Classification of linearly separable data with a perceptron
Neural Networks course (practical examples) © 2012 Primoz Potocnik
PROBLEM DESCRIPTION: Two clusters of data, belonging to two classes, are defined in a 2-dimensional input space. Classes are linearly separable. The task is to construct a Perceptron for the classification of data.
Contents
Define input and output data
close all, clear all, clc, format compact % number of samples of each class N = 20; % define inputs and outputs offset = 5; % offset for second class x = [randn(2,N) randn(2,N)+offset]; % inputs y = [zeros(1,N) ones(1,N)]; % outputs % Plot input samples with PLOTPV (Plot perceptron input/target vectors) figure(1) plotpv(x,y);
Create and train perceptron
net = perceptron; net = train(net,x,y); view(net);
Plot decision boundary
figure(1)
plotpc(net.IW{1},net.b{1});
