ML Recognition - ONEDOUBT.COM

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 linA 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:

Create a simple plotter
Create 500 random x y points
Display the x y points
Create a line function: f(x)
Display the line
Compute the desired answers
Display the desired answers

Create a Simple Plotter

Creating a simple plotter object is described in the .

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

<!DOCTYPE html>
<html>
<script src="myplotlib.js"></script>

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

//Plot the Points
plotter.plotPoints(numPoints, xPoints, yPoints, "blue");

</script>
</body>
</html>

Create a Line Function

Display the line in the plotter:

<!DOCTYPE html>
<html>
<script src="myplotlib.js"></script>

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

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

//Plot the Points and the Line
plotter.plotPoints(numPoints, xPoints, yPoints, "blue");
plotter.plotLine(xMin, f(xMin), xMax, f(xMax), "black");

</script>
</body>
</html>

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

<!DOCTYPE html>
<html>
<script src="myplotlib.js"></script>

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

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

//Plot the Line
plotter.plotLine(xMin, f(xMin), xMax, f(xMax), "black");

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

// Diplay Desired Result
for (let i = 0; i < numPoints; i++) {
let color = "blue";
if (desired[i]) color = "black";
plotter.plotPoint(xPoints[i], yPoints[i], color);
}
</script>
</body>
</html>

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.

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