Saravanan666/pypi-org huggingface dataset:
Package: l0bnb
L0BnB: Sparse Regression at ScaleHussein Hazimeh, Rahul Mazumder, and Ali SaabMassachusetts Institute of TechnologyIntroductionL0BnB is a scalable global optimization framework for solving linear regression problems penalized with a combination of the L0 and L2 norms. More concretely, given a data matrix X (with n samples and p features) and a response vector y, L0BnB solves the following problem to optimality:where the L0 norm counts the number of nonzeros in the coefficients vector B. Here the L0 norm performs variable selection, while the L2 norm adds shrinkage which can be effective in low-signal settings. L0BnB implements a custom branch-and-bound (BnB) framework that leverages a highly specialized first-order method to solve the node subproblems. It achieves over 3600x speed-ups compared to the state-of-the-art mixed integer programming (MIP) solvers, and can scale to problems where the number of features p ~ 10^7. For more details, check out our paperSparse Regression at Scale: Branch-and-Bound rooted in First Order Optimization(arXiv link).InstallationThe toolkit is implemented in Python 3. To install it, run the following command:pipinstalll0bnbA Quick Start in Pythonimportnumpyasnpfroml0bnbimportfit_pathfroml0bnbimportgen_synthetic"""For demonstration, we first generate a synthetic regression dataset (X,y)as follows: y = X*b + epsilon, where the true vector of coefficients bis sparse and has only 10 nonzero entries.We set the number of samples n=1000 and number of features p=10,000."""X,y,b=gen_synthetic(n=1000,p=10000,supp_size=10)print("Nonzero indices in b: ",np.nonzero(b)[0])"""Run L0BnB to solve the problem for a sequence of lambda_0's.By default, the sequence of lambda_0's is automatically chosen by the toolkit.Use max_nonzeros=10 to stop the regularization path when it exceeds 10 nonzeros.Here we fix lambda_2 = 0.01 (generally, this is data-dependent)."""sols=fit_path(X,y,lambda_2=0.01,max_nonzeros=10)"""sols is a list of solutions, each corresponding to a different lambda_0.Below we inspect the solution with index 4.The estimated coefficients vector "b_estimated" and the intercept term can be accessed as follows:"""b_estimated=sols[4]["B"]# a numpy array.intercept=sols[4]["B0"]# To check the nonzero indices in b_estimated:print("Nonzero indices in b_estimated: ",np.nonzero(b_estimated)[0])# The nonzero indices in b_estimated match that of b.# Predictions on the training data can be made as follows:y_estimated=np.dot(X,b_estimated)+intercept# For more advanced usage, check the documentation of fit_path:print(fit_path.__doc__)ReferencesIf you find L0BnB useful in your research, please consider citing the following paper:@article{hazimeh2020sparse,
  title={Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization},
  author={Hazimeh, Hussein and Mazumder, Rahul and Saab, Ali},
  journal={arXiv preprint arXiv:2004.06152},
  year={2020}
}
Package: l0n0lhttputils
简单服务器开发框架1.使用文档安装pip install l0n0lhttputils使用方法，以微信登录验证为例# 配置文件 config.yamlmysql_host:localhostmysql_port:13306mysql_user:rootmysql_password:'123'mysql_db:'test'token_expire_time:120listen_host:localhostlisten_port:12345appid:111secret_key:xxxxxxxximportloggingimportrequestsfroml0n0lhttputils.config_fileimportyaml_filefroml0n0lhttputils.http_serverimporthttp_server,webfroml0n0lhttputils.dbmysqlimportdbmysqlfroml0n0lhttputils.token_mgrimporttoken_mgr,check_token# 加载配置g_config=yaml_file("config.yaml")g_config.load_config()# mysqldb=dbmysql(g_config.get("mysql_host"),g_config.get("mysql_port"),g_config.get("mysql_user"),g_config.get("mysql_password"),g_config.get("mysql_db"))# 用户csrf tokeng_token_mgr:token_mgr=token_mgr()g_token_mgr.token_timestamp=g_config.get("token_expire_time")# http 服务器g_server=http_server(g_config.get("listen_host"),g_config.get("listen_port"))session_keys={}@g_server.route("get","/{prefix}/login")asyncdeflogin(request:web.Request):"""用户登录@code:微信提供的code"""# 获取用户的微信coderequest_data=awaitrequest.json()# 向微信验证用户的code是否正确，并获取用户的openidcheck_result=requests.get("https://api.weixin.qq.com/sns/jscode2session",params={'appid':g_config.get("appid"),"secret":g_config.get("secret_key"),"js_code":request_data['code'],"grant_type":'authorization_code'})# 判断验证时网络是否正常ifcheck_result.status_code!=200:returnweb.json_response({"errMsg":"向微信检查登录状态错误"})# 获取微信的验证结果，openidjson_value=check_result.json()openid=json_value['openid']# 生成本次会话的token，防止csrf攻击token=g_token_mgr.gen_token(openid)# 保存会话keysession_keys[openid]=json_value['session_key']# 检测是否有该用户,没有则创建该用户db.post("insert into `users` (`openid`) values (%s) on duplicate key update `openid` = values(`openid`)",[openid])# 验证成功，把会话token返回给用户returnweb.json_response({"errMsg":"OK","token":token,})@g_server.route("post","/{prefix}/user_data")@check_token(g_token_mgr)# check_token 会检测用户http请求中的  header中是否有 "token" headerasyncdefuser_data(request:web.Request):"""获取用户基本数据"""data=db.get("select * from `users` where `openid` =%s",[request.openid])ifnotdata:returnweb.json_response({"errMsg":"服务器错误"})# 验证成功，把会话token返回给用户returnweb.json_response({"errMsg":"OK","data":data,})froml0n0lhttputils.runnerimportrunif__name__=="__main__":logging.basicConfig(filename="testserver.log",level=logging.INFO)g_server.start()run()
Package: l0n0lnat
内网穿透（命令行）服务器命令usage: l0n0lnatserver [-h] listenhost listenport password

