semvec/pypi-packages huggingface dataset:
torch-eunn 0.2.1
An Efficient Unitary Neural Network implementation for PyTorch

# torch_eunn

This repository contains a simple PyTorch implementation of a Tunable Efficient
Unitary Neural Network (EUNN) Cell.

The implementation is loosely based on the tunable EUNN presented in this
paper: [https://arxiv.org/abs/1612.05231](https://arxiv.org/abs/1612.05231).


## Installation

```
    pip install torch_eunn
```


## Usage

```python
    from torch_eunn import EUNN # feed forward layer
    from torch_eunn import EURNN # recurrent unit
```

#### Note
The `hidden_size` ***and*** the `capacity` of the EUNN need to be ***even***, as explained in the section *"Difference with original implementation"*.

## Examples

* 00: [Simple Tests](examples/00_simple_tests.ipynb)
* 01: [Copying Task](examples/01_copying_task.ipynb)
* 02: [MNIST Task](examples/02_mnist.ipynb)


## Requirements

* [PyTorch](http://pytorch.org) >= 0.4.0: `conda install pytorch -c pytorch`


## Difference with original implementation


This implementation of the EUNN has a major difference with the original
implementation proposed in
[https://arxiv.org/abs/1612.05231](https://arxiv.org/abs/1612.05231), which is
outlined below.

In the original implementation, the first output of the top directional coupler
of a capacity-2 sublayer skips the second layer of directional couplers
(indicated with dots in the ascii figure below) to connect to the next
capacity-2 sublayer of the EUNN. The reverse happens at the bottom, where the
first layer of the capacity-2 sublayer is skipped. This way, a
`(2*n+1)`-dimensional unitary matrix representation is created, with `n` the
number of mixing units in each capacity-1 sublayer.
```
  __  __......
    \/
  __/\____  __
          \/
  __  ____/\__
    \/
  __/\____  __
          \/
  ......__/\__
```
For each capacity-1 sublayer with `N=2*n+1` inputs (`N` odd), we thus have `N-1`
parameters (each mixing unit has 2 parameters). Thus to have a unitary matrix
representation that spans the full unitary space, one needs `N` capacity-1
layers ***and*** `N` *extra* phases appended to the back of the capacity-`N`
sublayer to bring the total number of parameters in the unitary-matrix
representation to `N**2` (the total number of independent parameters in a
unitary matrix).

In the implementation proposed here, the dots in each capacity-2 sublayer are
connected onto themselves (periodic boundaries). This has the implication that
for each capacity-1 sublayer with `n` directional couplers, there are `N=2*n`
inputs and as many independent parameters. This means that we just need `N`
capacity-1 sublayers and **no** *extra* phases to span the full unitary space
with `N**2` parameters.

This, however, has the implication that the `hidden_size = N = 2*n` of the
unitary matrix should always be *even*. Also, because the forward pass is
defined per capacity-**2** sublayer (as opposed per capacity-1 sublayer in the
original implementation) the capacity has to be *even* as well.


## License

© Floris Laporte, 2018-2019.

Made available under the MIT license.
matics 1.3.2
一个包含矩阵，分数，计算，判断数类型，常量，方程的数学模块

README.md in English
# Matics
Matics is a math module.  But it's a bit good.

## Table of Contents
- [Matics](#Matics)
- [Lastest version](#Lastest-version)
- [Usage](#Usage)
  - [Step1](##Step1)
- [Examples](#Examples)
- [Thanks-For-Who](#Thanks-For-Who)
- [Plan](#Plan)
- [FAQ](#FAQ)

## Latest Version
Please look at [*matics-dist*](http://github.com/guozhenduo/matics-dist)



# Usage
See DOCS.md

## Step1:
```bash
$ cd matics
$ ./install.sh
$ ./test.sh
```

# Examples
Pleas look at [`EXAMPLES.md`](https://github.com/guozhenduo/matics/blob/master/EXAMPLES.md)

Author's email is [guozhenduo1@163.com](mailto:guozhenduo1@163.com). Welcome!!!

