Saravanan666/pypi-org huggingface dataset:
Package: learnblock
LearnBlockLearnBlock is an educational programming tool for learning programming. It has been designed to facilitate the learning process starting with a visual programming language and progressing towards a professional programming language.LearnBlock is robot-agnostic, i.e. the same program can be executed in several robots. Clients for new robots can be easily created adding device interfaces and implementing hardware access methods for those interfaces. Examples of existing clients can be foundhereMain featuresAmong other features, Learnblock includes the following:Available for different physical robots (EBO, Cozmo, Thymio and EV3) and simulated ones (EBO under RCIS and EV3 under V-REP).Robots can be programmed using different languages: visual language, Block-Text (textual representation of the visual language) and Python.New blocks can be created from code using the tool itself or external tools.A program can be run and stopped at any moment. When a program is interrupted, the robot is properly stopped and disconnected.InstallationBefore installing LearnBlock, some dependencies must be satisfied as follows:sudo apt install cmakeTo use all the features of LearnBlock, you will also need to install additional software by executing the following command:sudo apt install python3-zeroc-iceThen, you can easily install LearnBlock by running the following command:sudo pip3 install learnblockThe first time you executeLearnBlock, you will be asked for the creation of a workspace directory, where your projects will be saved by default. Once you specify a directory, you can start using LearnBlock and create programs for your favorite robot.
Package: learnbot-components
learnbot_componentsThis project has been designed as a tool to learn different topics, like programing, logic, math, emotions, physic, robotic, ...The methodology used to teach this knowledges is the following:
The kids make programs, with a IDE (Learnblock) this programs and this programs control a smal robot (EBO).LearnblockThis program is the main.  It is here where the kids make his/her programs. This programs can be created with language similar to scratch, that is to say, using blocks.EBOEBO is a small robot, that run some components that offer interfaces to control it.How to Install learnbot_components?For install learnbot_components first is necessary install some dependencies:Step 1: Install driver of motorscd ~
git clone https://github.com/pololu/drv8835-motor-driver-rpi.git
cd drv8835-motor-driver-rpi
sudo python setup.py installStep 2: Install learnbot_componentspip3 install learnbot_components
Package: learncube
!EXPERIMENTAL!Learncube PythonInstallationLearncube-Python is available on PyPI:$ pip install learncubeUsageVirtual Classroom APINote: For more information visit theLearncube documentationClassrooms APIPrerequisitesfromlearncubeimportClassroompublic_key="..."private_key="..."v_c=Classroom(public_key=public_key,private_key=private_key)Listresponse = v_c.list_virtual_classroom(room_token="...")CreateNote: you can pass any information into the **kwargs`data={'room_token':randint(0,100),'cancelled':False,'description':"This is a test",'start':datetime.now().isoformat(),'end':datetime.now().isoformat(),'max_participants':2,'audio_only':False,'return_url':"",'record_class':False}response=v_c.create_virtual_classroom(room_token=data['room_token'],cancelled=data['cancelled'],description=data['description'],start=data['start'],end=data['end'],max_participants=data['max_participants'],audio_only=data['audio_only'],return_url=data['return_url'],record_class=data['record_class'])Readresponse=v_c.read_virtual_classroom(uuid='...')UpdateNote: you can pass any information into the**kwargsdata={'room_token':randint(0,100),'cancelled':False,'description':"This is a