clear
 
%Load data
load fisheriris
x=meas(:,1:2);
y=categorical(species);
labels = categories(y);
figure(1)
gscatter(x(:,1),x(:,2),species,'rgb','osd');
xlabel('Sepal length');
ylabel('Sepal width');
 
%Learning by data
classifier{1}=ClassificationDiscriminant.fit(x,y);
classifier{2}=ClassificationTree.fit(x,y);
classifier{3}=ClassificationKNN.fit(x,y);
%classifier{4}=NaiveBayes.fit(x,y); 2019 ver
classifier_name={'Discriminant Analysis','Classification Tree','Nearest Neighbor'}; %'Naive Bayes'
 
%Check the result
[xx1,xx2]=meshgrid(4:.01:8,2:.01:4.5);
figure(2)
for ii=1:numel(classifier)
    ypred=predict(classifier{ii},[xx1(:) xx2(:)]);
    h(ii)=subplot(2,2,ii);
    gscatter(xx1(:),xx2(:),ypred,'rgb');
    title(classifier_name{ii},'FontSize',15)
    legend off
    axis tight
end
 
%Confusion Matrix
figure(3)
predictResult=predict(classifier{2},x);
y=categorical(y);
predictResult=categorical(predictResult);
plotconfusion(y,predictResult);

>> m=10;
>> b=0.1;
>> k=1000;
>> wn=sqrt(k/m)

wn =

    10

>> num=[4];
>> den=[1 0.5 0];
>> v=[-5 5 -5 5];
>> axis(v);

syms Y s y(t) t
 
laplace(10*diff(diff(y(t)))+0.4*diff(y(t))+4*y(t))
Y1=s*Y;
Y2=s*Y1;
sol=solve(10*Y2+0.4*Y1+4*Y-1,Y);
pretty(sol)
 
num=[1];
den=[10 0.4 4];
h=tf(num, den);
[A,B,C,D]=tf2ss(num, den)