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Syntax-highlighted view of JS of World: Perceptron (clone by Karl Murphy)





// Cloned by Karl Murphy on 15 Nov 2019 from World "Perceptron" by "Coding Train" project 
// Please leave this clone trail here.
 


// Port from
// https://github.com/nature-of-code/noc-examples-p5.js/tree/master/chp10_nn/NOC_10_01_Perceptron


// A list of points we will use to "train" the perceptron
let training = new Array(300);

const LearningConstant =  0.008;     // easier to watch if it is low 

// Coordinate space
let xmin = -1;
let ymin = -1;
let xmax = 1;
let ymax = 1;


    // set fixed width run header 
    AB.headerWidth ( 400 );
    
    

// function to draw a line
// y = ax + b
// original: y = 0.3 * x + 0.4

const a = AB.randomFloatAtoB ( 0.1, 0.9 );
const b = AB.randomFloatAtoB ( 0.1, 0.9 );

function f(x) 
{
  return ( a * x + b );
}

// classification is are you above or below line 
// perceptron should move towards the line 

    function getClassification ( x, y )
    {
     if (y < f(x)) return ( -1 );
     else return ( 1 );
    }




// Daniel Shiffman
// The Nature of Code
// http://natureofcode.com

// Simple Perceptron Example
// See: http://en.wikipedia.org/wiki/Perceptron

// Perceptron Class

// Perceptron is created with n weights and learning constant
class Perceptron 
{
  constructor(n, c) 
  {
    // Array of weights for inputs
    this.weights = new Array(n);
    // Start with random weights
    for (let i = 0; i < this.weights.length; i++) {
      this.weights[i] = random(-1, 1);
    }
    this.c = c; // learning rate/constant
  }

  // Function to train the Perceptron
  // Weights are adjusted based on "desired" answer
  train(inputs, desired, last_element) 
  {
    // Guess the result
    let guess = this.feedforward(inputs);
    // Compute the factor for changing the weight based on the error
    // Error = desired output - guessed output
    // Note this can only be 0, -2, or 2
    // Multiply by learning constant
    let error = desired - guess;
    // if (last_element == true && error == 0){
    //     throw new Error();
    // }
    //console.log("error is " + error);
    // Adjust weights based on weightChange * input
    for (let i = 0; i < this.weights.length; i++) 
    {
      this.weights[i] += this.c * error * inputs[i];
      console.log(this.weights)
    }
  }

  // Guess -1 or 1 based on input values
  feedforward(inputs) 
  {
    // Sum all values
    let sum = 0;
    for (let i = 0; i < this.weights.length; i++) 
    {
      sum += inputs[i] * this.weights[i];
    }
    // Result is sign of the sum, -1 or 1
    return this.activate(sum);
  }

  activate(sum) 
  {
    if (sum > 0) return 1;
    else return -1;
  }

  // Return weights
  getWeights() 
  {
    return this.weights;
  }
}



// The Nature of Code
// Daniel Shiffman
// http://natureofcode.com

// Simple Perceptron Example
// See: http://en.wikipedia.org/wiki/Perceptron

// Code based on text "Artificial Intelligence", George Luger

// A Perceptron object
let ptron;

// We will train the perceptron with one "Point" object at a time
let count = 0;



function setup() 
{
  createCanvas(800, 800);

  // The perceptron has 3 inputs 
  // x, y, and bias
  ptron = new Perceptron ( 3, LearningConstant ); 

  // Create a random set of training points and calculate the "known" answer
  for (let i = 0; i < training.length; i++) 
  {
    let x = random(xmin, xmax);
    let y = random(ymin, ymax);
    
    let answer = getClassification ( x, y );
 
    training[i] = 
    {
      input: [x, y, 1],
      output: answer
    };
  }
}


var step = 1;

function draw() 
{
    AB.msg ( "Line: y = " + a.toFixed(2) + " x + " + b.toFixed(2) +
            "<br> Step: " + step );
    step++;
    
  background('black');

  // Draw the line
  strokeWeight(3);
  stroke('lightblue');
  let x1 = map(xmin, xmin, xmax, 0, width);
  let y1 = map(f(xmin), ymin, ymax, height, 0);
  let x2 = map(xmax, xmin, xmax, 0, width);
  let y2 = map(f(xmax), ymin, ymax, height, 0);
  line(x1, y1, x2, y2);

  // Draw the line based on the current weights
  // Formula is weights[0]*x + weights[1]*y + weights[2] = 0
  stroke('white');
  let weights = ptron.getWeights();
  x1 = xmin;
  y1 = (-weights[2] - weights[0] * x1) / weights[1];
  x2 = xmax;
  y2 = (-weights[2] - weights[0] * x2) / weights[1];

  x1 = map(x1, xmin, xmax, 0, width);
  y1 = map(y1, ymin, ymax, height, 0);
  x2 = map(x2, xmin, xmax, 0, width);
  y2 = map(y2, ymin, ymax, height, 0);
  line(x1, y1, x2, y2);


  // Train the Perceptron with one "training" point at a time
  AB.msg ( "<br> Training on single point: " + count, 2 );
  last_element = training.length === count + 1;
  console.log("last_element", last_element);
  ptron.train(training[count].input, training[count].output, last_element);
  count = (count + 1) % training.length;
  console.log("count" + count)

  // Draw all the points
  AB.msg ( "<br> Drawing points 0 to " + (count-1), 3 );
  
  for (let i = 0; i < count; i++) 
  {
    strokeWeight(1);
    //let guess = ptron.feedforward(training[i].input);
 
    let x = map(training[i].input[0], xmin, xmax, 0, width);
    let y = map(training[i].input[1], ymin, ymax, height, 0);
       
  // original version: based on what the Perceptron would "guess" - shows how its guess changes over time
  //   if (guess > 0)

  // this version: correct answer 
  
    if ( getClassification ( training[i].input[0], training[i].input[1] ) == 1 )
    {
        stroke('lightgreen');
        fill('lightgreen');
    }
    else
    {
        stroke('lightpink');
        fill('lightpink');
    }
    
    ellipse(x, y, 12, 12);
  }
}