Saravanan666/pypi-org huggingface dataset:
Package: cpagym
RL-Accounting  会计强化学习RedefiningAccounting!
Developed byBfsu Ai-Accounting Team(c) 2023GithubInstall & Import 安装和导入For detailed installation instructions, see thedocumentation.pipinstallcpagymimportgymnasiumasgymimportcpagymascgimporttorchAccounting Envs 主要会计环境1. Moral_Hazard 道德风险Action SpaceNumActionUnit0Discharge of duty01Due diligence1Observation SpaceThe observation is andarraywith shape(6,)that provides information about the
two rotational joint angles as well as their angular velocities:NumObservationMinMax0Project success rate011Project success return01env=gym.make("Moral_Hazard-v1")#Instantiate the Environment载入道德风险环境state,info=env.reset(seed=2)#初始化n_steps=10for_inrange(n_steps):action=env.action_space.sample()#随机行动state,reward,terminated,truncated,info=env.step(action)print(observation,reward,terminated,truncated,info)ifterminatedortruncated:observation,info=env.reset()Accounting  RL-Algorithm 会计强化学习算法1. Train the Agent with DQNAccounting  RL-Agent 会计强化学习代理人agent=cg.Agent(state_size=1,action_size=2)#Instantiate the Agent载入会计智能体env=gym.make("Moral_Hazard-v1")state,info=env.reset(seed=2)action=agent.act(state,eps)#最优化行动next_state,reward,done,_,_=env.step(action)agent.step(state,action,reward,next_state,done)print(agent.memory.memory)配套智能会计包CPANLPCheck out:https://cpanlp.com/reinforcement
Package: cpalette
Color palette is a python library for working with various color formats such as RGB and HEX. This project is currently under development, but you can already use this library to work with colors. So far, color_palette supports only HEX and RGB, but other formats will be added in the future.PyPi-https://pypi.org/project/cpalette/GitHub-https://github.com/chebupelka8/CPaletteDownloadpipinstallcpaletteWikiHEXHEX("ffffff")->HEX(color="#FFFFFF")HEX("#FFFFFF").to_rgb()->(255,255,255)HEX("#3e3d3d").color->'#3E3D3D'HEX("#xxx")->ValueError:Wrongcolorat'#xxx'HEX("#QQQQQQ")->ValueError:Wrongcolorat'#QQQQQQ'RGBRGB(0,0,0)->RGB(r=0,g=0,b=0)RGB(255,255,255).to_hex(upper=True,hashtag=False)->FFFFFFRGB(30,30,30).color->(30,30,30)RGB(256,-2,400)->ValueError:Wrongcolorat(r=256,g=-2,b=400)RGB(100,100,100)+RGB(30,30,30)->RGB(r=65,g=65,b=65)# WARNING!!! Don't use this if you have more than 2 colorColorConverter.mix_rgb(RGB(100,100,100),RGB(30,30,30),RGB(255,255,255))->RGB(r=128,g=128,b=128)# If you have more than 2 colors, use this.ExamplesPreview colorfromcolor_paletteimportPreviewer,RGBPreviewer.preview(RGB(30,30,30))Convert colorsfromcolor_paletteimportRGB,HEX,ColorConverterprint(ColorConverter.hex_to_rgb(HEX("#dd33bd")))# RGB(r=221, g=51, b=189)print(ColorConverter.rgb_to_hex(RGB(156,48,39)))# HEX(color=#9C3027)Mix colorsfromcolor_paletteimportRGB,ColorConverterprint(ColorConverter.mix_rgb(RGB(100,100,100),RGB(255,55,88),RGB(79,23,54)))# RGB(r=144, g=59, b=80)
Package: cpalgo
cpalgoThe library is active since July 30, 2021.InstallationUse the package managerpipto install foobar.pipinstallcpalgoDevelopersNarayanaa S RContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseBSDTemplates to be covered in cpalgoAlgebraBinary ExponentitationEGCDLinear Diophantine EquationsLinear SievesQuery based OptimisationSparse TableRMQSquare Root DecompositionHeavy Light DecompositionRMQ - Euler TourGraph OptimisationArticulationDSU - KruskalTarjan off-lineFold Fulkerson - Max FlowDinic - Max FlowCondensation GraphFenwick TreeSegment TreeComputational GeometryConvex Hull - Graham's scanConvex Hull - Andrew MonotoneS&I NlogNLattice Points - Pick's algorithm