test",'start':datetime.now().isoformat(),'end':datetime.now().isoformat(),'max_participants':2,'audio_only':False,'return_url':"",'record_class':False}response=v_c.update_virtual_classroom(uuid=uuid,room_token=data['room_token'],cancelled=data['cancelled'],description=data['description'],start=data['start'],end=data['end'],max_participants=data['max_participants'],audio_only=data['audio_only'],return_url=data['return_url'],record_class=data['record_class'])Deleteresponse=v_c.delete_virtual_classroom(uuid=uuid)Participants APIPrerequisitesfromlearncubeimportParticipantspublic_key="..."private_key="..."p_c=Participants(public_key=public_key,private_key=private_key)Readresponse=p_c.read_participant(uuid='...')ListNote: you can pass any information into the**kwargsresponse=p_c.list_participants(room_token="...")Logs APIPrerequisitesfromlearncubeimportLogspublic_key="..."private_key="..."logs=Logs(public_key=public_key,private_key=private_key)Readresponse=logs.read_logs(uuid='...')ListNote: you can pass any information into the**kwargsresponse=logs.list_logs(room_token="...")OptionsUsing a custom api base pathBesides thepublic_keyandprivate_keyyou can provide anapi_base_pathattribute, which will overwrite the default endpoint.LinksBüffelheldLearncube
Package: learndash
LearnDash Python LibraryWarning, early alpha. Most functionality is missing.The LearnDash Python Library provides a simple wrapper for a LearnDash API.DocumentationSee theLearnDash API V2 Docs.InstallationpipinstalllearndashUsageimportlearndashlearndash.api_host=https://my-learndash-website.com# Auth is provided via wordpress user credentials when neededimportoslearndash.wordpress_un=os.environ.get('WORDPRESS_UN')learndash.wordpress_pw=os.environ.get('WORDPRESS_PW')# list Coursescourses=learndash.Course().list()print(courses)# retrieve specific Coursecourse=learndash.Course(12).retrieve()print(course)# add user to a coursecourse_id=1learndash.Course(1).users().update({'user_ids':[course_id]})Supported ResourcesThe Learndash V2 API is still in beta, and this library is still in development. Supported resources and examples are listed below.CourseRetrievelearndash.Course(1).retrieve()Listlearndash.Course().list()Course StepListlearndash.Course(1).steps().list()Updatelearndash.Course(1).steps().update({})Course PrerequisiteListlearndash.Course(1).prerequisites().list()Course UserListlearndash.Course(1).users().list()Updatelearndash.Course(1).users().update({'user_ids': []})Course GroupListlearndash.Course(1).groups().list()Updatelearndash.Course(1).groups().update()UserRetrievelearndash.User(1).retrieve()Listlearndash.User().list()User Course ProgressListlearndash.User(1).course_progress().list()User CourseListlearndash.User(1).courses().list()Updatelearndash.User(1).courses().update({'user_ids': []})User GroupListlearndash.User(1).groups().list()Updatelearndash.User(1).groups().update({})User Quiz ProgressListlearndash.User(1).quiz_progress().list()Configuring API PathsThe LearnDash Wordpress plugin allows admins to configure the paths for each API resource. By default, this library will use the LearnDash plugin's default paths, but you can reconfigure those paths.How To ConfigureSimply import the learndash module and overwrite the default path for an endpoint.importlearndashlearndash.path_courses='courses'# Leave out slashesConfigurable PathsAll configurable paths and their default values are below. Fromlearndash/__init__.py.path_assignments='sfwd-assignment'path_courses='sfwd-courses'path_course_steps='steps'path_course_prerequisites='prerequisites'path_course_users='users'path_course_groups='groups'path_essays='sfwd-essays'path_groups='groups'path_group_courses='courses'path_group_leaders='leaders'path_group_users='users'path_lessons='sfwd-lessons'path_price_types='price-types'path_questions='sfwd-question'path_question_types='question-types'path_quizzes='sfwd-quiz'path_quiz_statistics='statistics'path_quiz_statistics_questions='questions'path_topics='sfwd-topic'path_users='users'path_user_course_progress='course-progress'path_user_courses='courses'path_user_groups='groups'path_user_quiz_progress='course-progress'DependenciesRequires the requests library.
