Building prefix dict from the default dictionary ... DEBUG:jieba:Building prefix dict from the default dictionary ... Loading model from cache C:\Users\LY-AI\AppData\Local\Temp\jieba.cache DEBUG:jieba:Loading model from cache C:\Users\LY-AI\AppData\Local\Temp\jieba.cache Loading model cost 0.717 seconds. DEBUG:jieba:Loading model cost 0.717 seconds. Prefix dict has been built successfully. DEBUG:jieba:Prefix dict has been built successfully. C:\Users\LY-AI\Desktop\AI\vits-uma-genshin-honkai\python3.9.13\3.9.13\lib\site-packages\gradio\processing_utils.py:183: UserWarning: Trying to convert audio automatically from float32 to 16-bit int format. warnings.warn(warning.format(data.dtype)) Traceback (most recent call last): File "C:\Users\LY-AI\Desktop\AI\vits-uma-genshin-honkai\python3.9.13\3.9.13\lib\site-packages\gradio\routes.py", line 442, in run_predict output = await app.get_blocks().process_api( File "C:\Users\LY-AI\Desktop\AI\vits-uma-genshin-honkai\python3.9.13\3.9.13\lib\site-packages\gradio\blocks.py", line 1392, in process_api data = self.postprocess_data(fn_index, result["prediction"], state) File "C:\Users\LY-AI\Desktop\AI\vits-uma-genshin-honkai\python3.9.13\3.9.13\lib\site-packages\gradio\blocks.py", line 1326, in postprocess_data prediction_value = block.postprocess(prediction_value) File "C:\Users\LY-AI\Desktop\AI\vits-uma-genshin-honkai\app.py", line 42, in audio_postprocess return gr_processing_utils.encode_url_or_file_to_base64(data["name"]) AttributeError: module 'gradio.processing_utils' has no attribute 'encode_url_or_file_to_base64'

DICT智慧园区解决方案.pdf

DICT智慧园区解决方案.pdfDICT智慧园区解决方案.pdfDICT智慧园区解决方案.pdfDICT智慧园区解决方案.pdfDICT智慧园区解决方案.pdfDICT智慧园区解决方案.pdf

Building prefix dict from the default dictionary ... Loading model from cache C:\Users\86150\AppData\Local\Temp\jieba.cache Loading model cost 0.734 seconds. Prefix dict has been built successfully.这种情况是怎么回事儿？怎么解决呢？

这种情况一般是使用了Python中的jieba分词库时出现的，它是在使用默认字典构建词库时的输出信息，说明jieba分词库已经成功加载。这不是错误，所以不需要解决。但如果你觉得这种输出信息影响你的程序运行，你可以将...

Building prefix dict from the default dictionary ... Loading model from cache C:\Users\22530\AppData\Local\Temp\jieba.cache ['王者', '荣耀'] Loading model cost 0.605 seconds. Prefix dict has been built successfully.

这段提示信息来自于 jieba 分词库，它的作用是在使用 jieba 进行分词前，先构建一个词典，以提高分词效率。具体来说，这段提示信息包含了以下几个步骤： 1. 正在从默认词典中构建前缀词典。 2. 从缓存中加载...

如何解决Building prefix dict from the default dictionary ... Loading model from cache C:\Users\lenovo\AppData\Local\Temp\jieba.cache Loading model cost 0.999 seconds. Prefix dict has been built successfully. Traceback (most recent call last): File "C:/Users/lenovo/AppData/Local/Programs/Python/Python38/qqx.py", line 21, in <module> ranking.append(words_list[i]) IndexError: list index out of range

此外，你提到了使用了 jieba 库，你可以尝试在程序开头加上以下代码，以避免出现“Building prefix dict from the default dictionary ... Loading model from cache”的提示： python import jieba jieba....

D:\python\python.exe "E:/PyCharm practice/ceshi.py" Building prefix dict from the default dictionary ... Loading model from cache C:\Users\lenovo\AppData\Local\Temp\jieba.cache Loading model cost 0.632 seconds. Prefix dict has been built successfully. Traceback (most recent call last): File "E:/PyCharm practice/ceshi.py", line 53, in <module> combinations = list(set(combinations)) TypeError: unhashable type: 'list'

这个错误是因为你尝试将一个列表作为字典的键，但是列表是不可哈希的数据类型，不能用作字典的键。在你的代码中，你使用了set()函数将列表转换为集合，然后尝试将集合转换回列表，这可能导致其中某些元素的顺序发生...

Building prefix dict from the default dictionary ... Loading model from cache C:\Users\zyh\AppData\Local\Temp\jieba.cache Loading model cost 0.623 seconds. Prefix dict has been built successfully. Traceback (most recent call last): File "C:\Users\zyh\Desktop\大三下\python\测试.py", line 17, in <module> wc = WordCloud(background_color='white', axis_off=True) TypeError: init() got an unexpected keyword argument 'axis_off'

from wordcloud import WordCloud import jieba # 读取文本文件 with open('input.txt', 'r', encoding='utf-8') as f: text = f.read() # 使用 jieba 分词 words = jieba.cut(text) # 拼接分词结果为字符串 ...