Package: cpanel-api
CPanel API Client for PythonSupports cPanel API 2 and UAPI.Install$pipinstallcpanel-apiExamplesBasic usage:#!/usr/bin/env python# -*- coding: utf-8 -*-importloggingimportsysfrompprintimportpprintfromcpanel_apiimportCPanelApilogging.basicConfig(level=logging.WARNING,stream=sys.stderr)hostname='HOSTNAME_OR_IPADRESS'username='USERNAME'password='PASSWORD'client=CPanelApi(hostname,username,password)# Alternatively, to authenticate using a UAPI or cPanel API 2 token, use:# client = CPanelApi(hostname, username, '<TOKEN>', auth_type = 'utoken')# {'warnings': None, 'errors': None, 'data': {'port': '1243'}, 'metadata': {}, 'status': 1, 'messages': None}r=client.uapi.SSH.get_port()print('SSH port:',r.data.port)# get all public ssh keys# {'cpanelresult': {'postevent': {'result': 1}, 'apiversion': 2, 'data': [...], 'func': 'listkeys', 'event': {'result': 1}, 'module': 'SSH', 'preevent': {'result': 1}}}r=client.cpanel2.SSH.listkeys()pprint(r.cpanelresult.data)# retrieve keyr=client.cpanel2.SSH.fetchkey(name='id_rsa')# {"name": "id_rsa", "key": "ssh-rsa XXX"}print(r.cpanelresult.data[0].key)r=client.cpanel2.SSH.importkey(name='new_rsa.pub',key='*data*')pprint(r)# ...r=client.cpanel2.DomainLookup.getdocroot(domain='site.info')print(r.cpanelresult.data[0].reldocroot)# public_htmlFunction call syntax:client.api_version.ModuleName.function_name({'param':'value'})client.api_version.ModuleName.function_name(param='value')client.api_version.ModuleName.function_name({'param':'value'},param='value')client.api_cal('api_version','ModuleName','function_name',{'param':'value'},param='value')Whereapi_versioniscpanel2oruapi.Links:Official documentation.
Package: cpanel-xss-2023
About the Tool ⚒️cpanel_xss_2023is a simple Python script designed for finding CVE-2023-29489 vulnerability in cpanel.Features ⚙️CVE-2023-29489 Scanning:Identifies and scans for the CVE-2023-29489 vulnerability.URL Input:Supports scanning a single URL or reading multiple URLs from a file.Output Logging:Allows users to write the scan results to an output file.Telegram Notification:Option to create Telegram notifications for scan results.Prerequisites 🧩Before usingcpanel_xss_2023, make sure you have the following prerequisites installed:Python 3.x: Ensure you have Python 3.x installed on your system.Download PythonRequired Python Packages:Click: Install theclicklibrary using the following command:pipinstallclickRequests: Install therequestslibrary using the following command:pipinstallrequestsPyYAML: Install thePyYAMLlibrary using the following command:pipinstallPyYAMLTelegram Notification (Optional):If you plan to use the Telegram notification feature, you'll need to set up a Telegram bot and obtain your chat ID. Follow these steps:Create a Telegram bot using theBotFather.Obtain your chat ID using theget_id_bot.Usage 🚀cpanel_xss_2023-uhttps://example.com
cpanel_xss_2023-iurls.txt-oresults.txt
cpanel_xss_2023-uhttps://example.com-cyour_telegram_chat_idHelp Menu ❓u, --url:Specify the URL to scan for the CVE-2023-29489 vulnerability.
Example: cpanel_xss_2023 -uhttps://target.comi, --input:Read input URLs from a file.
Example: cpanel_xss_2023 -i target.txto, --output:Write scan results to an output file.
Example: cpanel_xss_2023 -i target.txt -o output.txtc, --chatid:Create Telegram notifications for scan results.