Package: learningcrux-dl
learningcrux-dlA utility to download courses/videos from learningcrux.com by usingYoutube-DLHow to useinstall from pypi.org$sudopipinstalllearningcrux-dlInstructionsThe tool can be accesed vialcx-dlcommand.
eg.$lcx-dl--urlhttps://www.learningcrux.com/course/some-courseMake sure you provide--urlor-uwhich is required.and it will download the whole course.You can also provide--output <your-path-here>to save on your desired directory.eg.$lcx-dl--urlhttps://www.learningcrux.com/course/some-course--output~/udemyand it will save in the given path.Command DocsFlagUsage-u--urlurl of course/video from learningcrux-o--outputOutput pathLimitationsCannot resume download. (will be fixed in future updates)Cannot download single video. (Releasing ASAP)Uses youtube-dl so downloading can be done sequencially (no multithreads for now)LicenseSwapnilSoni1999 (Unlicensed)Huge thanks to allContributors
Package: learning-orchestra-client
pythonClientPython client forlearningOrchestra.InstallationRequires Python 3.xpip install learning-orchestra-clientUsageEach interoperable REST API service described in Learning Orchestra is translated
into Python. Details atpython client docs.
Furthermore, some extra method calls are included into Python client API to simplify
even more the Machine Learning services. For instance, the REST API is asynchronous,
except for GET HTTP requests, but the Python client enables also the synchronous API calls.
The wait API method, useful to receive notifications from ML pipes, is another important
example to illustrate an extension of the original REST API.ExampleHerehas an example using theTitanic Dataset:Herehas an example using theSentiment Analysis On IMDb reviews:Herehas an example using theMNIST Dataset:
Package: learning-pipeline-plugin
learning-pipeline-pluginPlugin for Actcast application.
This plugin provides a base Pipe class for selecting and collecting data.UsageTo collect data, create a pipe that inherits fromlearning_pipeline_plugin.collect_pipe.CollectPipeBaseand defineinterpret_inputs().Example:fromtypingimportOptionalfromlearning_pipeline_plugin.collect_pipeimportCollectPipeBase,DataDictfromlearning_pipeline_pluginimportsender_taskclassCollectPipe(CollectPipeBase):definterpret_inputs(self,inputs)->Optional[DataDict]:img,probs,feature=inputsreturn{"image":img,"feature_vector":feature,"other_data":{"probabilities":probs}}interpret_inputs()gets the previous pipe output and must returnDataDictorNone.DataDictis TypedDict for type hint, and must have following properties:image: PIL.Imagefeature_vector: vector with shape (N,)other_data: any data used for calculating uncertaintyThen, create aSenderTaskinstance and pass it the pipeline_id parameter corresponding to your pipeline.defmain():[...]sender=sender_task.SenderTask(pipeline_id)Finally, instantiate yourCollectPipeand connect to other pipes:defmain():[...]collect_pipe=CollectPipe(...)prev_pipe.connect(collect_pipe)collect_pipe.connect(next_pipe)NotifierBy default, the information output by this plugin is logged as an actlog through the Notifier instance.
