iOS 14.5镜像包下载指南:Xcode调试与路径设置

下载需积分: 9 | ZIP格式 | 19.13MB | 更新于2025-01-03 | 195 浏览量 | 0 下载量 举报
收藏
资源摘要信息:"该资源是iOS 14.5的开发环境镜像包,压缩包文件名为'14.5(FromXcode_12.5_Release_Candidate_xip).zip'。该镜像包包含了Xcode 12.5的候选版,开发者可以通过安装此镜像包来获取iOS 14.5的开发调试环境。在安装完毕后,开发者可以在Xcode中直接调试iOS 14.5系统,前提是正确配置了Xcode的路径。如果在使用过程中遇到任何问题,建议重启电脑以解决问题。" 知识点详细说明: 1. iOS 14.5: 这是苹果公司推出的iOS系统的版本号,用于描述苹果设备上运行的操作系统。iOS 14.5版本主要增加了许多新功能,如AirTag支持、App隐私报告等,同时对于现有功能也进行了大量的优化和改进。对于开发者而言,随着新版本的发布,意味着需要更新自己的开发环境以支持新系统的功能和API。 2. Xcode: Xcode是苹果公司开发的一个集成开发环境(IDE),专门为Mac OS和iOS平台的应用开发而设计。它包括了一系列开发工具,如编译器、调试器和性能分析器,以及一个用于设计用户界面的图形编辑器。Xcode也提供了模拟器,允许开发者在没有真实设备的情况下测试他们的应用程序。 3. Xcode版本号: Xcode 12.5 Release Candidate指的是Xcode的候选发布版本,它是一个测试版本,旨在让开发者和测试者发现并报告潜在的问题,以便在最终版本发布前进行修正。在这个版本中,开发者可以体验到iOS 14.5的新特性和更新。 4. 镜像包: 镜像包通常用于软件安装,它是一个包含了操作系统、软件或系统状态完整映像的文件,可以通过特定的工具进行解压安装。在这个上下文中,'14.5(FromXcode_12.5_Release_Candidate_xip).zip'镜像包允许用户安装iOS 14.5版本的Xcode,包含所有必要的支持文件和配置。 5. 路径配置: 在安装完Xcode之后,为了能够调试iOS 14.5系统,开发者需要确保Xcode能够识别到正确的iOS设备支持文件的路径。路径通常位于'/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/DeviceSupport'。如果在调试过程中遇到问题,可能需要检查和修改这个路径。 6. 重启电脑: 在某些情况下,尤其是安装了新的开发环境或软件包后,可能需要重启电脑以确保所有的改动都已正确应用,并且新环境能够被系统完全识别。这是一个常见的故障排除步骤。 7. 标签应用: 在这里,标签"ios镜像 14.5 ios14.5"用于描述和分类资源。这些标签让开发者能够快速识别资源与iOS 14.5版本相关,并用于搜索和筛选资源。 8. 压缩文件的命名: 压缩包文件名为'14.5(FromXcode_12.5_Release_Candidate_xip).zip',这说明了文件的版本信息和来源,便于用户识别和下载正确的资源。 以上内容提供了关于iOS 14.5镜像包的详细知识点,包括Xcode的介绍、如何处理和安装镜像包,以及在调试过程中可能遇到的问题和解决方法。这对于iOS应用开发者来说是十分重要的信息。