Example: cpanel_xss_2023 --chatid your_telegram_chat_idh, --help:Display the help menu.Disclaimer ⚠️This script is intended for educational and ethical purposes only. Unauthorized use of this script to perform malicious activities is strictly prohibited. The developers are not responsible for any misuse or damage caused by this script.Version History 🕒v1.0: Find CVE-2023-29489 in cpanelv1.1: Find whether the endpoint is exploitable or notProfile Views 👁️License 🪪Author 👤@mdaseem03Connect at 💬
Package: cpanlp
Communicate in Business with pythonExamplesales increased 12% compared with fourth quarter 2021:North America segment sales increased 13% year-over-year to $93.4 billion, or increased 14% excluding changes in foreign exchange rates.International segment sales decreased 8% year-over-year to $34.5 billion, or increased 5% excluding changes in foreign exchange rates.AWS segment sales increased 20% year-over-year to $21.4 billion.sale1=p.Sale(quarter="Q4",amount=93.4,unit="billion dollars",growth_rate=13%,year=2022,segment="North America")sale2=p.Sale(quarter="Q4",amount=34.5,unit="billion dollars",growth_rate=-8%,year=2022,segment="International")sale3=p.Sale(quarter="Q4",amount=21.4,unit="billion dollars",growth_rate=20%,year=2022,segment="AWS")sales=[sale1,sale2,sale3]total_sales=0forsinsales:total_sales+=s.amountprint(f"Segment:{s.segment}, Sale:{s.amount}")print(f"Total Sales:{total_sales}")Install & ImportDependenciesscipynumpypandasFor detailed installation instructions, see thedocumentation.pipinstallcpanlpimportcpanlpaspFeaturesimportcpanlpasp# Keyword Analysiskeywordanalysis=Keyword()instances=keywordanalysis.analyze(text="Our concentrate operations typically generate net operating revenues by selling concentrates, syrups and certain finished beverages to authorized bottling operations")# Accounting Itemgold_asset=p.Asset(account="gold",debit=1000,date="2023-01-01")print(gold_asset.bubble)# Strategyhuawei=p.FinancialStrategy("huawei","defense",poison_pill(1000,0.1))# Language Decoratorside_effects=["financial instability","loss of reputation","decreased employee morale"]@with_side_effects(side_effects=side_effects)func()# Accounting Exceptionifabs(percent_change)>10:raiseAbnormalFluctuation(stock_name,percent_change)Module:CategoryModuleExampleAccountingAssetIntangible AssetLiabilityFinancial LiabilityEquityShareIncomeRevenueCashflowCashflowPolicyDividendPolicyReportIncomeSmoothingAuditAuditAudit OpinionBusinessMain Business，Capacity，Business Model，Value Chain，Operation${\color{purple}Decorator}$Estimate，Tense，Importance，With Effects，ValidatorCorporate LawContractLeaseControlVoting PowerEntityLLCProvisionSayOnPayDepartmentBoard Of Directors，Supervisory BoardEventAcquisition，Certification，Grants，Meeting，Resignation，Repurchase，Personnel，Registration，Shares，Lawsuit，StockHoldingIncrease${\color{purple}Exception}$Abnormal Fluctuation，Bubble，Winner CurseFinancial ManagementIncentivePromotion IncentiveSchemePonziInstitutionMarketCommodity，GoodsPerson${\color{red}Consumer}$，Employee，Entrepreneur，Manager，Investor，Partner，Shareholder，Supervisor，Creditor，Auditor，Beneficiary，Fiduciary，CraftsmanProjectPragmaticsPromiseRiskStakerHolderBank，Government，Media，Public，Rating AgencyStrategyLayout，Long Term Strategy，Financial StrategyTaxTax on BehaviorTransactionTaxTax on IncomePersonalIncomeTaxTax on PropertyRealEstateTaxTax on TurnoverVATTeamResearchTeamUtilityAccounting Gym-EnvCheck out:https://cpanlp.comPython Narrative packagespoliticsnlpfor Politicsreligionnlpfor Religionlawnlpfor Laweconomynlpfor Economycpanlpfor Businessmilitarynlpfor Military
Package: cpar
CPAR DatasetIntroductionCPAR Dataset consists of Devanagri(Hindi) Characters and Digits. This Dataset was collected by Mr. Rajiv Kumar.Quick TourCPAR contains 48 Characters and 9 Digits.InstallationUsing PIP via PyPI$ pip install cparManually via GIT$ git clone git://github.com/gaganmanku96/cpar
$ cd cpar
$ python setup.py installUsageCharacter Dataset$ from cpar.char import load_data
$ (x_train, y_train), (x_test, y_test) = load_data()Digit Dataset$ from cpar.digit import load_data
$ (x_train, y_train), (x_test, y_test) = load_data()CitationsKumar Rajiv, Amresh Kumar, and Pervez Ahmed. "A benchmark dataset for devnagari document recognition research." In 6th International Conference on Visualization, Imaging and Simulation (VIS'13), Lemesos, Cyprus, pp. 258-263. 2013.Kumar, Rajiv, and Kiran Kumar Ravulakollu. "HANDWRITTEN DEVNAGARI DIGIT RECOGNITION: BENCHMARKING ON NEW DATASET." Journal of theoretical & applied information technology 60, no. 3 (2014).
