semvec/pypi-packages huggingface dataset:
vnpy-arctic 1.0.4
arctic database manager for VeighNa quant trading framework.
License: MIT
Keywords: quant,quantitative,investment,trading,algotrading

# VeighNa框架的Arctic数据库接口

<p align="center">
  <img src ="https://vnpy.oss-cn-shanghai.aliyuncs.com/vnpy-logo.png"/>
</p>

<p align="center">
    <img src ="https://img.shields.io/badge/version-1.0.4-blueviolet.svg"/>
    <img src ="https://img.shields.io/badge/platform-linux|windows|mac-yellow.svg"/>
    <img src ="https://img.shields.io/badge/python-3.7|3.8-blue.svg" />
</p>

## 说明

基于arctic开发的MongoDB数据库接口。

## 使用

在VeighNa中使用Arctic连接MongoDB数据库时，需要在全局配置中填写以下字段信息：

|名称|含义|必填|举例|
|---------|----|---|---|
|database.name|名称|是|arctic|
|database.host|地址|是|localhost|
|database.port|端口|是|0|
|database.user|用户名|否|admin(若mongodb无认证则不填)|
|database.password|密码|否|123456(若mongodb无认证则不填)|
|database.database|实例|是|vnpy|
FaceChannel 0.0.3.4
A python interface for training Reinforcement Learning agents to play the Chef's Hat Card Game.
License: MIT
Requires Python: >=3.9.0


**FaceChannel Library - Facial Expression Recognition Models!**

![Screenshot](Images/demo.png)

This repository holds the FaceChannel Library. The FaceChannel contains different facial expression recognition models, and makes it easier to deploy and use them.

**Instalation and Documentation**

You can install the library using pip:

    pip install facechannel

and you can check the [full documentation](https://facechannel.readthedocs.io/en/latest/)  for more information.

Also check the examples folder for a full set of ready-to-use demos!


**Avaliable Models**

FaceChannel is a python library that holds several facial expression recognition models. The main idea behind the FaceChannel is to facilitate the use of this technology
by reducing the deployment effort. This is the current list of available models:



Model | Input Type | Output Type |
------------- | ------------- | -------------
FaceChannelV1  Cat  | Single Image (64x64) | ["Neutral", "Happiness", "Surprise", "Sadness", "Anger", "Disgust", "Fear", "Contempt"] |
FaceChannelV1  Dim  | Single Image (64x64) | ["Arousal", "Valence"] |
Self Affective Memory  | Single Image (64x64) | ["Arousal", "Valence"] |


* FaceChannelV1 -  [Barros, P., Churamani, N., & Sciutti, A. (2020). The facechannel: A fast and furious deep neural network for facial expression recognition. SN Computer Science, 1(6), 1-10](https://link.springer.com/article/10.1007/s42979-020-00325-6>)
* Self Affective Memory - [Barros, P., & Wermter, S. (2017, May). A self-organizing model for affective memory. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 31-38). IEEE.](https://www2.informatik.uni-hamburg.de/wtm/publications/2017/BW17/Barros-Affective_Memory_2017-Webpage.pdf)



**License**

All the examples in this repository are distributed under a Non-Comercial license. If you use this environment, you have to agree with the following itens:

- To cite our associated references in any of your publication that make any use of these examples.

- To use the environment for research purpose only.

- To not provide the environment to any second parties.



**Contact**

Pablo Barros - pablo.alvesdebarros@iit.it