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SENSE Wiki

Welcome to the SENSE Wiki! This comprehensive guide provides detailed information about the SENSE (Systematic Enhancement for Neural Selection and Evolution) framework, designed to help you understand, install, utilize, and contribute to this innovative machine learning system.

Overview of SENSE

SENSE is an advanced machine learning framework that integrates multiple AI methodologies into a cohesive, adaptive system. It aims to address the limitations of traditional static models by offering real-time adaptability and continuous learning capabilities. The framework combines evolutionary algorithms, reinforcement learning, online learning, anomaly detection, and system resource management to create a versatile tool for various complex applications.

Getting Started

Installation

Prerequisites

Python 3.6 or higher
TensorFlow 2.x
Git

Steps

Clone the Repository:

git clone https://github.com/pwnzersaurus/SENSE.git

Navigate to the SENSE Directory:
```
cd SENSE
```
Install Dependencies:
```
pip install -r requirements.txt
```

Configuration

Configure SENSE based on your specific requirements. This involves setting parameters such as state size, action size, learning rates, and other model-specific configurations. These settings can be adjusted in the configuration files located in the config directory.

Architectural Details

Evolutionary Algorithms

SENSE's evolutionary algorithms simulate natural selection processes to optimize machine learning models. Key elements include mutation (random alterations in the model parameters), crossover (combining aspects of two models), and selection (choosing the most effective models based on performance metrics).

Reinforcement Learning (SARSA Agent)

The SARSA Agent in SENSE employs a policy-based RL approach, optimizing decision-making in unpredictable environments. The agent’s behavior can be tuned using parameters like epsilon (exploration rate), gamma (discount factor), and alpha (learning rate).

Online Learning Module

This module enables SENSE to adapt to new data streams in real-time. It can be configured for various data frequencies and volumes, ensuring that the model remains relevant and up-to-date with the latest information.

Anomaly Detection

SENSE’s anomaly detection component monitors data for irregularities, helping maintain the integrity and reliability of the model. The sensitivity of this detection can be calibrated to match the specific requirements of the application.

System Resource Monitoring

This feature provides insights into the computational efficiency of SENSE, allowing users to optimize model complexity and resource allocation based on the system’s performance.

Usage and Examples

SENSE can be utilized in various fields such as autonomous systems, financial market analysis, and real-time data analytics. Example scripts and use cases are provided in the examples directory to demonstrate practical applications of the framework.

Development and Contribution

Contributors are encouraged to help enhance SENSE by submitting pull requests, reporting issues, or suggesting new features. Please follow the coding standards and contribution guidelines provided in the CONTRIBUTING.md file.

FAQs

How can I adjust the learning rate of the online learning module?
- The learning rate can be modified in the configuration file specific to the online learning module.
What are the system requirements for running SENSE?
- SENSE requires Python 3.6 or higher, TensorFlow 2.x, and sufficient computational resources based on the model complexity.

Troubleshooting

TODO

Release Notes