创建内网穿透服务器

positional arguments:
  listenhost  监听host
  listenport  监听端口
  password    密钥

optional arguments:
  -h, --help  show this help message and exit客户端命令usage: l0n0lnatclient [-h] serverhost serverport serverlistenport localhost localport password

创建内网穿透客户端

positional arguments:
  serverhost        监听host
  serverport        监听端口
  serverlistenport  服务器要监听端口
  localhost         本地服务host
  localport         本地服务端口
  password          密钥

optional arguments:
  -h, --help        show this help message and exit使用实例比如在内网有一个nginx(localhost:80)
服务器为 x.x.x.x

1.在服务器执行
l0n0lnatserver 0.0.0.0 12345 passwd

2.在客户端执行
l0n0lnatclient x.x.x.x 12345 8080 localhost 80 passwd

3.访问http://x.x.x.x:8080
Package: l0n0lrdp
简介软件主要功能是，可以在网页上实时查看另一台电脑上的屏幕内容。主要玩法：将手机，ipad，平板等作为电脑的第二屏幕安装第一步，安装python请到python官网或者miniconda官网第二步，安装l0n0lrdppip install l0n0lrdp参数l0n0lrdp -h
usage: l0n0lrdp [-h] [-l HOST:PORT]

录屏观看

options:
  -h, --help            show this help message and exit
  -l HOST:PORT, --listen HOST:PORT
                        The host and port to listen on使用方法第一步，启动服务器~$ l0n0lrdp -l x.x.x.x:5500
======== Running on http://x.x.x.x:5500 ========
(Press CTRL+C to quit)或者使用默认~$ l0n0lrdp 
======== Running on http://0.0.0.0:5500 ========
(Press CTRL+C to quit)第二步, 在被监测端和监测端浏览器打开目标网址http://x.x.x.x:5500/index.html被监测端 ID: 0监测端 ID: 1第三步，在监测端点击 [点击观看0号目标]第四步，在被监测端选择共享屏幕第五步，监测
Package: l0n0lutils
一些好用的函数和类1.配置文件yaml_file yaml 格式的配置文件config.yamlconfig.yaml
host: localhost
port: 123config.xlsx测试代码froml0n0lutils.filesimport*config=simple_load_config(yaml_file)print(config.get("host"))print(config.get("port"))config=simple_load_config(xl_file)print(config.get(1))print(config.get(2))print(config.data)config=simple_load_config(xl_kv_file)print(config.get(1))print(config.get(2))print(config.data)输出结果：localhost
1234
{'id': 1, 'a': 'asdf', 'b': 1.2, 'c': 4}
{'id': 2, 'a': 'adsf', 'b': 2.2, 'c': 5}
{1: {'id': 1, 'a': 'asdf', 'b': 1.2, 'c': 4}, 2: {'id': 2, 'a': 'adsf', 'b': 2.2, 'c': 5}, 3: {'id': 3, 'a': 'fda', 'b': 3.2, 'c': 6}, 4: {'id': 4, 'a': 'dae', 'b': 4.2, 'c': 7}}
asdf
adsf
{'int': 'string', 1.0: 'asdf', 2.0: 'adsf', 3.0: 'fda', 4.0: 'dae'}xl_file excel格式的配置文件
Package: l0n0lvedio2image
安装pip install l0n0lvedio2image介绍本工具需要在命令行中使用将视频转换为png图片usage: l0n0lvedio2img [-h] [-f FPS] vedio_path image_path

