semvec/pypi-packages huggingface dataset:
random-walk-env 0.0.4
A random walk environment for Gymnasium

## Random Walk
This environment reflects the Markov Reward Process (MRP) defined in Example 6.2 Random Walk of the book Reinforcement Learning An Introduction (2nd Ed) by Richard S. Sutton and 
Andrew G. Barto.  
The Random Walk Environment is a custom Gymnasium environment that simulates a simple random walk process. The agent starts in a predefined state and moves randomly to adjacent states (left or right) at each timestep. The states are arranged in a row, with terminal states at both ends. The rightmost terminal state rewards the agent with +1, while all other transitions have a reward of 0. The environment is ideal for experiments in reinforcement learning or Markov Reward Processes (MRPs), focusing on stochastic state transitions without actions.

## Installation
```
pip install random-walk-env
```

## Use with gymnasium

```{shell}
import random_walk_env
import gymnasium as gym

env = gym.make('random_walk_env/RandomWalk-v0')
```
## Note
As per the gymnasium API for the step function, an action must be provided. However, this value is ignored as this environment represents a Markov Reward Process (and not a Markov Decision Process).

The terminal states are represented by * (asterisk). Terminal states are the leftmost and the rightmost states.

You can set the number of (nonterminal) states and the index of the start state as the following. 
```
env = gym.make('random_walk_env/RandomWalk-v0', num_states=6, start_state=4) # *ABCD[E]F*
```

Default is ``` num_states=5, start_state=2 (*AB[C]DE*)```.
corrGraph 0.1.0
A python module built using graph theory to analyse how attributes/features in a dataset correlate with each other
Requires Python: >=3.8

# corrGraph
A python module built using graph theory to analyse how attributes/features in a dataset correlate with each other

## Project Goal

The goal of this project is to provide a comprehensive tool for analyzing the correlation between different attributes or features within a dataset using graph theory. By leveraging the capabilities of graph theory, this module aims to offer insightful visualizations and metrics that can help in understanding the relationships and dependencies among various features.

### Features

- **Graph Construction**: Build graphs where nodes represent features and edges represent the correlation between them.
- **Correlation Metrics**: Calculate various correlation metrics to quantify the strength and direction of relationships.
- **Visualization**: Generate visual representations of the correlation graph to easily identify clusters and key relationships.
- **Usability**: Simple and intuitive API for integrating with other data analysis workflows.

### Usage

Refer to the `corrGraph_usages` file for detailed examples and use cases demonstrating how to utilize the module effectively.

### Installation

To install the module, run:
```bash
pip install corrGraph
```

### Getting Started

Here's a quick example to get you started:
```python
from corrGraph import CorrGraph

# Load your dataset
data = pd.read_csv('some_data.csv')

# Initialize the CorrGraph object with a pandas correlation matrix
cg = CorrGraph(data.corr())

# Build the correlation graph
cg.get_graph()

# Visualize the graph
cg.visualize_graph()
```