Saravanan666/pypi-org huggingface dataset:
Package: conceptlearning
Concept LearningThis module contains functions to concept learning algorithmsInstallationRun the following command to install:pipinstallconceptlearningParametersdataThe training dataset consisting of featues and outcomes of type listAttributes# for Candidate Elimination Algorithm
    candidate_elimination() 

    # for Find S Algorithm
    initialize_hypothesis() 
    find_s()Usagefromconceptlearning.CandidateEliminationAlgorithmimportCandidateEliminationce=CandidateElimination(data)ce.candidate_elimination()fromconcetplearning.FindSAlgorithmimportFindSfs=FindS(data)fs.initialize_hypothesis()fs.find_s()
Package: concepttordf
concepttordf!TestsA small Python library for mapping a concept collection to theskos-ap-no specification.UsageInstall%pipinstallconcepttordfGetting startedTo create a SKOS-AP-NO concept collection:fromconcepttordfimportCollection,Concept,Definition# Create collection objectcollection=Collection()collection.identifier="http://example.com/collections/1"collection.name={"en":"A concept collection"}collection.name={"nb":"En begrepssamling"}collection.publisher="https://example.com/publishers/1"# Create a concept:c=Concept()c.identifier="http://example.com/concepts/1"c.term={"name":{"nb":"inntekt","en":"income"}}definition=Definition()definition.text={"nb":"ting man skulle hatt mer av","en":"something you want more of"}c.definition=definition# Add concept to collection:collection.members.append(c)# get rdf representation in turtle (default)rdf=collection.to_rdf()print(rdf)Will print the concept according to the specification:@prefixdcat:<http://www.w3.org/ns/dcat#>.
@prefixdct:<http://purl.org/dc/terms/>.
@prefixns1:<https://data.norge.no/vocabulary/skosno#>.
@prefixrdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>.
@prefixrdfs:<http://www.w3.org/2000/01/rdf-schema#>.
@prefixskos:<http://www.w3.org/2004/02/skos/core#>.
@prefixskosno:<http://difi.no/skosno>.
@prefixskosxl:<http://www.w3.org/2008/05/skos-xl#>.
@prefixvcard:<http://www.w3.org/2006/vcard/ns#>.
@prefixxml:<http://www.w3.org/XML/1998/namespace>.
@prefixxsd:<http://www.w3.org/2001/XMLSchema#>.

<http://example.com/collections/1>askos:Collection;rdfs:label"En begrepssamling"@nb;dct:publisher<https://example.com/publishers/1>;skos:member<http://example.com/concepts/1>.

<http://example.com/concepts/1>askos:Concept;ns1:betydningsbeskrivelse[ans1:Definisjon;rdfs:label"something you want more of"@en,"ting man skulle hatt mer av"@nb];skosxl:prefLabel[askosxl:Label;skosxl:literalForm"income"@en,"inntekt"@nb].DevelopmentRequirementspipx(recommended)pyenv(recommended)poetrynox%pipxinstallpoetry
%pipxinstallnox
%pipxinjectnoxnox-poetryInstall dev tools%gitclonehttps://github.com/Informasjonsforvaltning/concepttordf.git
%cdconcepttordf
%pyenvinstall3.8.2
%pyenvinstall3.9.6
%pyenvlocal3.8.23.9.6
%poetryinstallRun all tests%noxRun all tests with coverage reporting%nox-rstestsDebuggingYou can enter intoPdbby passing--pdbto pytest:nox-rstests----pdbYou can set breakpoints directly in code by using the functionbreakpoint().
Package: concept-xblock
ConceptXBlockThis is a simple XBlock which will allows one to tag problems with
concepts. To be useful, it requires a concept server. There is one in
the matching concept-tag-server repo.Usage:<Concept server="http://www.sample-concept-demo-server.org:7000/">This displays:In the bottom left, you can search for learning objectives, and drag
them into one of the top three bins. The bins are:Taught: For concepts which the item introduces. For example, a video
explaining the quadaratic equation would be tagged as teach the
quadratic equation. Perhaps should be renamed to 'introduced'?Exercised: For concepts which the problem is explicitly designed to
practice. For example, "Solve 5x^2+6x=7" would exercise the
quadratic equation.Required: For concepts implicitly required. For example, a physics
problem on the path of a rocket might have the quadratic equation
as a prerequisite, but not be explictly designed to exercise it.The edit button is a link back to the concept tag server, where we can
edit concept descriptions.The system has a few serious issues:Performance. This is trivial to fix, but the system currently makes
an AJAX request per objective on the page. Bulk requests would
solve this.500 errors when e.g. a concept is not on the concept server.No test cases.Shown as a student (rather than instructor) view. This is a Studio
limitation.Lack of configurability. It works on my taxonomy
(taught/exercised/required), and not yours.Horrific styling. In desperate need of a cleanup.It does have a few nice properties:Continues to develop edX-as-a-platform, rather than
edX-as-a-product.Natural path (via concept wiki) to coming up with a concept
taxonomy. Next steps will be to split and join objectives.Natural extension to other wise of tagging objectives (e.g. Bloom's
taxonomy, etc.)
Package: concept-x-converter
ConecptX_ConverterThis tool is designed for convertingConceptXactivation files into Word2Vec format. The resulting Word2Vec file can be used with gensim library in Python to findsemanticallysimilar words.This tool could be built as a binary file, or it could be run as a cargo project. The following sections describe how to build and run the tool.UsageGenerate a word2vec file from a ConceptX activation file.cargo run <input_file_name> <output_file_name>Replace <input_file_name> with the name of your ConceptX activation file, and <output_file_name> with the name you would like to give to your Word2Vec file.For example, if your input file is named my_conceptx_file.txt and you want to name your output file my_word2vec_file.txt, you would run the following command:cargo run text.in.tok.sent_len.activations-layer11.json output.txtThe output file will have the following format:<num_words> <dim>
word1:<line_num>:<position_num> val11 val12 val13 ... val1dim
word2:<line_num>:<position_num> val21 val22 val23 ... val2dim
...Wherenum_wordsis the number of words in the input file,dimis the dimension of the word vectors, andvalijis the jth value of the ith word vector.line_numandposition_numare the line number and position number of the word in the original context (This is designed for the purpose of retrieving the original context of the word from the input file).Then, in your Python code, you can load the word2vec file using the following code:import gensim

