semvec/pypi-packages huggingface dataset:
phap 4.0.0a1
Programing Helpful Algorithm Package
Requires Python: >=3.9

# PHAP
### *Programing Helpful Algorithm Package*
### Powered by Python 3.11
[![Apache License 2.0](https://img.shields.io/badge/license-Apache2.0-green.svg?style=flat)](https://choosealicense.com/licenses/apache-2.0/)

### English  | [简体中文](README_zh-CN.md)
#### *如果你不是在Github上阅读本说明，你可能无法打开非英语版本的说明文档*
#### *请前往[Github](https://github.com/DashStudio/phap "Github")网站，确保你能够成功地切换语言*

# Links
[Github](https://github.com/DashStudio/phap "Github") | [Pypi](https://pypi.org/project/phap/ "Pypi") | [Pypi (stralgo)](https://pypi.org/project/stralgo/ "Pypi (stralgo)")

# Versions
## Stable Version
+ v0.1.0 (stralgo)
+ v1.1.1 (stralgo)
+ v2.1.2
+ v2.2.1

## Latest Available Version
+ v3.1.0

## Latest Version
+ v4.0.0-alpha1

# To Use
## Read our development document
### *(Click the [Github](https://github.com/DashStudio/phap "Github") link to read this document，or you may can not to open the link)*
+ [Development Document](doc/README.md)

# Build
## Precondition
+ Install git and make tools
+ Install Python(the version 3.9 or the version 3.11)
+ Clone source code from source repository
```
git clone git@github.com:DashStudio/phap.git
```
### or
```
git clone https://github.com/DashStudio/phap.git
```

## Initialize packaging environment
```
make init
```
#### or
#### Try to install the package:
+ build
+ twine
#### This is the example command on Windows:
```
python -m pip install build
python -m pip install twine
```

## Build release
```
make build
```

# About the copyrights
## Current situation

The old part of the project PHAP and the old project,
are still under the MIT License.

The newer version after v3.0.0 are under the ***Apache License 2.0*** .

Let me see the [details](LICENSE).
cap6635 0.0.8
A Summary of AI Techniques covered in cap 6635
Requires Python: >=3.7

# CAP6635 Artificial Intelligence
[![Tests](https://github.com/nickumia/cap6635/actions/workflows/test.yml/badge.svg)](https://github.com/nickumia/cap6635/actions/workflows/test.yml)
[![PyPI version](https://badge.fury.io/py/cap6635.svg)](https://badge.fury.io/py/cap6635)

A summary of the AI techniques explored in Dr. Zhu's AI class

## Structure of Package

- `cap6635.agents` define the different search and decision-making algorithms.
    - See sub-directories for more details on the agents
- `cap6635.environment` creates and populates the world with various obstacles or other states.
- `cap6635.utilities` hosts various helper functions for searching, environment manipulation, animation and more.

### Development setup

```bash
pip install -r requirements.txt cap6635
```

## AI Examples

### Vacuums

Run the `vacuums.py` example with the optional paramters.  The output gets saved as a `vacuum?.gif` animation.

```bash
# Type of agent defaults to random type (if not provided)
# 1 --> Simple Reflex Vacuum
# 2 --> Model-based Vacuum
# 3 --> Goal-based Vacuum
# World Height & Width defaults to random int (if not provided)

python 1_vacuums.py [type_of_agent] [height_of_world] [width_of_world]
```

### n-Queens

```bash
# Hill Climbing
python 2_hill_climbing.py [number_of_queens]

# Simulated Annealing
python 3_simulated_annealing.py [number_of_queens]

# Genetic Algorithm
python 4_genetic.py [number_of_queens]
```

### Tic Tac Toe

```bash
# Minimax + Alpha-Beta Pruning
# Algorithm {1 --> Minimax, 0 --> Alpha-Beta Pruning}
# First Player {1 --> Human, 2 --> AI}
python 5_minimax.py [Algorithm] [First Player]

# e.g. Minimax (Alpha-Beta Pruning) - Player is X
python 5_minimax.py 0 1
```