semvec/pypi-packages huggingface dataset:
JBTools 0.7.1
My personal packages
License: MIT

JBTools
=======
These codes was written by Jean-Baptiste Tanguy. There are several classes or functions callable but there
are not really related. It's just about making the programs more easily handled.

What do we have in this package?
================================
* ```neural_language_model_char_based.py```: this allows to learn, load, save and use a neural character based language model
* ```simple_proba_language_model_char_based.py```: this allows to learn, load, save and use a simple statistical character based language model
* ```evaluate_ocr_results.py```: this allows to process several methods to evaluate an ocr result (cer, wer, language model).
django-simulated-inlines 0.0.0
A Django app to allow for admin inlines without relationship to the base model
License: MIT

# Django Simulated Inlines
[![Built with Spacemacs](https://cdn.rawgit.com/syl20bnr/spacemacs/442d025779da2f62fc86c2082703697714db6514/assets/spacemacs-badge.svg)](http://spacemacs.org)

## Description
This package extends the ModelAdmin interface with the possibility to create custom inlines
that can contain a list of any Model instances, the relationship with the base change_form is not needed.

## Features
  *  inlines can be collapsed
  *  supports inline subtitles
  *  pagination
  *  custom links for any inline cell
  *  dynamic fields
  *  action buttons

## Installation

1. Install: `pip install django_simulated_inlines`
2. add to INSTALLED_APPS:
```
    INSTALLED_APPS = [
        ...
        'django_simulated_inlines',
    ]
```

## How to Use
  * Import the ModelAdmin and Inline classes 

  ` from django_simulated_inlines.admin import SimulatedInline, SimulatedInlinesModelAdmin`

  * Declarating inlines:
  ```
  class ExampleInline(SimulatedInline):
      model = ExampleModel
      verbose_name = _("example")
      verbose_name_plural = _("examples")
      per_page = 20
      buttons = ("example_button", )
      collapse = True
      fields = (
          "id"
          ("custom_link", "/admin/custom/link/%s/"),
          "custom_field",
      )

      def custom_field(self, obj):
          """The return of this function is the value on the field cell"""
          return f"Custom field for {self.field}"
      custom_field.short_description = _("custom field")

      def example_button(self, request, object_id):
          """This function is called when the button is pressed"""
          execute_code()
      example_button.short_description = _("Execute example button")

      def get_subtitle(self, request, object_id):
          """Override this to define a subtitle for the inline"""
          return "this is a subtitle"

      def get_queryset(self, request, object_id, parent_queryset):
         """Override this to filter or customize de queryset"""
         return self.model.objects.filter(x=object_id).all()
  ```

  * Add the inlines to the  `SimulatedInlinesModelAdmin.simulated_inlines`.
  ```
  class ExampleModelAdmin(SimulatedInlinesModelAdmin):
      simulated_inlines = [ExampleSimulatedInline]
  ```

## Requirements
pyml-ensemble 0.1.2
A library for creating machine learning ensembles.
License: MIT

# Python Machine Learning Ensemble
A library used to create and manage adaptable machine learning ensembles in Python.

The ensemble is intended to use deep learning models as the models. However, rather than
including these models with the package there are ANN and LSTM models in the provided
examples (examples/models/). This was to remove the package's dependency on the
tensorflow package since many ensembles can be created without this package. There are
some other, smaller dependencies still to make the Ensemble usable out of the box.


## Under Construction
The initial version of PyML Ensemble is currently being developed. Feel free to use and contribute to the project but don't expect too much functionality. Additionally, the PyPi package has not been released yet.