Users can decide what information is output (and in what format), using a custom notifier.To customize it, define a custom notifier class inheriting from AbstractNotifier,
and definenotify()which gets a message as str.
Then, instantiate and pass it to the CollectPipe constructor.Example of introducing a message length limit:fromdatetimeimportdatetime,timezoneimportactfw_corefromlearning_pipeline_plugin.notifierimportAbstractNotfierclassCustomNotifier(AbstractNotfier):defnotify(self,message:str):iflen(message)>128:message=message[:128]+" <truncated>"actfw_core.notify([{"info":message,"timestamp":datetime.now(timezone.utc).isoformat(),}])defmain():[...]collect_pipe=CollectPipe(...,notifier=CustomNotifier())
Package: learning-to-rank
Learning_to_rank 一个传统学习排序算法库工具包说明•当前的Learning to rank 工具包，Ranklib基于java开发，TRanking基于Tensorflow开发，XGBoost，LightGBM基于树结构的模型•开发一个传统Learning to rank的工具包，涉及到神经网络部分用pytorch编写•熟悉老师上课的知识点 & 更简单，轻便的LTR模型用于实验与教学工作•实现算法的效率可以接近这些模型的最好水平算法实现Pointwise：MLPPairwise：RankNet，RankSVM， LambdaRank，RankBoostListwise：LambdaMar, ListNet, ListMLE实现流程数据处理，特征提取，评分函数构建需求安装需求
torch>=1.10.0
numpy>=1.17.2
scikit_learn>=0.23.2
pickle
tqdm>=4.48.2安装使用pip方式安装pip install learning-to-rank从github上clone$ git clone https://github.com/guankaisi/learning-to-rank.git
$ cd learning-to-rank
$ pip install -r requirements.txt
$ python setup.py install使用说明提供了test.py测试脚本首先调用你想要使用的模型，调用data_readerfromlearning_to_rank.listwiseimportLambdaMART,ListNet,ListMLEfromlearning_to_rank.pairwiseimportRankNet,LambdaRankfromlearning_to_rank.poinstwiseimportNRegressionfromlearning_to_rank.utilsimportdata_reader这里我们用LETOR 4.0 中的MQ2008进行实验用data_reader进行数据读取foriin[1,2,3,4,5]:print('start Fold '+str(i))training_data=data_reader('MQ2008/Fold%d/train.txt'%(i))test_data=data_reader('MQ2008/Fold%d/test.txt'%(i)定义模型，并通过model.fit()开始训练model = LambdaMART(training_data, number_of_trees=100, lr=0.001,max_depth=50)
model.fit()模型验证，定义ndcg@k中的k值average_ndcg,predicted_scores=model.validate(test_data,k=10)print(average_ndcg)这里面直接计算给出了ndcg，如果要计算其他指标，可以根据predicted_scores直接计算决策树可以直接可视化，层数太深可视化会不清晰，所以该可视化决策树层数只有5层importmatplotlib.pyplotaspltfromsklearnimporttreetree.plot_tree(model.trees[0])plt.show()达到效果上述LambdaMart模型：NDCG@10Fold10.7275983469048656Fold20.6850899950641907Fold30.6654757957913352Fold40.7038660130946692Fold50.699629816361395Average0.6963319934432912当前lambdaMART在LETOR4.0上的SOTA为：0.822891我实现的LambdaMart算法和sota相比有一定的距离可能的原因：（1）由于设备计算资源和时间限制，num_of_trees并没有设特别大（2）sklearn的决策树算法和XGBoost，lightGBM工具包相比有着效率和效果上的劣势
Package: learnkeyhole
使用U-net作为模型主体用于训练和预测匙孔图像目录仓库更新 Top News相关仓库 Related code性能情况 Performance所需环境 Environment训练步骤 How2train预测步骤 How2predict评估步骤 miou参考资料 ReferenceTop News2022-03:进行大幅度更新、支持step、cos学习率下降法、支持adam、sgd优化器选择、支持学习率根据batch_size自适应调整。BiliBili视频中的原仓库地址为：https://github.com/bubbliiiing/unet-pytorch/tree/bilibili2020-08:创建仓库、支持多backbone、支持数据miou评估、标注数据处理、大量注释等。相关仓库模型路径Unethttps://github.com/bubbliiiing/unet-pytorchPSPnethttps://github.com/bubbliiiing/pspnet-pytorchdeeplabv3+https://github.com/bubbliiiing/deeplabv3-plus-pytorch性能情况unet并不适合VOC此类数据集，其更适合特征少，需要浅层特征的医药数据集之类的。训练数据集权值文件名称测试数据集输入图片大小mIOUVOC12+SBDunet_vgg_voc.pthVOC-Val12512x51258.78VOC12+SBDunet_resnet_voc.pthVOC-Val12512x51267.53所需环境scipy==1.10.0
