simplifiedPerceptron.js

// Simplified from: https://github.com/Elyx0/rosenblattperceptronjs/blob/master/src/Perceptron.js 
class Perceptron {
  constructor(bias=1,learningRate=0.1,weights=[]) {
    this.bias = bias;
    this.learningRate = learningRate;
    this.weights = weights;
    this.trainingSet = [];
  }

  init(inputs,bias=this.bias) {
    // Initialize randomly and appending bias weight
    this.weights = [...inputs.map(i => Math.random()), bias];
  }

  adjustWeights(inputs,expected) {
    // ... 
    const actual = this.evaluate(inputs);
    if (actual == expected) return true; // Correct weights, return and don't touch anything.

    // Otherwise update each weight by adding the error * learningRate relative to the input
    this.weights = this.weights.map((w,i) => w += this.delta(actual, expected,inputs[i]));
    // ...
  }

  // Calculates the difference between actual and expected for a given input
  delta(actual, expected, input,learningRate=this.learningRate) {
    const error = expected - actual; // How far off were we
    return error * learningRate * input;
  }

  // Sum inputs * weights
  weightedSum(inputs=this.inputs,weights=this.weights) {
    return inputs.map((inp,i) => inp * weights[i]).reduce((x,y) => x+y,0);
  }

  // Evaluate using the current weights
  evaluate(inputs) {
    return this.activate(this.weightedSum(inputs));
  }

  // Heaviside as the activation function
  activate(value) {
    return value >= 0 ? 1 : 0;
  }
}