positional arguments:
  vedio_path         视频路径
  image_path         输出图片的路径

optional arguments:
  -h, --help         show this help message and exit
  -f FPS, --fps FPS  每多少帧输出一次例如:l0n0lvedio2img vedio.mp4 ./outimg -f 5执行完毕后，会在./outimg文件夹下生成视频每5帧的png图片将图片转换为mp4(h.264)视频usage: l0n0limg2vedio [-h] [-f FPS] image_path vedio_path

positional arguments:
  image_path         图片目录
  vedio_path         目标视频文件名

optional arguments:
  -h, --help         show this help message and exit
  -f FPS, --fps FPS  视频的帧率例如:l0n0limg2vedio ./outimg vedio2.mp4 -f 5执行完毕后，将会读取./outimg文件夹下图片, 按照文件名排序， 然后按照每5帧一张图，生成视频
Package: l10n
l10nA library and CLI for translating Python applications and libraries.Our main design principle is "Simple things should be simple, complex things should be possible". We streamline the workflow that fits 95% of the projects and allow to configure every small detail to make it possible to fit the remaining 5%. Think about l10n as a high-level and friendly library comparing to low-level and less friendlylocale,gettext, andbabel.Features:Simple. We stripped away all unnecessary steps and concepts. All that's left is what is actually relevant for Python.Type-safe. All other tools match translation.Explicit. No global state, no variable injection. You know exactly what gets translated and to what language.Zero-dependency runtime. There are a few small dependencies for CLI but they get installed only on your dev environment. On the production goes only one small library, l10n  itself.Pure Python. You can use it with PyPy, Numba, and any other interpreter.Zero configuration. The tool knows about the modern Python packaging and automatically discovers the project structure, name, version, and all other relevant metadata.Well documented. We make sure that you can pick up the tool without any prior knowledge of the topic.Compatible with other tools. We use*.poand*.mofiles that are compatible withgettexttoolchain and all other translation tools.Self-sufficient, no other tools required.Asyncio-compatible and race-condition-free.Fast. All teanslations on the production are compiled into a small and fast binary format.Small. We don't include any generated data into the library distribution. Just a little of Python code.Lazy. Any data is loaded only when you need it.Can be used in libraries. The compiled translations are automatically placed next to your Python code and discovered at runtime.Versatile. Most of the libraries in the wild are focused on web applications. This library fits any Python project.l10n in 30 secondsInstall l10n:python3-mpipinstall'l10n[cli]'And there are all the changes you need to do in your code to support translations for a string:froml10nimportLocaleslocales=Locales()defsay_hello(lang='en'):loc=locales[lang]msg=loc.get('Hello, world!')print(msg)Now, let's translate it to Ukrainian:Extract all strings from the code that need to be translated:python3-ml10nextract--langukTranslate all extracted strings using Google Translate:python3-ml10ntranslateCompile all translations into the binary format:python3-ml10ncompileThat's all! Now, your code supports translations:fromexampleimportsay_hellosay_hello(lang='uk')# Привіт Світ!If you want to manually adjust the translation text, just edit thelanguages/en.pofile and runcompileagain. You don't even need to restart your app!Read more in the documentation:l10n.orsinium.dev.
Package: l1ls
This is a large scale L1 regularized Least Square (L1-LS) solver written in
Python. The code is based on the MATLAB code made available onStephen Boyd’s l1_ls page.InstallationYou can install the bleeding edge directly from the source:pip install git+https://github.com/musically-ut/l1-ls.py.git@master#egg=l1lsThis package is also available onPyPi.pip install l1lsUsageThe module exposes two functions:l1ls(A, y, lmbda, x0=None, At=None, m=None, n=None,tar_gap=1e-3,quiet=False,eta=1e-3,pcgmaxi=5000), and,l1ls_nonneg(A, y, lmbda, x0=None, At=None, m=None, n=None,tar_gap=1e-3,quiet=False,eta=1e-3,pcgmaxi=5000)They can be used as follows:import l1ls as L
import numpy as np