model = gensim.models.KeyedVectors.load_word2vec_format('output.txt', binary=False)

# find the most similar word of the word "He", which is in the 0th line and 0th position of the original context

print(model.most_similar(positive=['He:0:0'], topn=5))Build Binarycargo build --releaseRun Binary./target/release/conceptx_converter <input_file_name> <output_file_name>
Package: concerns
# Concerns[Python](https://www.python.org/) implementation of the[Ruby](https://www.ruby-lang.org/)’s[ActiveSupport::Concern](https://api.rubyonrails.org/v5.1/classes/ActiveSupport/Concern.html)behavior.## Use examplesNote: the class context is the fourth argument of the[metaclass](https://www.python.org/doc/essays/metaclasses/) `__init__` method,formally “namespace”:```pythondef __init__(cls, name: str, bases: tuple, namespace: dict) -> None:...```It is a dictionary representation of the class body.### Using a class as mix-inIf the aspect is a class, its context will be merged to the target context onits factoring.```pythonfrom typing import NamedTuplefrom concerns import concernclass SerialMixin:def serialize(self) -> str:return json.dumps(self._asdict(), sort_keys=True)@classmethoddef deserialize(cls, data: str):return cls(**json.loads(data))class PersonBase(NamedTuple):name: strsurname: strclass Person(PersonBase):concern(SerialMixin)```### Using a mapping as default values mapIf the aspect is a mapping (a dictionary, by example), it itself will be mergedto the target context on its factoring.```pythonfrom concerns import concernclass Animal:# Too simple use, avoid doing itconcern({voice='grrr'})def __init__(self, *, voice: str = None):if voice:self.voice = voicedef say(self) -> None:print(self.voice)```### Function to change behaviorIf the aspect is a callable, the target class context will be passed asparameter to the aspect for processing before its factoring.```pythonfrom concerns import concernfrom collections.abc import MutableMappingfrom datetime import datetimedef make_timed(context: MutableMapping) -> None:"""Reusable aspect function to add creating time to objects"""def set_created_attr(attr: str) -> None:context[attr] = Nonedef new(cls, name: str, bases: tuple, _dict: dict):self = super().__new__(cls, name, bases, _dict)setattr(self, attr) = datetime.now()return selfcontext['__new__'] = newcontext['set_created_attr'] = set_created_attrclass Register:concern(make_timed)...  # more class attributesset_created_attr('created_at')```
Package: concert
Concertis a light-weight control system interface to control Tango and native
devices. It requires Python >= 3.7, for tests you will need >= 3.8. It can be
used as a library:from concert.quantities import q
from concert.devices.motors.dummy import LinearMotor

