// Cloned by Tristan Everitt on 31 Oct 2022 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(400);
const LearningConstant = 1; // 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 (10000 * 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
this.error = 0;
}
// Function to train the Perceptron
// Weights are adjusted based on "desired" answer
train(inputs, desired) {
// 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;
// Adjust weights based on weightChange * input
for (let i = 0; i < this.weights.length; i++) {
this.weights[i] += this.c * error * inputs[i];
}
this.error = error;
}
// 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) {
return sum > 0 ? 1 : -1;
}
// Return weights
getWeights() {
return this.weights;
}
getError() {
return this.error;
}
updateLearningRate(c) {
this.c = c;
}
getLearningRate() {
return this.c;
}
}
// 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;
let errors = 0;
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);
ptron.train(training[count].input, training[count].output);
if (ptron.getError() !== 0) {
errors++;
}
AB.msg("<br> Error Margin: " + errors, 3);
AB.msg("<br> Learning Rate: " + ptron.getLearningRate(), 4);
count = (count + 1) % training.length;
if (count === 0) {
if (errors === 0) {
console.log('Error zero on all points');
noLoop();
return;
} else {
console.log('Points with errors: ' + errors);
let prevC = ptron.getLearningRate();
let newC = prevC / 2;
console.log('Updating learning rate from ' + prevC + ' to ' + newC);
ptron.updateLearningRate(newC);
}
errors = 0;
}
// Draw all the points
AB.msg("<br> Drawing points 0 to " + (count - 1), 5);
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);
}
}