node.js - JavaScript: Simple Perceptron fails to train

Question

I tried to make simple perceptron for predicting true or false, where 1 means true and false means -1. but my perceptron fails to train itself and instead gives random values.

this is my class perceptron:

function f(x){
    //number is in negative it returns -1
    if(x<0){
        return -1
    //number is in positive it returns 1
    }else{
        return 1
    }
}

class Perceptron{
    constructor(){
        //two weights
        this.weights = new Array(2)
        //intitializing two random weights
        for(let i = 0; i<this.weights.length; i++){
            this.weights[i] = Math.random()*2-1
        }
    }
    //prediction
    guess(input){
        let sum = 0;
        for(let i = 0; i<input.length; i++){
           sum += this.weights[i]*input[i]//sum of all product of inputs and weights 
        }
        return f(sum)//returns -1 or 1
    }
    //training data
    train(inputs, target){
        this.lr = 0.1//learning rate
        let guess = this.guess(inputs)//answer comes either 1 or -1
        let err = target - guess//calc error
        for(let i = 0; i<this.weights.length; i++){
            this.weights[i] += err * inputs[i] * this.lr// re adjust the weights
        }
    }
}

export default Perceptron;

question from:https://stackoverflow.com/questions/65517156/javascript-simple-perceptron-fails-to-train

Answer 1

I put your code in a snippet, so you can run it here.

function f(x) {
  //number is in negative it returns -1
  if (x < 0) {
    return -1
    //number is in positive it returns 1
  } else {
    return 1
  }
}

class Perceptron {
  constructor() {
    //two weights
    this.weights = new Array(2)
    //intitializing two random weights
    for (let i = 0; i < this.weights.length; i++) {
      this.weights[i] = Math.random() * 2 - 1
    }
  }

  //prediction
  guess(input) {
    let sum = 0;
    for (let i = 0; i < input.length; i++) {
      sum += this.weights[i] * input[i] //sum of all product of inputs and weights 
    }
    return f(sum) //returns -1 or 1
  }

  //training data
  train(inputs, target) {
    this.lr = 0.1 //learning rate
    const guess = this.guess(inputs) //answer comes either 1 or -1
    const err = target - guess //calc error
    for (let i = 0; i < this.weights.length; i++) {
      this.weights[i] += err * inputs[i] * this.lr // re adjust the weights
    }
  }
}

const brain = new Perceptron;

let data = [{
    inputx: 1,
    inputy: 1,
    target: 1
  },
  {
    inputx: 0,
    inputy: 1,
    target: -1
  },
  {
    inputx: 1,
    inputy: 0,
    target: -1
  },
  {
    inputx: 0,
    inputy: 0,
    target: -1
  }
]
for (let i = 0; i < data.length; i++) {
  let inputs = [data[i].inputx, data[i].inputy]
  brain.train(inputs, data[i].target)
  let guess = brain.guess([1, 0])
  console.log(guess)
}