semvec/pypi-packages huggingface dataset:
sphinx-heigvd-theme 0.5.2
A Sphinx-doc theme based on sphinx press theme
License: MIT
Keywords: sphinx doc theme heig-vd vue.js

# Sphinx HEIG-VD Theme

[![Build Status](https://travis-ci.org/heig-vd-tin/sphinx-heigvd-theme.svg?branch=master)](https://travis-ci.org/heig-vd-tin/sphinx-heigvd-theme)

This theme is a fork of the [sphinx-press-theme](https://schettino72.github.io/sphinx_press_site/)
based on [VuePress](https://vuepress.vuejs.org/). It uses [Vue.js](https://vuejs.org/) & [Stylus](http://stylus-lang.com/)
managed by [webpack](https://webpack.js.org/) (through [vue-cli](https://cli.vuejs.org/)).

## Installation

First install the theme:

```
$ pip install git+https://github.com/heig-vd-tin/sphinx-heigvd-theme
```

On Sphinx project's ``conf.py``: set the theme name to ``heigvd``.

```
html_theme = "heigvd"
```

See details on [Sphinx theming docs](http://www.sphinx-doc.org/en/master/theming.html#using-a-theme).

## Development

To contribute to this theme you should first build the web assets:

```
cd ui
npm run build
```

Sphinx theme has a soft link to built assets. Once done, install the theme locally
with `pip install -e .`.

`docs` folder contains theme's own documentantion.

```
cd docs
make clean; make html
```
enhanceml 0.0.1
An ML enhancement package to improve outcomes
License: MIT

# enhanceml 

enhanceml (Enhance Machine Learning model) is a Python library to enhance the prediction of model using various statistical technique. This contains various sub-models which can be fitted over data to predict outcome.

* PBOB (Probability based Best Out of Best outcome): This method is used to enhance the prediction based on probability.


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install enhanceml.

```bash
pip install enhanceml
```
   or
```bash
https://github.com/sumitc29/enhanceml.git
```

## Usage

```python
from enhanceml import enhancer

knn=KNeighborsClassifier()
gnb=GaussianNB()

pbob_model=enhancer.PBOB(knn,gnb)

pbob_model.fit(x_train,y_train)
predictions=pbob_model.predict(x_test)
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License
[MIT](https://choosealicense.com/licenses/mit/)