Saravanan666/pypi-org huggingface dataset:
Package: nonebot-sanic
NoneBot简介NoneBot 是一个基于酷Q的 Python 异步 QQ 机器人框架，它会对 QQ 机器人收到的消息进行解析和处理，并以插件化的形式，分发给消息所对应的命令处理器和自然语言处理器，来完成具体的功能。除了起到解析消息的作用，NoneBot 还为插件提供了大量实用的预设操作和权限控制机制，尤其对于命令处理器，它更是提供了完善且易用的会话机制和内部调用机制，以分别适应命令的连续交互和插件内部功能复用等需求。NoneBot 在其底层与 酷Q 交互的部分使用python-aiocqhttp库，后者是CoolQ HTTP API 插件的一个 Python 异步 SDK，在Quart的基础上封装了与 CoolQ HTTP API 插件的网络交互。得益于 Python 的asyncio机制，NoneBot 处理消息的吞吐量有了很大的保障，再配合 CoolQ HTTP API 插件可选的 WebSocket 通信方式（也是最建议的通信方式），NoneBot 的性能可以达到 HTTP 通信方式的两倍以上，相较于传统同步 I/O 的 HTTP 通信，更是有质的飞跃。需要注意的是，NoneBot 仅支持 Python 3.7+ 及 CoolQ HTTP API 插件 v4.8+。示意图文档文档目前「指南」和「API」部分已经完成，「进阶」部分尚未完成，你可以在这里查看。贡献如果你在使用过程中发现任何问题，可以提交 issue或自行 fork 修改后提交 pull request。如果你要提交 pull request，请确保你的代码风格和项目已有的代码保持一致，遵循PEP 8，变量命名清晰，有适当的注释。
Package: nonebot-tuling
nonebot-tuling安装pipinstallnonebot-tuling配置fromnonebot.default_configimport*TULING_API_KEY='你的图灵机器人 API KEY'# TULING_API_KEY = ['key1', 'key2']  # 也可以同时填入多个，会依次尝试使用importnonebotfromdemoimportconfignonebot.init(config)if__name__=='__main__':nonebot.load_plugin('nonebot_tuling')nonebot.run()
Package: nonebridge
nonebridgeA adapter event bridge for nonebot2 makes plugins running on different adapters without any modify一个让你能够在不修改插件的情况下使其运行在不同adapter中的魔法bridge，开发目的是为了给Yuki Clanbattle提供Telegram支持还在开发中请勿生产环境使用女生自用插件，目前仅支持让为onebotv11编写的插件运行在自己写的nonebot-adapter-telegram上，仅会支持有限的消息类型和API模拟目前不支持主动发送消息和向非事件触发的聊天发送消息，支持主动向群聊使用send_group_msg发送群组消息了(必须在tg端收到任意消息后虚假的obv11 bot连接才会被注册)支持的接收类型纯文字(MessageSegment.text)图片(MessageSegment.image)支持的发送类型文字(MessageSegment.text)图片(MessageSegment.image)AT(MessageSegment.at)语音(MessageSegment.record)支持的额外APIOnebot v11 API对应的Telegarm APIget_group_infogetChat和getChatMemberCountget_group_member_listgetChatAdministrators(由于tg并没有提供相关API，仅能够直接获取管理员信息)get_group_member_infogetChatMembersend_group_msg---配置nonebridge所需的配置直接写入到nonebot2的.env文件内即可nonebridge_ob11_caption_ahead_photo: 将从telegram收到的带文字描述的图片消息中文字部分作为文字消息在ob11的消息段中前置以配合ob11中大部分插件的习惯写法，默认为True
nonebridge_httpx_hook: 安装httpx钩子以拦截获取qq头像的http api，默认为False
ob11_plugin_list: [] 需要强制处理为ob11消息的插件，该插件内的matcer将不会被tg消息触发使用方法同时安装并两个adapter，在bot.py紧随nonebot之后导入nonebridge，必须在任何adapter导入之前导入nonebridge，需要同时注册两个Adapter才能正常运行Example bot.pyimportnonebotimportnonebridgefromnonebot.adapters.onebot.v11importAdapterasOneBot_V11_Adapterfromnonebot.adapters.telegram.adapterimportAdapterasTelegram_Adapternonebot.init()driver=nonebot.get_driver()driver.register_adapter(OneBot_V11_Adapter)driver.register_adapter(Telegram_Adapter)nonebot.load_plugin("your_onebotv11_plugin")if__name__=="__main__":nonebot.run()