# *Thanks-For-Who*
I would like to thank [Bangyen](https://github.com/bangyen) for making this project better.
# Plan
No plan now.
# **FAQ**

1. Can I contribute?

 - That's for sure.As long as your code helps.

2. Which version is good?

 - 1.3.2

3. How do we update?

 - We update it every two weeks.


README.md in Chinese

# matics（为区别，所以首字母小写）
“matics”是个数学计算模块，我认为这个模块比较好。

## 目录
- [版本](#版本)
- [使用](#使用)
  - [步骤1](##步骤1)
- [实例](#实例)
- [鸣谢](#鸣谢)
- [计划](#计划)
- [常见问题回答](#常见问题回答)

# 版本
请见[*matics-dist*](http://github.com/guozhenduo/matics-dist)



# 使用
见DOCS.md

 ## 步骤1：
```bash
$ cd matics
$ ./install.sh
$ ./test.sh
```

 # 实例
请看到[`EXAMPLES.md`](https://github.com/guozhenduo/matics/blob/master/EXAMPLES.md)


作者的邮箱是guozhenduo1@163.com，欢迎来信

# 鸣谢
感谢Bangyen，他为这个项目做出了巨大的贡献。

# 计划
目前没有计划。

# 常见问题回答

1. 我可以做贡献吗？

 - 肯定的。只要你的代码足够好。

2. 那个版本好？

 - 1.3.2

3. 我们如何更新？

 - 两周一次
pandas-zmq 0.0.3
Communicate Pandas DataFrame over ZeroMQ connection
Requires Python: >=3.3

# pandas-zmq

Communicate [Pandas](https://pypi.org/project/pandas/) DataFrame over [ZeroMQ](https://pypi.org/project/pyzmq/) connection

## API

```
send_dataframe(socket, dataframe, secret='', alg='sha256', flags=0)
```

```
recv_dataframe(socket, secret='', alg='sha256', flags=0)
```

### Server

```
import pandas_zmq

# ...
pandas_zmq.send_dataframe(socket, df)
```


### Client

```
import pandas_zmq

# ...
df = pandas_zmq.recv_dataframe(socket)
```


## Security

WARNING: There are reported security issues with pickle, which is the backend serialization for this feature. A signature using a shared secret and a configurable hash algorithm has been added to mitigate this risk which will attempt to verify the data has not been altered. [See pickle documentation](https://docs.python.org/3/library/pickle.html).
byted-magellan-ai 1.0.0
Some computing tools with machine learning, python 3.6+.
Requires Python: >=3.6

# magellan_ai说明
这是一个旨在提供Machine Learning和Deep Learning标准计算工具的Python项目，定期发布到`pipy.org`。

目前提供以下模块：
* `magellan_ml`
  * `mag_util`
    * `metrics`: 提供了计算各种指标的工具方法，包括auc,ks,iv,psi等指标的计算。
  * `mag_nlp`: 提供了NLP相关的各种工具方法，包括分词，实体识别等功能。
  * `mag_case`: 提供各种教程。
  

# 在线安装
建议使用官方镜像，安装最新版本。

```python
pip install magellan-ai -i https://pypi.Python.org/simple/
```

# 本地安装
如果要给该项目贡献代码，可以在本地调试好后测试，本地安装方法

```python
$ git clone git@code.byted.org:cfalg/magellan_ai.git
$ cd magellan_ai
$ python install .
```

# 使用教程
以`magllan_ai.mag_util.mag_metrics`模块为例，安装完成之后，可以使用以下方法导入使用

```
from magllan_ai.mag_util import mag_metrics

mag_metrics.show_func()
mag_metrics.cal_auc()
mag_metrics.cal_psi()
```

# 打包发布

```
$ cd /path/to/magellan_ml
$ python setup.py sdist bdist_wheel
$ pip install twine
$ twine upload dist/*
```