network.js

class NeuralNetwork {
  constructor(neuronCounts) {
    this.name = getRandomName();
    this.generation = 0;
    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.generation++
    network.name = getRandomName();
    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;
  }
}