A = np.array([[1, 0, 0, 0.5], [0, 1, 0.2, 0.3], [0, 0.1, 1, 0.2]])
x0 = np.array([1, 0, 1, 0], dtype='f8')  # Original signal
y = A.dot(x0)                            # noise free signal
lmbda = 0.01                             # regularization parameter
rel_tol = 0.01

[x, status, hist] = L.l1ls(A, y, lmbda, tar_gap=rel_tol)
# answer_x = np.array([0.993010, 0.00039478, 0.994096, 0.00403702])If your matrixAis sparse, pass it inCSR formatformat for best performance.ReferenceS.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky. An
Interior-Point Method for Large-Scale l1-Regularized Least Squares,
(2007), IEEE Journal on Selected Topics in Signal Processing,
1(4):606-617.
Package: l298n
l298n ModuleThis module is designed to be used with two GPIO pins on the Raspberry Pi.UsageFirst, create an instance of l298n using 2 pin numbers from the board as
attributes. Use the GPIO number as opposed to the board number.froml298nimportl298nmotor=l298n(2,3)Then, using methods from l298n, a motor can be activated in the desired direction.motor.forwards()motor.backwards()motor.stop()ContributionsIf you see areas for improvement in this module, please submit an issue or
feel free to email me. All contributions are welcome.
Package: l2address
l2addressMicro library I'm planing to develop by using TDD approach, just to try it.Example usageimportl2addressmac=l2address.mac_address(0xff00ff)print(mac)# 00:00:00:ff:00:ffmac+=0xff00print(mac)# 00:00:00:ff:ff:ffmac-=l2address.mac_address('00:00:00:ff:ff:f0')print(mac)# 00:00:00:00:00:0fprint(mac==l2address.mac_address(0xf))# Trueprint(l2address.mac_address('ffffffffffff')+1)# 00:00:00:00:00:00print(l2address.mac_address(0xff,l2address.PeriodFormatter(4)))# 0000.0000.00ffFeaturesAt the current moment supports:Create MAC address from intCreate MAC address from strSum and Substitution of MAC addresses (and with ints)Convert MAC to stringDifferent formatting types (00:00:00:00:00:00, 0000.0000.0000, 00-00-00-00-00-00, and so on)Validation of format when parsing from string. There are predefined formats, but can be extended, see:Extending FormatsExtending Formatsimportl2addressfroml2address.utilsimportper_join# Just inherit l2address.Formatter and use new formatterclassMyMacStarFormatter(l2address.Formatter):defformat(self,value,max_value):returnper_join(self._to_clean_str(value,max_value),'*',2)def_get_validator_regexp(self,_str,max_value):returnr'^([\da-fA-F][\da-fA-F]\*?){6}'try:l2address.mac_address('AB*ab*bc*de*ff*ff')exceptValueError:print('Failed as expected')# Failed as expectedmac=l2address.mac_address('AB*ab*bc*de*ff*ff',MyMacStarFormatter())print(mac)# ab*ab*bc*de*ff*ffprint(mac.to_str(l2address.ColonFormatter()))# ab:ab:bc:de:ff:ffprint(l2address.mac_address(0xff).to_str(MyMacStarFormatter()))# 00*00*00*00*00*ffPlansMultiplication by intDifferent lengths of MAC address (there are 64 bit MAC addesses I believe)
Package: l2logger
Lifelong Learning Logger (L2Logger)Table of ContentsIntroductionLogger Term Definitions/GlossaryLogger Output FormatInterface/UsageExamplesTestsLog AggregationExampleUsageLog ValidationExampleUsageChangelogLicenseAcknowledgementsIntroductionThe Lifelong Learning Logger is a utility library provided for
producing logs in a convenient format for the provided l2metrics module,
but can also be used independently.Logger Term Definitions/GlossaryStrongly recommend starting here, detailed explanation of the terms used
throughout:docs/definitions.md.Logger Output FormatDetailed explanations of the logging output structure/format can be seen viadocs/log_format.md.Interface/UsageAt a high level, the library is used simply by creating an
instance of the logger object, then by invoking thelog_recordmember function on it at least once per experience.For a detailed explanation of the provided functions, seedocs/interface.md.ExamplesSee documentation in the examples folder atexamples/README.md.TestsSee documentation in the test folder attest/README.md.Log AggregationL2Logger provides a module for exporting an aggregated data table from an
L2Logger directory as a TSV, CSV, or Feather file.Aggregation ExampleThe following is a simple example for how to aggregate a log directory into a single TSV file:python-ml2logger.aggregate<path/to/log_directory>Aggregation Usageusage: python -m l2logger.aggregate [-h] [-f {tsv,csv,feather}] [-o OUTPUT] log_dir

