semvec/pypi-packages huggingface dataset:
enotification 0.0.7
A package to send an email when your function finished.
Requires Python: >=3.6

# enotification

## Introduction

`enotification` is a small package based on [zmail](https://github.com/zhangyunhao116/zmail), used to email you when your function is finished.

## Installation

`pip install enotification`

## Requirements

Since `enotification` is based on zmail, you should meet the requirements of zmail:

>Before using it, please ensure:
>
>- Using python3
>- Open SMTP/POP3 functions in your mail (For **@163.com** and **@gmail.com** you need to set your app private password)
>
>Then, all you need to do is just import zmail.

Note, since this package uses **f-string**, thus it requires Python>=3.6.

## QuickStart

`enotification` is designed as a decorator class

```python
from enotification import ENotification
import time

@ENotification(send_from="your email", password="your email password")
def test_func():
    print("test!")
    time.sleep(10)
    
if __name__ == "__main__":
    test_func()
    # your email would receive an email like this:
    # subject: This is a notification from enotification!
    # content: 
    # Your function test_func is finished!
    # Start time: 2021-04-23 11:13:54
    # End time: 2021-04-23 11:14:04
    # Time cost: 0:00:10!

```

## TODO

- [x] Ask user to input her email account or password if it is empty (using [getpass](https://docs.python.org/3/library/getpass.html) module). Usage: `@ENotification()`. **Note:** the `()` is must.
- [ ] 

## Q & A

If the notification email is not sent correctly, please read zmail's [Q&A](https://github.com/zhangyunhao116/zmail#qa) part to see more specific configurations.
ffl 0.3.4
Forcast Federated Learning
Requires Python: >=3.6

# Forcast Federated Learning

Forcast Federated Learning (FFL) is an open-source [Pytorch](https://pytorch.org/) based framework for machine learning on decentralized data. FFL has been developed to facilitate open experimentation with [Federated Learning (FL)](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html), an approach to machine learning where a shared global model is trained across
many participating clients that keep their training data locally. For example,
FL has been used to train
[prediction models for mobile keyboards](https://arxiv.org/abs/1811.03604)
without uploading sensitive typing data to servers.

FFL enables developers to use low level model aggregation into a federated model. Explicitly using individual data per client and sharing only the local models or model gradients. This helps bridge the gap from simulation, into simulation with isolated clients and private data and onto deployment.

## Installation

See the [install](docs/install.md) documentation for instructions on how to
install FFL as a package or build FFL from
source.

## Getting Started

See the [get started](docs/get_started.md) documentation for instructions on
how to use FFL.
STA-663-IBP 0.1
STA663 - Final Project: Implementation of Indian Buffet Process
Requires Python: >=3.6

# STA-663-Final-Project

Students: Raghav Agrawal, Peyton Chen

In our term final project for STA 663 - Statistical Computation, we studied the paper titled "Infinite Latent Feature Models
and the Indian Buffet Process" by Thomas L. Griffiths and Zoubin Ghahramani and implememented the algorithms described in the paper.

The `Package` directory contains all the files in our package, which includes the source code. 

The test codes are located in the `Test code` folder. 

The final report of our project can be found in the `Report` direcotry. 

Examples of the implememntation of our algorithms can be found in the `Example` directory.