Package: nonebug
NoneBug✨ NoneBot2 测试框架 ✨文档安装本工具为pytest插件，需要配合 pytest 异步插件使用。poetryaddnonebugpytest-asyncio-Gtest# 或者使用 anyiopoetryaddnonebuganyio-Gtestpdmaddnonebugpytest-asyncio-dGtest# 或者使用 anyiopdmaddnonebuganyio-dGtestpipinstallnonebugpytest-asyncio# 或者使用 anyiopipinstallnonebuganyio
Package: nonechat
Nonechat通用控制台聊天界面使用fromnonechat.infoimportEventfromnonechat.appimportFrontendfromnonechat.backendimportBackendclassExampleBackend(Backend):defon_console_load(self):print("on_console_load")defon_console_mount(self):print("on_console_mount")defon_console_unmount(self):print("on_console_unmount")asyncdefpost_event(self,event:Event):print("post_event")app=Frontend(ExampleBackend)app.run()
Package: nonecorn
Hypercorn is now hosted atgithub.com/pgjones/hypercorn.HypercornHypercorn is anASGIand
WSGI web server based on the sans-io hyper,h11,h2, andwsprotolibraries and inspired by
Gunicorn. Hypercorn supports HTTP/1, HTTP/2, WebSockets (over HTTP/1
and HTTP/2), ASGI, and WSGI specifications. Hypercorn can utilise
asyncio, uvloop, or trio worker types.Hypercorn can optionally serve the current draft of the HTTP/3
specification using theaioquiclibrary. To enable this install
theh3optional extra,pip install hypercorn[h3]and then
choose a quic binding e.g.hypercorn--quic-bindlocalhost:4433 ....Hypercorn was initially part ofQuartbefore being separated out into a
standalone server. Hypercorn forked from version 0.5.0 of Quart.QuickstartHypercorn can be installed viapip,$pipinstallhypercornand requires Python 3.7.0 or higher.With hypercorn installed ASGI frameworks (or apps) can be served via
Hypercorn via the command line,$hypercornmodule:appAlternatively Hypercorn can be used programatically,importasynciofromhypercorn.configimportConfigfromhypercorn.asyncioimportservefrommoduleimportappasyncio.run(serve(app,Config()))learn more (including a Trio example of the above) in theAPI usagedocs.ContributingHypercorn is developed onGithub. If you come across an issue,
or have a feature request please open anissue.  If you want to
contribute a fix or the feature-implementation please do (typo fixes
welcome), by proposing apull request.TestingThe best way to test Hypercorn is withTox,$pipenvinstalltox$toxthis will check the code style and run the tests.HelpThe Hypercorndocumentationis
the best place to start, after that try searching stack overflow, if
you still can’t find an answer pleaseopen an issue.