numpy==1.24.2
matplotlib==3.7.1
opencv_python==4.1.2.30
oonx==1.13.0
torch==1.7.1+cu110
torchvision==0.8.2+cu110
tqdm==4.64.1
pillow==9.4.0
h5py==3.8.0
PyQt5==5.15.7训练步骤一、训练voc数据集1、将我提供的voc数据集放入VOCdevkit中（无需运行voc_annotation.py）。2、运行train.py进行训练，默认参数已经对应voc数据集所需要的参数了。二、训练自己的数据集1、本文使用VOC格式进行训练。2、训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的SegmentationClass中。3、训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。4、在训练前利用voc_annotation.py文件生成对应的txt。5、注意修改train.py的num_classes为分类个数+1。6、运行train.py即可开始训练。预测步骤一、使用预训练权重a、VOC预训练权重下载完库后解压，如果想要利用voc训练好的权重进行预测，在百度网盘或者release下载权值，放入model_data，运行即可预测。img/street.jpg在predict.py里面进行设置可以进行fps测试和video视频检测。二、使用自己训练的权重按照训练步骤训练。在unet.py文件里面，在如下部分修改model_path、backbone和num_classes使其对应训练好的文件；model_path对应logs文件夹下面的权值文件。_defaults={#-------------------------------------------------------------------##   model_path指向logs文件夹下的权值文件#   训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。#   验证集损失较低不代表miou较高，仅代表该权值在验证集上泛化性能较好。#-------------------------------------------------------------------#"model_path":'model_data/unet_vgg_voc.pth',#--------------------------------##   所需要区分的类的个数+1#--------------------------------#"num_classes":21,#--------------------------------##   所使用的的主干网络：vgg、resnet50#--------------------------------#"backbone":"vgg",#--------------------------------##   输入图片的大小#--------------------------------#"input_shape":[512,512],#--------------------------------##   blend参数用于控制是否#   让识别结果和原图混合#--------------------------------#"blend":True,#--------------------------------##   是否使用Cuda#   没有GPU可以设置成False#--------------------------------#"cuda":True,}运行predict.py，输入img/street.jpg在predict.py里面进行设置可以进行fps测试和video视频检测。评估步骤1、设置get_miou.py里面的num_classes为预测的类的数量加1。2、设置get_miou.py里面的name_classes为需要去区分的类别。3、运行get_miou.py即可获得miou大小。Referencehttps://github.com/ggyyzm/pytorch_segmentationhttps://github.com/bonlime/keras-deeplab-v3-plus
Package: learnml
LearnMLLearnML is a Python module for Machine Learning and Deep Learning.InstallationRequirementsPython 3.6+Stable releaseTo install learnml, run this command in your terminal:pip install learnmlThis is the preferred method to install learnml, as it will always install the most recent stable release.If you don't havepipinstalled, thisPython installation guidecan guide you through the process.From sourcesThe sources for learnml can be downloaded from theGithub repo.You can either clone the public repository:git clone git://github.com/Vipul97/learnmlOr download thetarball:curl -OJL https://github.com/Vipul97/learnml/tarball/masterOnce you have a copy of the source, you can install it with:python setup.py installContributingPlease readCONTRIBUTING.mdfor details on code of conduct, and the process for submitting pull requests.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: learnpyOvercompensator9000
ovc is a module that simplifies python for young and enthusiastic programmers who find python to be complicatedHere are the functions of the module ovcDisplay() - this function is used to display text or any other value to the user via your display or monitor
ex - ovc.Display("Hello World!") -- Output: Hello World!
