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Pattern Recognition

Neural Networks are used in applications like Facial Recognition.

These applications use Pattern Recognition.

This type of Classification can be done with a Perceptron.

Perceptrons can be used to classify data into two parts.

Perceptrons are also known as a Linear Binary Classifiers.

Pattern Classification

Imagine a strait line (a linear graph) in a space with scattered x y points.

How can you classify the points over and under the line?

A perceptron can be trained to recognize the points over the line, without knowing the formula for the line.

Perceptron



How to Program a Perceptron

To program a perceptron, we can use a simple JavaScript program that will:

  1. Create a simple plotter
  2. Create 500 random x y points
  3. Display the x y points
  4. Create a line function: f(x)
  5. Display the line
  6. Compute the desired answers
  7. Display the desired answers

Create a Simple Plotter

Creating a simple plotter object is described in the AI Canvas Chapter.

Example

const plotter = new XYPlotter("myCanvas");
plotter.transformXY();

const xMax = plotter.xMax;
const yMax = plotter.yMax;
const xMin = plotter.xMin;
const yMin = plotter.yMin;

Create Random X Y Points

Create as many xy points as wanted.

Let the x values be random (between 0 and maximum).

Let the y values be random (between 0 and maximum).

Display the points in the plotter:

Example

const numPoints = 500;
const xPoints = [];
const yPoints = [];
for (let i = 0; i < numPoints; i++) {
  xPoints[i] = Math.random() * xMax;
  yPoints[i] = Math.random() * yMax;
}

Try it Yourself »


Create a Line Function

Display the line in the plotter:

Example

function f(x) {
  return x * 1.2 + 50;
}

Try it Yourself »


Compute Correct Answers

Compute the correct answers based on the line function:

y = x * 1.2 + 50.

The desired answer is 1 if y is over the line and 0 if y is under the line.

Store the desired answers in an array (desired[]).

Example

let desired = [];
for (let i = 0; i < numPoints; i++) {
  desired[i] = 0;
  if (yPoints[i] > f(xPoints[i])) {desired[i] = 1;}
}

Display the Correct Answers

For each point, if desired[i] = 1 display a black point, else display a blue point.

Example

for (let i = 0; i < numPoints; i++) {
  let color = "blue";
  if (desired[i]) color = "black";
  plotter.plotPoint(xPoints[i], yPoints[i], color);
}

Try it Yourself »


How to Train a Perceptron

In the next chapter, you will learn how to use the correct answers to:

Train a perceptron to predict the output values of unknown input values.