SAA.rar_5 to 1_adaline_adaline matlab code

close all, clear all, clc, format compact % define classes p1=[342 2322]'; p2=[588 1952]'; p3=[652 997]'; p4=[378 997]'; p5=[437 2761]'; p6=[669 2349]'; p7=[781 1136]'; p8=[459 1105]'; p9=[452 3081]'; p10=[717 2501]'; p11=[803 1210]'; p12=[494 1345]'; P=[p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12]; col1 = [p1 p5 p9]; col5 = [p2 p6 p10]; col7 = [p3 p7 p11]; col10= [p4 p8 p12]; % define output coding for classes T1 = [1 -1]'; T2 = [1 1]'; T3 = [-1 1]'; T4 = [-1 -1]'; T = [T1 T2 T3 T4 T1 T2 T3 T4 T1 T2 T3 T4]; % Plot input samples plot(col1(1,:),col1(2,:),'bs') hold on plot(col5(1,:),col5(2,:),'r+') plot(col7(1,:),col7(2,:),'go') plot(col10(1,:),col10(2,:),'m*') % set learning rate lr = maxlinlr(P,'bias'); % define a linear network net = linearlayer(0,lr); %configure the network according to the input and output net = configure(net,P,T); % define initial input weights and biases w = [[0 0]; [0 0]]; b=[-2;-3]; net.IW{1,1} = w; net.b{1} = b; % train the network net.trainParam.goal= 0; net.trainParam.epochs = 3000; [net, tr] = train(net,P,T); % plot the decicion boundary figure(1) plotpc(net.IW{1,1},net.b{1}); %output the weights and biases weights = net.IW{1,1}; biases = net.b{1}; % change the activation function to hardlims to start classification net.layers{1,1}.transferFcn = 'hardlims'; % classify pt1=[400 2500]'; pt2=[600 1200]'; pt3=[900 1300]'; pt4=[700 2400]'; pt5=[500 1000]'; pt6=[400 2000]'; pt7=[800 1500]'; pt8=[700 2700]'; pt9=[300 900]'; pt10=[1000 1500]'; ptest = [pt1 pt2 pt3 pt4 pt5 pt6 pt7 pt8 pt9 pt10]; for cnt = 1:10 cc=ptest(:,cnt) y = net(cc) out(:,cnt)=y; end