In this repository I made an artificial intelligence library and implemented some use cases.

Just copy the folder from lib and you're good to go.

For simple case AI use

var NeuralNetwork = preload("./Neural Network/Brain.gd")
var neural_network = NeuralNetwork.new(input_nodes: int, 
                                       hidden_nodes: int, 
                                       output_nodes: int)

neural_network.predict(inputs: Array)
neural_network.train(inptuts: Array, targets: Array)

For neuroevolution algorithms use

var NeuralNetwork = preload("./Neural Network/Brain.gd")
var neural_network = NeuralNetwork.new(input_nodes: int, 
                                       hidden_nodes: int, 
                                       output_nodes: int)

neural_network.predict(inputs: Array)
neural_network.duplicate()
neural_network.mutation_rate = mutation_rate
neural_network.mutate()

1. Simple perceptron

Here I implemented a simple perceptron, that has the role of doing linear separation of points based on a given function y=mx+b.

2. Multilayer perceptron

The first project scaled to n perceptrons, and now is a fully funcional neutral network with 3 layers, that can be trained and used for predictions. In this project I aproximated the XOR operator.

3. Matrix math

This is the barebone library of matrix operations. It contains a Matrix class with all the basic operations (both scalar and matrix wise) and a MatrixOperator class that contains static function for the matrices.

4. Color predictor

This is a program that predicts weather a color should be white or black, given the background color.

5. Neuroevolution

In this project I added more functionalities to the neural network, respectively mutate and duplicate

6. Flappy godot

In this project I implemented a flappy bird clone, and trained the neuroevolution algorithm to learn how to play

This project was inspired by Daniel Shiffman