ovc.Display(1) -- Output: 1UserInput(prompt,itype) - this allows the program to get a certain value from the user and the type of value could also be asked as well
ex - ovc.UserInput("Enter your name",'string') -- Output: Enter your name
Michael Davis
ovc.UserInput("Enter a number",'integer') -- Output: Enter a number
2Calculator(num1,num2,operator) - this function recieves two numbers and an operator(ex: +,-,*,/) to make a simple arithmetic calculation
ex - ovc.Display(ovc.Calculator(1,2,"+")) -- Output: 3Datatype(value,dtype) - this function allows the user to give a certain value and declare it's Datatype
ex - ovc.Display(ovc.Datatype(2,'integer')) -- Output: 2Bool(val) - This function would be used to delcare a variable in two ways, true or false
ex - a=ovc.Bool('is true') or a=ovc.Bool('is false')OpenFile(filename,mode) - this function would allow the program to open a text file to read data, write data or add data to it
ex - myfile=ovc.OpenFile('Myfile.txt','collect data')ReadfromFile(filehandle) - this function is used to read the contents of a text file
ex - paragraph = ovc.ReadfromFile(myfile)WriteToFile(filehandle) - this function is used to write some text to a text file
ex - ovc.WriteToFile(myfile,"My name is Jimmy Johnson")CloseFile(filehandle) - this function closes a text file that was opened in the program
ex - ovc.CloseFile(myfile)
Package: learn-python-ceg-test
Learn Python for Civil Engineering and Geoscience test answersA Python package for validating online courseLearn Python for Civil Engineering and Geosciencesanswers.Installation and updatingUse the package managerpipto install the last stable version of the package.pipinstalllearn-python-ceg-testUsageThe package allows for evaluating the answers to the exercises in sections 3, 4 and 6.Demo of some of the features:importlearn_python_ceg_test.check_answersascheckcheck.notebook_3(question_number=0,arguments=[car_info,message])ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseGNUAcknowledgmentsFinancial support for this project was provided through an open education grant from the Educational Management Team for the Civil Engineering and Geosciences Faculty at Delft. The content was first developed for Summer 2022 by Sandra Verhagen and a team of TA's in Jupyter notebooks that were auto-graded in Vocareum. For Summer 2023 a second round of funding was optained to update the content and adapt it to an entirely open and self-paced course without enrollement via a Jupyter Book, led by Robert Lanzafame. Special thanks goes to Miguel Mendoza Lugo who adapted the notebooks into the Jupyter Book format and implemented the interactive features, as well as Ahmed Farahat, who helped create the fun new visual features that make understanding the material easier in the Jupyter Book platform, as well as the In a Nutshell summaries.This Book is maintained and developed by staff of the Faculty of Civil Engineering and Geosciences of TU Delft, the Netherlands.
Package: learn-python-check
Learn Python for Civil Engineering and Geoscience check answersA Python package for validating online courseLearn Python for Civil Engineering and Geosciencesanswers.Installation and updatingUse the package managerpipto install the last stable version of the package.pipinstalllearn-python-checkUsageThe package allows for evaluating the answers to the exercises in sections 3, 4 and 6.Demo of some of the features:importlearn_python_check.check_answersascheckcheck.notebook_3(question_number=0,arguments=[car_info,message])ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseGNUAcknowledgmentsFinancial support for this project was provided through an open education grant from the Educational Management Team for the Civil Engineering and Geosciences Faculty at Delft. The content was first developed for Summer 2022 by Sandra Verhagen and a team of TA's in Jupyter notebooks that were auto-graded in Vocareum. For Summer 2023 a second round of funding was optained to update the content and adapt it to an entirely open and self-paced course without enrollement via a Jupyter Book, led by Robert Lanzafame. Special thanks goes to Miguel Mendoza Lugo who adapted the notebooks into the Jupyter Book format and implemented the interactive features, as well as Ahmed Farahat, who helped create the fun new visual features that make understanding the material easier in the Jupyter Book platform, as well as the In a Nutshell summaries.This Book is maintained and developed by staff of the Faculty of Civil Engineering and Geosciences of TU Delft, the Netherlands.
Package: learn-python-ms
Learning Java as MicroservicePython provides such and such and so and so. You might find
it most useful for tasks involving  and also . Typical usage
often looks like this::#!/usr/bin/env python

from towelstuff import location
from towelstuff import utils

if utils.has_towel():
    print "Your towel is located:", location.where_is_my_towel()(Note the double-colon and 4-space indent formatting above.)Paragraphs are separated by blank lines.Italics,bold,
andmonospacelook like this.A SectionLists look like this:FirstSecond. Can be multiple lines
but must be indented properly.A Sub-SectionNumbered lists look like you'd expect:hi theremust be goingUrls arehttp://like.thisand links can be
writtenlike this <http://www.example.com/foo/bar>_.
Package: learnregex
learnregexA pyschool story for learning regular expressions.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.Licenselearnregex is Copyright (c) 2016-2017 sophilabs, inc. It is free software, and may be
redistributed under the terms specified in thelicensefile.Aboutlearnregex is maintained and funded by sophilabs, inc. The names and logos for
sophilabs are trademarks of sophilabs, inc.History0.1.0 (2017-04-05)First release on PyPI.