Package: nonediag
nonediagNoneBot2 error diagnosing tool.NoneBot2 错误诊断工具。Installation | 安装在 bot 环境中安装 nonediagpip install nonediagUsage | 使用在命令行中输入下列指令：cd /path/to/botnonediag输入你的错误信息；输入完成后敲入 3-4 个换行即可。
Package: nonelib
NonelibRemove None's from data and calls.nonedict()>>>fromnonelibimportnonedict>>>nonedict(a=1,b=None,c=3){'a':1,'c':3}>>>nonedict({'a':1,'b':None,'c':3}){'a':1,'c':3}@nonewrap>>>fromnonelibimportnonewrap>>>@nonewrap>>>defs(lst,offset=3,limit=3):...returnlst[offset:offset+limit]>>>s([1,2,3,4,5,6,7,8,9,10],offset=None,limit=5)# offset=3 is in effect[4,5,6,7,8]nonelist()>>>fromnonelibimportnonelist>>>nonelist([1,None,3])[1,3]noneset()>>>fromnonelibimportnoneset>>>noneset({1,None,3}){1,3}noneiter()>>>fromnonelibimportnoneiter>>>forxinnoneiter([1,None,3]):...print(x)13
Package: nonemoji
Nonemoji自维护的 gitmoji-cli，删减了部分 emoji安装poetryaddnonemoji--dev在 pre-commit 中使用# .pre-commit-config.yamldefault_install_hook_types:[pre-commit,prepare-commit-msg]repos:-repo:https://github.com/pycqa/isortrev:5.10.1hooks:-id:isortstages:[commit]-repo:https://github.com/psf/blackrev:22.6.0hooks:-id:blackstages:[commit]-repo:https://github.com/nonebot/nonemojirev:v0.1.3hooks:-id:nonemojistages:[pre-commit-msg]使用快速提交使用如下命令，选择一个 emoji 并填写提交信息：nonemojicommit交互式使用nonemoji
Package: nonemptystr
nonemptystrNon-empty string.UsagepipinstallnonemptystrfromnonemptystrimportEmptyString,nonemptystrname:nonemptystr=nonemptystr("John")try:name=nonemptystr("")exceptEmptyString:print("The name is empty.")... withpydanticpipinstallnonemptystr,pydanticfromnonemptystrimportnonemptystrfrompydanticimportBaseModel,ValidationErrorclassRequest(BaseModel):user_id:nonemptystrtry:request=Request.model_validate({"user_id":""})print(f"user_id:{request.user_id}")exceptValidationError:print("user_id is empty")APIModulenonemptystrclassnonemptystr(obj: object)Subclass ofstr.
RaiseEmptyStringexception ifstr(obj)is empty string.classEmptyStringSubclass ofValueError.
Package: noneprompt
NonePromptPrompt toolkit for console interaction.Typingis fully supported.Asyncis also supported!InstallationpipinstallnonepromptPrompt UsageInputfromnonepromptimportInputPromptInputPrompt("What is your name?",validator=lambdastring:True).prompt()awaitInputPrompt("What is your name?",validator=lambdastring:True).prompt_async()ConfirmfromnonepromptimportConfirmPromptConfirmPrompt("Are you sure?",default_choice=False).prompt()awaitConfirmPrompt("Are you sure?",default_choice=False).prompt_async()ListfromnonepromptimportListPrompt,ChoiceListPrompt("What is your favorite color?",choices=[Choice("Red"),Choice("Blue")]).prompt()awaitListPrompt("What is your favorite color?",choices=[Choice("Red"),Choice("Blue")]).prompt_async()CheckboxfromnonepromptimportCheckboxPrompt,ChoiceCheckboxPrompt("Choose your favorite colors",choices=[Choice("Red"),Choice("Blue")]).prompt()awaitCheckboxPrompt("Choose your favorite colors",choices=[Choice("Red"),Choice("Blue")]).prompt_async()Choice DataYou can add data to choices. Result type can be inferred from the data type.fromnonepromptimportListPrompt,Choiceresult:Choice[str]=ListPrompt("What is your favorite color?",choices=[Choice("Red",data="#FF0000"),Choice("Blue",data="#0000FF"),],).prompt()print(result.data)Defaults and CancellationfromnonepromptimportInputPromptresult=InputPrompt("Press Ctrl-C to cancel.").prompt(default="Cancelled")assertresult=="Cancelled"fromnonepromptimportInputPrompt,CancelledErrortry:InputPrompt("Press Ctrl-C to cancel.").prompt()exceptCancelledError:# Do somethingpassStyle GuideSee the docstring of prompt classes for more information.fromnonepromptimportInputPromptfromprompt_toolkit.stylesimportStyleInputPrompt("What is your name?").prompt(style=Style([("input":"#ffffff"),("answer":"bold")]))Disable ansi colors:fromnonepromptimportInputPromptInputPrompt("What is your name?").prompt(no_ansi=True)Try from command linenoneprompt-h
Package: nonestrap
nonestrap中文文档在这里～Another NoneBot2 project bootstrap tool.InstallationInstall nonestrap via this command:pip install nonestrapUsage#installbootstrapfileinto./mybot/withonebot-v11adapterandwithoutany#extraplugin,underavirtualenvironment.nonestrap -a onebot-v11 mybot#installbootstrapfileinto./mybot/withbothonebot-v11anddingadapter#andAPScheduler,underavirtualenvironmentnonestrap -a onebot-v11 -a ding mybot nonebot-plugin-apscheduler#likethefirstonebutwithproduction.envfile.nonestrap -a onebot-v11 -e prod mybot#likethefirstonebutwithoutcreatinganewvirtualenvironment.#useitifyouhavealreadypreparedavirtualenvironment.nonestrap -a onebot-v11 -V mybot#likethefirstonebutkeepsbot.pyinsteadofbot.pycnonestrap -a onebot-v11 -C mybot
