semvec/pypi-packages huggingface dataset:
boml 0.1.2a1
A Bilevel Optimizer Library in Python for Meta Learning
License: MIT
Requires Python: >=3.5,<3.8
Keywords: meta-learning,bilevel-optimization,few-shot-lerning,python,Deep learning

.. BOML documentation master file, created by
   sphinx-quickstart on Mon Sep  7 09:30:26 2020.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Welcome to BOML's documentation!
================================
**Configuration & Status**

.. image:: https://travis-ci.com/dut-media-lab/BOML.svg?branch=master
   :target: https://github.com/dut-media-lab/BOML
   :alt: build status

.. image:: https://codecov.io/gh/dut-media-lab/BOML/branch/master/graph/badge.svg
   :target: https://github.com/dut-media-lab/BOML
   :alt: codecov
	
.. image:: https://readthedocs.org/projects/pybml/badge/?version=latest
   :target: https://github.com/dut-media-lab/BOML
   :alt: Documentation Status
	
.. image:: https://img.shields.io/badge/license-MIT-000000.svg
   :target: https://github.com/dut-media-lab/BOML
   :alt: License
	
.. image:: https://img.shields.io/github/languages/top/dut-media-lab/BOML
   :target: https://github.com/dut-media-lab/BOML
   :alt: Language
	
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/dut-media-lab/BOML
   :alt: Code style: black

BOML is a modularized optimization library that unifies several ML algorithms into a common bilevel optimization framework. It provides interfaces to implement popular bilevel optimization algorithms, so that you could quickly build your own meta learning neural network and test its performance.

**Key features of BOML**

- **Unified bilevel optimization framework** to address different categories of existing meta-learning paradigms. 
- **Modularized algorithmic structure** to integrate a variety of optimization techniques and popular methods.
- **Unit tests with Travis CI and Codecov** to reach 99% coverage, and following **PEP8 naming convention** to guarantee the code quality. 
- **Comprehensive documentations** using sphinx and **flexible functional interfaces** similar to conventional optimizers to help researchers quickly get familiar with the procedures.

**Related Links**

* `Go to the project home page <https://github.com/dut-media-lab/BOML>`_
* `Download the latest code bundle <https://codeload.github.com/dut-media-lab/BOML/zip/master>`_
BOML 0.1.2
A Bilevel Optimizer Library in Python for Meta Learning
License: MIT
Requires Python: >=3.5,<3.8
Keywords: meta-learning,bilevel-optimization,few-shot-lerning,python,Deep learning

.. BOML documentation master file, created by
   sphinx-quickstart on Mon Sep  7 09:30:26 2020.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Welcome to BOML's documentation!
================================
**Configuration & Status**

.. image:: https://travis-ci.com/dut-media-lab/BOML.svg?branch=master
   :target: https://github.com/dut-media-lab/BOML
   :alt: build status

.. image:: https://codecov.io/gh/dut-media-lab/BOML/branch/master/graph/badge.svg
   :target: https://github.com/dut-media-lab/BOML
   :alt: codecov
	
.. image:: https://readthedocs.org/projects/pybml/badge/?version=latest
   :target: https://github.com/dut-media-lab/BOML
   :alt: Documentation Status
	
.. image:: https://img.shields.io/badge/license-MIT-000000.svg
   :target: https://github.com/dut-media-lab/BOML
   :alt: License
	
.. image:: https://img.shields.io/github/languages/top/dut-media-lab/BOML
   :target: https://github.com/dut-media-lab/BOML
   :alt: Language
	
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/dut-media-lab/BOML
   :alt: Code style: black

BOML is a modularized optimization library that unifies several ML algorithms into a common bilevel optimization framework. It provides interfaces to implement popular bilevel optimization algorithms, so that you could quickly build your own meta learning neural network and test its performance.

**Key features of BOML**

- **Unified bilevel optimization framework** to address different categories of existing meta-learning paradigms. 
- **Modularized algorithmic structure** to integrate a variety of optimization techniques and popular methods.
- **Unit tests with Travis CI and Codecov** to reach 99% coverage, and following **PEP8 naming convention** to guarantee the code quality. 
- **Comprehensive documentations** using sphinx and **flexible functional interfaces** similar to conventional optimizers to help researchers quickly get familiar with the procedures.

