scored.txt 文件 with open('scored.txt', 'w') as f: for line in lines: # 获取学号、平时成绩、期末成绩 ...

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer from sklearn.naive_bayes import MultinomialNB import jieba good_comments = [] bad_comments = [] with open('D:\PyCharmProjects\爬虫测试\好评.txt', 'r', encoding='gbk') as f: for line in f.readlines(): good_comments.append(line.strip('\n')) # print(good_comments) with open('D:\PyCharmProjects\爬虫测试\差评.txt', 'r', encoding='gbk') as f: for line in f.readlines(): bad_comments.append(line.strip('\n')) # print(bad_comments) # stopwords = [] with open('StopWords.txt', 'r', encoding='utf-8') as f: stopwords = f.read().splitlines() good_words = [] for line in good_comments: words = jieba.cut(line, cut_all=False) words = [w for w in words if w not in stopwords] good_words.append(' '.join(words)) bad_words = [] for line in bad_comments: words = jieba.cut(line, cut_all=False) words = [w for w in words if w not in stopwords] bad_words.append(' '.join(words)) # 将文本转换为向量 vectorizer = CountVectorizer() X = vectorizer.fit_transform(good_words + bad_words) y = [1] * len(good_words) + [0] * len(bad_words) # 训练模型 clf = MultinomialNB() clf.fit(X, y) # 测试模型 test_text = '味道很差，不建议购买，还有不少烂的！' test_words = ' '.join(jieba.cut(test_text, cut_all=False)) test_vec = vectorizer.transform([test_words]) pred = clf.predict(test_vec) if pred[0] == 1: print('好评') else: print('差评')，为我给这段代码增加一个输出准确率的功能

scores = cross_val_score(clf, X, y, cv=5) print('准确率：', scores.mean()) 其中 cross_val_score 的参数说明： - clf：分类器 - X：特征矩阵 - y：标签向量 - cv：交叉验证的次数输出的结果...

4、实验内容:在文本文件scores.txt中保存了一些专业的课程平均分，如下图所示：编写一个Python程序，读入文本文件scores.txt中的数据，存放在二维列表中，并输出所有专业的分数；计算每个专业的最高分和最低分，按输出格式要求输出每个专业的名称、最高分、最低分以及对应的课程名(已知没有同分的情况)；输出格式参照下图：

with open("scores.txt","r",encoding="utf-8") as fi: # 以中文编码方式打开文本文件 for line in fi: # 遍历文件中每一行数据 scoreslist.append(line.strip().split()) # 将每一行数据去掉空格和换行符后，按...

用python写一个代码，能够读取("C:\\Users\\ASUS\\Desktop\\student.txt","r",encoding="UTF-8")的文件，内容如下学号姓名语文数学外语 Python 221 董浩 95 75 80 80 222 罗邦楹 87 64 77 85 223 陈锦炜 75 85 66 76 224 陈天才 59 59 59 59 225 李松林 95 99 95 92 能够统计每个课程的总分和总分的排名在文件中增多两列显示

with open("C:\\Users\\ASUS\\Desktop\\student.txt", "r", encoding="UTF-8") as f: content = f.readlines() # 去除每行末尾的换行符 content = [line.strip() for line in content] # 分离表头和数据 header =...

读取scores.txt文件生成数组

with open("scores.txt", "r") as f: students = [] for line in f: scores = list(map(int, line.strip().split())) students.append(scores) # 输出成绩 for i, scores in enumerate(students): print("学生...

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idx = np.argsort(self.scores).astype(int)[::-1]

补全代码def scoresToFile(scores): file = open("scores.txt","at") scores = [ ] #补全 file.writelines(scores) file.close()

with open('./score.txt','r') as f: lines=f.readlines() scores=[] for line in lines: scorelist=scores.append(list(line.strip().split(',')[1:])) scoreint=[int(score) for score in scorelist] scores.append(scoreint)

已知数据文件scores.txt中保存有学生信息，格式例如：张三 82 78 91

p_attn = F.softmax(scores, dim = -1)

import csv with open('./score.txt', 'r') as f: lines = f.readlines() scores = {} for line in lines: for i in range(1:4): player=[i] scorelist = line.strip().split(',')[1:] scoreint = [int(score) for score in scorelist] scores.get(player,scoreint)

读取scores.txt文件生成数组

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