semvec/pypi-packages huggingface dataset:
jSona 3.1
JSON format handler on the json built-in module
License: MIT

## jSona

##### JSON Format handler with bulit-in json module

Error tolerances are added to bulit-in json module.

Because those kind of works are really annoying.

---


### Installation

```code
pip3 install jSona-master/
```

---

### Projects

jSona have two major functions

* load : Loads data from json format file.

* save : Saves to json format file.

* loads, dumps : Equals loads and dumps functions in json module.

---

### Examples

* Script

```python
# from jSona import save, load
from pprint import pprint as pp
from jSona import save, load

sample_data = {'hello':['world', 'everyone~']}
save("sample.json", sample_data, cry=True)

load_data = load("sample.json", cry=True)
pp(load_data)
```

* Outputs

```python
SAVE SUCCESS TO [ sample.json ]
LOAD SUCCESS FROM [ sample.json ]
{'hello': ['world', 'everyone~']}
```

---


### Notices

###### Unauthorized distribution and commercial use are strictly prohibited without the permission of the original author and the related module.
jsonx-py 1.0.0
JSON flattening and expansion
License: MIT

# JSONx Python

A Python implementation of [JSONx](https://github.com/getformative/jsonx).

## Installation

```bash
pip install jsonx-py
```

## Unflattening JSON

```python
import jsonx

data = {
  "user.name": "Anakin Skywalker",
  "user.email": "anakin@jedi.galaxy",
}

jsonx.unflatten(data)
```
mlalgs 0.0.3
Machine Learning Algorithms from Scratch in Python
License: MIT
Requires Python: >=3.6

[![build status](https://travis-ci.com/amirhessam88/ml-algs.svg?branch=master)](https://travis-ci.com/github/amirhessam88/ml-algs)
[![License](https://img.shields.io/github/license/amirhessam88/ml-algs)](https://github.com/amirhessam88/ml-algs/blob/master/LICENSE)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/mlalgs)](https://pypi.org/project/mlalgs/)
![PyPI Version](https://img.shields.io/pypi/v/mlalgs)
[![Issues](https://img.shields.io/github/issues/amirhessam88/ml-algs)](https://github.com/amirhessam88/ml-algs/issues)
[![Forks](https://img.shields.io/github/forks/amirhessam88/ml-algs)](https://github.com/amirhessam88/ml-algs/network/members)
[![Stars](https://img.shields.io/github/stars/amirhessam88/ml-algs)](https://github.com/amirhessam88/ml-algs/stargazers)

<p align="center">
<a href="https://www.amirhessam.com/">
  <img src="https://raw.githubusercontent.com/amirhessam88/ml-algs/master/assets/design/logo.png" width="250"></img></a>
</p>

<h1 align="center">
    Machine Learning Algorithms from Scratch in Python
</h1>


## Installation

First, install Python 3.6 from https://www.python.org, and then run:

```
pip install mlalgs
```

## Implementations
- **Logistic Regression:** [Source](mlalgs/logistic_regression.py), [Notebook](notebooks/logistic_regression.ipynb)

## Citation
If you use the library in academic work, please consider citing
https://doi.org/10.1117/12.2304418 .

### Bibtex Entry:
```bib
@inproceedings{tahmassebi2018ideeple,
  title={ideeple: Deep learning in a flash},
  author={Tahmassebi, Amirhessam},
  booktitle={Disruptive Technologies in Information Sciences},
  volume={10652},
  pages={106520S},
  year={2018},
  organization={International Society for Optics and Photonics}
}
```
### APA Entry:

Tahmassebi, A. (2018, May). ideeple: Deep learning in a flash. In Disruptive
Technologies in Information Sciences (Vol. 10652, p. 106520S). International
Society for Optics and Photonics.