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Java Neural Network

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Overview

Java Neural Network is an innovative project aimed at implementing a neural network purely in Java without the use of external libraries or APIs. It is designed to be a flexible and extendable framework for various neural network experiments.

Features

  • Pure Java implementation
  • Customizable neural network architecture
  • No external dependencies
  • Simple and intuitive API
  • Comprehensive documentation and examples

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

  • Java JDK 1.8 or later
  • Basic understanding of neural networks and Java programming

Installation

  1. Clone the repository: 
    git clone https://github.com/noahbclarkson/Java-Neural-Network.git
  2. Navigate to the cloned directory: 
    cd Java-Neural-Network
  3. Compile and run the project using your preferred Java IDE or command line tools.

Usage

The Java Neural Network can be used to create, train, and test neural network models for various applications such as pattern recognition, data classification, and more.

Example

Here is a simple example of using the Java Neural Network to train a model:

import unprotesting.com.github.*;

public class Example {
    public static void main(String[] args) {
        // Create and train the neural network
        NeuralNetwork network = new NeuralNetwork();
        network.train(trainingData);

        // Test the neural network
        float[] testInput = { ... };
        float[] output = network.predict(testInput);
        System.out.println("Predicted output: " + Arrays.toString(output));
    }
}

Authors

License

This project is licensed under the Apache License 2.0 - see the LICENSE.md file for details

Note

This is a template for other neural networks but standalone, it checks whether a float array is sorted ascendingly, descendingly or not at all.

  • A result of 1, 0, 0 means the array the AI thinks the array is sorted ascendingly.

  • A result of 0, 1, 0 means the the AI thinks the array is sorted descendingly.

  • A result of 0, 0, 1 means the AI thinks the array is not sorted.

  • A result of 0, 0, 0 means the AI does not know / an error.