neuralnetwork.js

class NeuralNetwork{
    constructor(neuronCounts){
        this.levels=[];
        for(let i =0;i<neuronCounts.length-1;i++){
            this.levels.push(new Level(
                neuronCounts[i], neuronCounts[i+1]
            ))
        }
    }

    static feedForward(givenInputs, network){
        let outputs = Level.feedForward(givenInputs, network.levels[0]);
        for(let i = 1;i < network.levels.length;i++){
            outputs = Level.feedForward(
                outputs, network.levels[i]);
        }

        // console.log(outputs);
        return outputs;
    }

    static mutate(network,amount=1){
        network.levels.forEach(level => {
            for(let i=0;i<level.biases.length;i++){
                level.biases[i]=lerp(
                    level.biases[i],
                    Math.random()*2-1,
                    amount
                )
            }
            for(let i=0;i<level.weights.length;i++){
                for(let j=0;j<level.weights[i].length;j++){
                    level.weights[i][j]=lerp(
                        level.weights[i][j],
                        Math.random()*2-1,
                        amount
                    )
                }
            }
        });
    }
}

class Level {
    constructor(inputCount, outputCount) {
        this.inputs = new Array(inputCount);
        this.outputs = new Array(outputCount);
        this.biases = new Array(outputCount);

        this.weights = [];
        for (let i = 0; i < inputCount; i++) {
            this.weights[i] = new Array(outputCount);
        }

        Level.#randomize(this);
    }

    static #randomize(level){
        for(let i=0;i<level.inputs.length;i++){
            for(let j=0;j<level.outputs.length;j++){
                level.weights[i][j]=Math.random()*2-1;
            }
        }

        for(let i=0;i<level.biases.length;i++){
            level.biases[i]=Math.random()*2-1;
        }
    }

    static feedForward(givenInputs, level) {
        for (let i = 0; i < level.inputs.length; i++) {
            level.inputs[i] = givenInputs[i];
        }

        for (let i = 0; i < level.outputs.length; i++) {
            let sum = 0;
            for (let j = 0; j < level.inputs.length; j++) {
                sum += level.inputs[j] * level.weights[j][i]
            }

            if (sum > level.biases[i]) {
                level.outputs[i] = 1;

            }
            else{
                level.outputs[i] = 0
            }
        }

        return level.outputs
    }
}