semvec/pypi-packages huggingface dataset:
mlLang 1.0.2
Unified Machine Learning Language
License: MIT
Keywords: classifier model-learning xml

Unified Machine Learning Language (mlLang)
==========================================

**mlLang** takes hold of the Unified Machine Learning Language (mlLang) inside R. We have developed **mlLang** as an XML-based, unified language for machine learning. It standardizes all relevant steps to train superior models: preprocessing operations, model specification, and the tuning process. It thereby makes model tuning reproducible and documents the underlying process.

**mlLang** ships the converters for different programming language. For this purpose, it implements converters in two directions. 

(1) It automatically reads files in the unified machine learning language from custom XML files and then constructs a corresponding machine learning model in a given programming language. 

(2) It also supports the other direction and automatically converts machine learning models into XML files according to the unified machine learning language. 

Supported Languages
-------

Currently, the following languages are supported:

* [**R**](https://github.com/sfeuerriegel/mllang/tree/master/R):  Simply load [**mlLang for R**](https://github.com/sfeuerriegel/mllang/tree/master/R) when starting your programming session. Afterwards, the machine learning operations from caret are recorded and written to the disk in an open XML format. This file can be later loaded to reproduce models and training processes from machine learning. 

* **Python**: A Python variant for scikit-learn is currently in development.

License
-------

**mlLang** is released under the [MIT License](https://opensource.org/licenses/MIT)

Copyright (c) 2017 Stefan Feuerriegeln
pipi 1.0.1
shortcut install & freeze pip packages
License: MIT

pipi
====

.. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
   :alt: contributions welcome
   :target: https://github.com/mehmetkose/pipi/

shortcut install & freeze pip packages

Installation
~~~~~~~~~~~~

::

  pip install pipi


Usage
~~~~~

::

    pipi install [package_name]
    pipi i [package_name]
    pipi install requests
    pipi i requests

every single operation works like:

1. Create requirements.txt if not exists.
2. Try install the package if not installed.
3. Append package to the requirements.txt if the operation is successful.
kpfm 0.3
Kelvin Probe Force Microscopy (KPFM) signal processing utilities.
License: MIT
Keywords: kpfm

=============================
kpfm
=============================

.. image:: https://travis-ci.org/marohngroup/kpfm.svg?branch=master
    :target: https://travis-ci.org/marohngroup/kpfm

.. image:: http://readthedocs.org/projects/kpfm/badge/?version=latest
    :target: http://kpfm.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

Kelvin Probe Force Microscopy (KPFM) data analysis.


Motiviation
-----------

Collect useful code for custom Kelvin probe force microscopy experiments into a single repository.
The motivation for using a single repository is prior experience and `this article <http://danluu.com/monorepo/>`_.

Structure
---------

A tentative structure for the repository is

.. code-block:: none

    kpfm
    ├── __init__.py
    ├── _version.py
    ├── experiment
    │   └── __init__.py
    ├── tests
    │   ├── __init__.py
    │   └── test_kpfm.py
    ├── scratch
    │   ├── __init__.py
    │   └── temporary_code.py
    └── util
        ├── __init__.py
        └── plot.py

Each experiment is contained in its own module.
The ``util`` module collects useful functions and decorators that are useful across a range of modules.
Tests are placed in the ``tests`` module.
Scratch contains a place for code that will not be maintained.
If a particular experiment or procedure is useful over a longer period,
it can be moved to its own experiment module.

Development life cycle of new code
----------------------------------

1. Workup data and write provisional code in an IPython notebook
2. If many data files are going to be processed in this way, move the routine to a new module in ``scratch``.
     - Workups in ``scratch`` come with no guarantees that they will not break.
     - In practice, it would be useful to create a new copy of the temporary code if there is likely to be significant breakage.
3. If the experiment is useful enough, copy / edit the scratch code and move it to its own experiment folder. In doing so, we commit to leave the experiment in a usable, backward compatible state, which means consistent LabView code for collecting the data, as well as consistent, backward-compatible routines for working up the data.

The idea would be that with a powerful set of utility, signal processing, and data analysis tools in the ``kpfm`` package, many analyses could be performed quickly at (1) or (2).
Even having a file in scratch (perhaps copied occasionally as the data analysis or LabView code changes significantly) is a significant improvement over code copied between IPython notebooks.
The other consideration is that it is a large investment of time to maintain code, so that code related to a "one-off" experiment should be saved, but not maintained.

Features
--------

* TODO




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

The full documentation is at http://kpfm.rtfd.org.



History
-------

0.1.0 (2017-03-28)
++++++++++++++++++

* First release on GitHub.