semvec/pypi-packages huggingface dataset:
libra_py_001_07 0.0.1
Split Linearized Bregman Iteration
License: MIT
Keywords: sparsity regularization path Lasso variable-selection

Citing libra_py_001_07
=============

The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justified 
by the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry 'citation.bib <http://papers.nips.cc/paper/6288-split-lbi-an-iterative-regularization-path-with-structural-sparsity/bibtex>' ).

    Huang, Chendi and Sun, Xinwei and Xiong, Jiechao and Yao, Yuan. "Split LBI: An Iterative Regularization Path with Structural Sparsity" Advances in Neural Information Processing Systems 29, pp. 3369--3377 (2016)


libra_py_001_07: A Package for sparsity problem
============================================



Usage
-----
.. code-block:: python

    from libra_py_001_07 import lbi
    obj = lbi.LB(X,y,family='gaussian')
    obj.predict(X)


Tutorials and other information are available 'here <https://arxiv.org/abs/1604.05910>' and 
'here <https://www.sciencedirect.com/science/article/pii/S1063520316000038>'.

The R code is available as 'subrepository <https://cran.r-project.org/web/packages/Libra/index.html>'; the Matlab code is available as 'subrepository <https://github.com/yuany-pku/split-lbi>'.

If you have still **questions** after reading the documentation please open a issue at GitHub.

+----------------+------------------+-----------------------------+
| Family         | Solver           | Loss                        |
+================+==================+=============================+
| Gaussian       | LBI_Linear       | Square Loss                 |
+----------------+------------------+-----------------------------+
| Binomial       | LBI_Logit        | Logit Model                 |
+----------------+------------------+-----------------------------+

*Supported solvers and tasks*

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

**binary install**

``pip install libra_py_001_07``


Tests
-----
import libra_py_001_05
libra_py_001_06 0.0.1
Split Linearized Bregman Iteration
License: MIT
Keywords: sparsity regularization path Lasso variable-selection

Citing libra_py_001_05
=============

The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justified 
by the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry 'citation.bib <http://papers.nips.cc/paper/6288-split-lbi-an-iterative-regularization-path-with-structural-sparsity/bibtex>' ).

    Huang, Chendi and Sun, Xinwei and Xiong, Jiechao and Yao, Yuan. "Split LBI: An Iterative Regularization Path with Structural Sparsity" Advances in Neural Information Processing Systems 29, pp. 3369--3377 (2016)


libra_py_001_05: A Package for sparsity problem
============================================



Usage
-----
.. code-block:: python

    from libra_py_001_05 import lbi
    obj = lbi.LB(X,y,family='gaussian')
    obj.predict(X)


Tutorials and other information are available 'here <https://arxiv.org/abs/1604.05910>' and 
'here <https://www.sciencedirect.com/science/article/pii/S1063520316000038>'.

The R code is available as 'subrepository <https://cran.r-project.org/web/packages/Libra/index.html>'; the Matlab code is available as 'subrepository <https://github.com/yuany-pku/split-lbi>'.

If you have still **questions** after reading the documentation please open a issue at GitHub.

+----------------+------------------+-----------------------------+
| Family         | Solver           | Loss                        |
+================+==================+=============================+
| Gaussian       | LBI_Linear       | Square Loss                 |
+----------------+------------------+-----------------------------+
| Binomial       | LBI_Logit        | Logit Model                 |
+----------------+------------------+-----------------------------+

*Supported solvers and tasks*

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

**binary install**

``pip install libra_py_001_05``


Tests
-----
import libra_py_001_05
libra_py_001_05 0.0.1
Split Linearized Bregman Iteration
License: MIT
Keywords: sparsity regularization path Lasso variable-selection

Citing libra_py_001_05
=============

The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justified 
by the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry 'citation.bib <http://papers.nips.cc/paper/6288-split-lbi-an-iterative-regularization-path-with-structural-sparsity/bibtex>' ).

    Huang, Chendi and Sun, Xinwei and Xiong, Jiechao and Yao, Yuan. "Split LBI: An Iterative Regularization Path with Structural Sparsity" Advances in Neural Information Processing Systems 29, pp. 3369--3377 (2016)


libra_py_001_05: A Package for sparsity problem
============================================



Usage
-----
.. code-block:: python

    from libra_py_001_05 import lbi
    obj = lbi.LB(X,y,family='gaussian')
    obj.predict(X)


Tutorials and other information are available 'here <https://arxiv.org/abs/1604.05910>' and 
'here <https://www.sciencedirect.com/science/article/pii/S1063520316000038>'.

The R code is available as 'subrepository <https://cran.r-project.org/web/packages/Libra/index.html>'; the Matlab code is available as 'subrepository <https://github.com/yuany-pku/split-lbi>'.

If you have still **questions** after reading the documentation please open a issue at GitHub.