Package: cparse
cparseCode parsing command line tool.InstallationWithpipviaPyPipipinstallcparseClone the repository and installgitclonegit://github.com/luciancooper/cparse.gitcdcparse
pythonsetup.pyinstallDocumentationFull documentation can be foundherecparsecurrently includes 6 commandsls- list files in directorytree- print file treestat- directory file extension statspy- python code parsinghtml- html link parsingcss- css code parsinglsPrints a list of the files in a directory.cparsels[-r][-n<depth>][-d|-f][-a][-lim<count>][-fmt<format>][-exc<path>][-inc<path>][-wc<pattern>][-grep<regexp>][-ft<filetype>][-m|-M|-c|-C|-b|-B|-i|-I|-g|-G]<path>treePrints a tree representation of the contents of a directory.cparsetree[-d|-f][-a][-n<depth>][-fmt<format>][-exc<path>][-inc<path>][-wc<pattern>][-grep<regular-expression>][-ft<file-extension>][-m|-M|-c|-C|-b|-B|-i|-I|-g|-G]<path>statPrints a table displaying the file extension proportions of a directorycparsestat[-a]<path>pyParses the code of a provided python filecparsepy<path>htmlParses the links in an HTML file and represents them as a file tree.cparsehtml<path>cssParses CSS code, providing options to condense redundant rules and group identical selectors.cparsecss[-g][-c][-s]<path>Dependenciespydecorator
Package: cparser
A C parser based onparglare- a pure Python scannerless LR/GLR parser.Quick introcparseris a parser for C language. This example shows how to
collect all IDs from a simple C code.fromcparserimportCParserfromcparser.visitorimportASTVisitorcode="""
typedef struct {
    int element;
    int weight;
} Atom;

Atom helium;
"""classIDVisitor(ASTVisitor):"""Visitor that collects all identifiers in the code."""defvisit_id(self,node):print(node.value)returnnodeparser=CParser()ast=parser.parse(code)visitor=IDVisitor()visitor.visit(ast)# Output should look like this:# element# weight# Atom# Atom# heliumInstallationStable version:$pipinstallcparserDevelopment version:$gitclonehttps://github.com/typhoon-hil/cparser.git$pipinstall-ecparserLicenceMITPython versionsTested with 3.6-3.9
Package: cpast
🕓 CpastGit commit in the past.🗿 Usagepython-mcpast-dDATE-tTIME-mMESSAGEDate format:YYYY-MM-DDTime format:HH:MM:SS⚙️ Options-h, --helpShow help message and exit-d DATE, --date DATECommit date, example: 2021-12-25-t TIME, --time TIMECommit time, example: 22:13:05-m MESSAGE, --message MESSAGECommit message, example: "init :)"🧩 Installpipinstallcpast
Package: cpas-toolbox
Categorical Pose and Shape Evaluation ToolboxCPAS Toolbox is a package for evaluation of categorical pose and shape estimation methods. It contains metrics, datasets and methods.