motor = await LinearMotor()
await motor.set_position(10 * q.mm)
await motor.move(-5 * q.mm)or from a session and within an integratedIPythonshell:$ concert start

concert > from concert.devices.motors.dummy import LinearMotor
concert > motor = await LinearMotor()
concert > await motor.set_position(10 * q.mm)   # The asyncio blocking way
concert > task = motor.set_position(10 * q.mm)  # The asyncio non-blocking way
concert > motor.position = 10 * q.mm            # User convenience for blocking calls
concert > motor.position
10 <Unit('millimeter')>You can read more aboutConcertin the officialdocumentation.CitationIf you want to use Concert, we kindly ask you to acknowledge the respective
authorship not only by respecting the LGPL software license but also by linking
to our project website athttp://ufo.kit.eduand citing the following article:
Vogelgesang, M., Farago, T., Morgeneyer, T. F., Helfen, L., dos Santos Rolo, T.,
Myagotin, A. & Baumbach, T. (2016). J. Synchrotron Rad. 23, 1254-1263,https://doi.org/10.1107/S1600577516010195.
Package: concert-launcher
concert_launcherA minimal process launching automation tool based on YAML and TMUXQuickstartTailored to @alaurenzi 's laptop machine setup !pipinstall-e.cdconfig/example_alaurenzi# a folder containing launcher.yamlconcert_launcherruncartesio# run cartesio and its dependenciesconcert_launchermon# spawn tmux monitoring session on local machineconcert_launcherstatus# print process treeconcert_launcherkill[proc_name]# kill proc_name (or all)
Package: concha
ConchaA machine learning system for how deciding many things to make each day.Cafes, grocery stores, restaurants, donut shops, and panaderias face a fundamental
question every morning:How many should I make?"Concha uses data tracked by the point of sale service,
combined with local weather conditions to learn demand patterns.
Then it predicts how much to make of each product to maximize profit.Concha can interface with Square to get the sales history. It takes about 10 minutes to set up.Try it outYou can run concha entirely on Google Colab (a free deep learning platform).Run a concha simulation in a Colab notebookA Colab "notebook" is a bunch of code blocks you can run one by one by clicking the play button in the upper left corner (or by typing CTRL-ENTER).If you want to do more than run simulations and use it to predict how much to make/order for each day, you can run it from your Google Drive. Concha will save a file of predictions to your drive that you can open up with Google Sheets.This Medium article is a complete guide to setting up Concha and running it on Colab.Making Predictions from Your DataThe first step is to save the Google Colab notebooks (a kind of Google Drive file that can run Python code) on your own drive. Then you can set up access to 1.) The NOAA weather data, and 2.) Your Square data (your sales history.) Thesetup_do_oncenotebook shows exactly how it works and automates the process.Once you have setup access to your data and the weather, the model can learn from your sales history and predict the optimal quantity to produce by running code in themake predictionsnotebook. The predictions go out six days (the limit of the weather predictions).Local Installationpip install conchaPackage LayoutThe source code is in/src/concha.importers.pydefines the Square SDK agent.planner.pydefines the planner.model.pydefines the different estimators.product.pydefines product objects and methods.weather.pydefines the NOAA agent.The code is documented thoroughly, and you can see many other many other
settings that can be expirmented with to optimize production planning.Usage GuidesThese notebooks walk through how to use concha.01_run_a_simulation02_setup_do_once03_everyday_useNoteThis project has been set up using PyScaffold 3.2.3 and thedsproject extension0.4.
For details and usage information on PyScaffold seehttps://pyscaffold.org/.
Package: concierge-cli
Companion CLI forConcierge, the Configuration management & CI solution
for aligning your multitude of Git repositories.  Allows you to bulk-manage
properties of your Git repository projects.  Currently, GitLab is supported.Why Should I Use This Tool?Concierge-cli helps you analyze and bulk-update the repository projects you
manage (e.g. set topics on projects, generate project lists for ModuleSync).InstallationFrom PyPI:$pipinstallconcierge-cliNote:You need Python >= 3.6, otherwise your installation will not
succeed (python-gitlabv2.0.0 dropped support for older Pythons).
You can use Pipenv to work around this limitation:$pipinstallpipenv$pipenvshell--three$pipinstallconcierge-cliPreparationYou need anaccess tokenof a GitLab user to access resources through the
API. For groups and group membership this needs to be an administrator user.
You may also want todisable notificationsof the user(s) you plan to
perform bulk updates on, to avoid sending out massive amounts of emails.Set the GitLab URI and TOKEN as environment variables (if you want to avoid