+----------------+------------------+-----------------------------+
| Family         | Solver           | Loss                        |
+================+==================+=============================+
| Gaussian       | LBI_Linear       | Square Loss                 |
+----------------+------------------+-----------------------------+
| Binomial       | LBI_Logit        | Logit Model                 |
+----------------+------------------+-----------------------------+

*Supported solvers and tasks*

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

**binary install**

``pip install libra_py_001_05``


Tests
-----
import libra_py_001_05
shldn 1.2.1
Find divisions in Python code
License: MIT
Requires Python: >=3.6.0


# shldn 
Sheldon Cooper ALWAYS points out what (he thinks) is wrong with people and their work

## What is it?

`shldn` is a console-application that finds all the division operations in Python source code to facilitate transitioning Python 2 code to Python 3. It uses Python's [AST](https://docs.python.org/3/library/ast.html) module to process and parse the code. It, then traverses the abstract syntax tree to find all the division operations, and prints them in a normal or more readable format to the console. 

## Motivation

- Python 2 won't be supported past 2020 

    <img src="http://bestanimations.com/HomeOffice/Clocks/Grandfather/old-grandfather-clock-animated-gif-7.gif#.W1J27VgRJQ4.link" width=100> [tic tac tic tac...](https://pythonclock.org/)

- Division in Python 3 always returns a `float`; whereas division in Python 2 can return an `int`
- Critical for large Python 2 code bases that perform division operations



## Requirements
- Python 3

## Install

### Method 1: Install from [Pypi](https://pypi.org/) Method

```
pip3 install shldn
```

Or, if you use [pipenv](https://docs.pipenv.org/),
```
pipenv install shldn
```

### Method 2: Clone repository
Move to the desired directory to install [shldn](https://github.com/NablaZeroLabs/shldn)

```
git clone https://github.com/NablaZeroLabs/shldn.git
```

## Usage

### Method 1 (Installed from [Pypi](https://pypi.org/)):
In environment installed: 
1. Check if installed 
```
pip list
```
2. Execute
```
shldn [-h] [-hr] [-r] path
```

### Method 2 (Cloned repo):
1. Execute
```
python3 leonard.py [-h] [-hr] [-r] path

```
#### Arguments 
```
positional arguments:
  path                  path to python source file(s)

optional arguments:
  -h, --help            show this help message and exit
  -hr, --human_readable
                        set for friendlier output
  -r, --recursive       recursively check python files in path
```

## License
This project is licensed under the MIT License - see the [LICENSE.txt](https://github.com/NablaZeroLabs/shldn/blob/master/LICENSE.txt) file for details.

## More Resources
- [Porting Python 2 Code to Python 3](https://docs.python.org/3/howto/pyporting.html) by Brett Cannon
astrool 0.1.dev0
Python package for Astronomers in a Hurry
License: MIT
Requires Python: >=3.5
Keywords: astro,positional,astronomy,ephem,orbits,every-astro,orbital mechanics

.. astrool

astrool is an open source pure Python package dedicated to problems arising in life of
day to day Astronomy, such as orbit propagation, night sky plotting,
analemma plotting, conversion between position and velocity vectors and classical
orbital elements and orbit plotting.
It is released under the MIT license.

Requirements
============

astrool requires the following Python packages:

* NumPy, for basic numerical routines
* Astropy, for physical units and time handling
* ephem, for the planetary ephemerides using SPICE kernels
* matplotlib
* poliastro


Testing
=======

|codecov|

If installed correctly, the tests can be run using pytest::

  $ python -c "import astrool.testing; astrool.testing.test()"
  Running unit tests for astrool
  [...]
  OK
  $

Problems
========

If the installation fails or you find something that doesn't work as expected,
please open an issue in the `issue tracker`_.

.. _`issue tracker`: https://github.com/shreyasbapat/astrool/issues

Contributing
============

.. image:: https://img.shields.io/waffle/label/astrool/astrool/1%20-%20Ready.svg?style=flat-square
   :target: https://waffle.io/astrool/astrool
   :alt: 'Stories in Ready'

astrool is a community project, hence all contributions are more than
welcome! For more information, head to `CONTRIBUTING.rst`_.

.. _`CONTRIBUTING.rst`: https://github.com/shreyasbapat/astrool/blob/master/CONTRIBUTING.rst

Support
=======

|mailing|

Release announcements and general discussion take place on our `mailing list`_.
Feel free to join!

.. _`mailing list`: https://groups.io/g/astrool-dev

https://groups.io/g/astrool-dev


License
=======

|license|

astrool is released under the MIT license, hence allowing commercial
use of the library. Please refer to the COPYING file.

FAQ
===

What's up with the name?
------------------------

astrool is made up of astro + tools. So basically, we are trying to come up with an aggregated software.


What's the future of the project?
---------------------------------

astrool is a very new project and we aim to develop it further so that it can become one
package for all in basic astronomy.