Visit thedocumentationfor detailed usage instructions and API reference.Installationpipinstallcpas_toolboxCitationIf you find this library useful in your research, consider citingour publication:@article{bruns2022evaluation,
  title={On the Evaluation of {RGB-D}-based Categorical Pose and Shape Estimation},
  author={Bruns, Leonard and Jensfelt, Patric},
  journal={arXiv preprint arXiv:2202.10346},
  year={2022}
}DevelopmentUsepip install -e .to install the package in editable modeUsepip install -r requirements-dev.txtto install dev toolsUsepytest -rf --cov=cpas_toolbox --cov-report term-missing tests/to run tests and check code coverage
Package: cpath
Explainable AI with counterfactual pathsUsageInstall the Python package cpath via pippipinstallcpathand importimportcpathor from sourcepipinstall./cpathimportcpathOther importsfromimodels.util.data_utilimportget_clean_datasetimportnumpyasnpfromsklearn.treeimportDecisionTreeClassifier,DecisionTreeRegressorfromsklearn.ensembleimportRandomForestClassifierfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportbalanced_accuracy_scorefromsklearn.metricsimportroc_auc_scoreimportsysExample data setclf_datasets=[("breast-cancer","breast_cancer","imodels")]# Read in data setX,y,feature_names=get_clean_dataset('breast_cancer',data_source='imodels')# train-test splitX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20)Random Forest# number of treesntrees=10clf=RandomForestClassifier(n_estimators=ntrees)clf.fit(X_train,y_train)pred=clf.predict(X_test)Explain using cpathP=cpath.cpaths(clf,X_test,y_test)T=cpath.transition(P,X_test,y_test)IMP=cpath.importance(T)IMP["global"]CitationIf you find cpath please cite@misc{pfeifer2023explainable,
      title={Explainable AI with counterfactual paths}, 
      author={Bastian Pfeifer and Mateusz Krzyzinski and Hubert Baniecki and Anna Saranti and Andreas Holzinger and Przemyslaw Biecek},
      year={2023},
      eprint={2307.07764},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
Package: cpauto
cpautocpauto is a client library, written in Python, for the web APIs exposed
via Check Point R80 management server software. The Check Point R80
management APIs provide automation and integration capabilities that
were not available in previous versions of Check Point management server
software.https://sc1.checkpoint.com/documents/R80/APIs/#introductionBehold, the power of cpauto:>>> import cpauto
>>> cc = cpauto.CoreClient('admin', 'vpn123', '10.6.9.81')
>>> r = cc.login()
>>> r.status_code
200
>>> r.json()
{u'last-login-was-at': {u'posix': 1478636363481, u'iso-8601': u'2016-11-08T15:19-0500'}, u'uid': ...}
>>> n = cpauto.Network(cc)
>>> r = n.add('net_mgmt', { 'subnet': '10.6.9.0', 'subnet-mask': '255.255.255.0' })
>>> r.status_code
200
>>> r.json()
{u'domain': {u'domain-type': u'domain', u'name': u'SMC User', u'uid': u'41e821a0-3720-11e3-aa6e-0800200c9fde'}, ...}
>>> r = cc.publish()
>>> r.status_code
200
>>> r.json()
{u'task-id': u'01234567-89ab-cdef-8b0a-92e9635a47d3'}
>>> r = cc.logout()
>>> r.status_code
200
>>> r.json()
{u'message': u'OK'}InstallationTo install cpauto, simply:$ pip install cpautoEnjoy.DocumentationCopious documentation is available at:http://cpauto.readthedocs.io/Release History0.0.5 (2017-01-31)New FeaturesFull support for threat profiles.Bug FixesFixed issue discovered in the wait for task logic.0.0.4 (2016-12-09)New FeaturesFull support for sessions.The core client now optionally waits for tasks.MiscellaneousDocumentation is updated and hosted via readthedocs.io.0.0.3 (2016-11-23)New FeaturesFull support for service and application objects.MiscellaneousAll code confirmed to work with Python versions 2.7, 3.5, and in between.0.0.2 (2016-11-21)New FeaturesFull support for access and NAT objects.Support for some misc. powerful features.0.0.1 (2016-11-15)FeaturesFull support for policy package, simple gateway, host, group and network objects.
Package: cpbd
AboutCPBD is a perceptual-based no-reference objective image sharpness metric
based on the cumulative probability of blur detectiondeveloped at the
Image, Video and Usability Laboratory of Arizona State
University.[The metric] is based on the study of human blur perception for
varying contrast values. The metric utilizes a probabilistic model
to estimate the probability of detecting blur at each edge in the
image, and then the information is pooled by computing the
cumulative probability of blur detection (CPBD).This software is a Python port of thereference MATLAB
implementation.
To approximate the behaviour of MATLAB’s proprietary implementation of
the Sobel operator, it uses an implementationinspired by GNU
Octave.ReferencesCPBD is described in detail in the following papers:N. D. Narvekar and L. J. Karam, “A No-Reference Image Blur Metric
Based on the Cumulative Probability of Blur Detection (CPBD),” in
IEEE Transactions on Image Processing, vol. 20, no. 9, pp. 2678-2683,
Sept.