相关推荐

filetype

import pandas as pd import math as mt import numpy as np from sklearn.model_selection import train_test_split from Recommenders import SVDRecommender triplet_dataset_sub_song_merged = triplet_dataset_sub_song_mergedpd triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user','listen_count']].groupby('user').sum().reset_index() triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count':'total_listen_count'},inplace=True) triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged,triplet_dataset_sub_song_merged_sum_df) triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count']/triplet_dataset_sub_song_merged small_set = triplet_dataset_sub_song_merged user_codes = small_set.user.drop_duplicates().reset_index() song_codes = small_set.song.drop_duplicates().reset_index() user_codes.rename(columns={'index':'user_index'}, inplace=True) song_codes.rename(columns={'index':'song_index'}, inplace=True) song_codes['so_index_value'] = list(song_codes.index) user_codes['us_index_value'] = list(user_codes.index) small_set = pd.merge(small_set,song_codes,how='left') small_set = pd.merge(small_set,user_codes,how='left') mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']] data_array = mat_candidate.fractional_play_count.values row_array = mat_candidate.us_index_value.values col_array = mat_candidate.so_index_value.values data_sparse = coo_matrix((data_array, (row_array, col_array)),dtype=float) K=50 urm = data_sparse MAX_PID = urm.shape[1] MAX_UID = urm.shape[0] recommender = SVDRecommender(K) U, S, Vt = recommender.fit(urm) Compute recommendations for test users uTest = [1,6,7,8,23] uTest_recommended_items = recommender.recommend(uTest, urm, 10) Output recommended songs in a dataframe recommendations = pd.DataFrame(columns=['user','song', 'score','rank']) for user in uTest: rank = 1 for song_index in uTest_recommended_items[user, 0:10]: song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] # Get song details recommendations = recommendations.append({'user': user, 'song': song['title'], 'score': song['fractional_play_count'], 'rank': rank}, ignore_index=True) rank += 1 display(recommendations)这段代码报错了,为什么?给出修改后的 代码

102 浏览量
filetype

将上述代码放入了Recommenders.py文件中,作为一个自定义工具包。将下列代码中调用scipy包中svd的部分。转为使用Recommenders.py工具包中封装的svd方法。给出修改后的完整代码。import pandas as pd import math as mt import numpy as np from sklearn.model_selection import train_test_split from Recommenders import * from scipy.sparse.linalg import svds from scipy.sparse import coo_matrix from scipy.sparse import csc_matrix # Load and preprocess data triplet_dataset_sub_song_merged = triplet_dataset_sub_song_mergedpd # load dataset triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user','listen_count']].groupby('user').sum().reset_index() triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count':'total_listen_count'},inplace=True) triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged,triplet_dataset_sub_song_merged_sum_df) triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count']/triplet_dataset_sub_song_merged['total_listen_count'] # Convert data to sparse matrix format small_set = triplet_dataset_sub_song_merged user_codes = small_set.user.drop_duplicates().reset_index() song_codes = small_set.song.drop_duplicates().reset_index() user_codes.rename(columns={'index':'user_index'}, inplace=True) song_codes.rename(columns={'index':'song_index'}, inplace=True) song_codes['so_index_value'] = list(song_codes.index) user_codes['us_index_value'] = list(user_codes.index) small_set = pd.merge(small_set,song_codes,how='left') small_set = pd.merge(small_set,user_codes,how='left') mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']] data_array = mat_candidate.fractional_play_count.values row_array = mat_candidate.us_index_value.values col_array = mat_candidate.so_index_value.values data_sparse = coo_matrix((data_array, (row_array, col_array)),dtype=float) # Compute SVD def compute_svd(urm, K): U, s, Vt = svds(urm, K) dim = (len(s), len(s)) S = np.zeros(dim, dtype=np.float32) for i in range(0, len(s)): S[i,i] = mt.sqrt(s[i]) U = csc_matrix(U, dtype=np.float32) S = csc_matrix(S, dtype=np.float32) Vt = csc_matrix(Vt, dtype=np.float32) return U, S, Vt def compute_estimated_matrix(urm, U, S, Vt, uTest, K, test): rightTerm = S*Vt max_recommendation = 10 estimatedRatings = np.zeros(shape=(MAX_UID, MAX_PID), dtype=np.float16) recomendRatings = np.zeros(shape=(MAX_UID,max_recommendation ), dtype=np.float16) for userTest in uTest: prod = U[userTest, :]*rightTerm estimatedRatings[userTest, :] = prod.todense() recomendRatings[userTest, :] = (-estimatedRatings[userTest, :]).argsort()[:max_recommendation] return recomendRatings K=50 # number of factors urm = data_sparse MAX_PID = urm.shape[1] MAX_UID = urm.shape[0] U, S, Vt = compute_svd(urm, K) # Compute recommendations for test users # Compute recommendations for test users uTest = [1,6,7,8,23] uTest_recommended_items = compute_estimated_matrix(urm, U, S, Vt, uTest, K, True) # Output recommended songs in a dataframe recommendations = pd.DataFrame(columns=['user','song', 'score','rank']) for user in uTest: rank = 1 for song_index in uTest_recommended_items[user, 0:10]: song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] # Get song details recommendations = recommendations.append({'user': user, 'song': song['title'], 'score': song['fractional_play_count'], 'rank': rank}, ignore_index=True) rank += 1 display(recommendations)