using the--tokenand/or--urioptions):$exportCONCIERGE_GITLAB_URI=https://git.example.com/$exportCONCIERGE_GITLAB_TOKEN=<redacted>Usage PatternsManageproject topicsList projects by topicList (and merge) merge requests by projectManagegroup membershipand permissionsManage topicsList all projects (for a private GitLab) that have no topics yet:$concierge-cligitlab--urihttps://git.vs.hntopics--emptyList all projects “foo” or similar in group “bar” or similar: (on GitLab.com)$concierge-cligitlabtopicsbar/foo--emptySet topics on all those projects:$concierge-cligitlabtopicsbar/foo--empty--set-topicPuppet--set-topicAnsibleList all projectswithtopics now: (double-check)$concierge-cligitlabtopicsbar/fooList projectsPrint a YAML list of all projects matching a topic:$concierge-cligitlabprojects--topicPuppetUpdate the list of modules Concierge manages with a specific configuration:$concierge-cligitlabprojects--topicPuppet>configs/foo-bar/managed_modules.yml$gitadd-vconfigs/foo-bar/managed_modules.yml$gitstatus&&gitcommit-m'Added ...'&&gitpushMerge requestsList of all merge requests of a project, optionally matching labels:$concierge-cligitlabmrsmygroup/myproject--labelmylabelAdd--mergeyesto trigger merging all found requests.Group membershipList all groups where user is not yet a member of:$concierge-cligitlabgroups--no-membermy.user.nameAdd user to all those groups:$concierge-cligitlabgroups--no-membermy.user.name\--set-permissionmaintainerList a user’s group memberships and permissions:$concierge-cligitlabgroupsmy.user.nameRemove a user from selected groups:$concierge-cligitlabgroupsmy.user.name\--group-filtera-group-name\--set-permissionnoneFound a bug? Need a new feature?Please see ourContribution Guideif you want to help out!
Package: conciliator-python
conciliator-pythonPython library for the Conciliator APIIntroductionPython client library for Conciliator (https://expert.conciliator.ai/)Installation>>> pip install conciliator-pythonUsageimportconciliatorascccc.connect(username,pwd,tenant)forentityincc.Entity.list():print(entity.name)forfincc.File.list(entity)print(f.name)
Package: concisely
conciselyA bunch of text editing features for Wagtail. Please bear with us while we prepare more detailed documentation.Compatibilityconcisely' major.minor version number indicates the Wagtail release it is compatible with. Currently this is Wagtail 4.1.xInstallationInstall usingpip:pipinstallconciselyAddconciselyto yourINSTALLED_APPSsetting:INSTALLED_APPS=[# ...'concisely'# ...]
Package: conclas
conclasThe conclas is a client written in Python to use the service Conclas.InstallConclas Python Client can be installed using pip:$ pip install conclasIf you prefer to install manually, you must follow the following steps. First clone the repository client python conclas. This is thelink. After cloning the repository run the following command:$ python setup.py installExamplesThe example of using the client python conclas are in the examples folder. To test it run the following command:$ python examples/simple.pyRunning testsTo run the tests, run the following command:$ python tests/conclas/test_conclas.pyDocumentationTo understand more about the client python conclas, see ourdocumentation.
Package: conc-nouns
conc-nounsconc-nouns descrInstall itpipinstallgit+https://github.com/ffreemt/conc-nouns# poetry add git+https://github.com/ffreemt/conc-nouns# git clone https://github.com/ffreemt/conc-nouns && cd conc-nounsUse itfromconc_nounsimportconc_nouns
Package: concorde
Simple static site generator built from Markdown and JinjaThree commands to produce three things:concorde pages  renders Markdown files through a template, producing new pages
concorde index  takes a list of Markdown files that you presumably just rendered
                with pages, orders them by date, and renders them through a
                template to produce an index file
concorde rss    similar to concorde index, but produces an RSS feed insteadRunconcorde <command> helpfor more details.One distinguishing feature of Concorde is that the templates and the rendered
pages do not necessarily have to be HTML. In fact, Concorde was created because
of the need to quickly tack on a static blog generator to a site written in PHP.Concorde is designed to be easily paired with a Makefile to generate a site.
For example, given a site laid out like so:my-example-site
    site
        a-post.md
        another-post.md
    templates
        index.html
        blog-post.htmlA Makefile to build the HTML pages, index page, and RSS feed would look likeposts:=$(patsubst%.md,%.html,$(wildcardsite/*.md)).PHONY:allcleanall:site/index.htmlsite/rss.xml$(posts)clean:-rmsite/index.html-rmsite/rss.xml-rm$(posts)site/index.html:$(posts)templates/index.htmlconcordeindex--template=templates/index.html--output=$@--output-extension=.htmlsite/site/blog/rss.xml:$(posts)concorderss--output=$@--title="My Example Blog"--url="http://example.com/rss.xml"site/$(posts):site/*.mdtemplates/blog-post.htmlconcordepages--template=templates/blog-post.htmlsite/
Package: concordia
Concordia is a part of a suite of open-source machine learning packages
that allow organizations to more rapidly develop and deploy machine
learning models.Installationpip install concordiaDescriptionConcordia is a tracking and analytics tool for machine learning models
running in production.Using Concordia, you should be able to rapidly have confidence in your
shipped ML models.If everything’s working as expected, you should have quick proof that
you’re in a position to scale up this model.If things are not going according to plan, you should be able to see
that rapidly, and have a suite of information to hone in on the root
cause of those discrepancies.Basic Setupfrom concordia import Concordia
concord = Concordia()