Package: nonfig-sdk
Python SDKThe open source SDK will ease the integration with your platform if you are running on Python. For more information about the documentation, please check outdocs.nonfig.comThe package is already publishedhttps://pypi.org/project/nonfig-sdk/Example codefromnonfig.sdkimportNonfigoptions={'app_id':"c1e8293f-58be-4c55-9db4-b1c39cbc1dcb",# Replace with your App ID'app_secret':"XuuhXorEZqeRTJjHumGCgnPuZMdQgVvu",# Replace with your App Secret'debug':True,'cache_enable':True,'cache_ttl':60}nonfig=Nonfig(options)DocsWe have extensive documentation availablehereat the disposal. You shall be able to find example or sample codes too.GuidelinesPlease apply a correct label whether you are filing a Bug, Suggestion or just a question.Bug =bugSuggestion =featureQuestion =helpContributionThe project is maintained by the Nonfig's Core Team and following contributors (if any).
Package: nonholonomics
NonholonomicsSee theAUTHORSfile for the list of authors.
Currently we only have one. It gets lonely so join the team!License: New GNU License (see theLICENSEfile for details) covers all
files in the nonholonomics repository unless stated otherwise.See theCONTRIBUTINGfile for ways to contribute.Nonholonomics aims to become a central location for all things nonholonomic! We aim to centralize common methods for easy access and easy manipulation. Currently, the methods of solving nonholonomic systems in python are restricted to self built programs of by making use of Sympy's Lagrange multiplier method.Currently, nonholonomics is still in development. We have implemented a module to evaluate the nonholonomic Lagrange equations. This is a first step in producing modules to be tested.We also have a module for plotting purposes. In dynamics, we often plot the states of the system. The functions contained in this module aim to make this quick and easy.We have made a handful of examples for users to get familliar with. The examples are located in the examples directory and are simply numbered to keep naming short and easy.For a complte example using a holonomic linear system, take a look at example1.py for an indepth look at how to use most of the methods and classes.Example2.py and example3.py compare the implementation of constraints. The current program will handle systems with and with out nonholonomoic constraints.Example4.py shows the program can evaluate a nonlinear system. In this case it is the pendulum. There are comments to make the system linear to compare the results.We are working on implementing support for Lagrange multipliers and the principal of virtual power. If you would like to help please check out the contributing documentation.DownloadThe recommended installation method is through pip,
we are not on pypi yet, we use test.pypi until our first launch.to in install from test.pypi, use:$ pip install -i https://test.pypi.org/simple/ nonholonomicsYou can also get the latest version of nonholonomics from
<https://test.pypi.org/simple/nonholonomics>To get the git version do$ git clone https://github.com/ardiste3/nonholonomicsFor other options (tarballs, debs, etc.), please wait patiently or email your request in.ContributingWe welcome contributions from anyone, even if you are new to open
source.CleanTo clean everything (thus getting the same tree as in the repository):$ git clean -Xdfwhich will clear everything ignored by.gitignore, and:$ git clean -dfto clear all untracked files. You can revert the most recent changes in
git with:$ git reset --hardWARNING: The above commands will all clear changes you may have made,
and you will lose them forever. Be sure to check things withgit status,git diff,git clean -Xn, andgit clean -nbefore doing any
of those.BugsOur issue tracker is athttps://github.com/ardiste3/nonholonomics/issues. Please
report any bugs that you find. Or, even better, fork the repository on
GitHub and create a pull request. We welcome all changes, big or small,
and we will help you make the pull request if you are new to git.Brief HistoryThis started out as project in graduate school.