**Related Links**

* `Go to the project home page <https://github.com/dut-media-lab/BOML>`_
* `Download the latest code bundle <https://codeload.github.com/dut-media-lab/BOML/zip/master>`_
sche 1.0a5
Job scheduling for humans.
License: MIT
Requires Python: >=3.6
Keywords: schedule,periodic,jobs,scheduling,clockwork,cron,scheduler,job scheduling

sche
========


.. image:: https://api.travis-ci.org/dbader/schedule.svg?branch=master
        :target: https://travis-ci.org/dbader/schedule

.. image:: https://coveralls.io/repos/dbader/schedule/badge.svg?branch=master
        :target: https://coveralls.io/r/dbader/schedule

.. image:: https://img.shields.io/pypi/v/schedule.svg
        :target: https://pypi.python.org/pypi/schedule

`sche` is a fork of `schedule` -- Python job scheduling for humans.

An in-process scheduler for periodic jobs that uses the builder pattern
for configuration. Schedule lets you run Python functions (or any other
callable) periodically at pre-determined intervals using a simple,
human-friendly syntax.

Inspired by `Adam Wiggins' <https://github.com/adamwiggins>`_ article `"Rethinking Cron" <https://adam.herokuapp.com/past/2010/4/13/rethinking_cron/>`_ and the `clockwork <https://github.com/Rykian/clockwork>`_ Ruby module.

Features
--------
- A simple to use API for scheduling jobs.
- Very lightweight and no external dependencies.
- Excellent test coverage.
- Tested on Python 3.6+
- Timezone support.

Usage
-----

.. code-block:: bash

    $ pip install sche

.. code-block:: python

    import sche
    import time

    def job():
        print("I'm working...")

    sche.every(10).minutes.do(job)
    sche.every().hour.do(job)
    sche.every().day.at("10:30").do(job)
    sche.every(5).to(10).minutes.do(job)
    sche.every().monday.do(job)
    sche.every().wednesday.at("13:15").do(job)
    sche.every().minute.at(":17").do(job)

    # or use schedule expression
    sche.when("every 10 minutes").do(job)
    sche.when("every hour").do(job)
    sche.when("every day at 10:30").do(job)
    sche.when("every 5 to 10 minutes").do(job)
    sche.when("every monday").do(job)
    sche.when("every wednesday at 13:15").do(job)
    sche.when("every minute at :17").do(job)

    # or use decorator to register job(without arguments)
    @sche.when("every 10 minutes")
    def another_job():
        print("I'm working on another job...")

    sche.clear()  # remove all jobs

    # set job with timezone
    sche.timezone("+0800").every().day.at("00:00").do(job)

    sche.run_forever()  # blockingly

    # OR
    sche.run_forever_background()  # non-blockingly

Documentation
-------------

`sche`'s documentation lives at `sche.readthedocs.io <https://sche.readthedocs.io/>`_.

Please also check the FAQ there with common questions.


Meta
----

Daniel Bader - `@dbader_org <https://twitter.com/dbader_org>`_ - mail@dbader.org

Distributed under the MIT license. See `LICENSE.txt <https://github.com/dbader/schedule/blob/master/LICENSE.txt>`_ for more information.

https://github.com/dbader/schedule
root-finding-pkg-thanjira 0.0.4
Root-Finding optimization
License: MIT
Keywords: goldenSection,bisection,newton,secant,brent,quasi-newton,trust region

This package is python module which implementation of root-finding method in one dimensional and multidimensional. The methods that included in this module are Golden section seacrh, Bisection method, Newton-Raphson method, Secant method , Brent method method, newton method in n-Dimensional, Broyden-Fletcher-Goldfarb-Shanno (BFGS), and trust-region Dogleg.
thesaurus-test 0.0.4
A package to help analysts extract key terms
License: MIT

thesaurus_test
==============

.. image:: https://img.shields.io/pypi/v/thesaurus_test.svg
    :target: https://pypi.python.org/pypi/thesaurus_test/
    :alt: Latest PyPI version
 

.. image:: https://travis-ci.com/github/gniu25/package.png
   :target: https://travis-ci.com/github/gniu25/package
   :alt: Latest Travis CI build status

A package to help analysts extract key terms

Usage
-----

Installation
------------

Requirements
^^^^^^^^^^^^

Compatibility
-------------

Licence
-------

Authors
-------

`thesaurus_test` was written by `RCDC <guiran.niu@nih.gov>`_.