NeuralNetwork.js

class NN {
  constructor(inputs, hiddens, outputs, noH) {
    this.total = noH + 2;
    this.weights = [];
    this.biases = [];
    this.lr = 0.1;

    this.weights[0] = new Matrix(hiddens, inputs);
    this.weights[this.total - 2] = new Matrix(outputs, hiddens);

    this.biases[0] = new Matrix(hiddens, 1);
    this.biases[this.total - 2] = new Matrix(outputs, 1);

    for (let i = 1; i < this.total-2; i++) {
      this.weights[i] = new Matrix(hiddens, hiddens);
      this.biases[i] = new Matrix(hiddens, 1);
    }

    for (let i = 0; i < this.total-1; i++) {
      this.weights[i].randomize();
      this.biases[i].randomize();
    }
  }

  predict(ip) {
    let inputs = Matrix.fromArray(ip);

    let weightedSums = [];

    weightedSums[0] = Matrix.copyMatrix(inputs);

    for (let i = 1; i < this.total; i++) {
      weightedSums[i] = Matrix.matrixMult(this.weights[i - 1], weightedSums[i - 1]);
      weightedSums[i] = Matrix.matrixSum(weightedSums[i], this.biases[i - 1]);
      weightedSums[i] = Matrix.matrixMap(weightedSums[i], sigmoid);
    }

    let op = Matrix.toArray(weightedSums[this.total - 1]);

    return op;
  }

  train(ip, t){
    let inputs = Matrix.fromArray(ip);

    let weightedSums = [];

    weightedSums[0] = Matrix.copyMatrix(inputs);

    for (let i = 1; i < this.total; i++) {
      weightedSums[i] = Matrix.matrixMult(this.weights[i - 1], weightedSums[i - 1]);
      weightedSums[i] = Matrix.matrixSum(weightedSums[i], this.biases[i - 1]);
      weightedSums[i] = Matrix.matrixMap(weightedSums[i], sigmoid);
    }

    let output = Matrix.copyMatrix(weightedSums[this.total - 1]);

    let target = Matrix.fromArray(t);

    let errors = [];

    errors[this.total - 2] = Matrix.matrixSub(target, output);

    for(let i = this.total - 3;i >= 0;i--){
      let trans = Matrix.transpose(this.weights[i+1]);
      errors[i] = Matrix.matrixMult(trans, errors[i+1]);
    }

    let DSigWeightedSums = [];

    for(let  i = 0; i < weightedSums.length;i++){
      DSigWeightedSums[i] = Matrix.matrixMap(weightedSums[i], DSigmoid);
    }

    let gradients = [];
    let weights_deltas = [];

    for(let i = 0;i < this.total - 1;i++){
      gradients[i] = Matrix.hadamardMult(DSigWeightedSums[i+1], errors[i]);
      gradients[i].scalarMult(this.lr);
    }

    for(let i = 0;i < this.total - 1;i++){
      let trans = Matrix.transpose(weightedSums[i]);
      weights_deltas[i] = Matrix.matrixMult(gradients[i], trans);
    }

    for(let i = 0;i < this.total - 1;i++){
      this.weights[i].matrixSum(weights_deltas[i]);
      this.biases[i].matrixSum(gradients[i]);
    }
  }

  mutateNN(mr){
    for(let i = 0;i < this.weights.length;i++){
      this.weights[i].mutate(mr);
      this.biases[i].mutate(mr);
    }
  }

  copyNN(toCopy){
    this.total = toCopy.total;
    for(let i = 0;i < toCopy.weights.length;i++){
      this.weights[i].copyMatrix(toCopy.weights[i]);
      this.biases[i].copyMatrix(toCopy.biases[i]);
    }
  }
}