ml_predictor = load_ml_model()
concord.add_model(model=model, model_id='model123')Basic UsageIn your training environmentThe goal here is to save the features and predictions as you calcate
them in your training environment. Then, we can compare these to the
features and predictions coming from your live environment. We use the
row_ids to match rows across the two environments.df = load_my_data()
ml_predictor = train_ml_model()

predictions = ml_predictor.predict(df)

concord.add_data_and_predictions(model_id='model123', features=df, predictions=predictions, row_ids=df['my_row_identifier'])In your live environmentfrom concordia import load_concordia
concord = load_concordia()

data = get_live_data()

prediction = concord.predict(model_id='model123', features=data, row_id=data['my_row_identifier'])In your analytics environmentfrom Concordia import load_concordia
concord = load_concordia()

concord.analyze_prediction_discrepancies(model_id='model123')

concord.analyze_feature_discrepancies(model_id='model123')Infrastructure AssumptionsConcordia relies on MongoDB and Redis. These can be either local, or in
the cloud. You can specify DB credentials and connection options when
creating and loading Concordia.Database ConfigurationYou can easily specify your own DB connection. You’ll need to do this
both when creating the Concordia instance in the first place, as well as
when youload_concordia()to get access to that same Concordia
instance later.persistent_db_config = {
    'db': '__concordia_test_env'
    , 'host': 'localhost'
    , 'port': 27017
}

in_memory_db_config = {
    'db': 8
    , 'host': 'localhost'
    , 'port': 6379
}

concord = Concordia(in_memory_db_config=in_memory_db_config, persistent_db_config=persistent_db_config)

# To load this instance of Concordia later, use that same persistent_db_config
concord = load_concordia(persistent_db_config=persistent_db_config)What does Concordia do, under the hood?It ensures a consistent db schemaIt ensures a consistent way of saving data (predictions and
features), so that data can be matched up and compared laterIt builds in a suite of analytics tools for analyzing discrepanciesAll of this happens automatically for each model that you ship
Package: concordia-harmony
Concordia: Music Generation Using An SMT-based Harmony SolverOverviewSuppose you are composing some music. You have a beautiful melody, but you don’t know what chord progression to use. We proposeConcordia, an SMT-Based harmony-solving framework that helps composers and songwriters brainstorm musical ideas and test them out right away. Given some constraints ahead of time (e.g. baseline, melody, which chord to use),Concordiawill generate a harmony that satisfies the constraints. In the following sections, we will discuss the implementation of the harmony solver in more detail. This project made the following contributions:Describe how musical elements can be expressedas variables that an SMT-solver can understand.Provide an extensible frameworkfor constructing constraints around musical constructs.Provide An open-source and easy-to-install implementationof the harmony solver, available on here on Github and as a package onPyPI. An quick demo can be found inexample.ipynb.Supported ConstraintsConstraintDescriptionNote specificationUse specific notes at a certain point in the harmony.Chord specificationUse a specific chord/chord type at a certain position in the progression.Chord setOnly use chords specified to compose the harmony.Voice rangeSoprano cannot sing a really low note; some instruments can only play a small range of notes.Voice leadingYou might want to restrict the max interval of a jump in the melody.Chord progressionSome chords typically do not proceed to some other chords.Idiomatic usesSome music events happen with others (e.g. cadences, voice exchange).ArchitectureChangesTo push an update to PyPI, first update the version number inpyproject.toml, then push to GitHub.