semvec/pypi-packages huggingface dataset:
MVBLS 1.1
Multi-view Broad Learning Systerm
Requires Python: >=3.6

# Multi-view Broad Learning Systerm (MVBLS)


[![Python Versions](https://img.shields.io/pypi/pyversions/MVBLS.svg?logo=python&logoColor=white)](https://pypi.org/project/MVBLS)
[![Documentation Status](https://readthedocs.org/projects/mvbls/badge/?version=latest)](https://mvbls.readthedocs.io/)
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**Multi-view broad learning systerm (MVBLS)** [1] is a multi-view  framework that bases on BLS [2]. It is designed to be efficient with the following advantages:

- Support of classification and regression in supervised multi-view or multi-modal learning.
- Support of classification and regression in semi-supervised multi-view or multi-modal learning [3].
- Support of two or more views or modals.

## Get Started and Documentation

Our primary documentation is at https://mvbls.readthedocs.io/ and is generated from this repository. If you are new to MVBLS, follow [the installation instructions](https://mvbls.readthedocs.io/en/latest/Python-Intro.html) on that site. The preferred way to install MVBLS is via pip from [Pypi](https://pypi.org/project/MVBLS).

Next you may want to read:
- [**APIs & Parameters**](https://mvbls.readthedocs.io/en/latest/MVBLS.html) is an exhaustive list of customization you can make.
- [**Parameters Tuning**](https://mvbls.readthedocs.io/en/latest/Parameters-Tuning.html) is an exhaustive list of customization you can make.
- [**Examples**](https://mvbls.readthedocs.io/en/latest/Python-Examples.html) showing command line usage of common tasks.

## References

[1] Z. Shi, X. Chen, C. Zhao, H. He, V. Stuphorn and D. Wu, "Multi-view broad learning system for primate oculomotor decision decoding," in IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 28, no. 9, pp. 1908-1920, 2020.

[2] C. L. P. Chen and Z. Liu, "Broad learning system: An effective and efficient incremental learning system without the need for deep architecture," in IEEE Trans. on Neural Networks and Learning Systems, vol. 29, no. 1, pp. 10-24, 2018.

[3] T. Qiu, X. Liu, X. Zhou, W. Qu, Z. Ning and C. L. P. Chen, "An adaptive social spammer detection model with semi-supervised broad learning," in IEEE Trans. on Knowledge and Data Engineering, doi: 10.1109/TKDE.2020.3047857.
python-planfact 0.0.10.1
Simple python wrapper for some parts of the planfact API (https://apidoc.planfact.io/)
Requires Python: >=3.6

# Simple python wrapper for planfact.io API

### Resource

planfact API documentation: https://apidoc.planfact.io

### Installation

```pip install python-planfact```

### Example usage:

```python
import os
import planfact_api as pf

os.environ["PF_API_KEY"] = "YOUR_API_KEY_HERE"
currencies = pf.get_currencies()
```
antistasi_logbook 0.4.5
Antistasi Logbook
Requires Python: >=3.9

<center>
    <h1>Antistasi Logbook</h1>
</center>
<center>
    <img src="docs/images/app_icon.png" title="Antistasi Logbook" alt="image">
</center>

- **__Version:__** `0.4.5`
b2binpay-python 0.0.4
Library for working with b2binpay api
Requires Python: >=3.6

# b2binpay-py
> Python library for working with [B2binPay](https://docs.b2binpay.com/v2/en/api-reference.html?roistat_visit=584687)
# Install
`pip install b2binpay-py`

## Example 
```buildoutcfg
from b2binpay import client
import asyncio


async def main():
    c = await client.AsyncClient("API_KEY", "API_SERCET", test=True).connect()
    w = await c.get_wallets()
    await c.close_connection()


if __name__ == '__main__':
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())

```