实现精确匹配模式分词和搜索引擎模型分词，完成对所输入的文本进行分词，并输出分词结果，其中文本内容通过 input 从后台获取。要求测试输入：我来自北京清华大学预期输出： Building prefix dict from the default dictionary ... Dumping model to file cache /tmp/jieba.cache Loading model cost 1.252 seconds. Prefix dict has been built successfully. # Jieba接口的附加信息，说明分词过程的结果精确模式：我/来自/北京/清华大学搜索引擎模式：我 /来自 /北京 /清华 /华大 /大学

首先，jieba分词支持两种分词模式：精确模式和搜索引擎模式。精确模式是默认模式，会将文本尽可能地分成最小的词语，而搜索引擎模式则会将文本分成更多的词语，包括单字词语和非常常见的词语。下面是实现精确匹配...

Building prefix dict from the default dictionary ... Loading model from cache C:\Users\26316\AppData\Local\Temp\jieba.cache Loading model cost 0.617 seconds. Prefix dict has been built successfully. Traceback (most recent call last): File "E:\PycharmProjects\neural network learning\3\emotionLSTM.py", line 38, in <module> pn['sent'] = list(preprocessing.sequence.pad_sequences(pn['sent'], maxlen=maxlen, padding='pre', truncating='pre')) AttributeError: module 'keras.preprocessing.sequence' has no attribute 'pad_sequences' Pad sequences (samples x time) Process finished with exit code 1

根据您提供的错误信息，看起来是在使用Keras的preprocessing.sequence模块时出现了问题。具体地，它指出'keras.preprocessing.sequence'没有'pad_sequences'属性。这可能是因为您使用的Keras版本较旧，而'pad_...

在右侧编辑器中的 Begin-End 之间补充 Python 代码，实现精确匹配模式分词和搜索引擎模型分词，完成对所输入的文本进行分词，并输出分词结果，其中文本内容通过 input 从后台获取。测试说明平台将使用测试集运行你编写的程序代码，若全部的运行结果正确，则通关。测试输入：我来自北京清华大学预期输出： Building prefix dict from the default dictionary ... Dumping model to file cache /tmp/jieba.cache Loading model cost 1.252 seconds. Prefix dict has been built successfully. # Jieba接口的附加信息，说明分词过程的结果精确模式：我/来自/北京/清华大学搜索引擎模式：我 /来自 /北京 /清华 /华大用python写

import jieba # 获取输入文本 text = input() # 精确模式分词 seg_list = jieba.cut(text, cut_all=False) print("精确模式：" + "/".join(seg_list)) # 搜索引擎模式分词 seg_list = jieba.cut_for_search(text)...

D:\ProgramData\Anaconda3\python.exe C:/Users/tongt/PycharmProjects/pythonProject/svm（一对多）.py <class 'pandas.core.frame.DataFrame'> RangeIndex: 9261 entries, 0 to 9260 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 text 9261 non-null object 1 distance 9261 non-null object dtypes: object(2) memory usage: 144.8+ KB None Building prefix dict from the default dictionary ... Loading model from cache C:\Users\tongt\AppData\Local\Temp\jieba.cache Loading model cost 0.359 seconds. Prefix dict has been built successfully. forrtl: error (200): program aborting due to control-C event Image PC Routine Line Source libifcoremd.dll 00007FFF297E3B58 Unknown Unknown Unknown KERNELBASE.dll 00007FFFACA1D4B5 Unknown Unknown Unknown KERNEL32.DLL 00007FFFADF855A0 Unknown Unknown Unknown ntdll.dll 00007FFFAF0E485B Unknown Unknown Unknown

这是一个比较奇怪的错误信息，似乎是在运行 svm（一对多）.py 文件的时候出现了问题。其中的 forrtl 错误可能与 Fortran 相关，而这个程序中并没有使用到 Fortran，因此需要进一步排查代码或者环境是否存在问题。...

Building prefix dict from the default dictionary ... Loading model from cache C:\Users\14901\AppData\Local\Temp\jieba.cache Loading model cost 0.541 seconds. Prefix dict has been built successfully. Traceback (most recent call last): File "C:\Users\14901\Desktop\Python抓取猫眼近10万条评论并分析\新建文件夹\可视化.py", line 19, in <module> bg_image = plt.imread('xin.jpg') File "C:\Users\14901\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\pyplot.py", line 2123, in imread return matplotlib.image.imread(fname, format) File "C:\Users\14901\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\image.py", line 1541, in imread with img_open(fname) as image: File "C:\Users\14901\AppData\Local\Programs\Python\Python310\lib\site-packages\PIL\Image.py", line 3131, in open fp = builtins.open(filename, "rb") FileNotFoundError: [Errno 2] No such file or directory: 'xin.jpg'

这个错误提示是说找不到文件名为 'xin.jpg' 的图片文件。可能是以下几个原因导致的： 1. 文件名拼写错误 2. 文件不存在于指定的路径中 3. 权限问题，无法读取文件请检查文件名是否正确，文件是否存在于指定的路径...