Package: nonin
App-NoninStreams PPG and Status data from a Nonin Medical Pulse Oximeter (Xpod 3012 LP USB) over LSLInstallpip install noninExampleusage: nonin-lsl [-h] [--scan] [--port PORT]

Stream Nonin Medical Xpod 3012 LP USB Pulse Oximeter

optional arguments:
  -h, --help   show this help message and exit
  --scan       report the available devices
  --port PORT  which port to use
Package: nonlinear-benchmarks
nonlinear_benchmarksThe official dataloader ofnonlinearbenchmark.org. This toolbox can automatically download, load and split many of the datasets which can be found on the website.For intance,https://www.nonlinearbenchmark.org/benchmarks/wiener-hammerstein.importnonlinear_benchmarkstrain_val,test=nonlinear_benchmarks.WienerHammerBenchMark()sampling_time=train_val.sampling_time#in secondsu_train,y_train=train_val#or train_val.u, train_val.yu_test,y_test=test#or test.u,      test.yIn theWienerHammerBenchMarkmultiple options are provided. You can find the data files by usingraw_data=Trueand obtain the entire, not train-test splitted, dataset by usingtrain_test_split=False.Installpip install nonlinear-benchmarksDatasetsMultiple datasets have been implemented with an official train test split which are given below. (p.s. datasets without a official train test split can be found innonlinear_benchmarks.not_splitted_benchmarks)EMPStrain_val,test=nonlinear_benchmarks.EMPS()train_val_u,train_val_y=train_valtest_u,test_y=testCEDtrain_val,test=nonlinear_benchmarks.CED()(train_val_u_1,train_val_y_1),(train_val_u_2,train_val_y_2)=train_val(test_u_1,test_y_1),(test_u_2,test_y_2)=testCascaded_Tankstrain_val,test=nonlinear_benchmarks.Cascaded_Tanks()train_val_u,train_val_y=train_valtest_u,test_y=testWienerHammerBenchMarktrain_val,test=nonlinear_benchmarks.WienerHammerBenchMark()train_val_u,train_val_y=train_valtest_u,test_y=testSilverboxtrain_val,test=nonlinear_benchmarks.Silverbox()multisine_train_val=train_valtest_multisine,test_arrow_full,test_arrow_no_extrapolation=testNote that the test arrow full and the test arrow no extrapolation have some overlap.Error MetricsWe also provide error metrics innonlinear_benchmarks.error_metrics.fromnonlinear_benchmarks.error_metricsimportRMSE,NRMSE,R_squared,MAE,fit_index#generate example ouput data and predictiony_true=np.random.randn(100)y_pred=y_true+np.random.randn(100)/100print(f"RMSE:{RMSE(y_true,y_pred)}(Root Mean Square Error)")print(f"NRMSE:{NRMSE(y_true,y_pred)}(Normalized Root Mean Square Error)")print(f"R-squared:{R_squared(y_true,y_pred)}(coefficient of determination R^2)")print(f'MAE:{MAE(y_true,y_pred)}(Mean Absolute value Error)')print(f"fit index:{fit_index(y_true,y_pred)}(https://arxiv.org/pdf/1902.00683.pdf page 31)")
Package: non-linear-shrinkage
Non-Linear ShrinkageProvides a function that calculates an estimate of the covariance matrix shrunk using a non-linear analytic formula provided by
