semvec/pypi-packages huggingface dataset:
wired 0.4
An inversion-of-control (IoC) container for building decoupled, configurable, pluggable applications.
Requires Python: >=3.8
Keywords: ioc,inversion of control,dependency injection,service locator,singleton,service factory

=====
wired
=====

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

.. image:: https://github.com/mmerickel/wired/workflows/Build%20and%20test/badge.svg?branch=main
    :target: https://github.com/mmerickel/wired/actions?query=workflow%3A%22Build+and+test%22
    :alt: main CI Status

.. image:: https://readthedocs.org/projects/wired/badge/?version=latest
    :target: https://readthedocs.org/projects/wired/?badge=latest
    :alt: Documentation Status

Have a large application where you want to *decrease coupling* between components?
Need to *supply configuration* to your application's various services? Want to
make a *pluggable application* where others can supply services?

`Inversion of Control <https://en.wikipedia.org/wiki/Inversion_of_control>`_ and
`Dependency Injection <https://en.wikipedia.org/wiki/Dependency_injection>`_ are
two patterns commonly used for these goals.

``wired`` is an implementation of an inversion-of-control (IoC) container and
may be used as the core of a dependency injection (DI) framework or simply as
a way to separate config-time from runtime for services in an application. It
also provides caching such that a container maintains a local copy of each
service as they are instantiated.

`wired` aims to scale down to the simplest cases and up to very large, custom
systems. It has one dependency and that dependency has one dependency.

See https://wired.readthedocs.io or
``docs/index.rst`` in this distribution for detailed documentation.
overfit 0.0.1
An ML code generator
Requires Python: >=3.8

# overfit
ML training code generator. Not ready for use.

## Examples
```python
from overfit.unsupervised import UnsupervisedPipeline
fit = UnsupervisedPipeline(data = data,
                         target = 'target_name',
                         type = 'kmeans',
                         nfolds = 10
                         )
# fit.get_code(), print
fit.code_gen() # run

```