semvec/pypi-packages huggingface dataset:
hackermind 1.0.4
Crack the code in single- or multiplayer in this Mastermind masterpiece.
Requires Python: >=3.6

# Mastermind
It's codebreaker versus codebreaker. Pit yourself against your friends 
and find out who the real <i>Mastermind</i> is! The rules are simple. 
Players take turns trying to crack their secret code using logic and visual 
hints. The first person to prove they're a mastermind wins!

## Getting Started
---
Make sure you have Python 3.8.0 or newer installed and running on your machine. 
Open a terminal and browse to the project's root folder. Start the program by 
running the following command:
```
python3 mastermind 
```
You can also run the program from an IDE like Visual Studio Code. Start your IDE 
and open the project folder. Select the main module inside the hunter folder and 
click the "run" icon.

## Authors
---
- Dalton W
- Agnes H
- Kelton G
- Sarah A
- Mireya V
resautonet 0.2.1
Library for Autoencoder-based Residual Deep Network

# Library of Autoencoder-based Residual Deep Network (resautonet)

The python library of autoencoder based residual deep network (resautonet). 
Current version (2.0) just supports the KERAS package of deep learning and 
will extend to the others in the future. 

## Major modules

**model**

* resAutoencoder: major class to obtain a autoencoder-based residual 
      deep network by setting the arguments. See the class and its 
      member functions' help for details.  
* pmetrics: functions for regression metrics like rsquared and RMSE. 

**peranalysis**

* mulParPerAnalysis: major class for parallel performance analysis 
      You can setup many configure parameters for each network (a duty)
      and then run them to the effects in a parallel way. See this class 
      and its member functions' help for details.  

**data**

* data: function to access each of two datasets,  
         sim': simulated dataset in the format of Pandas's Data Frame,
         'pm2.5':string, the name for a real dataset of the 2015 PM2.5 
         and the relevant covariates for the Beijing-Tianjin-Tangshan
         area. It is sampled by the fraction of 0.8 from the
         the original dataset (stratified by the julian day).
         See this function's help for details.  
* simdata: function to simulate the test dataset,  
         The simulated dataset generated according to the formula:
         y=x1+x2*np.sqrt(x3)+x4+np.power((x5/500),0.3)-x6+
         np.sqrt(x7)+x8+noise
         See this function's help for details.

## Installation

You can directly install it using the following command for the latest version:
```
pip install resautonet -U
```
You can also clone the repository and then install:

```bash
git clone --recursive https://github.com/lspatial/resautonet.git
cd package 
pip install ./setup.py install 
```

With the `setup.py` file included in this example, the `pip install` command will
invoke CMake and build the resautonet module as specified in `CMakeLists.txt`.


## Note for installation and use 

**Compiler requirements**

resautonet requires a C++11 compliant compiler to be available.

**Runtime requirements**

resautonet requires installation of Keras with support of Tensorflow or other 
backend system of deep learning (to support Keras). Also Pandas and Numpy should 
be installed. 


## Use case 
The homepage of the github for the package, resautonet provides two specific 
examples for use of autoencoder based residual deep network:  
https://github.com/lspatial/resautonet 


## License

The resautonet is provided under a MIT license that can be found in the LICENSE
file. By using, distributing, or contributing to this project, you agree to the
terms and conditions of this license.

## Test call

```python
import resautonet as r
#Load the sample dataset for PM2.5  
simdata=r.data('pm2.5')
simdata.head()
```
## Collaboration

Welcome to contact Dr. Lianfa Li (Email: lspatial@gmail.com).
netrd 0.3.0
netrd: A library for network {reconstruction, distances, dynamics}

This library provides a consistent, NetworkX-based interface to various
utilities for graph distances, graph reconstruction from time series data, and
simulated dynamics on networks. 

Some resources that maybe of interest:

* A [tutorial](https://netrd.readthedocs.io/en/latest/tutorial.html) on how to use the library
* The API [reference](https://netrd.readthedocs.io/en/latest/) 
* A [notebook](https://nbviewer.jupyter.org/github/netsiphd/netrd/blob/master/notebooks/example.ipynb) showing advanced usage