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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]);
}
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){
level.inputs=[...givenInputs];
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;
}
} |