import pickle import numpy as np from keras.models import load_model from tensorflow.keras.preprocessing.sequence import pad_sequences # 导入字典 with open('word_dict.pk', 'rb') as f: word_dictionary = pickle.load(f) with open('label_dict.pk', 'rb') as f: output_dictionary = pickle.load(f) try: # 数据预处理 input_shape = 180 sent = "电视刚安装好，说实话，画质不怎么样，很差！" x = [[word_dictionary[word] for word in sent]] x = pad_sequences(maxlen=input_shape, sequences=x, padding='post', value=0) # 载入模型 model_save_path = 'model.h5' lstm_model = load_model(model_save_path) # 模型预测 y_predict = lstm_model.predict(x) label_dict = {v:k for k,v in output_dictionary.items()} print('输入语句: %s' % sent) print('情感预测结果: %s' % label_dict[np.argmax(y_predict)]) except KeyError as err: print("您输入的句子有汉字不在词汇表中，请重新输入！") print("不在词汇表中的单词为：%s." % err)

from keras.models import load_model from tensorflow.keras.preprocessing.sequence import pad_sequences # 导入字典 with open('word_dict.pk', 'rb') as f: word_dictionary = pickle.load(f) with open('...

class UserCache(UserMixin): REDIS_PREFIX = f"{PROJECT_NAME}-user" def init(self, alternative_id, userid=None, username=None, homepage=None, permissions=None): self.alternative_id = alternative_id self.userid = userid self.username = username self.homepage = homepage self.permissions = permissions or [] def get_id(self): return self.alternative_id def add_to_cache(self): values = { "alternative_id": self.alternative_id, "userid": self.userid, "username": self.username, "homepage": self.homepage, "permissions": ",".join(self.permissions), } redis_key = f"{self.REDIS_PREFIX}:{self.alternative_id}" redis_client.hmset(redis_key, values) redis_client.expire(redis_key, SIGNIN_LIFETIME) def del_user_cache(self): redis_client.delete(f"{self.REDIS_PREFIX}:{self.alternative_id}") @classmethod def load_from_cache(cls, alternative_id): redis_key = f"{cls.REDIS_PREFIX}:{alternative_id}" user_cache_dict = redis_client.hgetall(redis_key) if user_cache_dict: user_cache_dict['permissions'] = user_cache_dict.get('permissions', '').split(",") return cls(**user_cache_dict) 优化并提出建议，python3.8

以下是对代码的一些优化和建议： 1. 在类定义中，将 REDIS_PREFIX 定义为类变量，而... user_cache_dict['permissions'] = user_cache_dict.get('permissions', '').split(",") return cls(**user_cache_dict)

给下列代码加注释： def merge_accumulate_client_update(self, list_num_proc, list_state_dict, lr): total_num_proc = sum(list_num_proc) # merged_state_dict = dict() dict_keys = list_state_dict[0].keys() for state_dict in list_state_dict[1:]: assert state_dict.keys() == dict_keys # accumulate extra sgrad and remove from state_dict if self.use_adaptive and self.is_adj_round(): prefix = "extra." for state_dict in list_state_dict: del_list = [] for key, param in state_dict.items(): if key[:len(prefix)] == prefix: sgrad_key = key[len(prefix):] mask_0 = self.model.get_mask_by_name(sgrad_key) == 0. dense_sgrad = torch.zeros_like(mask_0, dtype=torch.float) dense_sgrad.masked_scatter_(mask_0, param) # no need to divide by lr self.control.accumulate(sgrad_key, dense_sgrad) del_list.append(key) for del_key in del_list: del state_dict[del_key]

# Remove the keys from the state_dict for del_key in del_list: del state_dict[del_key] 这段代码实现了一个merge_accumulate_client_update方法，主要功能是合并和累加list_state_dict中的状态字典...

MATLAB-四连杆机构的仿真+项目源码+文档说明

<项目介绍> - 四连杆机构的仿真 --m3_1.m: 位置问题求解 --m2_1.m: 速度问题求解 --FourLinkSim.slx: Simlink基于加速度方程的仿真 --FourLinkSim2.slx: Simscape简化模型仿真 --FourLinkSim3.slx: Simscape CAD模型仿真 - 不懂运行，下载完可以私聊问，可远程教学 1、该资源内项目代码都经过测试运行成功，功能ok的情况下才上传的，请放心下载使用！ 2、本项目适合计算机相关专业(如计科、人工智能、通信工程、自动化、电子信息等)的在校学生、老师或者企业员工下载学习，也适合小白学习进阶，当然也可作为毕设项目、课程设计、作业、项目初期立项演示等。 3、如果基础还行，也可在此代码基础上进行修改，以实现其他功能，也可用于毕设、课设、作业等。下载后请首先打开README.md文件（如有），仅供学习参考, 切勿用于商业用途。 --------

ridge_regression:用于岭回归的python代码（已实现以预测下个月的CO2浓度）

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Building prefix dict from the default dictionary ... Loading model from cache C:\Users\86150\AppData\Local\Temp\jieba.cache Loading model cost 0.734 seconds. Prefix dict has been built successfully.这种情况是怎么回事儿？怎么解决呢？

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