2011.N. D. Narvekar and L. J. Karam, “An Improved No-Reference Sharpness
Metric Based on the Probability of Blur Detection,” International
Workshop on Video Processing and Quality Metrics for Consumer
Electronics (VPQM), January 2010, http://www.vpqm.org
(pdf)N. D. Narvekar and L. J. Karam, “A no-reference perceptual image
sharpness metric based on a cumulative probability of blur
detection,” 2009 International Workshop on Quality of Multimedia
Experience, San Diego, CA, 2009, pp.
87-91.CreditsIf you publish research results using this code, I kindly ask you to
reference the papers of the original authors of the metric as stated in
the previous section as well as their reference implementation in your
bibliography. See also the copyright statement of the reference
implementation in thelicense
file.
Thank you!Installation$ pip install cpbdUsageIn [1]: import cpbd

In [2]: from scipy import ndimage

In [3]: input_image = ndimage.imread('/tmp/LIVE_Images_GBlur/img4.bmp', mode='L')

In [4]: cpbd.compute(input_image)
Out[4]: 0.75343203230148048Development$ git clone git@github.com:0x64746b/python-cpbd.git
Cloning into 'python-cpbd'...
$ cd python-cpbd
$ pip install -U '.[dev]'To quickly run the tests with the invocation interpreter:$ python setup.py testTo test the library under different interpreters:$ toxPerformanceThe following graph visualizes the accuracy of this port in comparison
with the reference implementation when tested on theimagesof theLIVE
database:
Package: cpboredbutton
Bored ButtonA python script which randomly opens a CP problem according to user difficultyRequirementspip3Installing the packagepip3 install cpboredbuttonRunning the FileRun the script withcpboredbutton [-cf Codeforces_Handle]CompletedImplemented for CodeforcesNext StepsMove Codeforces functions to classExpand to Codechef, Hackerrank & SPOJFront-End serviceAPI Service
Package: cp.buildout_postgres
FYI: I no longer use this code myself, and pay little attention to it.  if you use this code and want to adopt it, port it to python3/etc, let me know (and please do so)!the config options (with their defaults):version = 8.3.1
  url =
  http://ftp9.us.postgresql.org/pub/mirrors/postgresql/source/v%(version)s/postgresql-%(version)s.tar.gz
    md5link =
    http://ftp9.us.postgresql.org/pub/mirrors/postgresql/source/v%(version)s/postgresql-%(version)s.tar.gz.md5

      extra-options = --with-python --with-openssl --with-thread-safety
        user = daemon
          group = daemon
            ulimit = ulimit -S -n `ulimit -H -n`
              sysconf-dir = /etc
                log-dir = log
                  data-dir = data
                    pg-hba-extra =
                      pg-ident-extra =
                        pg-conf-extra =
Package: cpc
CPC is a command-line utility aimed towards competitive programmers.FeaturesAt any point in time, you can runcpcorcpc -hto get a list of what the utility has to offer, orcpc <command> -hfor details related to a particular command.Stress TestingCPC provides functionality to test an optimized program against bruteforce to compare their outputs withcpcst-b<bruteforce-executable>-o<optimized-executable>-tg<testcase-generator>It is advised to supply the arguments via the.cpcrcfile. This would shorten the command to:cpcstScraping SubmissionsUsers can scrape their submissions from various online judges. Even though support is limited, contribution is welcome to add support to other judges.Judges currently supported:Codeforces(Gym is also supported)SPOJInteractive scraper can be launched using:cpcscrapeConfigurationCPC can be configured using a.cpcrcfile. This would sit in the same repository as to where you will be operating from. If it exists in another directory, a path can be specified using:cpc<sub-command>-cf<path/to/.cpcrc>This is very useful for thestress testingcommand. Below is an example configuration:precommand:# To compile code into executablesg++ -std=c++17 testcase_generator.cpp -o generatorg++ -std=c++17 main.cpp -o optimizedg++ -std=c++17 bruteforce.cpp -o bruteforcecount:1000# Default -> 100bruteforce:bruteforce# Default -> bruteforceoptimized:optimized# Default -> optimizedtestcase_generator:generator# Default -> generatorInstallationInstallation via PIPThe installation process is very straight-forward. CPC is available as a PYPI package and can be installed via:pipinstallcpcManual installationTo install manually, clone from github. It is advised to usepython venv:gitclonehttps://github.com/Purhan/cpc.gitcdcpc
pipinstall--editable.