the working paper Ledoit and Wolf (2018), entitled ['Analytical Nonlinear Shrinkage of Large-Dimensional Covariance Matrices']
(http://www.econ.uzh.ch/static/wp/econwp264.pdf).Installationpip install non-linear-shrinkageUsageimport numpy as np
import nonlinshrink as nls
p = 2
n = 13
sigma = np.eye(p, p)
data = np.random.multivariate_normal(np.zeros(p), sigma, n)
sigma_tilde = nls.shrink_cov(data)The data is automatically demeaned unless otherwise specified. In the case where the data has been pre-processed and the effective degrees of freedom of the dataset is decreased, e.g. through an OLS regression, the user can specify this through a parameterkwhich signifies the degrees of freedom already subtracted. For example,import numpy as np
import nonlinshrink as nls
p = 2
n = 14
sigma = np.eye(p, p)
data = np.random.multivariate_normal(np.zeros(p), sigma, n) + np.arange(n)[:, np.newaxis] + 1
x = np.vstack((np.ones(n).T, np.arange(n).T)).T
betahat = np.linalg.solve(np.dot(x.T, x), np.dot(x.T, data))
datahat = np.dot(x, betahat)
res = data - datahat
sigma_tilde = nls.shrink_cov(res, k=2)  # corresponding to 2 degrees of freedomDevelopingPlease submit a PR! The shrinkage function itself is located innonlinshrink.py.If using conda, set up the project by doing the following:# Create conda env
conda env create -f environment.yml
# Activate env
conda activate non_linear_shrinkage
# Perform local editable install
pip install -e . --no-depsIf using pip or other venv manager:pip install -e .Then you can run the tests with the following command:pytest
Package: nonlinshrink
Non-Linear ShrinkageProvides a function that calculates an estimate of the covariance matrix shrunk using a non-linear analytic formula provided by
the working paper Ledoit and Wolf (2018), entitled ['Analytical Nonlinear Shrinkage of Large-Dimensional Covariance Matrices']
(http://www.econ.uzh.ch/static/wp/econwp264.pdf).Installationpip install nonlinshrinkUsageimport numpy as np
import nonlinshrink as nls
p = 2
n = 13
sigma = np.eye(p, p)
data = np.random.multivariate_normal(np.zeros(p), sigma, n)
sigma_tilde = nls.shrink_cov(data)The data is automatically demeaned unless otherwise specified. In the case where the data has been pre-processed and the effective degrees of freedom of the dataset is decreased, e.g. through an OLS regression, the user can specify this through a parameterkwhich signifies the degrees of freedom already subtracted. For example,import numpy as np
import nonlinshrink as nls
p = 2
n = 14
sigma = np.eye(p, p)
data = np.random.multivariate_normal(np.zeros(p), sigma, n) + np.arange(n)[:, np.newaxis] + 1
x = np.vstack((np.ones(n).T, np.arange(n).T)).T
betahat = np.linalg.solve(np.dot(x.T, x), np.dot(x.T, data))
datahat = np.dot(x, betahat)
res = data - datahat
sigma_tilde = nls.shrink_cov(res, k=2)  # corresponding to 2 degrees of freedomDevelopingPlease submit a PR! The shrinkage function itself is located innonlinshrink.py.