Aggregate data within a log directory from the command line

positional arguments:
  log_dir               Log directory of scenario

optional arguments:
  -h, --help            show this help message and exit
  -f {tsv,csv,feather}, --format {tsv,csv,feather}
                        Output format of data table
  -o OUTPUT, --output OUTPUT
                        Output filenameLog ValidationLogs generated by L2Logger should already be in the proper format for ingestion by the Metrics Framework. However, log validation can also be done manually using the providedvalidate.pymodule.Validation Examplepython-ml2logger.validate<path/to/log_directory>Validation Usageusage: python -m l2logger.validate [-h] log_dir

Validate log format from the command line

positional arguments:
  log_dir     Log directory of scenario

optional arguments:
  -h, --help  show this help message and exitNote: This script only validates one instance of a scenario output; it does not run recursively on a directory containing multiple scenario logs.ChangelogSeeCHANGELOG.mdfor a list of notable changes to the project.LicenseSeeLICENSEfor license information.AcknowledgementsPrimary development of Lifelong Learning Logger (L2Logger) was funded by the DARPA Lifelong Learning Machines (L2M) Program.Â© 2021-2022The Johns Hopkins University Applied Physics Laboratory LLC
Package: l2rpn-baselines
L2RPN_BaselinesRepository hosting reference baselines for theL2RPN challengeInstallRequirementspython3 >= 3.6Instal from PyPIpip3installl2rpn_baselinesInstall from sourcegitclonehttps://github.com/rte-france/l2rpn-baselines.gitcdl2rpn-baselines
pip3install-U.cd..
rm-rfl2rpn-baselinesContributeWe welcome contributions: see thecontribute guidefor details.Get started with a baselineSay you want to know how you compared with the "PPO_SB3" baseline implementation in this repository (for the
sake of this example).Train it (optional)As no weights are provided for this baselines by default (yet), you will first need to train such a baseline:importgrid2opfroml2rpn_baselines.PPO_SB3importtrainenv=grid2op.make("l2rpn_case14_sandbox")res=train(env,save_path="THE/PATH/TO/SAVE/IT",iterations=100)You can have more information about extra argument of the "train" function in theCONTRIBUTEfile.Evaluate itOnce trained, you can reload it and evaluate its performance with the provided "evaluate" function:importgrid2opfroml2rpn_baselines.PPO_SB3importevaluateenv=grid2op.make("l2rpn_case14_sandbox")res=evaluate(env,load_path="THE/PATH/TO/LOAD/IT",nb_episode=10)You can have more information about extra argument of the "evaluate" function in theCONTRIBUTEfile.
Package: l2tscaffolder
l2tscaffolderl2tscaffolder is a tool that provides scaffolders for various open source projects. It can be used to bootstrap plugin or parser generation for tools like plaso, to make development work easier.Project statusTravis-CICodecovReadTheDocsPyPiDocumentationThe purpose of the l2t scaffolder tool is to simplify development of various
open source forensics tools, eg. plaso, timesketch, turbinia, etc.The tool simply provides a UI prompting the user to answer few questions, and
then generates templates for all files needed to write a parser or a plugin for
the appropriate tool, that is it provides scaffolding for the necessary boiler
plate code that is sometimes associated with creating new plugins or parsers.UsageIn essence the tool can be simply run as:$ l2t_scaffolder.pyThe tool will then guide you towards creating all the necessary files to
generate a parser, plugin or a module for the given tool. Another way to run
the tool is:$ l2t_scaffolder.py <PROJECT>eg:$ l2t_scaffolder.py plasoThis will run the scaffolder tool to generate a plugin or a parser for plaso.Also see:http://l2tscaffolder.readthedocs.ioRequirementsPython 3.6+, Python 2 is not supported.InstallationThe simple mechanism is to use pip within a virtualenv setup.Setupvirtualenv.And then use pip3 inside the virtualenv:$ pip3 install l2tscaffolderBackgroundThe original tool was called PlasoScaffolder, which was written byClaudia Saxeras part of her BSc and