82 浏览量
filetype

ERROR: Exception: Traceback (most recent call last): File "E:\Python\phthon397\lib\site-packages\pip\_internal\cli\base_command.py", line 173, in _main status = self.run(options, args) File "E:\Python\phthon397\lib\site-packages\pip\_internal\cli\req_command.py", line 203, in wrapper return func(self, options, args) File "E:\Python\phthon397\lib\site-packages\pip\_internal\commands\install.py", line 315, in run requirement_set = resolver.resolve( File "E:\Python\phthon397\lib\site-packages\pip\_internal\resolution\resolvelib\resolver.py", line 75, in resolve collected = self.factory.collect_root_requirements(root_reqs) File "E:\Python\phthon397\lib\site-packages\pip\_internal\resolution\resolvelib\factory.py", line 471, in collect_root_requirements req = self._make_requirement_from_install_req( File "E:\Python\phthon397\lib\site-packages\pip\_internal\resolution\resolvelib\factory.py", line 433, in _make_requirement_from_install_req cand = self._make_candidate_from_link( File "E:\Python\phthon397\lib\site-packages\pip\_internal\resolution\resolvelib\factory.py", line 204, in _make_candidate_from_link self._link_candidate_cache[link] = LinkCandidate( File "E:\Python\phthon397\lib\site-packages\pip\_internal\resolution\resolvelib\candidates.py", line 295, in __init__ super().__init__( File "E:\Python\phthon397\lib\site-packages\pip\_internal\resolution\resolvelib\candidates.py", line 156, in __init__ self.dist = self._prepare() File "E:\Python\phthon397\lib\site-packages\pip\_internal\resolution\resolvelib\candidates.py", line 227, in _prepare dist = self._prepare_distribution() File "E:\Python\phthon397\lib\site-packages\pip\_internal\resolution\resolvelib\candidates.py", line 305, in _prepare_distribution return self._factory.preparer.prepare_linked_requirement( File "E:\Python\phthon397\lib\site-packages\pip\_internal\operations\prepare.py", line 508, in prepare_linked_requirement return self._prepare_linked_requirement(req, parallel_builds) File "E:\Python\phthon397\lib\site-packages\pip\_internal\operations\prepare.py", line 570, in _prepare_linked_requirement dist = _get_prepared_distribution( File "E:\Python\phthon397\lib\site-packages\pip\_internal\operations\prepare.py", line 61, in _get_prepared_distribution return abstract_dist.get_pkg_resources_distribution() File "E:\Python\phthon397\lib\site-packages\pip\_internal\distributions\wheel.py", line 26, in get_pkg_resources_distribution with ZipFile(self.req.local_file_path, allowZip64=True) as z: File "E:\Python\phthon397\lib\zipfile.py", line 1257, in __init__ self._RealGetContents() File "E:\Python\phthon397\lib\zipfile.py", line 1324, in _RealGetContents raise BadZipFile("File is not a zip file") zipfile.BadZipFile: File is not a zip file WARNING: You are using pip version 21.2.3; however, version 23.1.2 is available. You should consider upgrading via the 'E:\Python\phthon397\python.exe -m pip install --upgrade pip' command.

111 浏览量