For running the tests dogit clone https://github.com/matzhaugen/analytic_shrinkage.git
cd analytic_shrinkage
pip install -e . # install the package
pytest
Package: nonlocalbox
A friendly API for simulating nonlocal no-signalling correlations.InstallationThe easiest way to installNonlocalBoxis to use thepipcommand:python-mpipinstallnonlocalboxYou may need to replacepythonwith the correct Python interpreter, e.g.,python3.UsageThe following example illustrates a simple game between Alice and Bob. First, we
need to create two instances ofNonlocalBox, one for Alice and one for Bob:fromosimportenvironfromnonlocalboximportNonlocalBoxalice_game=NonlocalBox(environ["ALICE_API_KEY"])bob_game=NonlocalBox(environ["BOB_API_KEY"])In the current state, neither of them are in any role. Suppose that Alice invites
Bob for a simulation, whose username is known by Alice (which is ‘bob’ in this case).
Alice wants to use Popescu-Rohrlich Box of box ID 1 and names it ‘hellothere’:alice_game.invite("bob",1,'hellothere')print(alice_game.box_id)# this is arbitrary4In the server side, Bob is automatically added to this box. They both should
initialize the newly created box with ID 4. This will set the role ‘Alice’
to Alice and ‘Bob’ to Bob (since there won’t be any box in Bob’s list with ID 4):alice_game.initialize(4)bob_game.initialize(4)They can use the nonlocal boxes to run a simulation.Suppose Alice sendsx = 0are her input to the box with transaction ID20220311001and
Bob sendsy = 0with the same transaction ID. Note that forx = y = 0the results should
be correlated:print(alice_game.use(0,"20220311001"))0print(bob_game.use(0,"20220311001"))0Now suppose Bob will be the first to sendy = 1with an incremented transaction ID, and
Alice also sendsx = 1. The results should be anticorrelated:print(bob_game.use(1,"20220311002"))1print(alice_game.use(1,"20220311002"))0
Package: non-local-detector
non_local_detectorInstallationGPU InstallationIn order to use GPU with the conda or mamba environment, you need to install the following packages:Using mamba or conda (mamba recommended):mambainstalljaxlib=*=*cuda*jaxcuda-nvcc-cconda-forge-cnvidia
mambainstallnon_local_detector-cedenoor from the environment file:mambacreateenv-fenvironment_gpu.ymlor via pip:pipinstallnon_local_detector[gpu]CPU Installationmambacreateenv-fenvironment.ymlor via pip:pipinstallnon_local_detector
Package: nonlocalpooling
Non-Local PoolingUpdate 2.0.1Now NonLocalPool2d will not use PixelShuffle by default since it may harm the performance. NonLocalPool3d still uses PixelShuffle3d to reduce the token number.Update 1.2.0Now you can determine the output channel just like all the other learnable pooling methods (former version force out_channel=in_channel). However, if you specify out_channel, MaxPool would not work then since they cannot be added together. Leave out_channel to None to make the module works like before.To use NonLocalPooling for your PyTorch project:Step 1pip install nonlocalpoolingStep 2from nonlocalpooling.pool import NonLocalPool2d, NonLocalPool3dNon-Local Pooling can be used to substitue your original PyTorch pooling methods.
Package: nonneg-rescal
This folder contains the code for the non-negative RESCAL Tensor-Factorization.================================================================================Please cite:============Denis Krompass, Maximilian Nickel, Xueyan Jiang and Volker Tresp.Non-Negative Tensor Factorization with RESCAL.ECML Workshop on Tensor Methods for Machine Learning, 2013.Bibtex:@inproceedings{KrompassECML2013,author    = {Denis Krompa\ss and Maximilian Nickel and Xueyan Jiang and Volker Tresp},title     = {Non-Negative Tensor Factorization with RESCAL},booktitle = {ECML Workshop on Tensor Methods for Machine Learning},year      = {2013}}================================================================================python packages required:-------------------------numpyscipysklearnWe compile numpy with OpenBlas:https://github.com/xianyi/OpenBLAS