integrated into the log2timeline organization for purpose of maintenance.l2tscaffolder is a rewrite of the original PlasoScaffolder tool, reusing parts
of it, and rewriting other parts to make the tool easier to extend to other
open source projects and scaffolders, the original tool was written for plaso
and only supported SQLite plugins.
Package: l2x-synthetic
l2x_syntheticExposes synthetic dataset generation code fromL2Xas apippackage. To install, run:pipinstalll2x-syntheticYou can now create the synthetic datasets like:froml2x_syntheticimportXORGeneratorgenerator=XORGenerator(n_samples=100)X,y=generator.get_data()Which generates new data every time you callget_data()✨. Userandom_stateto create reproducible data generation.APIAvailable generators:XORGeneratorfroml2x_syntheticimportXORGeneratorOrange Skin generatorfroml2x_syntheticimportOrangeGeneratorNon-linear additive generatorfroml2x_syntheticimportAdditiveGeneratorSwitch generator: combines orange labels and non-linear additivefroml2x_syntheticimportSwitchGeneratorGenerator APIAll generators are of the following type:classl2x_synthetic.DataGenerator:name:str=None# contains a human-friendly name for the generator.n_samples:int=100random_state:Optional[int]=Nonedefget_data(self)->Tuple[np.ndarray,np.ndarray]:...defget_dataframe(self)->pd.DataFrame:...Development dependenciespipinstall-rrequirements.txtAboutSee the original repo:https://github.com/Jianbo-Lab/L2X/
Package: l33tapi
l33t.ltd python api wrapperDescriptionPython 2/3 library for theanti-captchaweb siteGetting APi keyRegister atl33t website. Go to the main page and click Profile. Follow "Infromation" tab. Here you can see your API key and balance in rubles(в‚Ѕ)Functionsget balanceget threadsget free theadssolve REcaptchasolve image captchaOfficial APi doc is avialablehereExamplesGet balanceimportlibsolver=ApiWrapper("your-api-key")print(solver.get_balance())Get using api keyimportlibsolver=ApiWrapper("your-api-key")print(solver.get_key())Updating keyimportlibsolver=ApiWrapper("your-api-key-1")solver.update_key("your-api-key2")Get all threads & free threadsimportlibsolver=ApiWrapper("your-api-key")all_threads=solver.get_threads()free_threads=solver.get_free_threads()pinr(f"{all_threads}threads are possible and{free_threads}are free")Send REcapthca v2 to serverimportlibsolver=ApiWrapper("your-api-key")cap_data=solver.solve_recaptcha(cap_version=2,sitekey="6Le-wvkSAAAAAPBMRTvw0Q4Muexq9bi0DJwx_mJ-",is_json=1,page_url="https://www.google.com/recaptcha/api2/demo")captcha_id=cap_data["request"]Send REcaptcha v3 to serverimportlibsolver=ApiWrapper("your-api-key")cap_data=solver.solve_recaptcha(cap_version=3,k_value="6LfwIo4UAAAAADauXCK0Ke_jIWNSW-z49N-IUj43",is_json=1,page_url="https://demo.codeforgeek.com/recaptcha-v3/")captcha_id=cap_data["request"]Sending image captcha to serverimportlibsolver=ApiWrapper("your-api-key")cap_data=solver.solve_imgcaptcha(captcha_str="long_base64_string_of_captcha",is_json=1)captcha_id=cap_data["request"]Get captcha answerimportlibsolver=ApiWrapper("your-api-key")print(solver.get_cap_answer(cap_id=326119,is_json=1))# cap_id is captcha_id from the previous exmaples
Package: l3c
L3C - Life Long Learning In ContextL3C provide abundant environments for Lifelong Learning (L3) in Context.In-Context Learning entails a different learning process than that of gradient descent, as it involves feeding data through prompts instead. Our objective is to enhance lifelong learning research by utilizing prompts, which involve agents that learn through interaction in a forward-only manner.Environments UpdatingMazeWorlds: 2D/3D maze generators for task generalizationMetaLM: Meta language model datasetBandits: Bandits task generalization